Little is known about how differences in water availability within the “super humid” tropics can influence the distribution of understory plant species and the composition of understory plant communities. We investigated variation in the physiological drought tolerances of understory plants and plant communities across a large elevation and precipitation gradient. We established 58 understory plots along a gradient of 400 – 3600 m asl elevation and 1000 – 6000 mm yr-1 rainfall in and around Manu National Park in southeastern Peru. Within the plots, we sampled all understory woody plants and measured three metrics of physiological leaf drought tolerance - turgor loss point (TLP), cuticular conductance (Gmin), and solute leakage (SL) - and assessed how the community-level means of these traits related to mean annual precipitation (MAP) and elevation (in the study gradient temperature decreases linearly and vapor pressure deficit increases monotonically with elevation). We did not find any correlations between the three metrics of leaf drought tolerance, suggesting that they represent independent strategies for coping with low water availability. Despite being widely used metrics of leaf drought tolerance, neither TLP nor Gmin showed any significant relationships with elevation or MAP. In contrast, SL, which has only recently been developed for use in ecological field studies, increased significantly at higher precipitations and at lower elevations (i.e., plants in colder and drier habitats have lower SL, indicating greater drought tolerances). Our results illustrate that differences in water availability may play a strong role in structuring plant communities even in in the wet tropics and highlight the potential for SL assays to be an efficient and effective tool for measuring drought tolerances in the field.

In order to investigate the vertical distribution pattern of soil fauna community in low-altitude mountain areas. On July 8th, 2022, a low hill was selected as the study area to collect soil arthropods were collected through trap. Leaf litter, vegetation type and distribution quantity were investigated at each sampling site while soil fauna were collected. Also, the soil physical and chemical parameters were measured simultaneously. The results of one-way ANOVA showed that there were significant differences (P

A dynamic impact test was performed on as-rolled AZ31 alloy with gradient microstructure under various strains. The microstructural evolution and mechanical properties were systematically investigated. As the strain rate gradually increases, an increasing number of twins are formed, facilitating dynamic recrystallization (DRX), and the mechanical properties are also gradually improved as well. The microstructure becomes heterogeneous at higher strain rates, but the peak stress decreases. The impact process results in significantly higher performance due to microstructural refinement, work hardening by dislocations and precipitates. In addition, both the adiabatic shear band and the adjacent crack experience a temperature rise that exceeds the recrystallization temperature of the alloy. This observation also explains the presence of ultrafine recrystallized grains within the adiabatic shear band and the appearance of molten metal around the crack.

The gradient descent algorithm has become the standard algorithm for computing the extreme values of functions, but for multivariate functions this algorithm is mostly ineffective. This is because the convergence rate of each element is inconsistent in most cases. However, in this paper, we found that the gradient sign is a self-optimizing operator, ensuring that the convergence rate is consistent across all elements. This also explains, from an optimization perspective, the success of the Fast Gradient Sign Method (FGSM) in generating adversarial samples that are indistinguishable from the normal input, but can easily fool neural networks. We also found that the fractional order gradient is also self-optimizing, and that the convergence speed of this algorithm can be controlled by adjusting the order of the gradient. Experiments suggest that this algorithm not only generates adversarial samples faster than other algorithms, but that a single source image can generate many such samples. This algorithm is also more effective than others at generating adversarial samples from simple images.

This study investigates the impact of plasma gradient modification and gradient illumination on the optoelectronic properties of buckypaper (BP), a flexible and large-scale material composed of multi-walled carbon nanotubes (MWCNTs). The BP samples were subjected to argon ion plasma treatment at varying power levels and durations, thereby creating different carrier concentration gradients on the surface. The photovoltage and photocurrent responses of the samples were then measured under uniform full illumination and gradient illumination conditions. The findings revealed that both plasma gradient modification and gradient illumination significantly enhanced the optoelectronic performance of BP. Notably, the combined application of these two methods yielded superior results compared to the application of either method alone. Specifically, the optimal plasma power for improving BP was found to be 20W. Under conditions of plasma gradient modification and gradient illumination, a photovoltage of 267.76 μV was generated, which represents a 21.44 times increase, and a photocurrent of 15.69 μA, reflecting a 32.69 times enhancement. The mechanism underlying this optoelectronic effect can be attributed to the presence of π-bonds in the carbon atoms. These π-bonds are excited by photons, resulting in the generation of small voltages and currents. This study underscores the potential of BP as an optoelectronic material and introduces a novel approach to enhance its optoelectronic properties through plasma gradient modification and gradient illumination.

The attitude dynamics and orbit dynamics of large rigid space structures are coupled with each other under gravity gradient, which may affect the stability of the large rigid space structures. In this paper, the gravity gradient stability of a large rigid space structure is studied under both small and large disturbances. Based on the rigid body dynamics and orbit dynamics, an accurate dynamic model without any linearization of the large rigid space structure is established via the natural coordinate formulation (NCF), which is able to describe the large overall motions of the structures. By using the generalized-α algorithm, the gravity gradient stability of the large rigid space structure is simulated and analyzed via various examples, including the influence of large disturbance angles, the positions at the stabilization and unstabilization regions. Finally, the relationship between spinning stability and gravity gradient stability is also investigated via a large spinning space structure.

Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently this fact is not considered either in laboratory experiments or field pollution degree measurements, causing the existing insulation selection method to be deficient. In this paper a systematical research on insulator flashover voltage gradient correction involving types of soluble pollution constituents was presented. Taking typical type glass insulator as the sample, its flashover tests polluted by typical soluble chemicals NaCl, NaNO₃, KNO₃, NH₄NO₃, MgSO₄, Ca(NO₃)₂ and CaSO₄ were carried out. Then the flashover gradient correction was made combining the flashover performance of each soluble constituent, the ESDD contribution of the seven constituents, as well as the saturation performance of CaSO₄. The correction was well verified with the flashover test results of insulator polluted by three types of soluble mixture. Research results indicate that the flashover gradient correction method proposed by this paper performs well in reducing the calculating error. It is recommended to carry out component measurements and flashover gradient correction to better select outdoor insulation configuration.

For many engineering applications it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in finite plasticity or fluid mechanics. In many approaches length scale parameter have been introduced that are related to a specific micro structure. An alternative approach is possible, if a thermodynamically consistent framework is used for material modelling as shown in the present contribution. However, even if sophisticated and thermodynamically consistent material models can be established there are still not yet standard experiments to determine higher order material constants. In order to contribute to this ongoing discussion system identification based on the method of self-adaptive filtering is proposed in this paper. To evaluate the effectiveness of this approach it has been applied to second-order gradient materials considering longitudinal vibrations. Based on thermodynamically consistent models that have been solved numerically it has been possible to prove that system identification based on self-adaptive filtering can be used effectively for both narrow-band and broadband signals in the field of second-order gradient materials. It has also been found that the differences identified for simple materials and gradient materials allow for condition monitoring and detection of gradient effects in the material behavior.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

Questions：SAP in mountainous cities of China serve as crucial carriers for species and wild food, constituting potential green infrastructure. These patches should be recognized as locally specific resources. This study investigates the transformation characteristics of the landscape patterns of SAP, along with their elements, across urban-rural gradients, providing foundational support for the planning of conservation and utilization strategies. Location: Yongchuan area of Chongqing in southwest China. Methods: The study investigated urban-rural gradient-based transformation of SAP. A four-direction transect space was selected and divided into uniform sample squares. Fragstats 4.3 calculated the landscape index of SAP, and Spearman correlation coefficients in SPSS were used to evaluate the influence of landform and artificial factors on agroforestry patch transformation. Results: 1) Patch area (PA), Plaque density (PD), and CLUMPY of SAP systematically transform, showing increased SAP areas and higher connectivity with greater distance from the city, resulting in reduced patch fragmentation. However, Edge density (ED) and Largest plaque index (LPI) exhibit irregular transformation patterns. 2) With increased distance from the city, woodland patch area and connectivity increase, leading to decreased fragmentation, dispersal, and lower fractal dimensions. Conversely, Cultivated land follows the opposite trend. garden land patch area, fractal dimension, fragmentation, and dispersal all increase, while connectivity decreases. 3) Landscape pattern transformations of SPA are significantly influenced by artificial factors. Woodland patches are influenced similarly by landform and artificial factors. In contrast, cultivated land and garden land patches are more significantly impacted by artificial factors. Conclusions: SAP exhibit differentiated conservation and utilization measures across urban-rural gradients. Small patches located far from urban centers may benefit from systematic conservation measures to prevent further fragmentation and erosion in peri-urban areas.

