Search Results (166)

The innovative aquaculture equipment known as high-density polyethylene (HDPE) floating rafts has gained popularity among fishermen in the southeast coastal regions of China. Compared to deep-water anti-wave fish cages, the construction costs of HDPE floating rafts are 50% to 75% less. There is a dearth of comprehensive publicly available records of HDPE floating rafts sea trial data, despite substantial numerical studies on the motion response of aquaculture fish cages and scale model experiments under controlled-wave conditions. This study involves sea trial techniques under operational and extreme environmental conditions for motion responses of HDPE floating rafts, presents a comprehensive procedure for sea trials of HDPE floating rafts, summarizes the issues encountered during the trials, and suggests solutions. Using MATLAB for independent programming, motion videos and photos collected from the sea trials are processed for image capture, yielding the original time history curve of vertical displacement. Based on the sea trials’ data, including motion displacement, acceleration, mooring line force, overall deformation patterns, and current and wave data, recommendations are provided for the design and layout of HDPE floating rafts. Based on the Fast Fourier Transform (FFT) method for spectral analysis, the influence of interference items on the observational data is eliminated; the rationality of the observational data is verified in conjunction with the results of the Gabor Transform. This study offers a scientific analytical method for the structural design and safe operation of HDPE floating rafts and provides a reference for subsequent numerical simulations. Full article

Forest fires can profoundly impact the hydrological response of river basins, modifying vegetation characteristics and soil infiltration. This results in a significant increase in surface flow and channel runoff. In response to these effects, many researchers from different areas of earth sciences are committed to determining emergency measures to rehabilitate river basins, intending to restore their functions and minimize damage to soil resources. This study aims to analyze the mapping detection capacity of burned areas in a river basin in Brazil based on images acquired by AMAZÔNIA-1/WFI and the AQ1KM product. The effectiveness of the AMAZÔNIA-1 satellite in this regard is evaluated, given the importance of the subject and the relatively recent introduction of the satellite. The AQ1KM data were used to analyze statistical trends and spatial patterns in the area burned from 2003 to 2023. The U-Net architecture was used for training and classification of the burned area in AMAZÔNIA-1 images. An increasing trend in burned area was observed through the Mann–Kendall test map and Sen’s slope, with the months of the second semester showing a greater occurrence of burned areas. The NIR band was found to be the most sensitive spectral resource for detecting burned areas. The AMAZÔNIA-1 satellite demonstrated superior performance in estimating thematic accuracy, with a correlation of above 0.7 achieved in regression analyses using a 10 km grid cell resolution. The findings of this study have significant implications for the application of Brazilian remote sensing products in ecology, water resources, and river basin management and monitoring applications. Full article

Benzoxazinoids (BXs) are unique bioactive metabolites with protective and allelopathic properties in maize in response to diverse stresses. The production of BXs involves the fine regulations of BXs biosynthetic gene cluster (BGC). However, little is known about whether and how the expression pattern of BGC members is impacted by biotic and abiotic stresses. Here, maize BGC was systemically investigated and 26 BGC gene members were identified on seven chromosomes, for which Bin 4.00–4.01/4.03–4.04/7.02 were the most enriched regions. All BX proteins were clearly divided into three classes and seven subclasses, and ten conserved motifs were further identified among these proteins. These proteins were localized in the subcellular compartments of chloroplast, endoplasmic reticulum, or cytoplasmic, where their catalytic activities were specifically executed. Three independent RNA-sequencing (RNA-Seq) analyses revealed that the expression profiles of the majority of BGC gene members were distinctly affected by multiple treatments, including light spectral quality, low-temperature, 24-epibrassinolide induction, and Asian corn borer infestation. Thirteen differentially expressed genes (DEGs) with high and specific expression levels were commonly detected among three RNA-Seq, as core conserved BGC members for regulating BXs biosynthesis under multiple abiotic/biotic stimulates. Moreover, the quantitative real-time PCR (qRT-PCR) verified that six core conserved genes in BGC were significantly differentially expressed in leaves of seedlings upon four treatments, which caused significant increases in 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) content under darkness and wound treatments, whereas a clear decrease in DIMBOA content was observed under low-temperature treatment. In conclusion, the changes in BX metabolites in maize were regulated by BGC gene members in multiple stress presences. Therefore, the identification of key genes associated with BX accumulation under biotic/abiotic stresses will provide valuable gene resources for breeding maize varieties with enhanced capability to adapt to environmental stresses. Full article

