The objectives of the study were to determine the spatial rate of CO₂ flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R² = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R² = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO₂ to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring.

The article presents the results of studies aimed to identify the effect of foliar treatments of winter wheat, sugar beets and soybeans with mineral and bioorganic fertilizers. Their positive effect on increasing the concentration of mineral forms of nitrogen in soil was revealed. The crop types have a greater influence on the size of nitrous oxide emissions: in winter wheat crops - 679 µg/m3, soybeans - 637.2 µg/m3, sugar beets - 576.8 µg/m3. The amounts of nitrous oxide emissions were lower in the variants without the use of phosphorus-potassium fertilizers. The Ruter AA fertilizer showed the greatest efficiency on winter wheat crops - 42.7 c/ha. Soybean grain yield of 44.2 c/ha was also achieved by using Ruter AA, as well as Geo-humate together with phosphorus-potassium fertilizers. On the background of the actual supply of phosphorus and potassium, maximum yield was obtained when using N30 on the leaf - 39.3 c/ha. Foliar feeding ensured a yield of sugar beet root crops of up to 67.1-77.4 c/ha on the backdrop of the application of phosphorus-potassium fertilizers with the advantage of using Amino turbo. The largest yield of sugar beet roots without use of mineral phosphorus and potassium was ensured by N30 treatment of the leaf in the phases of 4-6 and 8 leaves - 63.2 t/ha. Of the organo-mineral fertilizers and biostimulants, the best result was shown by the use of Ruter A - 62.6 t/ha. We believe that foliar feeding of crops can be a sustainable farming method that can increase crop yields and reduce N2O emissions.

Present study on land use land cover analysis and taluka wise combination of cropped area, carried out in Beed district of Maharashtra state in India and studied 11 talukas of the district. Secondary data on land use land cover and area in various crops has been collected, compiled and analyzed from various secondary sources. Simple descriptive statistics used to examine the agricultural land use land cover and J.C. Weaver’s crop combination technique was used for taluka wise analysis of crop combinations and on the basis of highest to smallest area engaged in particular crop, crops have been ranked. The study showed that, in the district as a whole, percentage of uncultivated land other than fallow was about 46 percent as compare to net sown area is only 35 percent in total geographical area (1063855ha) of Beed district. The study further revealed that about 42 percent area engaged in food crops and about 58 percent area occupied by nonfood crops, which includes cereals (25%), cotton (19%), pulses (13%), oilseed (12%), sugarcane (5%) and fruits and vegetables (3%). Results of Weaver’s crop combination technique showed that the Wadwani taluka demarcated as most diversified taluka by means of maximum number (sixteen) of crops combinations with the lowest variance value (14) found in the present investigation. Further, the study revealed that the high crop diversity found in Dharur taluka with eighteen crop combinations and the taluka has given first rank amongst others. To derive the crop diversity ranking, taluka wise maximum to minimum number of crop combinations derived in different talukas and given ranks accordingly. The study concluded that, agricultural land use analysis is essential for solving and optimizing the land use problem, maintaining socio-ecological balance, best utilization of scarce resources. The authors enlightened that the rational use of agricultural land has important for food security and biodiversity which increase the productivity and profitability of the agriculture sector and it indicates the decision making ability of farmers in study area.

The use of chemical pesticides in agriculture contributes to soil, water and air pollution, biodiversity loss and can harm non-target species. The European Commission has already established a Harmonized Risk Indicator to quantify the progress in reducing the risks linked to pesticides. Therefore, there is an increasing need to promote biopesticides or so-called low-risk pesticides (LRP). Extract from Melaleuca alternifolia – tea tree oil (TTO) is known for its antiseptic, antimicrobial, antiviral, antifungal, and anti-inflammatory properties. Tea tree oil has been extensively studied in pest management as well as in the pharmaceutical and cosmetic industry and there are already products based on its active substances on the market. This review focuses on the overall evaluation of tea tree oil in terms of effectiveness and safety as a biopesticide for the first time. The collected data can be an added value for further evaluation of tea tree oil in terms of the authorization extension as a fungicide in 2026.

Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

Irrigation is a useful crop enhancement procedure up to the point where free surface water appears. Thereafter, water can begin to flow into waterways, leaching nutrients and giving rise to environmental damage, as well as being a waste of a precious resource. The current work addresses the problem of measuring free water on the surface of agricultural soils by a real-time acoustic remote sensing method. Directional acoustic transmitter and receiver arrays are used to define a ‘footprint’ on the ground from which changes in reflectance are sensed. These arrays are mounted on a moving irrigator. Chirp signals are used to provide along-path resolution and to ensure robustness against unwanted acoustic background noise from farm machinery and the irrigator. Field measurements have been conducted above a well-defined ‘quadrat’ with controlled and measured water content, and also with the instrument mounted on an operational irrigator. A structured light camera mounted above the footprint is used to validate surface water fraction. It is found that the areal fraction of free water on the soil surface can be reliably estimated from changes in the amplitude of the reflected sound waves. The mechanism giving rise to the observed acoustic reflectivity changes is discussed and a model is developed which agrees with normalized intensity observations with a coefficient of determination R2 between 0.65 and 0.83. The rms error between model predictions and observations is comparable to the rms variation of the measurements, indicating that there is insignificant error due to the choice of model.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

Recent technological advancements in conservation genetics and genomics have resulted in diverse tools for aiding conservation of species. The precision and resolution of high throughput sequencing technologies provide valuable insights to aid conservation decisions, but these technologies are often financially unfeasible or unavailable in resource constrained countries. Inter-Simple Sequence Repeat (ISSR) markers, when combined with sensitive automated detection systems, provide a simple, cheap means to investigate genetic diversity and discriminate closely related species. Here we apply this technology to assess genetic diversity and taxonomic delimitation in the Encephalartos eugene-maraisii species complex, a highly threatened, taxonomically dubious group of cycads in South Africa. Our analyses support taxonomic singularity of E. dyerianus, E. dolomiticus and E. eugene-maraisii. Relationships between E. nubimontanus and E. cupidus remain uncertain. E. middelburgensis samples showed no clustering but had poor amplification success. This study demonstrates the suitability of automated ISSR fingerprinting as a method for plant conservation studies, especially in resource-constrained countries, and we make recommendations as to how this methodology can be effectively implemented.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

Detecting and monitoring changes in soil salinity through remote sensing provides an opportunity for field assessment in regions where on-site measurements are limited. This investigation, carried out in Siwa Oasis, Egypt, aimed to assess the efficacy of several soil salinity indices derived from Landsat 5 and 7 satellite images. To achieve this, 56 on-site ground measurements were utilized for evaluation purposes. The study aimed to improve the correlation between EC and index values and explore the relationship between salinity and changes in land cover. Eleven spectral indices were calculated for nine scenes captured in different months from August to December. The first approach involved stacking the data to identify the index with the strongest correlation with ground EC values, without the need for complex analysis or EC value ranging. In the second approach, the ground EC measurements were classified into seven different salinity categories, and the correlation coefficient was calculated between each index and each salinity category. The third approach analyzed the data temporally, considering different salinity levels. Lastly, a spatial correlation analysis was conducted between EC and index values in the fourth scenario. The initial approach revealed a weak correlation due to substantial variation in EC values. However, the SI index demonstrated the highest correlation coefficient of 0.38. In the second scenario, the S2 index exhibited the highest correlation of 0.96 for moderate salinity samples. The third scenario showed that the S1 index achieved the highest correlation value of 0.99 for moderately saline areas. In the fourth scenario, the SI index exhibited the strongest correlation among all four ponds with correlation coefficients of 0.23, 0.23, 0.18, and 0.61. Notably, the correlations observed in the second and third scenarios demonstrated higher correlation coefficients compared to both the first and fourth scenarios. The study highlighted significant variations in EC levels related to land cover types and pond elevation. Additionally, remote sensing methods detected a 48% increase in total vegetated area over 17 years, showing the potential of remote sensing techniques in salinity monitoring for expanding agriculture and improving land management.

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

In view of the fact that the field monitoring information can more intuitively and accurately reflect the stability state of slopes, this paper takes the warning threshold of slope deformation rate monitoring as the slope stability evaluation standard, and puts forward a method for solving the safety coefficient of rocky slopes. The discrete element method 3DEC, which is suitable for rocky slopes, is selected as the numerical calculation tool, and the convergence criterion of its strength reduction method is modified to the slope deformation rate threshold, and the method is realized by the bifurcation method through its built-in FISH programming language. The results of the classical case show that, by selecting the slope deformation rate threshold in the appropriate interval, the results of this paper's method are very close to those of the finite unit stress method and the limit equilibrium method, which verifies the reliability of this paper's method. Further, the method of this paper is applied to an open-pit mine slope project, based on the slope deformation on-site monitoring data, through the time series prediction method to determine the slope deformation rate warning threshold, using the threshold as an evaluation criterion to solve the slope's coefficient of safety, the calculation results show that: the slope's coefficient of safety in natural working conditions is 1.086, basically stable, suffered from continuous rainfall, the slope body is gradually saturated, the slope's coefficient of safety is reduced to 0.987, the slope's safety is reduced to 0.987. After continuous rainfall and gradual saturation of the slope, the coefficient of safety decreased to 0.987, destabilizing and destroying, which is consistent with the site conditions.

:Peat substrate is made from peat from drained peatlands, which is a limited resource. A realistic estimate is that 50% of the world’s wetlands have been lost. Peat is used in horticulture, especially for the cultivation of vegetables in greenhouses. The consequences of peatland exploitation are an in-crease in the greenhouse effect and a decrease in carbon stocks. Wood fiber can be used as an al-ternative to peat. The chemical properties of the growing media interact and change continuously due to the small volume of the growing media, which is limited by the growing container. This study aims to gain new knowledge on the impact of nutrient changes in the microbial degradation of carbon compounds in wood fiber and mixtures with peat substrate on the content and uptake of nutrients required by plants. Cucumbers (Cucumis sativus L.) variety “Dirigent H” developed in the Netherlands, were cultivated in growing media of peat substrate and wood fiber: (1) peat substrate (PS); (2) wood fiber (WF); (3) wood fiber and peat substrate 50/50 v/v (WF/PS 50/50); (4) wood fiber and peat substrate 25/75 v/v (WF/PS 25/75). The rates of fertilization were the following: (1) con-ventional fertilization (CF); (2) N13; (3) N23; (4) N30. The experiment was carried out in three repli-cations. As the amount of wood fiber increased, the humidity and pH of the growing medium in-creased. Fertilization of cucumbers with different rates of nitrogen influenced the nutrient uptake. Plants grown in 50/50 and 25/75 growing media had the best Cu uptake when fertilized with N23. When the plants grown in the wood fiber media and the 50/50 media were fertilized with N13, N23, and N30, the Mn content in the growing media at the end of the growing season was significantly lower than the Mn content in the media with conventional fertilization. Thus, nitrogen improved the uptake of Mn by plants grown not only in the wood fiber but also in combinations with peat sub-strate. Growing plants in wood fiber and fertilizing them with N13 can result in optimum uptake of micronutrients. The number and mass of cucumber fruits per plant were influenced by the amount of wood fiber in the growing media and the application of nitrogen fertilizer. The highest number of fruits and mass of fruits per plant was obtained significantly higher when cucumbers were grown on WF/PS 50/50 growing media with additional N13 fertilization.

: Greenhouse plants are usually grown in containers filled with growing media. Peat is the main component of the growing media used in greenhouses. However, peat substrate is made from peat from drained peatlands, which is a limited resource. Wood fiber can be an alternative to use as a growing media for controlled-climate crops. The chemical properties of the growing media interact and change continuously due to the small volume of the growing media, which is limited by the vegetative container. Therefore, this study aims to gain new knowledge on the impact of nutrient changes in the microbial degradation of carbon compounds in wood fiber and mixtures with peat substrate on the content and uptake of nutrients required by plants. Cucumbers (Cucumis sativus L.) were cultivated in growing media of peat substrate and wood fiber: (1) peat substrate (PS); (2) wood fiber (WF); (3) wood fiber and peat substrate 50/50 v/v (WF/PS 50/50); (4) wood fiber and peat substrate 25/75 v/v (WF/PS 25/75). The rates of fertilization were the following: (1) conventional fertilization (CF); (2) N13; (3) N23; (4) N30. Fertilization of cucumbers with different rates of nitrogen influenced the nutrient uptake. Plants grown in WF/PS 50/50 and WF/PS 25/75 growing media had the best Cu uptake when fertilized with N23. When the plants grown in the wood fiber media and the 50/50 media were fertilized with N13, N23, and N30, the Mn content in the growing media at the end of the growing season was significantly lower than the Mn content in the media with conventional fertilization. Thus, nitrogen improved the uptake of Mn by plants grown not only in the wood fiber but also in combinations with peat substrate. Growing plants in wood fiber and fertilizing them with N13 can result in optimum uptake of micronutrients. The number and mass of cucumber fruits per plant were influenced by the amount of wood fiber in the growing media and the application of nitrogen fertilizer. The highest number of fruits and mass of fruits per plant was obtained significantly higher when cucumbers were grown on WF/PS 50/50 growing media with additional N13 fertilization.