(1) Background: Depressional wetlands are highly vulnerable to changes in temperature and rainfall, affecting littoral vegetation, thus HydroGeoMorphic (HGM) wetland types. This study aimed to assess the utility of Sentinel-2A vegetation indices in enhancing delineation and sub-classify depressional HGM types. (2) Methods: We sampled 84 pairs of four vegetation indices from 10x10m plots along 14 belt transects in eight depressional wetlands in Mpumalanga, South Africa. We evaluated vegetation index differences between and within wetlands, focusing on lit-toral gradients. (3) Results: Significant differences (Bonferroni p

The movement of liquid droplets on the energy gradient surface has attracted extensive attention. Inspired by biological features in nature, such as the periodic spindle-shaped nodes in spider silks and conical-like barbs of cacti, the structure-property-function relationship of multifunctional gradient surfaces. In this study, a series of specific patterns are fabricated by 3D printing technology, followed by modification via the atmospheric pressure plasma treatment and liquid phase chemical deposition, resulting in enhancing the ability of water droplets of 5μL to travel 18.47mm on a horizontal plane and 22.75mm against gravity up to 20° tilting angle. Additionally, analysis techniques have been employed including contact angle analyzer, ESCA and laser confocal microscope to evaluate the sample performance. This work could further be applied to many applications related to microfluidic devices, drug delivery and water/fog collection.

As a new type of high-performance material, gradient structural steel is widely used in engineering fields due to its unique microstructure and excellent mechanical properties. For the prevalent fatigue failure problem, the rate of change of the local grain size gradients along the structure (referred to as the gradient rate) is a key parameter in the design of gradient structures, which significantly affects the fatigue performance of gradient structural steel. In this work, a method of ‘Voronoi primary + secondary modeling’ is adopted to successfully establish three typical high-strength steel models corresponding to the convex, linear, and concave type gradient rates for gradient structures, focusing on the stress-strain response and crack propagation in structural steel with different gradient rates under cyclic loading. It is found that the concave gradient rate structural model is dominated by finer grains with larger volume fraction, which is conducive to hindering fatigue crack propagation and has the longest fatigue life; the linear structure is the second one, and the convex structure is the shortest one. The simulation results in this work are consistent with the relevant experimental phenomena. Therefore, when regulating the gradient rate, priority should be given to increasing the volume fraction of fine grains and designing a gradient rate structure dominated by fine grains to improve the fatigue life of the material.

Quantifying the landscape pattern change can effectively demonstrate the ecological progresses and the consequences of urbanization. Based on remotely sensed land cover data in 1994, 2000, 2006 and a gradient analysis with landscape metrics at landscape- and class- level, we attempted to characterize the individual and entire landscape patterns of Shanghai metropolitan during the rapid urbanization. We highlighted that a roadscape transect approach that combined the buffer zone method and the transect-based approach was introduced to describe the urban-rural patterns of agricultural, residential, green, industrial, and public facilities land along the railway route. Our results of landscape metrics showed significant spatiotemporal patterns and gradient variations along the transect. The urban growth pattern in two time spans conform to the hypothesis for diffusion–coalescence processes, implying that the railway is adaptive as a gradient element to analyze the landscape patterns with urbanization. As the natural landscape was replaced by urban landscape gradually, the urban fringe expanded radically. The results also showed that the desakota region expanded its extent widely. Satellite towns witnessed the continual transformation from the predominantly rural landscape to peri-urban landscape. Furthermore, the gap between urban and rural areas remained large especially in public service. More reasonable urban plans and land use policies should push to make more of an effort to transition from the urban-rural separation to coordinated urban-rural development. This study is a meaningful trial in demonstrating a new form of urban–rural transects to study the landscape change of large cities from a strategic viewpoint. By combining gradient analysis with landscape metrics, we addressed the process of urbanization both spatially and temporally, and provided a more quantitative approach to urban studies.

Increased anthropogenic pressure on forest resources leads to deforestation and forest degradation, significantly limiting the regeneration capacity of native woody species and consequently the restoration of miombo woodlands in anthropized habitats within the rural area of Lubumbashi. This study assessed the diversity and natural regeneration capacity of miombo species through floristic inventories in three different habitats (unexploited forests, degraded forests, and post-cultivation fallows). The results reveal that for the adult’s stratum unexploited and degraded forests exhibit higher dendrometric (density, mean square diameter, basal area) and floristic parameter (taxa, genera, families) values compared to post-cultivation fallows. Furthermore, the regeneration of miombo woody species is higher in degraded forests (21 taxa; 105 juveniles/plot). However, regarding the sapling’s stratum (1 cm ≤ dbh < 10 cm), the three habitats display nearly similar situations. Additionally, the floristic composition and diversity of unexploited and degraded forests show a significantly higher similarity (76.50%) among them compared to these habitats and the post-cultivation fallows (56.00%). These findings indicate that miombo woodlands have the potential to regenerate and maintain floristic diversity. To sustain this natural regeneration capacity of miombo woody species and promote the restoration of forest mass and its floristic diversity, it is imperative to determine the rotation period after habitat exploitation and regulate anthropogenic activities and late bush fires, particularly in anthropized habitats at the village level.

In vertical coordinates, (x,y,z), the gradient Richardson number Ri=N^2⁄S^2 is the square of the ratio between buoyancy frequency N and vertical-shear amplitude S, where N^2=-(g⁄ρ) ∂ρ⁄∂z and S^2=(∂u⁄∂z)^2+ (∂v⁄∂z)^2, with ρ potential density, (u,v) the horizontal velocity components and g gravity acceleration, so that a decrease in stratification brings a decrease in dynamic stability and vice versa. In the isopycnic coordinate system, (x,y,ρ), however, Ri=M^2⁄〖S_ρ〗^2 , is the ratio between 〖M^2≡N〗^(-2) and the squared diapycnal shear 〖S_ρ〗^2=(ρ⁄g)^2 [(∂u⁄∂ρ)^2+ (∂v⁄∂ρ)^2 ], so a decrease in stratification leads to an increase in dynamic stability and vice versa. The apparently different role of stratification arises because S and S_ρ are related through the stratification itself, S_ρ=S⁄N^2 or, equivalently, τ=〖t_o〗^2⁄t_d , which is interpreted as a natural oscillation time τ≡S_ρ that equals the buoyancy or oscillation time t_o=N^(-1) normalized by the ratio t_o⁄t_d , where the deformation time is t_d=S^(-1). Here we follow simple arguments and use field data from three different situations (island shelf-break, Gulf Stream and Mediterranean outflow) to endorse the usefulness of the isopycnal approach. In particular, we define the reduced squared vertical σ^2= S^2-N^2 and reduced squared diapycnal shear 〖σ_ρ〗^2= 〖S_ρ〗^2-M^2, which are positive for unstable conditions and negative for stable conditions. It turns out that Ri and σ^2 remain highly variable for all stratification conditions; in contrast, the mean 〖σ_ρ〗^2 values display a univocal tendency, its magnitude decreasing with stratification. We propose 〖σ_ρ〗^2 and 〖S_ρ〗^2 to be good indexes for the occurrence of effective mixing under high stratification conditions.