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10–20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC. Full article

The use of artificial water points for wildlife in African savannah ecosystems has been widely criticised for affecting the distribution of wildlife and initiating changes in the heterogeneity of natural landscapes. We examined the spatiotemporal variations in the landscape before and after the installation of an artificial water point by integrating the analysis of vegetation and soil spectral response patterns with a supervised learning random forest model between 2002 and 2022 in Chobe Enclave, Northern Botswana. Our results revealed that the study area is characterised by animal species such as Equus quagga, Aepyceros melampus, and Loxodonta africana. The findings also showed that the main vegetation species in the study area landscape include Combretum elaeagnoides, Vachellia luederitzii, and Combretum hereroense. The artificial water point induced disturbances on a drought-vulnerable landscape which affected vegetation heterogeneity by degrading the historically dominant vegetation cover types such as Colophospermum mopane, Dichrostachys cinerea, and Cynodon dactylon. The immediate years following the artificial water point installation demonstrated the highest spectral response patterns by vegetation and soil features attributed to intense landscape disturbances due to abrupt high-density aggregation of wildlife around the water point. Landscapes were strongly homogenised in later years (2022), as shown by overly overlapping spectral patterns owing to an increase in dead plant-based material and senescent foliage due to vegetation toppling and trampling. The landscape disturbances disproportionately affected mopane-dominated woodlands compared to other vegetation species as indicated by statistically significant land cover change obtained from a random forest classification. The woodlands declined significantly (p < 0.05) within 0–0.5 km, 0.5–1 km, 1–5 km, and 5–10 km distances after the installation of the water point. The results of this study indicate that continuous nonstrategic and uninformed use of artificial water points for wildlife will trigger ecological alterations in savannah ecosystems. Full article

The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’s susceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests (Pinus halepensis Mill. and Pinus pinaster Aiton) to wildfires, analyzing two major forest fires that occurred in Yeste (Spain) in 1994 and 2017, affecting over 14,000 and 3200 hectares, respectively. Four recovery regions were identified based on fire severity—calculated using the delta Normalized Burn Ratio (dNBR) index—and recurrence: areas with high severity in 2017 but not in 1994 (UB94-HS17), areas with high severity in 1994 but not in 2017 (HS94-UB17), areas with high severity in both fires (HS94-HS17), and areas unaffected by either fire (UB94-UB17). The analysis focused on examining the recovery patterns of three spectral indices—the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), and Normalized Burn Ratio (NBR)—using the Google Earth Engine platform from 1990 to 2023. Additionally, the Relative Recovery Indicator (RRI), the Ratio of Eighty Percent (R80P), and the Year-on-Year average (YrYr) metrics were computed to assess the spectral recovery rates by region. These three spectral indices showed similar dynamic responses to fire. However, the Mann–Kendall and unit root statistical tests revealed that the NDVI and NDMI exhibited distinct trends, particularly in areas with recurrence (HS94-HS17). The NDVI outperformed the NBR and NDMI in distinguishing variations among regions. These results suggest accelerated vegetation spectral regrowth in the short term. The Vegetation Recovery Capacity After Fire (VRAF) index showed values from low to moderate, while the Vulnerability to Fire (V2FIRE) index exhibited values from medium to high across all recovery regions. These findings enhance our understanding of how vegetation recovers from fire and how vulnerable it is to fire. Full article

This study investigates the historical variability in annual average precipitation in the northwest region of Mexico, aiming to evaluate the cumulative impact of precipitation on soil degradation and associated risks posed by rainfall. Despite being known as “The Agricultural Heart of Mexico”, the region’s soil has experienced significant damage to its granulometric structure due to unpredictable rainfall patterns attributed to climate change. Sixteen historical series of average annual rainfall were analyzed as stationary stochastic processes for spectral analysis. The results revealed exponential decay curves in each radial spectrum, indicating a linear relationship between frequency and amplitude. These curves identified initial impulses correlated with moments of severity for structural damages caused by rainfall-induced degradation. The degradation process, exacerbated by water stress, accelerates, as evidenced by maps illustrating approximately 75% soil damage. In the context of climate change and the uncertainty surrounding soil responses to extreme meteorological events, understanding this phenomenon becomes crucial. Recognizing the dynamic nature of soil responses to environmental stressors is essential for effective soil management. Emphasizing the need to employ numerical processes tailored to new environmental considerations related to observed soil damages is crucial for sustainable soil management practices in any region. Full article