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven a promising method for fast SOM content estimation. However, soil physical properties significantly affect the sensitivity of satellite hyperspectral imaging to SOM, leading to poor generalization ability of the estimation model. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on the spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root mean squared height, RMSH), and soil bulk weight (SBW), a soil spectral correction strategy was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPD) exceeding 1.4 in almost all bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, root mean square error (RMSE) of 5.74 g/kg, and RPD of 1.68. The prediction accuracy, R2, RMSE, and RPD of the model after migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction strategy based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. The performance comparison highlighted the advantages of considering soil physical properties in regional-scale SOM prediction.

Timely and accurate acquisition of information on distribution of the crop planting structure in Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in western China, is crucial for promoting fine management of agriculture and ensuring food security. In this study, the Object-Based Image Classification (OBIC), the Random Forest (RF) and Convolutional Neural Network (CNN) models were employed to classify the crop planting structure of four representative test areas in Qingyang City in a precise manner. Firstly, different optimal segmentation scales for various crops were selected using the Estimation of Scale Parameter 2 (ESP2) tool and the Ratio of Mean Difference to Neighbors(ABS) to Standard Deviation (RMAS)model. The images were then segmented through multiresolution segmentation combined with the Canny Edge Detection algorithm. Secondly, the L1 regularized logistic regression model was utilized to select and optimize 39 spatial feature factors including spectral, textural, geometric, and index features, in conjunction with phenological factors. Lastly, under the multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model, combined with object-based multiresolution segmentation, was used to classify the crop planting structure. The findings show that: Thanks to the Canny Edge Detection algorithm, we can obtain a more complete boundary of the segmented objects and improve the separability. The optimal segmentation scales for corn, vegetables, and buckwheat were found to be 55, 70, and 35, respectively, while wheat and apple had optimal segmentation scales of 65. In addition to phenological characteristics, the number of selected spatial features for corn, vegetables, buckwheat, wheat, and apple were 9, 7, 16, 12, and 10, respectively. The CNN model demonstrated high consistency with the RF model in the classification results, but the accuracy of RF model is higher than that of CNN model on the whole. The overall accuracy of classification using RF model in four test areas registered 91.93%, 94.92%, 89.37% and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields.

Revealing the spatial distribution mechanism of the coupling effect between soil water and salt is essential for managing saline-alkali farmland. This study utilized the multifractal method to analyze soil water and salt under varying water content levels in arid saline-alkali farmland. Soil samples were collected on the second (S1), fifth (S2), eighth (S3), eleventh (S4), and fourteenth (S5) days after a rainfall event. The findings showed that a significant decrease in soil water content, contrasting with an increase in soil salt content throughout the soil layers post-rainfall. As water content decreased, the spatial variability of soil water initially increased from S1-3 and then decreased, while the spatial variability of soil salt decreased. The spatial distribution between soil water and salt exhibited a high correlation at S3-4 (with relative water content of soil ranging from 0.52 to 0.75) due to their coupling effect. However, soil salt was unevenly leached by rainfall at high water content levels (S1-2) and precipitated at low water content levels (S5), resulting in low spatial variability correlations between soil water and salt. This study elucidated the coupling process of soil water and salt, identifying their spatiotemporal distribution mechanism in dryland agricultural areas.

For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

Spatial and temporal variation of the free electron concentration in the ionosphere affects SAR interferograms, in particular at low radar frequencies. In this work the identification, estimation and compensation of ionospheric path delay phases in PALSAR-3 and NISAR-L interferograms is discussed. Both these L-band sensors acquire simultaneously SAR data in a main spectral band and in an additional, spectrally separated, narrower second band to support the mitigation of ionospheric path delays. The methods presented permit separating the dispersive and the non-dispersive phase terms based on the double-difference interferogram between the two available spectral bands and the differential interferogram of the main band. The applicability of the proposed methods is demonstrated using PALSAR-3-like data that was simulated based on PALSAR-2 SM1 mode data.

：Non-Euclidean data, such as social networks, and citation relationships between documents, has node information and structural information. Graph Convolutional Network(GCN) can automatically learn node features and association information between nodes. The core ideology of the graph convolutional network is to aggregate node information by using edge information, thereby generating a new node feature. In the process of updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node, the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the Adaptive Attention Mechanism(AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution(MHGC). Finally, we adopt cross-entropy(CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on AAM, MHGC and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). Experiments show that the classification accuracy has achieved outstanding performances on Cora, Citeseer and Pubmed datasets.

The existing segmentation-based scene text detection methods mostly need complicated post-processing, and the post-processing operation is separated from the training process, which greatly reduces the detection performance. The previous method, DBNet successfully simplified post-processing and integrated the post-processing into a segmentation network. However, the training process of the model took a long time for 1200 epochs and the sensitivity to texts of various scales was lacking, leading to some text instances being missed. Considering the above two problems, we design the text detection Network with Binarization of Hyperbolic Tangent(HTBNet). First of all, we propose Binarization of Hyperbolic Tangent (HTB), optimized along with which, the segmentation network can expedite the initial convergent speed by reducing the amount of epochs from 1200 to 600. Because features of different channels in the same scale feature map focus on the information of different regions in the image, to better represent the important features of all objects in the image, we devise the Multi-Scale Channel Attention(MSCA). Meanwhile considering that multi-scale objects in the image cannot be simultaneously detected, we propose a novel module named Fused Module with Channel and Spatial(FMCS), which can fuse the multi-scale feature maps from channel and spatial dimension. Finally we adopt cross entropy as the loss function, which measures the difference between predicted values and ground truths. The experimental results show that HTBNet compared with lightweight models has achieved competitive performance and speed on Total-Text(F-measure:86.0%, FPS:30) and MSRA-TD500 (F-measure:87.5%, FPS:30).

Batch and transport experiments were used to investigate the remediation of a loamy sand soil contaminated with Cr(VI) using zero valent iron nanoparticles (nZVI) stabilized by the carboxy methyl cellulose (CMC-nZVI). The effect of pH, ionic strength (IS), and flow rate on the removal efficiency of Cr(VI) were investigated under equilibrium (uniform transport) and non-equilibrium (Two-site sorption) transport using the Hydrus-1D model. The overall removal efficiency ranged from 70 to over 90% based on the chemical characteristics of the CMC-nZVI suspension and the transport conditions. The concentration and pH of the CMC-nZVI suspension had the most significant effect on the removal efficiency and transport of Cr(VI) in the soil. The average removal efficiency of Cr(VI) was increased from 24.1 to 75.5% when the concentration of CMC-nZVI nanoparticles was increased from 10 to 250 mg L-1, mainly because of the increased total surface area at larger particle concentration. Batch experiments showed that removal efficiency of Cr(VI) was much larger under acidic conditions. The average removal efficiency of Cr(VI) reached 90.1 and 60.5 % at pH 5 and 7, respectively. The Two-site sorption model described quite well (r2 = 0.96-0.98) the transport of Cr(VI) in soil as compared to the uniform transport model (r2 = 0.81-0.98). The average retardation of Cr(VI) was 3.51 and 1.61 at pH 5 and 7, respectively, indicating earlier arrival for the breakthrough curves and shorter time to reach maximum relative concentration at lower pH. The Hydrus-1D model has proven to be an efficient tool for assessing the removal of Cr(VI) pollutants, offering innovative, cost-effective, and environmentally friendly remediation methodologies.

Our study aimed to assess sprinkler irrigation's impact on the activity of selected soil enzymes concerning nitrogen metabolism and oxidation-reduction processes in the soil with different doses of inorganic nitrogen fertilizers. The soil Alfisol was sampled from the experimental field of spring barley in the University Research Center in the central part of Poland in the moderate transitory climate during the growing seasons 2015–2017. The index resistance (RS) was derived to recognise the resistance enzymes of drought. In the maturity phase, nitrate reductase activity was at an 18% higher level in irrigated soils and the activity of other enzymes was higher in no-irrigated treatments by 25% in dehydrogenase, 22% in peroxydase 33% in catalase and 17% in urease case. The development stage of barley influenced nitrate reductase activity. Enzymatic activity has been changing in the examined years of the research, depending on the weather conditions. During the maturity stage, the soil's lower ammonium nitrogen content resulted from higher spring barley uptake due to drought stress. Irrigation probably contributes to increased leaching of nitrate in soil. The highest index of resilience was presented in the soil catalase activity.

This review provides a comprehensive and updated information on chalcones, presenting their sources, methods of identification and properties putting emphasis on applications in agricultural industry. Vast exploitation of synthetic pesticides led to the weed and pest resistance issue, causing economic loss but more importantly health risk due to pesticide overuse. Because one of the aim of the European Green Deal 2030 is the Farm to Fork strategy which targets on reducing by 50% the use of chemical pesticides by 2030, and push towards natural pesticides which are more environmentally friendly and restore natural resources, chalcones and their derivatives seem to fit perfectly. They have herbicidal, fungicidal, bactericidal and antiviral properties. Moreover, chalcones are naturally occurring substances, known for their favorable health effect for years, working on many areas. This review is complementary to the previous ones describing chalcones use in agriculture, updates them but additionally shows methods of detection.

Phosphite (Phi) has gained attention in agriculture due to its biostimulant effect on crops. This molecule has been found to benefit plant performance by providing protection against pathogens, improving yield and fruit quality, and improving nutrient and water use efficiency. It is still unclear how Phi enhances plant growth and protects against multiple stresses. It has been hypothesized that Phi acts by directly affecting the pathogens and interacting with the plant cellular components and molecular machinery to elicit defense responses. This study delves into elucidating the mechanisms underlying Phi’s beneficial effects on plants, revealing a complex interplay with fundamental signaling pathways. A RNA-seq study of Arabidopsis seedlings under optimal and limiting phosphate conditions helped us unveil Phi’s role in promoting plant growth by activating the expression of genes involved in the biosynthesis and signaling pathways associated with abscisic acid (ABA), salicylic acid (SA), and jasmonic acid (JA). Expression of ABA-related genes, known for their involvement in stress response and development regulation, is triggered by Phi treatment, contributing to enhanced resilience and growth. Simultaneously, the activation of the SA pathway, associated with defense responses, suggests Phi’s potential in bolstering plant immunity. Moreover, Phi influences JA biosynthesis and signaling, which are crucial for defense against herbivores and pathogens, thereby strengthening plants’ defenses. Our findings reveal a multifaceted mechanism through which Phi benefits Arabidopsis development. Understanding its intricate interplay with key signaling pathways opens avenues for leveraging Phi as a strategic tool to enhance plant resilience, immunity, and growth in agricultural and ecological contexts.

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e. professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

Crop production in Afghanistan suffers from limited phosphorus (P) availability, which severely hinders national agriculture sustainability. This present study hypothesized that deep fertilizer placement could significantly enhance uptake of immobile P and thus tissue P accumulation and crop yield. A two-year pot experiment growing two maize (Zea mays) hybrid cultivars (Xida-789 and Xida-211) was therefore conducted to test these hypotheses under three contrasting fertilizer placement methods (broadcast, side band, and deep band). In doing so, P concentrations in both maize tissues and soils were compared at 45, 60, and 115 days after sowing (DAS) under nine combinations of nitrogen (N) and P fertilizer rates (kg ha-1: N112P45, N112P60, N112P75, N150P45, N150P60, N150P75, N187P45, N187P60, N187P75). Results have shown that deep band placement significantly increased P uptake efficiency, leading to greater P concentration and accumulation in maize tissues compared to other two fertilization methods. This improved P uptake was attributed to several factors associated with deep placement, including reduced P fixation, enhanced root access to P, and moisture availability for P uptake. Additionally, deep band placement combined with higher N application rates (N187 and N150) further enhanced plant P uptake by promoting P availability and utilization mechanisms. Deep band placement also resulted in significantly higher total soil P, Olsen-P, and P use efficiency than broadcast and side band methods, indicating a more efficient P fertilization strategy for maize that can improve growth and yield. This present study also found positive correlations between P concentration in plant organs and soil Olsen-P, highlighting the importance of adequate soil P levels for optimal plant growth. Overall, our results have showed that deep band fertilizer placement emerged as a superior strategy for enhancing P uptake efficiency, utilization, and maize productivity compared to broadcast and side band placement. This approach can potentially optimize P fertilizer utilization, improve maize growth and yield, while minimizing environmental P losses associated with broadcast applications.

Soil water balance (SWB) in woody crops is sometimes difficult to estimate with one-dimensional models because these crops do not completely cover the soil and usually have a deep root system, particularly when cropped under rainfed conditions in a Mediterranean climate. In this study, the actual crop evapotranspiration (ETc act) is estimated with the soil water balance model SIMDualKc which uses the dual-Kc approach (relating the fraction of soil cover with the crop coefficients) to improve the estimation of the water requirements of a rainfed vineyard, using data from a deep soil profile. The actual basal crop coefficient (Kcb act) obtained using the SIMDualKc model was compared with the Kcb act estimated using the A&P approach, which is a simplified approach based on measurements of the fraction of ground cover and crop height. Spectral vegetation indices derived from Landsat-5 satellite data were used to determine the fraction of ground cover (fc VI) and thus, the density coefficient (Kd). The SIMDualKc model was calibrated using soil water content (SWC) measurements down to a depth of 1.85 meters, which significantly improved the conditions for using a SWB estimation model. The test of the model was performed using a different SWC dataset. A good agreement between simulated and field measured SWC was observed for both data sets along the crop season, with RMSE<12.0 mm, NRMSE <13%. The calibrated Kcb values were 0.15, 0.60 and 0.52 for the initial, mid-season, and end season, respectively. The ratio between actual (ETc act) and crop evapotranspiration (ETc) was quite low between veraison and maturity (mid-season), corresponding to 36%, indicating that the rainfall was not sufficient to satisfy the vineyards water requirements. Spectral vegetation indices used to compute fc VI were unable to fully track the plants’ conditions when water stress. However, ingestion of data from remote sensing showed promising results that could be used to support decisions making in irrigation scheduling. Further studies on the use of the A&P approach using remote sensing data are required.