Gradient-domain image processing is a technique where, instead of operating directly on the image pixel values, the gradient of the image is computed and processed. The resulting image is obtained by reintegrating the processed gradient. This is normally done by solving the Poisson equation, most oftenly by means of a finite difference implementation of the gradient descent method. However, this technique in some cases lead to severe haloing artefacts in the resulting image. To deal with this, local or anisotropic diffusion has been added as an ad-hoc modification of the Poisson equation. In this paper, we show that a version of anisotropic gradient-domain image processing can result from a more general variational formulation through the minimisation of a functional formulated in terms of the eigenvalues of the structure tensor of the differences between the processed gradient and the gradient of the original image. Example applications of linear and non-linear local contrast enhancement and colour image daltonisation illustrate the behaviour of the method.

Coastal dunes worldwide are increasingly under pressure from the adverse effects of human activities. Therefore, more and more restoration measures are being taken to create conditions that help disturbed coastal dune ecosystems regenerate or recover naturally. However, many projects lack (open-access) monitoring observations needed to signal whether further actions are needed, and hence to learn by doing. This submission presents an open access data set of 37 high-resolution digital elevation models and 24 orthomosaics collected before and after the excavation of five artificial foredune trough blowouts ("notches") in winter 2012/2013 in the Dutch Zuid-Kennemerland National Park, one of the largest coastal dune restoration projects in northwest Europe. These high-resolution data provide a valuable resource for improving understanding of the biogeomorphic processes that determine the evolution of restored dune systems, as well as developing guidelines to better design future restoration efforts with foredune notching.

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of a terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and solution based on prediction of the weight mask by an encoder-decoder architecture, subse-quently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. Each sample corresponds to a 10 by 10 meter area of the stream channel and adjacent land. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. The proposed solution was compared with existing methods for measuring WSE in small streams using a drone. The results indicate that the solution outperforms previous photogrammetry-based methods and is second only to the radar-based method, which is considered the most accurate method available. The solution is characterized by a high degree of explainability and generalization.

This study addresses the problem of accurately predicting azimuth and elevation angles of signals impinging on an antenna array employing Machine Learning (ML). Using the information obtained at a receiving system when a transmitter’s signal hits it, a Decision Tree (DT) model is trained to estimate azimuth and elevation angles simultaneously. Simulation results demonstrate the robustness of the proposed DT-based method, showcasing its ability to predict the Direction of Arrival (DOA) in diverse conditions beyond the ones present in the training dataset, i.e., the results display the model’s generalization capability. Additionally, the comparative analysis reveals that DT-based DOA estimation outperforms the state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm, establishing DTs as competitive alternatives for DOA estimation in signal reception systems.

Questions: Elevation, biodiversity, and forest structure are commonly correlated, but their relationships near the positive extremes of biodiversity and elevation are unclear. We asked 1) How does forest structure vary with elevation in a high biodiversity, high topographic complexity region? 2) Does forest structure predict vascular plant biodiversity? 3) Is plant biodiversity more strongly related to elevation or to forest structure? Location: Great Smoky Mountains National Park, USAMethods: We used terrestrial LiDAR scanning (TLS) to characterize vegetation structure in 12 forest plots. We combined two new canopy structural complexity metrics with traditional TLS-derived forest structural metrics and vascular plant biodiversity data to investigate correlations among forest structure metrics, biodiversity, and elevation. Results: Forest structure varied widely across plots spanning the elevational range of GRSM. Our new measures of canopy density (Depth) and structural complexity (σDepth) were sensitive to structural variations and effectively summarized horizontal and vertical dimensions of structural complexity. Vascular plant biodiversity was negatively correlated with elevation, and more strongly positively correlated with vegetation structure variables. Conclusions: The strong correlations we observed between canopy structural complexity and biodiversity suggest that structural complexity metrics could be used to assay plant biodiversity over large areas in concert with airborne and spaceborne platforms.

Cosmogenic nuclides are widely used to constrain the landscape history of glaciated areas. At nunataks in continental polar regions with extremely arid conditions, cosmogenic nuclides are often the only method available to date the ice thinning history of the glacier. However, the amount of cosmogenic isotopes accumulated at the surface of nunataks depends not only on the length of time that rock has been exposed since the last deglaciation, but on the full history of the surface, including muon production under ice, exposure during previous interglacials, subaerial weathering rate, glacial erosion rate, and uplift rate of the nunatak. The NUNAtak Ice Thinning model (NUNAIT) simulates the cosmonuclide accumulation on vertical profiles, fitting the aforementioned parameters to a set of multi-isotope apparent ages from samples taken at different elevations over the ice-sheet surface. The NUNAIT calculator is an easy-to-use tool that constrains parameters that describe the geological history of a nunatak from a set of surface exposure ages.

Elevation of the intermediate amino acid metabolite Homocysteine (Hcy) causes Hyperhomocysteinemia (HHcy), a metabolic disorder frequently associated with mutations in the methionine-cysteine metabolic cycle as well as with nutritional deficiency and aging. Previous literature suggest that HHcy is a strong risk factor for cardiovascular diseases. Severe HHcy is well established to correlate with vascular pathologies primarily via endothelial cell death. Though moderate HHcy is more prevalent and associated with an increased risk of cardiovascular abnormalities in later part of life, its precise role in endothelial physiology is largely unknown. In this study, we report that moderate elevation of Hcy causes endothelial dysfunction through impairment of their migration and proliferation. We established that unlike severe elevation of Hcy, moderate HHcy is not associated with suppressed endothelial VEGF/VEGFR signaling and oxidative stress induction. We further showed that moderate HHcy induces a sub-lethal ER stress that causes defective endothelial migration through abnormal actin cytoskeletal remodeling. We also found that sub-lethal increase of Hcy causes endothelial proliferation defect by suppressing mitochondrial respiration and concomitantly increases glycolysis to compensate the consequential ATP loss and maintain overall energy homeostasis. Finally, analyzing an available microarray dataset, we confirmed that these hallmarks of moderate HHcy are conserved in adult endothelial cells as well. Thus, we identified adaptive UPR and metabolic rewiring as two key mechanistic signatures in moderate HHcy-associated endothelial dysfunction. As HHcy is clinically associated with enhanced vascular inflammation and hypercoagulability, identifying these mechanistic pathways may serve as future targets to regulate endothelial function and health.

ST-segment elevation myocardial infarction (STEMI), a major cause of mortality worldwide, has been shown through epidemiological studies to have a relationship with short-term exposure to air pollutants; however, the association between air pollutants and the outcome of STEMI has not been well studied. The aim of this study was to estimate the impact of air pollutants on the outcomes of STEMI. Data on particulate matter <2.5 μm (PM2.5), <10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) at each of the 11 air monitoring stations in Kaohsiung City were collected between January 1, 2012, and December 31, 2017. Medical records of non-trauma patients aged > 20 years who had visited the ED with a principal diagnosis of STEMI were extracted. The primary outcome measure was in-hospital mortality. After adjusting for potential confounders and meteorological variables, we found that an increase in the interquartile range (IQR) in NO2 was associated with an elevated risk of in-hospital mortality in patients with STEMI. Moreover, during the warm season, an IQR increase in NO2 corresponded to a higher risk of in-hospital mortality in patients with STEMI on lag 3 (OR=3.266; 95% CI:1.203–8.864, p=0.02). Conversely, an IQR increase in PM10 was associated with an increased risk of in-hospital mortality in patients with STEMI on lag 3 (OR=2.792; 95%CI:1.115–6.993, p=0.028) during the cold season. Our study suggests that exposure to NO2 (during warm season) and PM10 (during cold season) may contribute to a higher risk of poor prognosis in patients with STEMI.

The Combined Elevation Test (CET) is a musculoskeletal screening technique (MST) replicates the streamline position in swimming and is commonly used in various sports. Although the CET is widely used, no normative data exist within an adolescent population. Therefore, the purpose of this study was to develop a normative data set for the CET within an adolescent population and to evaluate the influence various demographic and anthropometric variables. Data was collected for 416 participants aged between 8 and 18 years old. Age and arm span showed a significant correlation with CET scores (arm span r_s (105) = .478, p = .000, age r_s (416) = .238 p = .000). Regression analysis further quantified the influence of arm span and age on CET scores accounting for 23.1% and 5.3% of variability respectively. These results can be used as a reference point for clinicians and coaches who are using the CET within their assessment.