Disaster prevention and the mitigation of earth fissures is a key issue in the sustainable development of urban land. Structures directly avoiding earth fissures are not conducive to the rational planning and efficient utilization of urban construction. The Su-Xi-Chang area, which consists of the cities of Suzhou, Wuxi, and Changzhou, surrounded by Taihu Lake, has developed bedrock buried-hill earth fissures that are rare in the rest of the country. Existing research results have identified the genesis mechanisms, distribution patterns, and developmental characteristics of this type of fissure. Not only does the slow-variable activity of earth fissures cause direct damage to surface and underground structures, but in addition, when an earthquake occurs, the presence of earth fissures may cause the seismic response of the site to be altered or even strengthened, leading to unknown damage or the possible destruction of structures near the fissures. However, no studies have been conducted to assess the dynamic effects of bedrock-buried-hill earth fissure sites. Therefore, in this research, based on six typical bedrock-buried-hill-type earth fissures in the Su-Xi-Chang area, and in order to accurately reveal the dynamic amplification effect law of the earth fissure sites, systematic spectral analyses and comparisons of the microtremor signals were carried out by using the linear analysis method (Direct Fourier Transform Analysis) and the nonlinear analysis method (Hilbert–Huang Transform). The results show that bedrock-buried-hill-type earth fissures have a significant amplification effect on the dynamic response of the site; the amplification effect of bedrock-buried-hill fissure sites follows the same attenuation pattern, and the furthest range of the dynamic response on the site is about 25 m, beyond which the original seismic fortification level can be maintained; the extreme value of the amplification factor of the two sides of this type of site, as derived from the Fourier and HHT methods, is about double, and the nearest earth fissure region should be considered to have a raised seismic fortification intensity of more than double the original. The Hilbert–Huang transform method has good applicability for processing microtremor data, and nonlinear signal analysis methods can be considered comprehensive for future microtremor signal processing. Full article

The applicability of remote sensing enables the prediction of nutritional value, phytosanitary conditions, and productivity of crops in a non-destructive manner, with greater efficiency than conventional techniques. By identifying problems early and providing specific management recommendations in bean cultivation, farmers can reduce crop losses, provide more accurate and adequate diagnoses, and increase the efficiency of agricultural resources. The aim was to analyze the efficiency of vegetation indices using remote sensing techniques from UAV multispectral images and Sentinel-2A/MSI to evaluate the spectral response of common bean (Phaseolus vulgaris L.) cultivation in different phenological stages (V4 = 32 DAS; R5 = 47 DAS; R6 = 60 DAS; R8 = 74 DAS; and R9 = 89 DAS, in 99 days after sowing—DAS) with the application of doses of magnesium (0, 250, 500, and 1000 g ha⁻¹). The field characteristics analyzed were mainly chlorophyll content, productivity, and plant height in an experimental area by central pivot in the midwest region of Brazil. Data from UAV vegetation indices served as variables for the treatments implemented in the field and were statistically correlated with the crop’s biophysical parameters. The spectral response of the bean crop was also detected through spectral indices (NDVI, NDMI_GAO, and NDWI_GAO) from Sentinel-2A/MSI, with spectral resolutions of 10 and 20 m. The quantitative values of NDVI from UAV and Sentinel-2A/MSI were evaluated by multivariate statistical analysis, such as principal components (PC), and cophenetic correlation coefficient (CCC), in the different phenological stages. The NDVI and MCARI vegetation indices stood out for productivity prediction, with r = 0.82 and RMSE of 330 and 329 kg ha⁻¹, respectively. The TGI had the best performance in terms of plant height (r = 0.73 and RMSE = 7.4 cm). The best index for detecting the relative chlorophyll SPAD content was MCARI (r = 0.81; R² = 0.66 and RMSE = 10.14 SPAD), followed by NDVI (r = 0.81; R² = 0.65 and RMSE = 10.19 SPAD). The phenological stage with the highest accuracy in estimating productive variables was R9 (Physiological maturation). GNDVI in stages R6 and R9 and VARI in stage R9 were significant at 5% for magnesium doses, with quadratic regression adjustments and a maximum point at 500 g ha⁻¹. Vegetation indices based on multispectral bands of Sentinel-2A/MSI exhibited a spectral dynamic capable of aiding in the management of bean crops throughout their cycle. PCA (PC1 = 48.83% and PC2 = 39.25%) of the satellite multiple regression model from UAV vs. Sentinel-2A/MSI presented a good coefficient of determination (R² = 0.667) and low RMSE = 0.12. UAV data for the NDVI showed that the Sentinel-2A/MSI samples were more homogeneous, while the UAV samples detected a more heterogeneous quantitative pattern, depending on the development of the crop and the application of doses of magnesium. Results shown denote the potential of using geotechnologies, especially the spectral response of vegetation indices in monitoring common bean crops. Although UAV and Sentinel-2A/MSI technologies are effective in evaluating standards of the common bean crop cycle, more studies are needed to better understand the relationship between field variables and spectral responses. Full article