Maintaining sufficient levels of plant nutrients in the soil and controlling certain heavy metals, which can be toxic to the environment, are critical to ensure sustainable agricultural production. The study aimed to assess the linkage of crop-specific agricultural practices established by farmers in the Kvemo Kartli region (Georgia) with metal accumulation in soils of agricultural lands being subject to influence from polluted irrigation water in the past. In particular, we tried to identify the primary sources of micronutrients, including iron (Fe), copper (Cu), manganese (Mn), nickel (Ni), and zinc (Zn), and toxic elements such as cadmium (Cd) and lead (Pb), and share of the contaminated irrigation water and other factors related to agricultural practices under different land uses, such as intensive and extensive arable farming, vineyards, orchards, and permanent pastures having the least disturbed soil. Based on principal component analysis, five primary sources were identified and categorized according to farmers' interviews and previous studies conducted in the region. The results showed that increased concertation of plant-available Cu, Zn, Cd and Pb were mainly associated with irrigation water and intensive use of fungicides; Fe, Mn, and Ni were closely linked to several factors, such as the mineralogical composition of soils, mineral and organic fertilizers inputs, and atmospheric deposition from diffuse sources, where exhausts from transport are probably the primary source. During our study, we attempted to differentiate irrigation water inputs from fungicides using simulation based on irrigation pattern and irrigation water quality on the one hand and fungicide application rate and their metal content on the other. The simulation revealed that the intensive application of fungicides, especially in vineyards, is more significant in enriching soils with Cu and Zn than irrigation water. Identification of factorial dependences was supported by statistical analysis and application of a number of contamination assessment methods: Contamination Factor (CF), Pollution Load Index (PLI), single-factor Pollution Index (PI), Nemerow’s comprehensive pollution index (PIN), Enrichment Factor (EF), and Geo-Accumulation Index (Igeo). Applied environmental indices indicate that the soils under the former and existing vineyards are the most enriched with Cu and Zn, highlighting the significance of agricultural practices on heavy metals accumulation in soils of agricultural lands.

Proximal Junctional Kyphosis (PJK) as one of the most discussed problems after corrective surgery in adolescent idiopathic scoliosis (AIS), is still not fully clarified and seems to be multifactorial. Biome-chanical and few clinical studies have shown the influence of destruction of posterior ligaments by re-section of spinous processes and some parameters concerning rod contouring as risk factors for PJK. To verify these results 192 patients with AIS and corrective surgery via posterior approach between 2009 and 2017 were included. Radiographic parameters were analyzed preoperatively (preOP), postoperatively (postOP), and with a mean follow up (FU) of 27 months. The participants were divided into two groups (PJK group and non-PJK group). The incidence of PJK was 15.6%. Contrary to the results of biomechanical studies, we could not find any significant influence of the spinous process resection. However, the PJK group had significantly larger preOP T4-T12 kyphosis (31.1°±13.93° vs. 23.3°±14.93°, p=.016). Furthermore, the PJK group showed a significantly larger rod contour angle (RCA) (8.0°±4.44° vs. 5.9°±3.28°, p=.003) and mismatch of postOP proximal junctional angle (PJA) and RCA (3.5°±5.72° vs. 0.9°±4.86°, p=.010) compared to the non-PJK group. An increase of the mismatch of postOP PJA and RCA (OR=1.14, p=.008) and a high RCA are risk factors for PJK and need to be focused on by surgeons.

Smallsats are becoming the predominant electro-optical Earth observation (EO) imaging platforms. While atmospheric correction of smallsat data enhances its utility, an established pathway to do so may constrain accuracy and utility. The alternative, Closed-form Method for Atmospheric Correction (CMAC), developed for smallsat application provides surface reflectance derived solely from scene statistics. In a prior paper, CMAC closely agreed with Land Surface Reflectance Code (LaSRC) software for correction of the four VNIR bands of Landsat-8/9 images for conditions of low to moderate atmospheric effect over quasi-invariant warehouse-industrial targets. Those results were accepted as surrogate surface reflectance to support analysis of CMAC and LaSRC reliability for surface reflectance retrieval in two contrasting environments: shortgrass prairie and barren desert. Reliability was defined and tested through a null hypothesis: the same top-of-atmosphere reflectance under the same atmospheric condition will provide the same estimate of surface reflectance. Evaluated against the prior surrogate surface reflectance, the results found decreasing error with increasing wavelength for both methods. From 58 comparisons across the four bands, LaSRC average absolute error ranged from 0.59% (NIR) to 50.30% (blue). CMAC error was well constrained from 0.01% (NIR) to 0.98% (blue) sustaining the null hypothesis for reliability.

The atmospheric lightning is one of the natural disasters which need to be considered in the sustainable development. The National Remote Sensing Centre (NRSC) has installed 46 lightning detection sensors (LDS) across the country. These sensors work in radio frequencies having their detection range up to 1000 km with more than 98% detection efficiency within 300 km range. The network is put in a way that sensors have about 50% sensor-to-sensor overlap in their detection ranges to enable high geolocation accuracy and maintain redundancy. The NRSC-LDS network (LDSN) estimates the current associated with each cloud to ground (CG) lightning flash which can be utilized for the risk assessment. The current estimates from one network to the other need to be inter-calibrated and harmonized. Present study compares the LDSN current estimates with Lightning Location Network (LLN) estimates and results are discussed with their impact on the estimation of vulnerable zones. A case study of lightning-induced fire event is presented to showcase the importance of the currents associated with lightning for future planning.

With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most of the cellular systems such as Bluetooth indoor positioning system due to multipath and non-line-of-sight (NLOS) propagation, the indoor positioning accuracy is severely affected. In this paper a comprehensive algorithm is investigated that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates obtained using particle filtering, which allows us to explore new optimised methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by about 25.7% on average.

This paper is aimed at studying the temporal evolution of the surface displacements occurred over the past few years in the ocean-reclaimed platforms of the Shanghai megacity (China), which are mainly ascribable to consolidation processes of large dredger fills and alluvial deposits. With respect to previous analyses carried out over the same area, this work provides a joint multi-platform differential interferometry synthetic aperture radar (DInSAR) analysis, based on the application of the advanced Small BAseline Subset (SBAS) algorithm. This led us to retrieve long-term deformation time-series that are helpful for a better understanding of the on-going deformation phenomena. To this aim, we have exploited two sequences of SAR data collected by the ASAR/ENVISAT and by the COSMO-SkyMed (CSK) sensors, respectively, spanning the whole time period from 2007 to 2016. Unfortunately, the large time gap (of about three years) existing between the available ASAR/ENVISAT and CSK datasets gave rise to additional difficulties for their combination. Nevertheless, this problem has been faced by benefiting from the knowledge of a time-dependent model describing the temporal evolution of the expected deformations affecting the Shanghai ocean-reclaimed platforms.

Outliers seriously affect the accuracy in geometric model fitting. Previous works in coping with outliers involve threshold selection and scale estimation. However most scale estimators suppose the inlier distribution as a Gaussian model, which usually poorly meets the cases in geometric model fitting. Outliers, considered as the points with big residuals to all the true models, share common items with big values in quantized residual preferences, thus making the outliers gather away from the inliers in quantized residual preference space. In this paper we make use of this outlier consensus in quantized residual preference space by extending the usage of energy minimization to combine the model error and the spatial smoothness in quantized residual preference space for outlier detection. The energy minimization based outlier detection process follows an alternate sampling and labeling framework. After the outlier detection, an ordinary energy minimization method is employed to optimize the inlier labels, which also follows the framework of alternate sampling and labeling. The experimental results in this study show that the energy minimization based outlier detection method can detect most of the outliers in the data. Furthermore, the proposed energy minimization based inlier segmentation process segments the inliers into different models quite accurately. Overall, the performance of the proposed method is better than most of the state-of-the-art methods.

The impact of antecedent soil moisture content on soil erosion is a complicated phenomenon, which is still under argument with contradictions in research results. Hence, the objective of this study is to investigate the impact of antecedent soil moisture content to soil loss on clay soil by conducting two-year field experiments under natural rainfall on runoff plots with length of 10 meters, width of 3 meters, and uniform slope of 9%. Volumetric soil moisture sensors were used to log soil moisture changes, and soil moisture readings were used along with rainfall records to quantify the antecedent soil moisture conditions for each rainfall event. Results of this study show duration of the effective erosion event outranks the antecedent volumetric soil moisture content. Field study also suggests that accumulative rain falling within 48 hours (Pp48) prior to an effective erosion event strongly correlated with soil loss per Rainfall-Runoff Erosivity Index (Soil Loss / EI30), particularly when the duration of an effective erosion event is either between 3 ~ 7 hours or 10~ 30 hours. Hence, Pp48 can be considered as an alternative to replace antecedent soil moisture content in RUSLE 2’s cover and management factor.

N-Acyl-homoserine lactones, the Quorum sensing signaling molecules predominantly found in gram-negative bacteria, which regulate several bacterial genes including virulence and antibiotic resistant genes. The study was aimed to identify and characterize QS and QQ bacteria from different samples. 5 samples with different ecological background were collected from soil and 10 samples from hospital setup. 31 different bacteria were isolated with either QS or QQ activities all together. CV026 and A136 biosensor strains were used for the detection of QS and QQ positive strains. QS activity was observed by cross streaking test of bacteria against CV026, it was affirmed that 13 isolates from the soil and 5 from hospital equipment’s showed positive QS activity. QQ activity of each isolate was tested by well diffusion assay, C6-HSL and C12-HSL were our candidate AHL molecules. The AHL molecule degradation was detected in 4 isolates of soil and none from the samples obtained from hospital setup. The total of 6 strongly positive QS and QQ isolates were identified and selected for 16S rRNA gene sequencing. The phylogenetic analysis revealed that these isolates were closely related to Pseudomonas, Bacillus and Exiguobacterium genera. In contrast, 1 Gram positive bacterial isolate was also purified with QS potential.

Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the actor critic (A2C) framework and a particle filter gated recurrent unit for localization is proposed. Performance is studied in a randomized 20 x 20 m convex and non-convex environment across a range of signal-to-noise ratio (SNR)s for a single detector and single source. RAD-A2C performance is compared to both an information-driven controller that uses a bootstrap particle filter and to a gradient search (GS) algorithm. We find that the RAD-A2C has comparable performance to the information-driven controller across SNR in a convex environment and at lower computational complexity per action. The RAD-A2C far outperforms the GS algorithm in the non-convex environment with greater than 95% median completion rate for up to seven obstructions.

The mineralogical indices, e.g., Quartz Index (QI), Carbonate Index (CI), Mafic Index (MI) for ASTER multispectral thermal infrared (TIR) data have been applied to various geological materials. The indices are proved to be robust in extracting geological information at the local scale. Considering the relatively narrow spatial coverage of each ASTER scene compared to LANDSAT, many scenes are needed when mosaicking the images to be mapped at regional scales. We develop a system to search ASTER data for a target area given the vast and expanding ASTER data archive. The data is then conditioned, prioritized, and the indices are calculated before finally mosaicking the imagery. The maps of the indices covering the very wide region of the central Tibetan Plateau are produced with this system. The characteristic features of the indices relating to their geology in the study area are analyzed and discussed. Many interesting lithological and structural information that are not currently well understood in the central Tibetan Plateau, the highest and most extensive plateau in the world, with an average elevation of over 4,500 meters above sea level, for example, distributions of the mafic-ultramafic rocks along the suture zones, the quarzitic and marine sedimentology accreted to the Eurasian continent and sulfate layers related to the Tethys and neo-Tethys geological setting can be retrieved from the processed ASTER images.

This article describes the history of research and development of surface-penetrating radar, starting from the 60s in the USSR, and ending today in Russia and neighboring states formed after the collapse of the USSR. The outstanding role played in the initial period of this technology development by Prof. M.I. Finkelstein and his colleagues is highlighted. Their research had a significant impact on subsequent developments in modern Russia and the neighboring countries.

This study explores the potential of digital technologies for preserving cultural heritage through the case study of the Raya Gopura, an incomplete monument in Melukote, India. Employing a combination of photogrammetry (terrestrial and drone-based) and terrestrial laser scanning, the research team captured highly detailed 3D models of the structure. This non-destructive approach offers a comprehensive digital record for future research and preservation. Furthermore, consultations with experts yielded two possible purposes for the Raya Gopura: an entrance to a planned fort or a gateway to a temple. These insights inform a conjectural architectural drawing of the potentially completed monument. The 3D models hold significant potential for various applications, including tourism, education, and architectural research, with the potential for immersive experiences through VR and AR technologies. In conclusion, this study demonstrates the effectiveness of digital tools in heritage preservation, not only safeguarding the Raya Gopura but also offering valuable insights for future research and public engagement.