Applications of gradient method for nonlinear optimization in development of Gradient Neural Network (GNN) and Zhang Neural Network (ZNN) are investigated. Particularly, the solution of the matrix equation AXB=D which changes over time is studied using the novel GNN model, termed as GGNN(A,B,D). The GGNN model is developed applying GNN dynamics on the gradient of the error matrix used in the development of the GNN model. The convergence analysis shows that the neural state matrix of the GGNN(A,B,D) design converges asymptotically to the solution of the matrix equation AXB=D, for any initial state matrix. It is also shown that the convergence result is the least square solution which is defined depending on the selected initial matrix. A hybridization of GGNN with analogous modification GZNN of the ZNN dynamics is considered. The Simulink implementation of presented GGNN models is carried out on the set of real matrices.

Initial choice of Learning Rate is a key part of gradient based methods and has a great effect on the performance of the Deep Learning Model.This paper studies the behavior of multiple gradient based optimization algorithm which are commonly used in Deep Learning and compare their performance on various learning rate. As observed popular choice of optimization algorithms are highly sensitive to various choice of learning rates. Our goal is to find which optimizer has an edge over others for a specific setting. We look at two datasets namely MNIST and CIFAR10 for benchmarking. The results are quite surprising, and it will help us to choose a learning rate more efficiently.

Daltonisation refers to the recolouring of images such that details normally lost by colour vision deficient observers become visible. This comes at the cost of introducing artificial colours. In a previous work, we presented a gradient-domain colour image daltonisation method that outperformed previously known methods both in behavioural and psychometric experiments. In the present paper, we improve the method by (i) finding a good first estimate of the daltonised image, thus reducing the computational time significantly, and (ii) introducing local linear anisotropic diffusion, thus effectively removing the halo artefacts. The method uses a colour vision deficiency simulation algorithm as an ingredient, and can thus be applied for any colour vision deficiency, and can even be individualised if the exact individual colour vision is known.

Artificial Intelligence is dominated by Artificial Neural Networks (ANNs). Currently, the Batch Gradient Descent (BGD) is the only solution to train ANN weights when dealing with large datasets. In this article, a modification to the BGD is proposed which significantly reduces the training time and improves the convergence. The modification, called Instance Eliminating Back Propagation (IEBP), eliminates correctly-predicted-instances from the Back Propagation. The speedup is due to the elimination of unnecessary matrix multiplication operations from the Back Propagation. The proposed modification does not add any training hyperparameter to the existing ones and reduces the memory consumption during the training.

Video quality assessment (VQA) plays an important role in video applications for quality evaluation and resource allocation. It aims to evaluate the video quality consistent with the human perception. In this letter, a hierarchical gradient similarity based VQA metric is proposed inspired by the structure of the primate visual cortex, in which visual information is processed through sequential visual areas. These areas are modeled with the corresponding measures to evaluate the overall perceptual quality. Experimental results on the LIVE database show that the proposed VQA metric significantly outperforms the state-of-the-art VQA metrics.

Data creation is often the only way for researchers to produce basic geospatial information for the pursuit of more complex tasks and procedures such as those that lead to the production of new data for studies concerning river basins, slope morphodynamics, applied geomorphology and geology, urban and territorial planning, detailed studies, for example, in architecture and civil engineering, among others. This exercise results from a reflection where specific data processing tasks executed in Google Sketchup (Pro version, 2018) can be used in a context of interoperability with Geographical Information Systems (GIS) software. The focus is based on the production of contour lines and Digital Elevation Models (DEM) using an innovative sequence of tasks and procedures in both environments (GS and GIS). It starts in Google Sketchup (GS) graphic interface, with the selection of a satellite image referring to the study area—which can be anywhere on Earth's surface; subsequent processing steps lead to the production of elevation data at the selected scale and equidistance. This new data must be exported to GIS software in vector formats such as Autodesk Design Web format—DWG or Autodesk Drawing Exchange format—DXF. In this essay the option for the use of GIS Open Source Software (gvSIG and QGIS) was made. Correcting the original SHP by removing “data noise” that resulted from DXF file conversion permits the author to create new clean vector data in SHP format and, at a later stage, generate DEM data. This means that new elevation data becomes available, using simple but intuitive and interoperable procedures and techniques which confgures a costless work flow.

Glypicans are a family of heparan sulfate proteoglycans that are attached to the outer plasma membrane leaflet of the producing cell by a glycosylphosphatidylinositol anchor. Glypicans are involved in the regulation of many signalling pathways, including those that regulate the activities of Wnts, Hedgehog (Hh), Fibroblast Growth Factors (FGFs) and Bone Morphogenetic Proteins (BMPs), among others. In the Hh signalling pathway, glypicans have been shown to be essential for ligand transport and the formation of Hh gradients over long distances, for the maintenance of Hh levels in the extracellular matrix and for its unimpaired reception in distant recipient cells. Recently, two mechanistic models have been proposed to explain how Hh can form the signalling gradient and how glypicans may contribute to it. In this review, we describe the structure, biochemistry and metabolism of glypicans and their interactions with different components of the Hh signalling pathway that are important for the release, transport and reception of Hh.

The functioning of forest ecosystems depends on the taxonomic and ecological diversity of soil fungi. Urbanization is increasing worldwide and is regarded a major driver of environmental change altering local species assemblages in urban forest. We investigated whether the degree of urbanization and local forest characteristics affect the soil fungal community in 20 beech forests located along a rural–urban gradient in the city of Basel and its suburbs (Switzerland). We analyzed their soil fungal communities by DNA metabarcoding of the rDNA ITS2 region and related these data to local forest vegetation characteristics and soil properties. The number of fungal OTUs in the 20 forests examined ranged from 170 to 303. Richness, diversity and evenness of fungal OTUs were all significantly affected by the degree of urbanization, but in different ways. OTU richness was highest in forests in areas with a low degree of urbanization and lowest in forests in rural areas. In contrast, the diversity of OTUs increased with increasing degree of urbanization. Different fungal phyla and fungal guilds showed distinct patterns in their relative abundance along the rural-urban gradient. The degree of urbanization reduced the relative abundance of symbiotrophic fungi, but increased that of saprotrophic and pathotrophic fungi. Our results show that urbanization alters soil fungal community, which in turn can lead to changes in forest ecosystems.

Salinity acts as a critical environmental filter on microbial communities in natural systems, negatively affecting microbial diversity. However, how salinity affects the community assembly remains unclear. This study used Wendeng multi-pond saltern as a model to evaluate the prokaryotic community composition and diversity and quantify the relative importance of ecological processes across salinity gradients. Results showed that low saline salterns (45-80 g/L) exhibited higher bacterial diversity than those in high saline salterns (175-265 g/L). The relative abundance of taxa assigned to Halanaerobiaceae, Haloferacaceae, Desulfohalobiaceae, Phormidiaceae, Rohodobacteraceae, and Nitrococcaceae was higher with increasing salinity. Salinity and pH were the primary environmental factors that directly or indirectly determined the composition and diversity of prokaryotic communities. Microbial co-occurrence network dynamics were more complex in the sediment than in water of salterns. An infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis (iCAMP) showed that microbial community assembly in sediment and water differed. Our findings provide more information about microbial community structure and the importance of various ecological processes in controlling microbial community diversity and succession along salinity gradients in water and sediment environments.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