The production of “Nocciola Romana” hazelnuts in the province of Viterbo, Italy, has evolved into a highly efficient and profitable agro-industrial system. Our approach is based on a hierarchical framework utilizing aggregated data from multiple temporal data and sources, offering valuable insights into the spatial, temporal, and phenological distributions of hazelnut crops To achieve our goal, we harnessed the power of Google Earth Engine and utilized collections of satellite images from Sentinel-2 and Sentinel-1. By creating a dense stack of multi-temporal images, we precisely mapped hazelnut groves in the area. During the testing phase of our model pipeline, we achieved an F1-score of 99% by employing a Hierarchical Random Forest algorithm and conducting intensive sampling using high-resolution satellite imagery. Additionally, we employed a clustering process to further characterize the identified areas. Through this clustering process, we unveiled distinct regions exhibiting diverse spatial, spectral, and temporal responses. We successfully delineated the actual extent of hazelnut cultivation, totaling 22,780 hectares, in close accordance with national statistics, which reported 23,900 hectares in total and 21,700 hectares in production for the year 2022. In particular, we identified three distinct geographic distribution patterns of hazelnut orchards in the province of Viterbo, confined within the PDO (Protected Designation of Origin)-designated region. The methodology pursued, using three years of aggregate data and one for SAR with a spectral separation clustering hierarchical approach, has effectively allowed the identification of the specific perennial crop, enabling a deeper characterization of various aspects influenced by diverse environmental configurations and agronomic practices.The accurate mapping and characterization of hazelnut crops open opportunities for implementing precision agriculture strategies, thereby promoting sustainability and maximizing yields in this thriving agro-industrial system. Full article

Over prolonged exposure to groundwater conditions, semi-rigid base asphalt pavements can undergo significant changes in their internal moisture field, resulting in substantial variations in the pavement’s stiffness and, consequently, affecting the overall load-bearing capacity and stability of the road structure. This paper employs FWD non-destructive testing equipment to assess its mechanical performance and conduct data analysis and conducts a mechanical response study of asphalt road surfaces, considering the influence of roadbed moisture levels. Using the dynamic analytical theory, the fundamental equations and stiffness matrices for a linear elastic half-space model were established, leading to the development of a computational model for the mechanical response of semi-rigid base asphalt pavements under FWD dynamic loading, with an examination of the surface deflection in relation to changes in groundwater levels. Numerical examples and engineering applications were employed to validate the proposed model. The research findings indicate: With the passage of time, surface deflection values initially increase and then decrease, exhibiting a sinusoidal variation pattern similar to that of the applied load. As the distance from the loading center increases, the moment of peak deflection continually lags behind. The average absolute relative error between the results obtained using the method proposed in this study and the traditional ABAQUS finite element method was only 0.70%. The correlation coefficient between the theoretically computed deflection curve and the measured deflection curve using the spectral element method was greater than 0.9, with an average absolute relative error of 4.92% between the theoretical peak deflection and the measured peak deflection. As the groundwater level rises, surface deflection noticeably increases, with an approximately 40% increase in deflection values at the loading center. These research findings can be utilized to analyze the dynamic deflection of semi-rigid base asphalt pavements under various groundwater conditions, providing significant practical value for assessing road structural performance and serviceability. Full article