The Microwave Humidity and Temperature sounder (MWHTS) on board the Fengyun (FY)-3C satellite measure the outgoing radiance form the Earth surface and atmospheric constituents. MWHTS makes measurements in the isolated oxygen absorption line near 118 GHz and the vicinity of strong water vapor line around 183 GHz, can provide fine vertical distribution structure of both atmospheric humidity and temperature. However, in order to obtain the accurate soundings of humidity and temperature by the physical retrieval method, bias between the observed radiance and those simulated by radiative transfer model from the background or first guess profiles must be correct. In this study, two bias correction methods are developed through the correlation analysis between MWHTS measurements and air mass identified by the first guess profiles of the physical inversion, one is the linear regression correction (LRC) and the other is neural networks correction (NNC), representing the linear and nonlinear nature between MWHTS measurements and air mass, respectively. Both correction methods have been applied to MWHTS observed brightness temperatures over the geographic area (180° W-180° E, 60° S-60° N). The corrected results are evaluated by the probability density function of the difference between corrected observations and simulated values and the root mean square error (RMSE) with respect to simulated observations. The numerical results show that the NNC method perform better, especially in MWHTS channels 1 and 7-9 whose peak weight function heights are close to the surface. In order to assess the effects of bias correction methods proposed in this study on the retrieval accuracy, a one-dimensional variational system was built and applied to the MWHTS uncorrected and corrected brightness temperatures to estimated atmospheric temperature and humidity profiles, The retrieval results show that the NNC has better performance which is to be expected. An indication of the stability and robustness of NNC method is given which suggests that the NNC method has promising application perspectives in the physical retrieval.

This research investigates precision enhancement in regional quasi-geoid refinement through ADS80 tri-linear array scanning stereoscopic imagery for aerial triangulation, coupled with GNSS gravity-potential modeling. Acquiring stereoscopic imagery and analyzing triangulation accuracy with the ADS80 camera, we performed this study over the Qinghai-Tibet Plateau's elevated, desolate terrain, collecting 593 GNSS points following high-precision stereoscopic imagery model. Utilizing 12 gravity satellite models, we computed geoid heights and China 1985 Yellow Sea elevations for 28 benchmarks and GNSS points, thereby refining the Qinghai Province Quasi-Geoid Model (QPQM) using geometric techniques. The findings reveal that POS-assisted ADS80 stereoscopic imagery yields high-precision triangulation with maximal horizontal and elevation accuracies of 0.083/0.116 cm and 0.053/0.09 cm, respectively, across five control point arrangements. The RMSE of normal heights for 1985, processed via these GNSS points, achieved decimeter precision. By applying error corrections from benchmarks to the 1985 elevation data from gravity satellites and performing weighted averaging, the precision of EGM2008, SGG-UGM-2, and SGG-UGM-1 models improved to 8.61 cm, 9.09 cm, and 9.38 cm, respectively, surpassing the QPQM by 9.22 cm to 9.99 cm. This research demonstrates that the proposed methods can significantly enhance the precision of regional quasi-geoid surfaces. Additionally, these methods offer a novel approach for rapidly establishing regional quasi-geoid models in the uninhabited areas of the Qinghai-Tibet Plateau.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

In the Vietnamese Mekong delta, soil quality and crop yield are steadily declining under rice monocultures with three crops per year. The objective of this study was to evaluate the medium-term effects of rotating rice with upland crops and adding organic amendments on rice yield and relate it to soil quality. A field trial with split-plot design including two factors and three replicates was carried out from 2017 to 2020, over the course of nine consecutive cropping seasons. Crop rotations and organic amendments were applied as main-plot and subplot factors, respectively. The rotations were: 1) rice–rice–rice (R–R–R), 2) soybean–rice–rice (So–R–R), and 3) sesame–rice–rice (Se–R–R), while organic amendment treatments included: i) no amendment (NO-AM); ii) compost of rice straw and cow manure (RS+CM), and iii) sugarcane compost (SGC); the composts were applied at a rate of 2.0 t ha-1. The rotation cycle started with the so-called spring–summer (SS), followed by summer–autumn (SA) and ended with the winter–spring (WS) season. Rice yield significantly (P < 0.05) increased by organic amendments after nine growing seasons (2019–2020 WS), with an increment of 5.1% for RS+CM (7.07 ton/ha) and 6.1% for SGC (7.14 ton/ha). Contrary to our expectation, rotations with upland crops did not significantly increase rice yield. Rice yield significantly and positively correlated with an integrated soil quality index SQI (r = 0.86) for the topsoil (0–15 cm), but not for the subsoil (15–30 cm). The increased availability of soil nutrients (Si and marginally also P) and improved soil physical properties probably induced by organic amendments, along with other soil properties under study, cumulatively attributed to enhanced rice yield. Use of a SQI involving several soil quality indicators enables to overall quantify the importance of soil fertility for rice yield versus other factors and it provides an effective means of quantifying the integrated effect of improved management. Moreover, integrating a wide range of soil quality indicators in a SQI ensures its applicability across diverse settings, including different crop rotations and various soil types.

Our work is regarding the analysis of land use changes, in the light of “saving soil” against the expansion due to unearned plus value of land: The loss of natural and agricultural surface in front of the expanding urban environment is a critical aspect of unsustainability of urban development, especially in the way it was carried out in the past decades. The measure of the physical transition of land use and characters from a more natural condition of land surface to a new artificial one, joint with a parallel analysis of the increase of land value due to such change is nowadays a major land-policy tool. The interplay of urban economics regulation with planning, reveals new key issues in urban governance and environmental preservation. In this paper it will be shown some experiment about the impact assessment of soil take, related with the seek of valorization of property inside the planning process. Our paper reports as well about the experimental activity carried out inside the MITO Lab of the Polytechnic of Bari, where reports about property values and environmental values have been produced, specially looking at the reality of the Apulia, a southern Italian Region, that is rich of farmlands and coastlines, often invaded by constructions with a severe loss of nature, landscape and ecosystems services.

In this paper, a distributed algorithm is proposed to solve a consensus convex optimization problem. It is a Jacobi-proximal alternating direction method of multipliers with a damping parameter $\gamma$ in the iteration of multiplier. Compared with existing algorithms, it has the following nice properties: (1) The restriction on proximal matrix is relaxed substantively, thus alleviating the weight of the proximal term. Therefore, the algorithm has a faster convergence speed. (2) The convergence analysis of the algorithm is established for any damping parameter $\gamma\in(0,2]$, which is larger ones in the literature. In addition, some numerical experiments and an application to a logistic regression problem are provided to validate the effectiveness and the characteristics of the proposed algorithm.

Background: The aim of the present study was to determine the correlation between obesity, serum levels of leptin and proximal gastric cancer. Methods: Sixty-four gastric cancer patients operated on with curative intent were included in the study. We determined the correlation between the preoperative serum levels of leptin and the tumor`s location. Results: Serum leptin levels were correlated significantly with the proximal third location (p = 0.04), gastric outlet obstructing tumors (p < 0.0001), CRP levels (p = 0.03) and BMI (p < 0.0001). Patients with high serum levels of leptin had significantly more intestinal types of gastric cancer (p = 0.033) and better differentiation compared to patients with low serum leptin levels (p = 0.009). The linear regression model determined the proximal tumor location (beta: 0.467; p = 0.045), BMI (beta: 0.657; p = 0.001), high preoperative serum albumin (beta: 0.563; p = 0.016) and the presence of pyloric stenosis (beta: 0.525; p = 0.006) as related significantly to serum leptin levels. The Cox proportional hazard model identified age (HR: 0.003; 95%CI: 0-0.794; p = 0.041), preoperative serum levels of leptin (HR: 0.125; 95%CI: 0.018-0.887; p = 0.037) and the number of extracted LNs (HR: 0.001; 95%CI: 0-0.677; p = 0.038) as independent prognostic factors. Conclusions: Serum levels of leptin were significantly elevated in patients with proximal gastric cancer, suggesting that the leptin`s effect might be due to its systemic secretion. This might explain the higher incidence of proximal gastric cancer in obese patients. Elevated serum leptin levels were an independent prognostic factor.

The Katerini-Kolindros aquifer system (KKas) is a critical freshwater resource, essential for the local and regional economy. To understand contamination sources, groundwater flow paths, and assess the aquifer's health, an extensive dataset of 751 samples from 113 wells collected between 2010 and 2020 was analyzed using multivariate statistical analysis (MVSA) and hydrogeochemical diagrams. The prevailing water types identified were Ca-HCO₃ and Mg-HCO₃, with mixed water types resulting from various factors affecting groundwater quality. The chemistry of groundwater in the KKas is shown to be affected by ion exchange processes and to a lesser extent by reverse ion exchange processes. No indications of sea-water intrusion have been identified in the water wells monitored. On the other hand, nitrate contamination has been detected in specific water wells spatially associated with urban-type land uses and activities. It is considered that nitrate contamination originates primarily from septic tanks and urban sewage networks or secondarily from fertilization of football pitches, cemeteries or urban waste management practices.

In Ethiopia, farmers have changed cropland to Eucalyptus globulus plantations for construction, fuelwood and economic benefits. However, it has adverse effects on soil properties. The investigation was, therefore, to evaluate the impact of Eucalyptus globulus plantations on soil properties in northwestern Ethiopia. About 30 soil samples were collected from two soil depths (0-20 and 20-40 cm) and three land uses (Eucalyptus globulus plantations, cropland and grazing land) with five replications. Soil properties were analyzed using a two-way analysis of variance (ANOVA). Based on the result, soil properties such as silt particles, moisture content (MC), organic matter (OM), total nitrogen (TN) and cation exchange capacity (CEC) were significantly higher under the soil of Eucalyptus globulus plantations than in cropland and grazing land. Whereas the lower contents of bulk density (BD), soil pH, available phosphorus (AP), electrical conductivity (EC) and exchangeable bases (Ca, Mg, K, and Na) were recorded in Eucalyptus globulus plantations. Besides, clay, BD, MC, pH, EC, CEC, percent base saturation (PBS) and basic cations increased with soil depth while decreasing sand, silt, TP, OM and AP. The basic cations ( Ca, Mg, K), PBS and AP decreased in the soil of the 25-year old Eucalyptus globulus plantation compared to cropland by 38%, 36%, 43 %, 45% and 47% and grazing land by 56%, 60%, 76 %, 60% and 56%, respectively. Therefore, appropriate and effective land use management principles should be implemented in the study area.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and re-analysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, to capture the nuanced dynamics of Cyprus's atmospheric conditions. With a temporal resolution of 15 minutes and a spatial of 0.05°x0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models' accuracy but also highlights their proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), of -2 to -4% per decade have been found in Autumn, especially marked on the island’s Northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of -2 to -3% per decade that were found in Spring do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the Daylight Integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 extracted articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, ISPRS Journal Of Photogrammetry And Remote Sensing as the leading journals. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by USA (11.76%), France (5.88 %), Spain (4%) and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most commonly used data sources. Benchmark datasets such as ISPRS Vaihingen &amp; Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder-decoder, and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue, and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

This study investigates how root environment acidification impacts tea seedlings and the role of magnesium (Mg) in mitigating these effects. We examine varying pH and Mg levels' influence on tea seedlings' resistance to abiotic stress, focusing on antioxidant capacity and nutritional content. In a hydroponic experiment, we varied root pH (3.5, 5.0, 6.5) and Mg concentrations (0.01, 0.4, 0.8 mM), assessing parameters like antioxidant capacity, peroxidative damage, and nutritional content at 1, 7, 15, and 30 days post-treatment. Root environment acidification and Mg deficiency worsened peroxidative damage in tea plant leaves and roots. Increased Mg supplementation enhanced antioxidant enzyme activity, reducing malondialdehyde and mitigating oxidative damage from root environment acid stress. Under acid stress, 0.8 mM Mg significantly increased tea leaf polyphenols, amino acids, and water-soluble extracts. Mg notably boosted chlorophyll content, surpassing lower Mg levels at pH 5. Additionally, Mg reversed root vitality inhibition induced by acid stress, leading to increased nitrogen, potassium, and Mg concentrations in leaves, promoting balanced nutrient absorption. Mg supplementation is crucial for enhancing tea plant antioxidant capacity, alleviating growth inhibition from root environment acid stress, and improving chlorophyll content and root vitality, highlighting Mg's significance in tea cultivation and broader agricultural practices.

Global warming leads in general to a reduction in precipitations and non-rainfall water contributions such as dew. The present study, carried out in Gujarat (India) for 17 years between 2005 and 2021, shows however a rare increase of the rain falls and dew condensation, the latter related to an increase in relative humidity and a decrease in wind amplitudes. Rain was obtained from meteo stations. Dew was calculated from an energy model and meteo data from 15 meteo stations (14 in India and 1 in Pakistan) and compared to early and dedicated measurements in 13 sites. Maps of dew, rain and their relative contributions during the period are provided. They show that, while rain mainly occurs during the monsoon (June to September), dew forms during the dry season (October to April). This water resource was mapped over the Gujarat state of India by using a kriging process. According to the site, the dew/rain ratios can represent between 4.6% (Ahmedabad) to 37.2% (Jamnagar) on the whole period of 17 years. This positive evolution, which is observed since 2015-2017, is likely to continue in the future.

Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. There are many studies focused on flood impact assessments for administrative districts but fewer on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and rainfall data, combined with crop growing season data to construct a crop flood damage assessment index (CFAI). First, an analytic hierarchy process (AHP) was used to assign weights to the impact factors; then, the weighted composite score method was used to calculate the CFAI; and finally, the impact was classified as slight, moderate, or severe based on the magnitude of the CFAI . This method was used for the Missouri River floods of 2019 in the U.S. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to validate the results. The combined approach achieved a high degree of consistency in spatial distribution. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.

Water is a fundamental resource for ecosystems, humans, and the development of all economic sectors; it is necessary to identify and evaluate its environmental pressures or impacts. The water footprint (WF) is an appropriate indicator for the consumption of water used to produce a product. The present study uses this tool to evaluate the green and blue water requirements and the sustainability of irrigation water use for agroindustrial avocado production in Ziracuaretiro, Michoacán (2012-2021). Our analysis was based on aggregating weather and soil data at the municipal level and official government databases of avocado cultivated surface, fruit production, and water rights concessions. The analysis considers the homogeneity of information throughout the study area. We estimated that rainfed plantations require 839.03 m³/ton and irrigated plantations require 2355.80 m³/ton, with an average of 1597.47 m³/ton. In addition, we determined that avocado cultivation can demand up to 124.3% of agricultural water concessions in this municipality. In addition to the WF estimates, the analysis indicates that such studies are fundamental for decision-makers to develop and implement water use-efficiency strategies and shows the need for further research related to water consumption of avocado as a crop at more detailed scales to reduce uncertainty.

Channel bends are one of the most important characteristic features of natural streams. These bends often create the conditions for a hyporheic zone, which has been recognized as a critical component of stream ecosystems. The streambed vertical hydraulic conductivity (K_v), vertical hydraulic gradient (VHG) and Darcian flux (DF) in the hyporheic zone were estimated at 61 locations along a channel bend of the Beiluo River during July 2015 and January 2016. All the streambed attributes showed great spatial variability along the channel bend. Both upward fluxes and downward fluxes occurred during the two test periods, most of studied stream sections were controlled by downwelling, indicating stream water discharge into the subsurface. The average downward flux was higher at the downstream side than at the upstream side of the channel bend, especially in July 2015. The distribution of streambed sediment grain size has a significant influence on the variability of K_v; high percentages of silt and clay sediments generally lead to low K_v values. Higher K_v at the depositional left bank at the upstream site shifted toward the erosional right bank at the downstream site, with K_v values positively correlated with the water depth. This study suggested that the variabilities of K_v and VHG were influenced by the stream geomorphology and that the distribution of K_v was inversely related, to a certain extent, to the distribution of VHG across the channel bend. K_v and VHG were found to have opposite effects on the DF, and the close relationship between K_v and DF indicated that the water fluxes were mainly controlled by K_v.

This study explores the potential of digital technologies for preserving cultural heritage through the case study of the Raya Gopura, an incomplete monument in Melukote, India. Employing a combination of terrestrial photogrammetry, drone-based photogrammetry and terrestrial laser scanning, the research team captured highly detailed 3D models of the structure. This non-destructive approach offers a comprehensive digital record for future research and preservation. Furthermore, for the conjecture for the Raya Gopura two possible purposes: an entrance to a planned fort or a gateway to the temple are perceived through the collective intelligence (CI) approach. These insights inform a conjectural architectural drawing of the potentially completed monument. The 3D models hold significant potential for various applications, including tourism, education, and architectural research, with the potential for immersive experiences through VR and AR technologies. The study demonstrated the effectiveness of digital tools in heritage preservation, not only safeguarding the Raya Gopura but also offering valuable in-sights for future research and public engagements.

South Africa (SA) is grappling with a severe food insecurity crisis exacerbated by socioeconomic challenges. This resulted in the Department of Basic Education (DBE) advocating for the establishment of vegetable gardens at schools. However, information on the suitability of the soils in SA for sustainably growing Beetroot is unavailable. This study aimed to assess the suitability of soils for sustainable Beetroot production at various schools in the Kgakotlou circuit. Four Secondary Schools were selected: Mothimako, Madipoane, Klaas Mothapo and Phomolong. A detailed soil survey was carried out to study soils in vegetable gardens. Soil samples were collected for laboratory analysis of physicochemical properties. The soil suitability was evaluated using the physicochemical properties of the soils and optimal soil requirements for Beetroot production. We found various soils in the schools, including Cambisols (Hutton and Nkonkoni), Leptosol (Glenrosa), Plinthosol (Westleigh), and Acrisols (Tshiombo and Tubatse). Portions of the gardens on Hutton and Westleigh soil were moderately suitable (S2), limited by electrical conductivity (EC). Meanwhile, portions of Tshiombo, Tubatse, and Nkonkoni were marginally suitable (S3), limited by pH and EC. Portions on Glenrosa soil at Klaas Mothapo and Phomolong were not suitable (NS) and were limited by shallow depth, soil pH and EC. The results reveal the importance of considering soil suitability before establishing vegetable gardens in school settings. Indexing soil suitability using the physicochemical properties of soil resources for sustainable beetroot production is proposed for schools in the Kgakatlou circuit and other parts of SA.

Soil organic matter (SOM) is essential for nutrient cycling and soil carbon (C) accumu-lation, both of which are heavily influenced by the quality and quantity of plant litter. Since SOM dynamics in relation to plant diversity are poorly understood we investigated the effects of willow variety and mixture, and site on the soil C stocks, SOM chemical composition and ther-mal stability. Using pyrolysis-field ionization mass spectrometry we analysed the top 10 cm of soil from two 7-year-old experimental sites in Germany and Sweden, with monocultures and mixtures of two willow varieties (Salix spp.) belonging to different species. Overall, site had the strongest effect on SOM quality. Results showed significant variability across sites for willow identity and mixture effects on C accumulation and SOM chemistry. In the German site (Ros-tock), yearly soil C accumulation was higher (p < 0.05) for variety ‘Loden’ (1.0 Mg C ha-1 year-1) compared to ‘Tora’ (0.5 Mg C ha-1 year-1), whilst in the Swedish site (Uppsala), both varieties ex-hibited similar soil C accumulation rates of around 0.6 Mg C ha-1 year-1. Willow variety identity significantly affected SOM quality at both sites, while mixing had minor effects. Our findings emphasize the significance of site-specific context and variety or species identity in shaping soil C accumulation in willow plantations.

Background: Antibiotic resistance is an urgent issue everywhere in the globe. It is necessary to look into new sources of antibiotics to address this issue. Aim of the study: studying the purification of Corallopyronin B from various soil conditions in Egypt, as well as the antibacterial effectiveness of Corallopyronin B in preclinical animal testing and randomized human clinical trials phases 1/2. Type of the study: Screening experimental study. Methodology: Several soil conditions in Egypt were examined to create bacterial isolates that generated the antibiotic chemical Corallopyronin B. Using reversed-phase HPLC, Myxopyronin B was purified. The test antibiotic's minimum inhibitory concentration( MIC) and in vitro antibacterial activity were ascertained using the paper disc diffusion assay and the broth microdilution technique. Furthermore, in stages 1/2 of randomized clinical trials including human and animal models, pharmacokinetics, adverse drug reactions, and the in vivo antibacterial spectrum were discovered. Results: The soil bacterial isolate Corallococcus coralloides DSM 2259, grown on a Casein yeast peptone( CYP) plate, produced Corallopyronin B from its culture supernatant. At MICs more than 100 mcg/ml, the test antibiotic inhibited the growth of many Gram -ve bacteria, including Escherichia coli, while preventing the growth of numerous Gram +ve bacteria, with MICs ranging from 1 to 10 mcg/ml. Eukaryotic cells, on the other hand, including those in humans and fungi, were unharmed. The test antibiotic was shown to have a bactericidal effect by inhibiting bacterial DNA-dependent RNA polymerase( RNLP). In phases 1/2 of randomized human clinical trials, when 600 mg of the dose per 70 kg of body weight was administered SC, the Cmax was 8.6 mcg/ml at Tmax 1 hour; T1/2 reached 136 min following first-order kinetics of elimination. It stopped acting around 6-7 hours after SC was administered. In the preclinical and randomized human clinical trial phases 1/2, less than 6 percent of experimental candidates had uncommon toxicity, showing reduced bile flow. Protein binding with plasma albumin was detected which reached about 83%. Conclusion: The current work was noteworthy since it involved the production of the bactericidal antibiotic Corallopyronin B from Corallococcus coralloides DSM 2259 which was isolated from several soil environments in Egypt.

Oncomelania Hupensis is the exclusive intermediary host of Schistosoma japonicum in China. The alteration of its habitat and population distribution directly affects the safety of millions of individuals residing in the Yangtze River Economic Belt (YREB), and the ecological stability. Therefore, it is crucial to analyse the influence of climate change on the distribution of O. Hupensis in order to achieve accurate control over its population. This study utilised the MaxENT model to forecast possible snail habitats by utilising snail distribution data obtained from historical literature. The subsequent outcomes were achieved: The primary ecological factors influencing the distribution of O. Hupensis are elevation, minimum temperature of coldest month, and precipitation of wettest month; Furthermore, future climate scenarios indicate a decrease in the distribution area and a northward shift of the distribution centre for O. hupensis; Specifically, those in the upstream will move northeast, while those in the midstream and downstream will move northwest; These changes in suitable habitat area, the average migration distance of distribution centers across different climate scenarios, time periods, and sub-basins within the YREB, resulting in uncertainty. This study offers theoretical justification for the prevention and control of O. hupensis along the YREB.

Thromboembolic conditions, such as stroke and myocardial infarction [MI], are the primary causes of mortality globally. High efficiency was demonstrated by fibrinolytic enzymes isolated from microbial sources in dissolving various blood clots. The study aimed to investigate and produce bacterial fibrinolytic enzymes from various Egyptian soil conditions using recombinant DNA technology. Fifty soil samples were examined to determine the production of fibrinolytic enzymes. The plasmin plate technique assessed the thrombolytic activities of the strong fibrinolytic enzyme-producing bacterial isolates. In addition to determining the molecular mass using a mass spectrometer and the western blot method, the activity and thermostability of the enzymes were characterized. An affinity chromatography approach was used to purify the fibrinolytic enzymes. However, bacterial recombinant DNA technology produced the pure bacterial fibrinolytic enzyme. Several dosage formulations for the formulation of fibrinolytic enzymes were tested. By using affinity chromatography and the SDS-PAGE test, the isolated bacterial fibrinolytic enzyme in this investigation was almost 83% pure. Bacillus cereus ATCC 4342 was identified as the predominant bacterial isolate secreting fibrinolytic enzymes. It was discovered that the fibrinolytic enzyme activity of fifty soil samples ranged from 0.167 U/ml to 1.853 U/ml. The extracellular enzyme extracted from Bacillus cereus culture supernatant demonstrated a relatively stable fibrinolytic activity over 18 hours at a pH range of 7.4–10, as well as stability with maximum productivity at 43℃ for over 60 minutes. In this investigation, the enzyme was referred to as Fibrin-proteinase[ FP]. The fibrin clots were directly thrombolyzed by the FP. The calculated values for the specific activity and molecular mass of the FP were approximately 40.71 units/mg of protein and approximately 30 KDa, respectively. It was observed that the presence of Fe+3 marginally boosted protease activity, but the presence of EDTA decreased it. On the other hand, adding monovalent cations like K+1 and Na+1 significantly increased the fibrinolytic activity of FP. The enzyme exhibited activity throughout the temperature range of 20 to 65℃, peaking at 55℃. The ideal range for the generation of protease was 35–43℃ and 8.0–10. The thermostable fibrinolytic enzyme FP, which was isolated from Bacillus cereus ATCC 4342 in various soil environments in Egypt during the current study, was a promising fibrinolytic enzyme because it demonstrated a more extensive and effective fibrinolytic activity and fewer side effects than the fibrinolytic enzymes that are currently available worldwide. It is advised that further research be done on optimizing the right formulation of bacterial fibrinolytic enzymes in the future to help dissolve different types of blood clots and, as a result, lower the high death rate that exists worldwide.