The quality of photogrammetric-based derived products like orthophotos, digital terrain models (DTMs) and digital line maps as well as the global digital elevation models (DEM) are rigorously dependent on the accuracy of image orientation. This paper evaluates the vertical accuracy of aerial photogrammetric Digital Terrain Model (DTM), Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), and TerraSAR-X's twin satellite of TanDEM-X (TDX) datasets against in-situ orthometric heights computed from ellipsoidal heights and the 2008 Earth Gravitational Model (EGM2008) derived geoid heights in Ethiopia. The quality of the four global digital elevation models was also validated against the aerial photogrammetric DTM measurements. Besides, the accuracies of the photogrammetric DTM and the four DEM products were checked for their compliance to the American Society for Photogrammetry and Remote Sensing (ASPRS) standards as well as the Ethiopian national vertical data evaluation standards. The study showed that the photogrammetric DTM is in a good agreement with the reference orthometric heights compared to SRTM, ASTER and TDX datasets. More precisely, the result has an absolute accuracy of 1.67 m at Linear Error (LE) 95% confidence level, while the absolute accuracy of SRTM3 arc seconds (SRTM3) at LE 90% (11.91 m) is better than its product specification (16 m). The absolute accuracy of SRTM1 arc second (SRTM1) (7.70 m at LE 90%) surpasses that of SRTM3, whereas the absolute accuracy of ASTER DEM is somehow below its product specification. TDX also has the same vertical accuracy (10.29 m at LE 90%) compared to its product specification (10 m). Furthermore, the vertical accuracy of the photogrammetric DTM meets the100 cm vertical accuracy of the 2015 ASPRS standard. However, it does not meet the Ethiopian national vertical data accuracy requirement standard, i.e., RMSEz of ± 0.45 m. In general, the photogrammetric DTM, SRTM1, and TDX have been proven a superior product over the SRTM3 and ASTER DEMs, and better to use these products for high-level precision and accuracy required applications.

Modern fighter aircraft increasingly need to conjugate aerodynamic performance and low observability. In this paper we showcase a methodology for a gradient-based bidisciplinary aero-stealth optimization. The shape of the aircraft is parametrized with the help of a CAD modeler, and we optimize with the SLSQP algorithm. The drag, computed with the help of a RANS method, is used as the aerodynamic criterion. The stealth criterion, a function is derived from the radar cross section in a given cone of directions, weighed with a function whose goal is to cancel the electromagnetic intensity in a given direction. Stealth is achieved passively by scattering back the electromagnetic energy away from the radar antenna and no energy is absorbed by the aircraft, which is considered as a perfect conductor. A Pareto front is identified by varying the weights of the aerodynamic and stealth criteria. The Pareto front allows for an easy identification of the CAD model corresponding to a chosen aero-stealth trade-off.

Fluid flow and chemokine gradients play a large part in not only regulating homeostatic processes in the brain, but also in pathologic conditions by directing cell migration. Tumor cells in particular are superior at invading into the brain resulting in tumor recurrence. One mechanism that governs cellular invasion is autologous chemotaxis, whereby pericellular chemokine gradients form due to interstitial fluid flow (IFF) leading cells to migrate up the gradient. Glioma cells have been shown to specifically use CXCL12 to increase their invasion under heightened interstitial flow. Computational modeling of this gradient offers better insight into the extent of its development around single cells, yet very few conditions have been modelled. In this paper, a computational model is developed to investigate how a CXCL12 gradient may form around a tumor cell and what conditions are necessary to affect its formation. Through finite element analysis using COMSOL and coupled convection-diffusion/mass transport equations, we show that velocity (IFF magnitude) has the largest parametric effect on gradient formation, multidirectional fluid flow causes gradient formation in the direction of the resultant which is governed by IFF magnitude, common treatments and flow patterns have a spatiotemporal effect on pericellular gradients, exogenous background concentrations can abrogate the autologous effect depending on how close the cell is to the source, that there is a minimal distance away from the tumor border required for a single cell to establish an autologous gradient, and finally that the development of a gradient formation is highly dependent on specific cell morphology.

Intra-articular fractures are a major cause of post-traumatic osteoarthritis (PTOA). Despite adequate surgical treatment, the long-term risk for PTOA is high. Previous studies reported that joint injuries initiate an inflammatory cascade characterized by elevation of synovial pro-inflammatory cytokines, which can lead to cartilage degradation and PTOA development. This review summarizes the literature on the post-injury regulation of pro-inflammatory cytokines and the markers of cartilage destruction in patients suffering from intra-articular fractures. METHODS We searched Medline, Embase, and Cochrane databases (1960–February 2020) and included studies that were performed on human participants and included control groups. Two investigators assessed the quality of the included studies using Covidence and the Newcastle-Ottawa Scale. RESULTS Based on the surveyed literature, several synovial pro-inflammatory cytokines, including interleukin (IL)-1β, IL-2, IL-6, IL-8, IL-12p70, interferon-y, and tumor necrosis factor-α, were significantly elevated in patients suffering from intra-articular fractures compared to control. A simultaneous elevation of anti-inflammatory cytokines such as IL-10 and IL-1RA was also observed. In contrast, IL-13, CTX-II, and aggrecan concentrations did not differ significantly between the compared cohorts. CONCLUSIONS Overall, intra-articular fractures are associated with an increase in inflammation-related synovial cytokines. However, more standardized studies which focus on the ratio of pro- and anti-inflammatory cytokines at different time points are needed.

The digital elevation model (DEM) is one of the key geospatial datasets used in many fields of engineering and science for countless applications. In this contribution, we assess the vertical accuracy of the Advanced Land Observing Satellite (ALOS) World 3D-30m (AW3D30) DEM using the runway method (RWYM). The RWYM utilizes the longitudinal profiles of runways which are reliable and ubiquitous reference data. A reference dataset used in this project consists of 36 runways located at various points throughout the world. The same dataset was previously used to test the accuracy of WorldDEM^TM. Our study indicates that AW3D30 has a remarkably high RMSE of 1.78 m (one σ). However, while analyzing the results, it has become apparent that it also contains a widespread elevation anomaly. We conclude that this anomaly is the result of uncompensated sensor noise and the data processing algorithm (downsampling of the higher resolution data). We believe that this issue should be communicated to the user community. Also, we would like to note that the traditional accuracy assessment of a DEM, e.g., statistical assessment of the elevation differences = model – reference, does not allow for identification of these type of anomalies in a DEM.

The magnetic navigation system (MNS) with gradient and uniform saddle coils is one of the effective systems to manipulate various medical magnetic robot due to its compact structure and uniformity of the magnetic field and field gradient. However, it has limited operating time and large inductance effect due to the high current density and numerous turns of coils, respectively. In this paper, we propose an optimization methodology of the MNS considering the current density and inductance of the coils. We introduce objective functions, constraints, and design variables of the MNS. Then, the MNS was designed by an optimization algorithm. The constructed MNS was compared with the conventional MNS, and it could generate 22 % stronger magnetic field or field gradient while the maximum three-dimensional rotating magnetic field was improved by 42 %. We also demonstrated an unclogging performance of a helical robot was improved by 54 % with the constructed MNS.

Unmanned aerial vehicles (UAV) thermal imagery offers several advantages in environmental monitoring, as it can provide a low-cost, high-resolution, and flexible solution to measure the temperature of the surface of the land. Limitations related to the maximum load of the drone lead to use of lightweight uncooled thermal cameras whose internal components are not stabilized to a constant temperature. Such cameras suffer from several unwanted effects that contribute to the increase in temperature measurement error from ±0.5 °C in laboratory conditions, to ±5 °C in unstable flight conditions. This article describes a post processing procedure, that reduces the above unwanted effects. It consists of following steps: i) devignetting using single image vignette correction algorithm, ii) georeferencing of images using EXIF data, scale-invariant feature transform (SIFT) stitching, and gradient descent optimisation, and iii) temperature calibration by minimisation of bias between overlapping thermal images using gradient descent optimisation. The solution was tested in several case studies of river areas, where natural water bodies were used as a reference temperature benchmark. In all tests, the precision of the measurements was increased. The root of the mean of the Square of Errors RMSE on average was reduced by 39.0% and Mean of the absolute value of Errors MAE by 40.5%. The proposed algorithm can be called self-calibrating, as in contrast to other known solutions is fully automatic, uses only field data and does not require any calibration equipment or additional operator effort. A Python implementation of the solution is available on GitHub.