The selection of an affordable method to fabricate plasmonic metasurfaces needs to guarantee complex control over both tunability and reproducibility of their spectral and morphological properties, making plasmonic metasurfaces suitable for integration into different sensing devices. Displacement Talbot lithography could be a valid solution thanks to the limited fabrication steps required, also providing the highly desired industrial scalability. Fabricated plasmonic metasurfaces are represented by a gold nanohole array on a glass substrate based on a triangular pattern. Scanning electron microscopy measurements have been recorded, showing the consistency of the surface features with the optimized design parameters. Reflectance and transmittance measurements have also been carried out to test the reliability and standardization of the metasurface’s optical response. Furthermore, these plasmonic metasurfaces have also been successfully tested for probing refractive index variations in a microfluidic system, paving the way for their use in sensitive, real-time, label-free, and multiplexing detection of bio-molecular events. Full article

In response to the limitations posed by noise interference in complex environments and the narrow focus of existing diagnosis methods for jointless track circuit faults, an innovative approach is put forward in this study. It involves the application of the continuous wavelet transform (CWT) for signal preprocessing, along with the integration of a deep belief network (DBN) and a genetic algorithm (GA) to improve the least-squares support vector machine (LSSVM) model for intelligent time–frequency fault diagnosis. Initially, the raw induced voltage signals are transformed using continuous wavelet transformation resulting in wavelet time–frequency representations that combine temporal and spectral information. Subsequently, these time–frequency representations are fed into the deep belief networks, which perform semi-supervised dimensionality reduction and feature extraction, thereby uncovering distinct fault characteristics in the track circuit. Finally, the genetic algorithms are employed to improve the kernel function and penalty factor parameters of the least-squares support vector machine, thus establishing an optimal DBN-GA-LSSVM diagnostic model. Experimental validation demonstrates the effectiveness of the proposed time–frequency intelligent network model by leveraging the advantages of deep belief networks in hierarchical feature extraction and the superior performance of the least-squares support vector machine in addressing high-dimensional pattern recognition problems with limited samples. The achieved accuracy rate on the testing dataset reaches an impressive 99.6%. Consequently, this comprehensive approach provides a viable solution for data-driven track circuit fault diagnosis. Full article

Corals are habitat-forming organisms on tropical and sub-tropical reefs, often displaying diverse phenotypic behaviors that challenge field-based monitoring and assessment efforts. Symbiont chlorophyll (Chl) is a long-recognized indicator of intra- and inter-specific variation in coral’s response to environmental variability and stress, but the quantitative Chl assessment of corals at the reef scale continues to prove challenging. We integrated field, airborne, and laboratory techniques to test and apply the use of reflectance spectroscopy for in situ and reef-scale estimation of Chl a and Chl c2 concentrations in a shallow reef environment of Kāne‘ohe Bay, O‘ahu. High-fidelity spectral signatures (420–660 nm) derived from field and airborne spectroscopy quantified Chl a and Chl c2 concentrations with demonstrable precision and accuracy. Airborne imaging spectroscopy revealed a 10-fold range of Chl concentrations across the reef ecosystem. We discovered a differential pattern of Chl a and Chl c2 use in symbiont algae in coexisting corals indicative of a physiological response to decreasing light levels with increasing water depth. The depth-dependent ratio of Chl c2:a indicated the presence of two distinct light-driven habitats spanning just 5 m of water depth range. Our findings provide a pathway for further study of coral pigment responses to environmental conditions using field and high-resolution airborne imaging spectroscopy. Full article

Brazil is the largest producer and exporter of coffee beans in the world. Given this relevance, it is important to monitor the crop to prevent attacks by pests. This study aimed to detect leaf miner (Leucoptera coffeella) infestation in a newly planted crop based on vegetation indices (VI) derived from aerial images obtained by a multispectral camera embedded in a remotely piloted aircraft (RPA) using random forest (RF). The study was conducted on the Cafua farm in the municipality of Lavras in southern Minas Gerais. The images were collected using a multispectral camera attached to a remotely piloted aircraft (RPA). Collections were carried out on 30 July 2019 (infested crop) and 16 December 2019 (post chemical control). The RF package in R software was used to classify the infested and healthy plants. The t test revealed significant differences in band means between healthy and infested plants, favouring higher means in healthy plants. VI also exhibited significant differences, with EXR being higher in infested plants and GNDVI, GOSAVI, GRRI, MPRI, NDI, NDRE, NDVI and SAVI showing higher averages in healthy plants, indicating distinct spectral responses and light absorption patterns between the two states of the plant. Due to the spectral differences between the classes, it was possible to classify the infested and healthy plants, and the RF algorithm performed very well. Full article