Transport of carbon, major and trace element by rivers in permafrost-affected regions is one of the key factor of circumpolar aquatic ecosystem response to climate warming and permafrost thaw. While seasonal and annual export fluxes (yields) of carbon (C) and inorganic solutes are fairly well known for all large Arctic rivers, spatial variations in elementary concentration along the river length and among its tributaries remain poorly understood. Moreover, the landscape factors controlling riverine element concentration in permafrost-affected regions are still poorly constrained. This is especially true for the largest river of Eastern Siberia, the Lena River, which drains through continuous permafrost zones with highly variable lithology and vegetation. Here we present the results of C, major and trace element measurements over a 2600-km transect of the Lena River main stem (upper and middle reaches) including its 30 tributaries, conducted at the peak of the spring flood. There were two main group of solutes in the main stem depending on their spatial pattern: i) elements that decreased their concentrations downstream, from SW to NE (Cl, SO4, DIC, Li, B, Na, Mg, K, Ca, As, Sr, Mo, Sb, Ba and U), which probably reflected a decrease in the proportion of carbonate rocks in the watershed and the degree of groundwater feeding, and ii) elements that increased their concentrations downstream (Al, Ti, Cr, Fe, Ga, Rb, Y, Zr, Nb, Cs, REEs, Hf and Th), which was tentatively linked to an increase in organic C stock in soils, larch forest coverage and enhanced mobilization of lithogenic elements from silicate soil minerals. Based on landscape parameters of Lena tributaries, we tested the impact of major environmental factors on major and trace element spatial pattern. Among all the variables, the proportion of sporadic permafrost on the watershed strongly controlled concentrations of soluble highly mobile elements (Cl, B, DIC, Li, Na, K, Mg, Ca, Sr, Mo, As and U). Another important factor of element concentration control in the Lena River tributaries was the coverage of watershed by light (B, Cl, Na, K, U) and deciduous (Fe, Ni, Zn, Ge, Rb, Zr, La, Th) needle-leaf forest (pine and larch). The latter, however, could also reflect the DOC-enhanced transport of low-soluble trace elements in the NW part of the basin. This part of the basin is dominated by silicate rocks and continuous permafrost, as compared to carbonate rock-dominated and groundwater-affected SW part of the Lena River basin. Overall, the impact of rock lithology and permafrost on major and trace solutes of the Lena River basin during the peak of spring flood was mostly detected at the scale of the main stem. Such an impact for tributaries was much less pronounced, because of the dominance of surface flow and lower hydrological connectivity with deep groundwater in the latter. Future changes in the river water chemistry linked to climate warming and permafrost thaw at the scale of the whole river basin are likely to be linked to changes in spatial pattern of dominant vegetation, rather than to the permafrost regime. We argue that comparable studies of large, permafrost-impacted rivers during most contrasting seasons, including winter baseflow, should allow efficient prediction of future changes in riverine ‘inorganic’ hydrochemistry induced by permafrost thaw.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the [email protected] metric, and 72.03%, 65.37%, and 38.91% in the more stringent [email protected]:0.95 metric, respectively.

Background: Antibiotic resistance is an urgent issue everywhere in the globe. It is necessary to look into new sources of antibiotics to address this issue. Aim of the study: studying the purification of Corallopyronin B from various soil conditions in Egypt, as well as the antibacterial effectiveness of Corallopyronin B in preclinical animal testing and randomized human clinical trials phases 1/2. Type of the study: Screening experimental study. Methodology: Several soil conditions in Egypt were examined to create bacterial isolates that generated the antibiotic chemical Corallopyronin B. Using reversed-phase HPLC, Myxopyronin B was purified. The test antibiotic's minimum inhibitory concentration( MIC) and in vitro antibacterial activity were ascertained using the paper disc diffusion assay and the broth microdilution technique. Furthermore, in stages 1/2 of randomized clinical trials including human and animal models, pharmacokinetics, adverse drug reactions, and the in vivo antibacterial spectrum were discovered. Results: The soil bacterial isolate Corallococcus coralloides DSM 2259, which was grown on a Casein yeast peptone( CYP) plate, produced Corallopyronin B from its culture supernatant. At MICs more than 100 mcg/ml, the test antibiotic inhibited the growth of many Gram -ve bacteria, including Escherichia coli, while also preventing the growth of numerous Gram +ve bacteria, with MICs ranging from 1 to 10 mcg/ml. Eukaryotic cells, on the other hand, including those in humans and fungi, were unharmed. The test antibiotic was shown to have a bactericidal effect by inhibiting bacterial DNA-dependent RNA polymerase( RNLP). In phases 1/2 of randomized human clinical trials, when 600 mg of the dose per 70 kg of body weight was administered SC, the Cmax was 8.6 mcg/ml at Tmax 1 hour; T1/2 reached 136 min following first-order kinetics of elimination. It stopped acting around 6-7 hours after SC was administered. In the preclinical and randomized human clinical trial phases 1/2, less than 6 percent of experimental candidates had uncommon toxicity, which showed up as reduced bile flow. Protein binding with plasma albumin was detected which reached about 83%. Conclusion: The current work was noteworthy since it involved the production of the bactericidal antibiotic Corallopyronin B from Corallococcus coralloides DSM 2259 which was isolated from several soil environments in Egypt.

Volcanic activity essentially consists of the transfer of heat from the Earth’ interior to the surface. The precise signature of this heat transfer relates directly to the processes underway at and within a particular volcano and this can be observed, at a safe distance, remotely, using infrared sensors that are present on Earth-orbiting satellites. For over 50 years, scientists have perfected this art using sensors intended for other purposes, and they are now in a position to determine the particular sort of activity that characterizes different volcanoes. This review will describe the theoretical basis of the discipline and then discuss the sensors available for the task and the history of their use. Challenges and opportunities for future development in the discipline are then discussed.

Within environmental perception, automatic navigation and object detection, computer vision is a crucial and demanding field with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles with sensing ability. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which has spawned the creation of a large number of related applications. This paper presents the results of a detailed review of over 50 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement of computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the most recent advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis between conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM (Simultaneous Localization and Mapping) algorithms in the context of autonomous vehicles with sensing capability was explored. Furthermore, we explored a thorough and efficient architecture for utilizing these techniques in autonomous robotic vehicles into unmanned green and low-carbon smart factories, underlining novel advances as well as potential prospects for future research.

Background: Antibiotic resistance is an overwhelming serious difficulty globally. This necessitates the exploration of novel sources of antibiotics to overcome this challenge. Aim of the study: Bacterial Myxopyronin A production from various soil environments Egypt; as well as testing its antimicrobial activity in preclinical animal testing and randomized human clinical trials phases 1/2. Type of the study: Screening experimental study. Methodology: Different soil environments in Egypt were screened for the growth of bacterial isolates producing Myxopyronin A as an antimicrobial agent. Purification of Myxopyronin A was performed via reversed-phase HPLC. Paper disc diffusion assay, as well as the Broth dilution technique, were exploited to assess the invitro antimicrobial and minimum inhibitory concentration( MIC) of the test antibiotic. Furthermore, in vivo antimicrobial spectrum, adverse drug reactions, and pharmacokinetics were detected during animal model testing stages and human randomized clinical trials phases 1/2. Results: From the culture supernatant of the Myxobacterium Myxococcus fulvus 124B02 which was the predominant soil bacterial isolate grown on the Casein yeast peptone plate, Myxopyronin A was produced. The test antibiotic blocked the growth of many Gram +ve bacteria with MICs less than 100 mcg/ ml. In contrast, it inhibited the growth of a few Gram -ve bacteria such as Escherichia coli at MICs greater than 100 mcg/ ml. On the other hand, Eukaryotic cells such as fungal and human cells were not affected. Prokaryotic DNA-dependant-RNA polymerase( RNLP) was noticed to be inhibited via the test antibiotic suggesting its bactericidal action. The maximum concentration was 7-8 mcg/ ml at a maximum time of 2 hours when 600 mg dose was orally administered in randomized human clinical trials phases 1/2, and T1/2 reached 2.5 hours following first-order kinetics of elimination. The duration of its action was nearly 12 hours after oral administration. Rare toxicity was detected during preclinical and randomized human clinical trials phases 1/2 in the form of mild diarrhea and cholestatic jaundice in less than 5 % of experimental candidates. Conclusion: The present study was promising due to the bactericidal antibiotic Myxopyronin A production from Myxococcus fulvus 124B02 isolated from different soil environments in Egypt.

In GNSS/IMU integrated navigation systems, factors such as satellite occlusion and non-line-of-sight conditions can lead to degradation of satellite positioning results, Subsequently affecting the overall accuracy of the integrated navigation system. To address this issue and ef-fectively utilize historical pseudorange information from satellites, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints are con-structed using satellite ‘s position from previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint can serve as an alternative solution for individual satellites in case of signal anomalies, ensuring the integrity and continuity of the graph optimi-zation model. Additionally, this paper conducts an analysis of the graph optimization model constructed using these virtual constraints, comparing it with traditional integrated navigation graph optimization model, and analyzes and reconstructs the marginalization process based on these virtual constraints. The experimental results, compared with tightly coupled Kalman fil-tering and the original graph optimization method on a set of real-world data, demonstrate that the introduction of virtual pseudoranges maintains high positioning accuracy. The RMSE error between it and the original graph optimization remains within 5%, affirming the high feasibility of this approach.

The long-term use of fertilizers and pesticides in conventional cultivation has resulted in a decrease in soil productivity and vegetable yields in greenhouses. However, only a limited number of studies have explored the impacts of organic cultivation on greenhouse soil. Specifically, there is a scarcity of research examining the alterations in soil aggregate mediated organic carbon and microbial communities, which are known to enhance soil productivity. This study investigated the properties of soil aggregates, including the levels of organic carbon fractions, microbial community, and enzyme activity with the three aggregate classes microaggregates (&lt;0.25 mm), small macroaggregates (2-0.25 mm) and large macroaggregates (&gt;2 mm). These investigations were conducted under three different cultivation methods in a greenhouse: conventional cultivation (CC), integrated cultivation (IC), and organic cultivation (OC) in greenhouse. The results showed that (1) OC and IC promoted the formation of small macro-aggregates and enhanced aggregate stability compared to CC, (2) SOC in the three size fractions of OC increased by 98.99%-92.06% compared to CC; EOC increased by 98.47%-117.59%; POC increased by 138.59%-208.70%; MBC increased by 104.71%-230.61%; DOC increased by 21.93%-40.90%, respectively; (3) Organic cultivation significantly increased enzyme activity in all three particle size aggregates and increased the relative abundance of bacteria in micro-aggregates as well as the relative abundance of fungi in small macro-aggregates. Structural equation model (SEM) analysis revealed that organic farming practices fostered the development of smaller macro-aggregates, elevated microbial and enzyme activities within soil aggregates, and facilitated the conversion of soil nutrients and carbon sequestration. consequently, this led to an increase in soil organic carbon content and improved vegetable yield in greenhouse environments. In conclusion, the soil carbon content and vegetable yield are enhanced through long-term organic cultivation in greenhouses, primarily by increasing the proportion of small macro-aggregates.

Background: Across the world, antibiotic resistance is a critical challenge. To address such a problem, it has to be done to investigate new antibiotic sources. Aim of the study: Study of the antibacterial activity of Corallopyronin A in preclinical animal testing and randomized human clinical trials stages 1/2, as well as investigating the purification of Corallopyronin A from different soil conditions in Egypt. Type of the study: Screening experimental study. Methodology: Egypt's various soil conditions were tested to develop bacterial isolates that produced the antibiotic compound Corallopyronin A. Reversed phase HPLC was used to purify Myxopyronin A. The broth microdilution method and the paper disc diffusion assay determined the test antibiotic's minimum inhibitory concentration( MIC) and in vitro antibacterial activity. Moreover, in vivo antibacterial spectrum, adverse medication responses, and pharmacokinetics were found in phases 1/2 of randomized clinical studies involving humans and animal models. Results: Corallopyronin A was generated from the culture supernatant of the soil bacterial isolate Corallococcus coralloides M2, cultivated on a Casein yeast peptone( CYP) plate. The test antibiotic prevented the growth of several Gram-ve bacteria, including Escherichia coli, at MICs higher than 100 mcg/ ml; while blocking the development of many Gram +ve bacteria, with MICs ranging from 3 to 15 mcg/ ml. However, eukaryotic cells—such as those found in fungi and humans—were unaffected. A bactericidal consequence of the test antibiotic was detected in the inhibition of prokaryotic DNA-dependent RNA polymerase( RNLP). Conclusion: The synthesis of the bactericidal antibiotic Corallopyronin A from Corallococcus coralloides M2 isolated from different soil settings in Egypt intrigued the current investigation.