Polynomial recurrence bounds for a class of stochastic differential equations with a gradient type drift and an additive Wiener process are studied without Lyapunov functions.

The mechanical properties and stability of hydrophobic surface structures prepared by traditional methods are still the main technical bottlenecks restricting the broad application of hydrophobic systems on workpiece surfaces. In this contribution, we propose a technique called selective laser shock peening (SLSP) to enable large-scale high efficient, low-cost manufacturing of hydrophobic metal surfaces with enhanced mechanical properties for durable applications. Using the method of experimental investigation combined with numerical calculation, the hydrophobic properties, mechanical properties, and tribological properties of the samples prepared under SLSP, all-laser shock peening (ALSP), and non-laser shock peening (NLSP) are studied. The SLSP process could prepare a 3D gradient structure material with surface structures, a two-phase (strong phase, soft phase) distribution on the surface, and a multi-level gradient distribution in the thickness direction. Compared with the 2D gradient structure prepared by the traditional process, 3D gradient structures by SLSP have more significant advantages in improving the wetting behavior and the mechanical properties of the material, which proves SLSP to be a novel method to fabricate functional metal surface structures, with highly high engineering application value.

The feasibility of using full ion kinetics, instead of gyrokinetics, in simulating low-frequency Ion-Temperature-Gradient (ITG) instabilities in tokamaks has recently been demonstrated by Sturdevant et al. [Physics of Plasmas 24, 081207 (2017)]. In that work, a variational integrator was developed to integrate the full orbits of ions in toroidal geometry, which proved to be accurate in capturing both the short-time scale cyclotron motion and long time scale drift motion. The present work extends that work in three aspects. First, we implement a relatively simple full orbit integrator, the Boris integrator, and demonstrate that the accuracy of this integrator is also sufficient for simulation of ITG instabilities. Second, the equilibrium magnetic configuration is extended to general toroidal configuration specified numerically, enabling simulation of realistic equilibria reconstructed from tokamak experiments. Third, we extend that work to the nonlinear regime and investigate the nonlinear saturation of ITG instabilities. To verify the new numerical implementation of the orbit integrator and magnetic configuration, the linear electrostatic ITG frequency and growth rate are compared with those given in Sturdevant's work and good agreement is found.

Introduction: The Elevation Training Mask 2.0 (ETM) has been introduced as a novel tool to allow for respiratory muscle training and altitude exposure during exercise that can improve performance and hematological markers in elite and well-trained athletes. Thus, the aim of the present study was to assess the effect of wearing ETM while training on erythropoietin (EPO), vascular endothelial growth factor (VEGF), peak oxygen consumption (VO2max), lactate, and 1000-m kayaking performance in elite Kayaking girls. Methods: Thirty elite Kayaking girls (14 to 18-year-old) were equally and randomly assigned into mask, no mask, and control groups. The mask and no-mask groups completed 8 weeks of repeated sprint training, while the control group performed their routine kayaking training programs. Pre- and post-training tests included EPO, VEGF, VO2max, lactate, and 1000-m kayaking performance. Results: 1000-m time trial significantly decreased for mask (p < 0.001) and control (p = 0.035) groups, and was significantly lower in mask group than control (p = 0.014) and no mask (p = 0.009) groups. EPO did not show any significant changes for all groups. VEGF was increased significantly for mask (p = 0.04) and no mask (p = 0.014) groups. Lactate was decreased significantly for mask group (p = 0.025). VO2max increased significantly for no mask group (p = 0.021). Conclusion: Wearing the ETM while participating in 8 weeks of repeated sprint training might improve specific blood markers and endurance performance, especially anaerobic pathways. Performing repeated sprint training while wearing ETM has the potential to enhance performance in Kayak racing.

Here we evaluate the chemistry of mineral soil solutions collected by suction lysimeters in a small mountain catchment that was affected by acidification-related spruce die-back. The aim was to obtain new insights into spatial patterns of nutrient partitioning during acidification recovery. This was achieved by comparing nutrient concentrations in soil solutions, collected along a V-shaped valley, with those in runoff. Five nests of suction lysimeters were installed in the 33-ha UDL catchment at different topographic positions (hilltops, slopes and valley). Following a 1-year equilibration, monthly samples of soil solutions were collected over a 2-year period. In the vicinity of each lysimeter nest, soil pits were excavated for a study of soil chemistry. Soil solutions were analyzed for SO42-, NO3-, DOC, NH4+, Na+, K+, Ca2+, Mg2+, and total dissolved Al concentrations, DOC and pH. For a P release estimation, ammonium oxalate extraction of soil samples was performed. Comparison of soil water data from this study with other European studies indicated that major environmentally relevant chemical species at UDL had concentrations similar to median concentrations across numerous previously acidified sites. CEC (≤ 58 meq kg-1) and BS (≤ 13 %), however, were significantly lower at UDL than at other European sites, documenting incomplete recovery from acidification. Spatial trends and seasonality in soil water chemistry support belowground inputs from mineral-stabilized legacy pollutants and soil nutrients. Seasonal variability of soil water sulfate and nitrate was significant. High nitrate in soil solutions during summer originated during the preceding dormant season, and high sulfate concentrations observed during the winter originating from recycled organic S during the summer. Higher concentrations of SO42-, NO3-, and base cations in runoff than in soil solutions may be explained by lateral surficial runoff leaching pollutants and nutrients from very shallow soil horizons. Nearly 30 years after peak acidification, solutions in mineral subsoil at UDL exhibited similar concentrations of SO42-, Ca2+ and Mg2+ as median values at the Pan-European International Co-operative Program (ICP) Forest sites, yet NO3- concentrations were an order of magnitude higher than the ICP sites. Calcium applied on soil surface by liming 32 to 11 years ago affected runoff more than soil solutions, suggesting contribution of both shallow soil water and groundwater to runoff.

Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's Disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and Magnetic Resonance Imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and Mini-Mental State Exam (MMSE). A Residual Network with 50 layers (ResNet-50) predicted CDR presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4,139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine Learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

Presepsin is a diagnostic and prognostic biomarker of sepsis; however, elevated presepsin levels have also been documented without sepsis. This study aims to retrospectively analyze the laboratory parameters and Sequential Organ Failure Assessment (SOFA) score affecting presepsin levels in 567 patients. Some patients with elevated presepsin levels exhibited renal dysfunction or elevation of biliary enzymes despite a low SOFA score. The univariate regression analysis revealed a close correlation between presepsin levels and SOFA score, serum creatinine (CRE), blood urea nitrogen, and biliary enzymes. In addition, a multivariate regression analysis revealed that SOFA score, alkaline phosphatase (ALP), and CRE independently affected presepsin levels significantly. The analysis of covariance (ANCOVA) revealed that presepsin levels were significantly higher in patients with hepatobiliary disease. Besides, we found that patients who presented with the dilatation of intra- or extrahepatic bile ducts and the elevation of ALP or total bilirubin exhibited remarkable high presepsin levels in the bile. Furthermore, the presepsin production in the liver’s Kupffer cells was established by immunostaining in patients who received surgical liver resection. Overall, this study elucidates that biliary enzymes’ elevation affects presepsin levels, presepsin exists in high concentrations in the bile, and is positive in Kupffer cells.

The thermal conditions of transitional (ranging from warm to cold) coldwater streams impact the ranges and resource availabilities for biota inhabiting these lotic systems. With ongoing climate change and increasing land modifications, thermal boundaries may shift, altering thermal transition zones and their biotic communities. The objective of this study was to investigate the condition of trout across three forks of the Whitewater River catchment and to investigate factors influencing fish community composition and distribution. Each fork was characterized into three separate sections: headwater (coolwater), middle (warmwater), and lower (coldwater). Springs were identified throughout each fork, with greatest concentrations in the lower sections of each fork. Using single-pass electrofishing, we sampled 61 sites across the three forks in the Whitewater River system (North = 21 sites, Middle = 19, South = 21), and catch statistics were used to calculate diversity, trout abundance, and trout condition. In general, diversity increased, and trout were healthier but less abundant in middle and headwater sections, whereas diversity decreased slightly, trout condition decreased, and trout abundance increased in lower reaches, with changes differing somewhat among forks. Canonical correlation analysis had strong significant correlations showing simpson diversity and condition increase going upstream with high non trout abundance and catch rates while trout catch rates and reach width decrease. The Whitewater River is a catchment exhibiting transitional temperature-pattern characteristics with generally low fish community diversity and trout conditions that range from thin, normal, and robust. Dominated by a changing landscape (agriculture) and intensifying climate change, we may begin to see stream temperatures increase along with species diversity. Understanding how spring temperature influences species composition and distribution can bring potential stressors to light increasing our understanding of thermal conditions and to help mitigate the negative impacts from land use and climate change.

Background: A significant percentage of younger patients with myocardial infarction have premature coronary artery disease (CAD). The aims of this study were to analyze all-cause mortality and major adverse cardiovascular events (MACE- cardiovascular death, non-fatal reinfarction and stroke and target vessel revascularization) during eight-year follow-up in STEMI patients with premature CAD. Method: we analyzed 2,560 STEMI patients without previous CAD and without cardiogenic shock at admission who were treated with primary PCI. CAD was classified as premature in men aged <50 years and women <55 years. Results: Of the 2,560 patients analyzed 630(24.6%) patients had premature CAD. Patients with premature CAD have fewer comorbidities and better initial angiographic findings, as compared to patients without premature CAD. The incidence of non-fatal adverse ischemic events was similar to the incidence in older patients. Premature CAD was an independent predictor for lower mortality (HR 0.50, 95%CI 0.28-0.91) and MACE (HR 0.27, 95%CI 0.15-0.47). In patients with premature CAD, EF<40% was the only independent predictor of mortality (HR 5.59 95%CI 2.18-8.52) and MACE (HR 4.18, 95%CI 1.98-8.13). Conclusion: Premature CAD was an independent predictor for lower mortality and MACE. In patients with premature CAD, EF<40% was an independent predictor of eight-year mortality and MACE.

Lightning has received a lot of attention in scientific literature during the recent decade, not only because it is an impressive atmospheric phenomenon but also its associations with severe storms that cause unprecedented damages to agriculture, electric power networks, property, and life. This study assessed the Spatio-temporal characteristics of lightning occurrence with elevation in Uganda using lightning flash and elevation datasets for a period of fifteen years (1998-2013). Datasets used in this study included daily lightning flashes as captured by Lightning Imaging Sensor (LIS) aboard on Tropical Rainfall Measurement Mission (TRMM) satellite and elevation data in form of Digital Elevation Model (DEM) obtained from the Shuttle Radar Topography Mission (SRTM). Spatio-temporal results indicated that ~80% of areas with an elevation that ranges from 800-1200 m above mean sea level (masl) in Uganda had severe lightning occurrences and ~20% of areas with an elevation greater than 1200 m (masl) had severe lightning occurrences. The country received an enhanced number of lighting events with the highest number in 1999. Subsequently, a reduced trend was observed from 2002 to 2007 followed by an increment in the number of lightning events in (2010, 2011, 2012, and 2013). The intensity of the events decreased gradually though two peaks were observed, (1998-2001) and (2010-2013). Furthermore, results indicate escalations in the frequency and duration of lightning events from 60 times in 1998 to approximately 200 times in 2013 and from 1000 microseconds in 1998 to more than 2000 microseconds in 2013. Generally, the country experienced an enhanced increase in lighting occurrences over the study period which therefore calls for urgent actions to combat the root cause and also provide effective measures to reduce the impacts of lightning strikes.

Magnetorheological (MR) fluids have been known to react to magnetic fields of sufficient magnitudes. While in the presence of the field the material develops a yield stress. The tunable property has made it attractive in e.g. semi-active damper applications in vibration control domain in particular. Within the context of a given application the fluids can be exploited in at least one of the fundamental operating modes of which the so-called gradient pinch mode has been least explored. Contrary to the other operating modes, the MR fluid volume in the flow channel is exposed to a non-uniform magnetic field in such a way that the Venturi-like contraction is developed in a flow channel solely by means of a solidified material in the regions near the walls rather than mechanically driven changes of the channel’s geometry. The pinch mode rheology of the material has made it a potential candidate in developing on a new category of MR valves. By convention, a pinch mode valve features a single flow channel with poles over which non-uniform magnetic field is induced. In this study the authors examine ways of extending the dynamic range of pinch mode valves by employing a number of such arrangements (stages) in series. To accomplish this, the authors developed a prototype of a multi-stage (three-stage) valve, and then compared its performance against that of a single-stage valve across a wide range of hydraulic and magnetic stimuli. To summarize, improvements in the pinch mode valve dynamic range are evident, however, at the same time it is hampered by the presence of serial air gaps in the flow channel.

Radiation is the major driver of snowmelt, and hence its estimation is critically important. Net radiation reaching the forest floor is influenced by vegetation density. Previous studies in mid-latitude conifer forests have confirmed that net radiation decreases and then subsequently increases with increasing vegetation density, for clear sky conditions. This leads to existence of a net radiation minimum at an intermediate vegetation density. With increasing cloud cover, the minimum radiation shifts toward lower densities, sometimes resulting in a monotonically increasing radiation with vegetation density. The net radiation trend, however, is expected to change across sites, affecting the magnitude and timing of individual radiation components. This research explores the variability of net radiation on snow-covered forest floor for different vegetation densities along a latitudinal gradient. We especially investigate how the magnitude of minimum/maximum radiation and the corresponding vegetation density change with the site geographical location. To evaluate these, the net radiation is evaluated using the Forest Radiation Model at six different locations in predominantly white spruce (Picea glauca) canopy cover across North America, ranging from 45 to 66°N latitudes. Results show that the variation of net radiation with vegetation density considerably varies with latitude. In higher latitude forests, the magnitude of net radiation is generally smaller, and the minimum radiation is exhibited at relatively sparser vegetation densities, under clear sky conditions. For interspersed cloudy sky conditions, net radiation non-monotonically varies with latitude across the sites, depending on the seasonal sky cloudiness and air temperature. Latitudinal sensitivity of net radiation is lower on north-facing hillslopes than on south-facing sites.

Synthetic polymer-based gradient foams have considered as promising category of functionally graded materials with unique properties. In this study, the carbon dioxide (CO₂) foaming technology has used for PET-PEN (Polyethylene Terephthalate - Polyethylene Naphthalate) copolymer towards porous functional materials with thermal insulation with reasonable mechanical strength. Through scanning electron microscope based morphological characterization, a potential to fabricate gradient foam structures with micro-pores has identified. It has shown that variation of post-foaming temperature can tune the pore size distribution although the very high post-foaming temperature tends to cause structural instability. Thermal measurement data set the limits of operation, confirmed by simultaneous differential scanning calorimeter and thermo-gravimetric analysis. Mechanical stress and thermal conductivity also has measured to find rationale of thermal insulation with reasonable mechanical strength and to elucidate the actual 3D grid foam of copolymer.