BACKGROUND: Thromboembolic disorders including Myocardial infarction[ MI] and stroke represent the leading causes of death worldwide. Fibrinolytic enzymes extracted from microbial sources showed high efficacy in the dissolution of different blood clots. AIM OF THE STUDY: Screening and the bacterial recombinant DNA manufacture of bacterial fibrinolytic enzymes from different soil environments in Egypt. METHODOLOGY: A total of 50 soil samples were screened for their fibrinolytic enzyme production. The potent fibrinolytic enzyme-producing bacterial isolates were evaluated for their thrombolytic activities using the plasma plate method. The characterization of the enzyme activity and thermostability were carried out; also, the determination of molecular mass using a mass spectrometer and western blot technique was performed. The purification of the fibrinolytic enzymes was done through the affinity chromatography technique. On the other hand, the production of the purified bacterial fibrinolytic enzyme was achieved via bacterial recombinant DNA technology. Formulation of fibrinolytic enzymes in different dosage forms was tried. RESULTS: In the present study, the purified bacterial fibrinolytic enzyme was approximately 83% pure through affinity chromatography and SDS-PAGE assay. The predominating bacterial isolate secreting fibrinolytic enzymes was found to be Bacillus cereus. The fibrinolytic enzyme activities of 50 soil samples were found to be in the range of 0.167-1.853 U/ ml. The fibrinolytic activity of the extracellular enzyme refined from the culture supernatant of Bacillus cereus was relatively stable over the PH range from 7.4-10 for 18 hours and also it showed stability with the highest productivity at a temperature of 43 0C for nearly 60 minutes. The enzyme was called fibrin-proteinase[ FP] in the present study. The FP degraded the fibrin clots via direct thrombolysis. The specific activity and the molecular mass of the FP were estimated to be nearly 40.71 Units/ mg protein and nearly 30 KDa respectively. Protease activity was noticed to be slightly increased in the presence of Fe+3; While it was inhibited in the presence of EDTA. On the other hand, the FP fibrinolytic activity was enhanced greatly after the addition of monovalent cations such as K+1 and Na +1. The enzyme was also active over the temperature range of 20- 65 0C with the highest activity at 55 0C. The optimum temperature and PH of protease production was at 35-43 0C and PH 8.0-10. Conclusion: During the present study, the thermostable fibrinolytic enzyme FP purified from Bacillus cereus in different soil environments in Egypt was a promising fibrinolytic enzyme because it showed a broader efficacious fibrinolytic activity as well as less adverse effects than the current fibrinolytic enzymes globally. It is recommended in the future advanced exploration of the optimization of suitable formulation of bacterial fibrinolytic enzymes to aid in the dissolution of various blood clots and as a consequence overcoming the high rate of mortality globally.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

Background: Across the world, antibiotic resistance is a critical challenge. To address such a problem, it has to be done to investigate new antibiotic sources. Aim of the study: Study of the antibacterial activity of Corallopyronin A in preclinical animal testing and randomized human clinical trials stages 1/2, as well as investigating the purification of Corallopyronin A from different soil conditions in Egypt. Type of the study: Screening experimental study. Methodology: Egypt's various soil conditions were tested to develop bacterial isolates that produced the antibiotic compound Corallopyronin A. Reversed phase HPLC was used to purify Myxopyronin A. The broth microdilution method and the paper disc diffusion assay determined the test antibiotic's minimum inhibitory concentration( MIC) and in vitro antibacterial activity. Moreover, in vivo antibacterial spectrum, adverse medication responses, and pharmacokinetics were found in phases 1/2 of randomized clinical studies involving humans and animal models. Results: Corallopyronin A was generated from the culture supernatant of the soil bacterial isolate Corallococcus coralloides M2, cultivated on a Casein yeast peptone( CYP) plate. The test antibiotic prevented the growth of several Gram-ve bacteria, including Escherichia coli, at MICs higher than 100 mcg/ ml; while blocking the development of many Gram +ve bacteria, with MICs ranging from 3 to 15 mcg/ ml. However, eukaryotic cells—such as those found in fungi and humans—were unaffected. A bactericidal consequence of the test antibiotic was detected in the inhibition of prokaryotic DNA-dependent RNA polymerase( RNLP). Conclusion: The synthesis of the bactericidal antibiotic Corallopyronin A from Corallococcus coralloides M2 isolated from different soil settings in Egypt intrigued the current investigation.

This study outlines the development of an innovative method utilizing UHPLC-PDA for the simultaneous identification of polyphenols and bitter acids (alpha-acids, beta-acids, and iso-alpha-acids) in beer samples. The resulting chemical profiles were leveraged to distinguish the characteristics of four bergamot-flavored beers, each representing distinct styles (IPA, Lager, Blanche, and ALE), produced on a pilot-scale plant. In a streamlined 29-minute analysis, twenty-nine polyphenols and fourteen bitter acids were successfully identified under optimized separation conditions. Rigorous validation, encompassing parameters such as Limit of Detection (LOD), Limit of Quantification (LOQ), R-squared (R2), repeatability, and reproducibility, was conducted for polyphenol quantification using constructed calibration curves with seven levels. Exploring polyphenolic components as potential discriminators among different beer styles, a total of thirty-two polyphenolic compounds were identified and quantified, including characteristic bergamot peel polyphenols like neoeriocitrin, naringin, and neohesperidin. The determination of bitter units and total phenols further aided in style differentiation. Multivariate analysis provided additional insights into variations among specific beer styles, revealing discrepancies in the presence or relative concentrations of specific compounds linked to brewing ingredients and processes. This research enhances to trace the fingerprinting of the chemistry governing beer quality through a straightforward and cost-effective analytical approach, readily applicable in common laboratories.

This study aims to improve the shaft with hexagon joints to be a type not requiring welding or bolts in the static load test . In order to evaluate the bearing capacity of helical piles, two sites were selected to conduct pile installation for the field test and the pile load test. For the pile load test, the static pile load test and the dynamic pile load test were carried out, and torque was measured during pile installation for the field test to compare and analyze expected bearing capacity and thus assess the feasibility of the method for estimating the bearing capacity. The field pile load test revealed the bearing capacity of the gravity grout pile was the same or greater than 600kN in the static pile load test in accordance with AC 358 Code. The non-grout pile showed the bearing capacity the same or smaller than 600kN, suggesting gravity grouting is required. Moreover, the field pile load test was used to establish the bearing capacity equation considering the torque in pile installation, and a small number of samples were used to establish the equation which can be used as a basic data.

The soil organic matter (SOM) content and stability in natural gypsum soils and soils from mining/industrial areas were compared, in terms of their physical, chemical and micromorphological properties, to determine whether the old brownfield soils were acting as carbon (C) sinks, counteracting increasing carbon dioxide concentrations in the atmosphere. The mining/industrial soils were 50–400 years old, so had been left sufficiently long for full self-restoration. Despite having very different origins, both natural and brownfield soils stored similar amounts of SOM, and had similar pH values, calcium carbonate contents and textures. They were also fully covered with similar grass plant communities. The mining/industrial soils exhibited similar microstructures to the gypsum soils and were often quite porous. The most striking difference between them and the natural gypsum soils was that the mining/industrial soils were strongly polluted with zinc, lead and cadmium. The 50-year-old brownfield soils differed from the others in terms of having a lower state of SOM transformation, with a much higher C/N ratio, lower aggregate water resistance index, lower dehydrogenase activity and greater areas of undecomposed or slightly decomposed plant residues. These parameters in the older (150–400 years) brownfield soils were similar to those of the natural soils.

As the fast growth of information technology (IT), the safety of image transmission and the storing of images are increasingly concerned. Traditional image encryption algorithms have certain limitations in transmission and security, so there is an urgent need for a secure and reliable image encryption algorithm. A universal compression sensing (CS) image encryption (IE) algorithm based on DNA-triploid mutation (DTM) is presented in this paper. Firstly, by using CS algorithm an image is compressed while obtaining a range of chaotic sequences by iteration of chaotic map. Then DNA sequences are generated by encoding the image, and based on the DTM, new mutant DNA sequences are generated according to specific rules. Next, the chaotic sequences are operated at the DNA level to do confusion and diffusion operations on the image to ensure the security of the data. At last, DNA decoding is carried out to obtain the compressed-encrypted image. Both simulation experiments and performance tests fully show that a high level of security and reliability of the proposed algorithm in protecting image privacy is achieved.

Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

Determining precise boundaries and protective zones for historical and cultural objects enables their effective preservation. This is particularly crucial in the face of increasing tourism, urbanization, and natural disasters. Accurately defining boundaries and protective zones can aid in the planning of scientific research, archaeological excavations, and other studies aimed at exploring the history and cultural significance of these objects. The article presents the framework for establishing protective zones around historical and cultural heritage sites existing in Ukraine, using the example of the Citadel defensive complex located in Lviv (Ukraine). It proposes general and detailed conceptual models for the combined application of remote and non-invasive methods for investigating historical and cultural heritage sites. This conceptual article introduces the theory of integrating radar satellite imaging with ground-based georadar imaging. Additionally, it presents a software module that has been developed to analyze collected data on two-dimensional historical and cultural heritage objects, refine their boundaries, and establish protective zones around them.

The next-generation communication systems demand integration of sensing, communication, and power transfer (PT) capabilities, requiring high spectral efficiency, energy efficiency, and low cost, while also necessitating robustness in high-speed scenarios. Integrated sensing and communication systems (ISACS) exhibit the ability to simultaneously perform communication and sensing tasks using a single RF signal, while simultaneous wireless information and power transfer (SWIPT) systems can handle simultaneous information and energy transmission, and orthogonal time frequency space (OTFS) signals are adept at handling high Doppler scenarios. Combining the advantages of these three technologies, a novel cyclic prefix (CP) OTFS based integrated simultaneous wireless sensing, communication, and power transfer system (ISWSCPTS) framework is proposed in this work. Within the ISWSCPTS, the CP-OTFS matched filter (MF) based target detection and parameters estimation (MF-TDaPE) algorithm is proposed to endow the system with sensing capabilities. To enhance the system’s sensing capability, a waveform design algorithm based on CP-OTFS ambiguity function shaping (AFS) is proposed, which is solved by iteratively method. Furthermore, to maximize the system’s sensing performance under communication and PT Quality of Service (QoS) constraints, a semidefinite relaxation (SDR) beamforming design (SDR-BD) algorithm is proposed, solved using through SDR technique. Simulation results demonstrate that the ISWSCPTS exhibits stronger parameter estimation performance in high-speed scenarios compared to orthogonal frequency division multiplexing (OFDM), waveform designed by CP-OTFS AFS demonstrates superior interference resilience, and beamforming designed by SDR-BD strikes a balance in the overall performance of the ISWSCPTS.

Successful fertilization treatments targeted to improve stand productivity while reducing operational complexities and cost depend on a clear understanding of soil nutrient availability under varying environmental conditions. Soil nutrient data collected from 154 forest sites throughout the Inland Northwest, USA were analyzed to examine soil nutrient characteristics on different geologic soil parent materials and to rank soil fertility. Results show that soil parent material explains significant differences in soil nutrient availability. Soils developed from volcanic rocks have the highest CEC and are relatively high in P, K, S, Mg, Cu, Ca, and B, but generally poor in N. Forest soils developed from plutonic rocks exhibit the lowest CEC and are low in N, S, K, Mg, Cu, and Ca, but higher in P. Some soils located on mixed glacial till are low only in K, Cu, Mg, and Ca, but many glacial soils are relatively rich in other nutrients, albeit the second lowest CEC. Soils developed from metasedimentary and sedimentary rocks are among those with lowest soil nutrient availability for P and B. Sulfur was found to have the highest concentrations in metasedimentary influenced soils and the least in sedimentary derived soils. Our results should be useful in designing site-specific fertilizer and nutrient management prescriptions for forest stands growing on soils developed from these major geologies within the Inland Northwest region of the United States.

Moso bamboo is famous for fast growing and biomass accumulation, as well as high annual output for timber and bamboo shoots. These high outputs require high nutrient inputs to maintain and improve stand productivity. Soil nitrogen (N), phosphorus (P) and potassium (K) are important micronutrients for plant growth and productivity. Due to high variability of soils, analysing spatial patterns of soil N, P and K stocks is necessary for scientific nutrient management in Moso bamboo forests. In this study, soils were sampled from 138 locations across Yong’an City and ordinary kriging was applied for spatial interpolation of soil N, P and K stocks. Soil N stock showed a strong spatial dependence while soil N and P stocks presented a moderate spatial dependence, indicating soil N was mainly controlled by intrinsic factors while soil N and P stocks were controlled by both intrinsic and extrinsic factors. Different spatial patterns were observed for soil N, P and K stocks across the whole study area, indicating that fertilizations with different ratios of N:P:K should be applied for different sites to maintain and improve stand productivity. The total soil N, P and K stocks within 0-60 cm were 0.624, 0.020 and 0.583 Tg, respectively.

BACKGROUND: Thromboembolic disorders including Myocardial infarction[ MI] and stroke represent the leading causes of death worldwide. Fibrinolytic enzymes extracted from microbial sources showed high efficacy in the dissolution of different blood clots. AIM OF THE STUDY: Screening and the bacterial recombinant DNA manufacture of bacterial fibrinolytic enzymes from different soil environments in Egypt. METHODOLOGY: A total of 50 soil samples were screened for their fibrinolytic enzyme production. The potent fibrinolytic enzyme-producing bacterial isolates were evaluated for their thrombolytic activities using the plasma plate method. The characterization of the enzyme activity and thermostability were carried out; also, the determination of molecular mass using a mass spectrometer and western blot technique was performed. The purification of the fibrinolytic enzymes was done through the affinity chromatography technique. On the other hand, the production of the purified bacterial fibrinolytic enzyme was achieved via bacterial recombinant DNA technology. Formulation of fibrinolytic enzymes in different dosage forms was tried. RESULTS: In the present study, the purified bacterial fibrinolytic enzyme was approximately 83% pure through affinity chromatography and SDS-PAGE assay. The predominating bacterial isolate secreting fibrinolytic enzymes was found to be Bacillus cereus. The fibrinolytic enzyme activities of 50 soil samples were found to be in the range of 0.167-1.853 U/ ml. The fibrinolytic activity of the extracellular enzyme refined from the culture supernatant of Bacillus cereus was relatively stable over the PH range from 7.4-10 for 18 hours and also it showed stability with the highest productivity at a temperature of 43 0C for nearly 60 minutes. The enzyme was called fibrin-proteinase[ FP] in the present study. The FP degraded the fibrin clots via direct thrombolysis. The specific activity and the molecular mass of the FP were estimated to be nearly 40.71 Units/ mg protein and nearly 30 KDa respectively. Protease activity was noticed to be slightly increased in the presence of Fe+3; While it was inhibited in the presence of EDTA. On the other hand, the FP fibrinolytic activity was enhanced greatly after the addition of monovalent cations such as K+1 and Na +1. The enzyme was also active over the temperature range of 20- 65 0C with the highest activity at 55 0C. The optimum temperature and PH of protease production was at 35-43 0C and PH 8.0-10. Conclusion: During the present study, the thermostable fibrinolytic enzyme FP purified from Bacillus cereus in different soil environments in Egypt was a promising fibrinolytic enzyme because it showed a broader efficacious fibrinolytic activity as well as less adverse effects than the current fibrinolytic enzymes globally. It is recommended in the future advanced exploration of the optimization of suitable formulation of bacterial fibrinolytic enzymes to aid in the dissolution of various blood clots and as a consequence overcoming the high rate of mortality globally.

The accurate assessment and monitoring of soil erosion is of great significance for guiding food production and ensuring ecological security, and it is a current research hotspot. In this paper, remote sensing and geographic information systems (GISs) are combined with the Revised Universal Soil Loss Equation (RUSLE model) to carry out research on soil erosion monitoring and make a quantitative evaluation. According to five factors, including rainfall erosivity, soil erodibility, topography, vegetation cover, crop management and water and soil conservation measures, the distribution of the soil erosion rate in Jilin Province in 2019 was mapped, and the soil erosion rate was divided into 5 levels according to the degree of erosion, including very slight, slight, moderate, severe and extremely severe erosion. Based on the segmented S-slope factor model and the unique topographical features of the study area, the relationships among the soil erosion rate, erosion risk level, erosion area, erosion amount and slope angle (θ) were systematically analysed, and a slope angle of 15° was identified as the threshold for soil erosion on sloped farmland in Jilin Province. The total soil erosion in Jilin Province was 402.14×106 t in 2019, the average soil erosion rate was 21.6 t·ha-1·a-1, and the average soil loss thickness was 1.6 mm·a-1; these values were far greater than the soil erosion rate risk threshold of 10 t ·Ha-1·a-1. Thus, the province has a strong level of soil erosion. We conclude that soil degradation is accelerating, and food production and the ecological environment will face severe challenges. It is suggested that soil erosion control should be carried out according to different types and slopes of land, with an emphasis on the management of forestland and farmland because forestland and farmland are currently the first types of land to be managed in Jilin Province. This paper aims to explore a timely, fast, efficient and convenient soil erosion monitoring and evaluation method and provide effective monitoring tools for agricultural water and soil conservation, ecological safety management and stable food production in Jilin Province and similar black soil areas.

Heavy metals constitute pollutants particularly common in air, water, and soil. They are present in both urban and rural environments, on land and in marine ecosystems, causing serious environmental problems since they do not degrade easily, remain almost unchanged for long periods, and bioaccumulate. Detection, though especially quantification, is a systematic process as monitoring of their levels needs to be carried out at regular intervals since there is often seasonal variation. The need for rapid and low-cost determination of metals is therefore considerable. In the present study atomic absorption spectroscopy (AAS), using a flame or graphite furnace, and square wave anodic stripping voltammetry (SWASV), using a bismuth thin film electrode, a sensitive voltammetric method is being compared and analytically validated. Atomic absorption spectroscopy (AAS) represents a well-established analytical technique while anodic stripping voltammetry is being criticized for its applicability in complicated sample matrices such as soil samples. This sample-handling challenge is being investigated in the present study. The results show that both methods AAS and ASV were satisfactorily correlated and showed that the concentration of metals is lower than the limit values, but with increasing trend. Therefore, continuous monitoring of metal levels in the urban complex of the city is necessary and a matter of great importance. Regarding Cd, the detection limits of Cd are lower compared to those of AAS when using the graphite furnace, while an overestimation was noted when using flame-AAS in comparison with those found with SWASV. The SWASV method has the advantage of being cheaper and faster, enabling the simultaneous determination of heavy elements across the range of concentrations that these elements can occur in Mediterranean soils. Additionally, we applied a dsDNA-modified electrode which can be used in the speciation analysis of Cu(I) and Cu(II). The changes in the characteristic peaks of guanine and adenine can be applied in the redox speciation analysis of copper in soil which represents an issue of great importance.

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. Prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart-sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart-sensing chairs, with a specific focus on the strategies used for posture detection and classification, as well as the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, and Google Scholar databases yielded 34 pertinent studies that utilize non-invasive methods for posture monitoring. The analysis reveals that Force Sensing Resistors (FSR) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy, due to constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.

In this study the effects of three different main preparatory tillage operations [ploughing at 0.4 m (P40) and 0.20 m (P20) depth and minimum tillage at 0.20 m depth (MT) each of them carried out at two different soil water contents (WC) [low, 58% (LH) and high, 80% (HH) of field capacity] were investigated. The results obtained in this research show high values of soil strength in term of Penetration resistance (CI) and shear strength (SS) particularly in deeper soil layers at lower water content. Fossil-fuel energy requirements both for P40 LH and P20 LH were 25 and 35% higher with respect to the HH treatments and tractor slip were very high (P40 LH = 32.4%) with respect to the P40 HH treatment (16%). Therefore soil water content had significantly influenced tractor performance during soil ploughing, particularly at 0.40 m depth while MT was not influenced at all. A significant correlation between grain yield and soil penetration resistance was found highlighting how soil strength may be good indicator of its productivity. Obtained results during these field tests allowed considering MT and P20 treatments more suitable for this type of soil in climate change scenarios.

This paper presents the design and analysis of a novel 3-degree-of-freedom (3-DOF) parallel ma-nipulator equipped with self-sensing Ni-Ti (Nitinol) actuators. The manipulator's architecture and mechanical design are elucidated, emphasizing the integration of Nitinol actuators. The self-sensing technique implemented in a previous work was extended to a 20 mm actuator length, and the actuator was used to design the 3 DOF manipulator. Kinematic analyses were conducted to eval-uate the manipulator's performance under various operating conditions. A dynamic model was implemented for the dynamic dimensioning of the actuators, which work synergistically with a bias spring. The manipulator was realized, and a control strategy was implemented. Experimental tests, although documenting some positioning accuracy issues, show the efficacy and potential applica-tions of the proposed manipulator in robotics and automation systems, highlighting the advantages of self-sensing Nitinol actuators in small parallel manipulator designs.

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium that remains a prevalent clinical and environmental challenge. Quorum sensing (QS) molecules are effective biomarkers in pinpointing the presence of P. aeruginosa. This study aimed to develop a convenient-to-use whole-cell biosensor using P. aeruginosa reporters individually encapsulated within alginate-poly-L-lysine (alginate-PLL) microbeads to specifically detect the presence of bacterial autoinducers. The PLL-reinforced microbeads were prepared using a two-step method involving ionic crosslinking and subsequent coating with thin layers of PLL. The alginate-PLL beads showed good stability in the presence of a known cation scavenger (sodium citrate), which typically limits the widespread applications of calcium alginate. In media containing synthetic autoinducers, such as N-(3-oxo dodecanoyl) homoserine lactone (3-oxo-C12-HSL) and N-butanoyl-L-homoserine lactone (C4-HSL) or cell-free supernatants of planktonic or flow cell biofilm effluent of wild P. aeruginosa (PAO1), the encapsulated bacteria enabled a dose-dependent detection of the presence of these QS molecules. The prepared bioreporter beads remained stable during prolonged storage at 4 and -80 °C and were ready for on-the-spot sensing without the need for recovery. This whole-cell biosensor demonstrates the practicality of the encapsulated bioreporter for bacterial detection based on specific QS molecules.

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy lidar (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence at multiple spatial scales ≤ 10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more evenly distributed by height and depth, respectively, as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5-4 meters, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Our study highlights the potential, and limitations, for fusing lidar and radar technologies to quantitatively couple above- and belowground ecosystem structure.

Ammonium sulfate has been utilized in agriculture; however, there is a dearth of research on its application in maize cultivation subsequent to the implementation of nitrification inhibitors or coating. This study aims to analyze the impacts of various combinations of ammonium sulfate fertilizers on soil nutrients, plant nutrient uptake, yield, and fertilizer utilization efficiency in maize cultivation to ascertain the optimal and stabilized disposal method for ammonium sulfate. A completely randomized design was employed with five treatments (AU, the control using urea; AS, treatment using ammonium sulfate; ASN, treatment using ammonium sulfate with a nitrification inhibitor; ASG, treatment using oil-coated ammonium sulfate; ASD, treatment using oil-humic acid-coated ammonium sulfate). Results showed that: (1) Compared with AU and AS, ASN, ASG and ASD decreased the leaching rates of TN, NH4+-N and NO3-N to the 10-20 cm soil layer, and more residual N was obtained in 0-10 cm soil layer. The first-order kinetic equation Nt=N0(1-e-kt) can better fit the process of nitrogen accumulation and release, and the N release rate constant is AU>CK>AS>ASH>ASN>ASD; (2) Compared with AU and AS treatments, the plant dry weight, grain dry weight, spike width, spike length, and yields of maize increased by 8.85-11.08%, 12.98-14.15%, 2.95-3.52%, 5.50-5.65%, and 43.21-51.10% under ASG treatment respectively. The impact of effective spike number on maize yield was found to be significant, as demonstrated by the path analysis conducted in this study. Furthermore, the accumulation levels of nitrogen, phosphorus, and potassium within above-ground plants significantly increased under the ASG treatment compared to the AU and AS treatments. The partial productivity of nitrogen, phosphorus, and potassium increased by 1.43-1.51-fold under ASG treatment, while grain nitrogen balance, grain phosphorus balance, and grain potassium balance increased by 1.41-1.58-fold, 1.51-1.95-fold, and 1.15-l.96-fold respectively. The oil-coated ammonium sulfate, therefore, exhibited the optimal slow-release effect of nutrients, thereby achieving superior performance in enhancing maize production and efficiency.

The proximal part of the colon offers opportunities to prolong the absorption window following oral administration of a drug. In this work, we used computer simulations to understand how the hydrodynamics in the proximal colon might affect the release from dosage forms designed to target the colon. For this purpose, we developed and compared three different models: a completely-filled colon, a partially-filled colon and a partially-filled colon with a gaseous phase present (gas-liquid model).The highest velocities of the liquid were found in the completely-filled model, which also shows the best mixing profile, defined by the distribution of tracking particles over time. No significant differences with regard to the mixing and velocity profiles were found between the partially-filled model and the gas-liquid model. The fastest transit time of an undissolved tablet was found in the completely-filled model. The velocities of the liquid in the gas-liquid model are slightly higher along the colon than in the partially-filled model. The filling level has an impact on the exsisting shear forces and shear rates, which are decisive factors in the development of new drugs and formulations.

SOC stock (SOCS) changes caused by land use changes are still unclear, and understanding this response is essential for many environmental policies and land management practices. In this study, we investigated the temporal-spatial and vertical distribution characteristics of SOCS on the Western Sichuan Plateau (WSP) using the sparrow search algorithm-random forest regression (SSA-RFR) models with excellent model applicability and accuracy. The temporal-spatial varia-tions in the SOCS were modeled using 1080 soil samples and a set of nine environmental covariates. We analyzed the effect of land use changes on the SOCS on the WSP. The total SOCS increased by 18.03 Tg C from 1990 to 2020. The results of this study confirmed that the SOCS in the study area has increased significantly since 2010, with an increase of 27.88 Tg C compared to the total SOCS in 2010. We found that the spatial distribution of the SOCS increased from southeast to northwest, and the vertical distribution of the SOCS in the study area decreased with increasing soil depth. Forest and grassland are the main sources of SOCS, the total SOCS in the forest and grassland accounted for 37.53 and 59.39% of the total SOC pool in 2020, respectively. Expansion of the wetlands, forest, and grassland areas could increase SOCS in the study area.

We investigate the possible laboratory origins of the highly pathogenic avian influenza (HPAI) H5N1 clade 2.3.4.4b genotype B3.13, currently affecting various animal species and causing sporadic human infections. The first detection of HPAI H5N1 clade 2.3.4.4b in the Netherlands in 2020 raises concerns about earlier gain-of-function research. The proximal origins of HPAI H5N1 Clade 2.3.4.4b may be the USDA Southeast Poultry Research Laboratory (SEPRL) in Athens, Georgia and the Erasmus Medical Center in Rotterdam, the Netherlands. Genetic analysis indicates that genotype B3.13 emerged in 2024 and exhibits genetic links to genotype B1.2, which was identified to have originated in Georgia in January 2022 after the start of serial passage research with H5Nx clade 2.3.4.4 in mallard ducks at the USDA Southeast Poultry Research Laboratory (SEPRL) in Athens, Georgia in April 2021. Genotype B1.2 was found in a bottlenose dolphin (Tursiops truncatus) in March 2022 in Florida, indicating sudden new adaptations to different animal species. The NP gene of H5N1 clade 2.3.4.4b (genotype B3.13), which is causing outbreaks in cattle, was reported to have likely originated from an avian influenza A virus derived from mallard ducks. Significant mutations found in recent human cases, including PB2 E627K and PB2 M631L, also suggest links to serial passage experiments. However, further investigation is urgently needed to confirm these findings and to identify all H5N1 laboratory leaks that may have occurred with a focus on mallard ducks and other migratory waterfowl, which have the potential to infect a large number of poultry and livestock facilities around the world. A moratorium on gain-of-function research including serial passage of H5N1 is indicated to prevent a man-made influenza pandemic affecting animals and humans.