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity to appraise water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water's appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region's WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that random forest and gradient boosting eclipsed other algorithms, achieving an accuracy rate of 99%. In close pursuit were SVM and XGBoost, registering accuracy scores of approximately 95% and 93%, respectively, while KNN and Decision Trees garnered accuracy rates of 88% and 87%, respectively. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization

Traumatic brain injury (TBI) affects millions of people each year. Previous studies using the central fluid percussion injury (CFPI) model in adult male rats indicated that elevated intracranial pressure (ICP) was associated with long-term effects, including neuronal cell loss and increased sensory sensitivity post-injury and secondary ICP elevation, that were not seen following injury alone. Investigations also indicated that Cathepsin B (Cath B), a lysosomal cysteine protease, may play a role in the pathological progression of neuronal membrane disruption; however, the specific impact of Cath B inhibition following CFPI remains unknown. Thus, the focus of this study was to evaluate the effects of Cath B inhibition via intracerebroventricular infusion of CA-074Me 2w following injury with or without secondary elevation of ICP. This was accomplished using adult male rats continuously infused with CA-074Me or 10% DMSO as a vehicle control for 2w following either sham injury, a CFPI only, or a CFPI with subsequent ICP elevation to 20mmHg. We assessed Cath B activity in the lateral neocortices and liver, evaluated protein levels of Cath B and Cath B binding partners AIF, Bcl-XL, and Bak. We also conducted histological analyses of total cell counts to assess for cell loss, membrane disruption, and Cath B localization. Finally, we investigated somatosensory changes with the whisker nuisance task. Overall, this study demonstrated that Cath B is not a direct driver of membrane disruption, however, administration of CA-074Me alters Cath B localization and reduced hypersensitivity, emphasizing Cath B being an important component in late secondary pathologies.

Backgroundː Previous studies have shown that both serum creatinine and D-dimer levels were associated with atherosclerotic coronary artery disease (CAD). We aimed to determine whether DCR is associated with coronary Gensini score in patients with ST-elevation myocardial infarction (STEMI). Methodsː 337 STEMI patients with complete D-dimer and creatinine and other necessary information were included in the analysis. According to the values of the DCR, patients were divided into the lower DCR group (DCR ≤ 1.42, n = 173) and the higher DCR group (DFR > 1.42, n = 174), and the differences between the two groups were compared. Multivariate linear and multivariate logistic regression analyses were performed to determine independent predictors of Gensini score. Resultsː High DCR group had higher Gensini score compared with low DCR group (P < 0.05). DCR was positively correlated with Gensini score (r=0.493, P < 0.001). Multiple linear regression analysis showed that Previous MI (r=11.312, P=0.035) and DCR (r=5.129, P<0.001) were independent risk factors associated with Gensini score. Multivariate logistic regression analysis showed that, compared to the group 1, DCR is independent risk factor for Group 2, Group 3, Group 4 (P <0.001). DCR is positively correlated with coronary Gensini score in STEMI patients and can be used as an independent predictor of higher Gensini score. Conclusionsː As a new and useful clinical marker, DCR is positively correlated with coronary Gensini score in STEMI patients and can be used as an independent predictor of higher Gensini score.

Pollution of urban surface dust with hazardous elements (HEs) is a serious environmental issue due to its toxicity and potential hazardous effects. Surface dust samples were collected from core urban, urban, and suburban gradients in the Urumqi city of the arid NW China, and the concentrations of six HEs, such as arsenic (As), cadmium (Cd), nickel (Ni), lead (Pb), mercury (Hg), and chromium (Cr), were determined. The pollution load index (PLI) and the US EPA health risk assessment model were applied to analyze and compare the pollution levels and potential health risk of HEs in surface dusts in different urbanization gradients. The obtained results indicate that the average concentrations of Hg, Cd, and Ni in surface dust decrease in the order of core urban > urban > suburban, whereas the average concentrations of As, Cr, and Pb decrease in the order of urban > core urban > suburban. The PLI of HEs in surface dust decreased in the order of core urban > urban > suburban. The concentrations of HEs in the core urban and urban gradients are relatively higher than in the suburban gradient. Furthermore, the total non-carcinogenic and carcinogenic risk index of investigated HEs in surface dust decrease in order of urban > core urban > suburban, for both adults and children. In addition, the pollution of surface dust with HEs in all urbanization gradients is more harmful to the children's health than to the adults. Overall, the potential non-carcinogenic and carcinogenic health risks of the investigated HEs, instigated primarily by oral ingestion of surface dust, are found to be within the acceptable range. However, urbanization can effects the accumulation of HEs in surface dust, and Cr is the main non-carcinogenic risk factor, whereas Cd is the main carcinogenic risk factor among the analyzed HEs in surface dust in all urbanization gradients.

Accurate and efficient traffic incident detection methods can effectively alleviate traffic congestion caused by traffic incidents, prevent secondary accidents and improve the safety of urban road traffic.Aiming at the problems that the traditional machine learning event detection method cannot fully extract the parameter characteristics of traffic flow and is not suitable formulti-dimensional and non-linear massive data, we propose a new traffic event detection method(CNN-XGBoost).This method combines the respective advantages of Convolution Neural Network(CNN) and Extreme Gradation Boosting (XGBoost). Firstly, we preprocessed the original freeway traffic incident detection data set by constructing initial variable set, data normalization, data balance processing and dimension reorganization. Secondly,we use CNN network to automatically extract the deep features of event detection data, and use XGBoost as a classifier to classify the extracted features for expressway traffic event detection.Finally, we use the data set of Hangzhou expressway microwave detector in China to carry out simulation experiments on CNN-XGBoost. The experimental results show that compared with XGBoost, CNN, Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT) and other methods, CNN-XGBoost method can effectively improve the accuracy of expressway traffic event detection and has better generalization ability.

Cerebrotendinous xanthomatosis (CTX) is a rare inherited metabolic disease attributed to the mutation of the gene CYP27A1, resulting in sterol 27-hydroxylase deficiency characterized by deposition of cholestanol and cholesterol in several tissues, like the central nervous system and tendons. Furthermore, cataracts, gallstones, diarrhea and premature atherosclerosis have been reported. Nonetheless, clinical development is extremely heterogeneous in CTX. We report here two cases of CTX genetic alteration in the absence of cholestanol elevation in plasma and tendons but with prominent xanthomas. We propose that CTX may not be characteized by increased plasma cholestanol concentration due to alteration in the sterol 27-hydroxylase gene, but is a more complex pathology that may result from additional genetic alterations that require further analyses.

In this case study, an active runway of a civilian airport in Zonguldak, Turkey, is used to assess the suitability of spaceborne digital elevation models (DEMs) to model an anthropogenic structure. The tested DEMs include the ASTER, the AW3D30 m, the SRTM-1”, the SRTM-3”, the SRTM-X, the TanDEM-3”, and the WorldDEMTM. A photogrammetric high accuracy DEM was also available for the tests. As a reference dataset, a line leveling survey of the runway using a Leica Sprinter 150/150M instrument was performed. The selection of a runway as a testbed for this type of investigation is justified by its unique characteristics, including its flat surface, homogenous surface material, and availability for a ground survey. These characteristics are significant because DEMs over similar structures are free from environment- and target-induced error sources. The most accurate DEM is the WorldDEMTM followed by the SRTM-3” and TanDEM-3”, with vertical errors (LE90) equal to 1.291 m, 1.542 m, and 1.56 m, respectively. This investigation uses a method for identifying the vertical errors in DEMs that is known as the runway method.

Thermally-induced moisture flow in unsaturated soils is a complex coupled thermal-hydro process involving moisture flow both in vapor and liquid phases. Thermally-induced moisture flow in unsaturated sands is usually less pronounced than fine soils due to their low field capacity. It is affected by many factors, including soil density, particle size, moisture content, and gravity-induced flow. Besides, soil moisture sensors need to be highly accurate and calibrated for temperature effects in order to monitor the thermally induced moisture flow accurately. A silty sand with 35% non-plastic fines was selected to study the coupled thermo-hydro processes in a closed heat cell. The heat cell consists of two temperature-controlled heat exchanger plates at the top and bottom, heat flux sensors, moisture sensors, thermocouples, and heat conductivity sensors. The soil was prepared at various initial moisture content and tested under a constant 1-D temperature gradient. The soil moisture sensors were first calibrated in the test soil at room temperature and then at elevated temperatures. The soil moisture redistribution, temperature and thermal conductivity profiles were determined. The critical moisture content, corresponding to maximum moisture movement, was also determined. The results offer new insights into coupled moisture-heat flow in unsaturated sands.

We hypothesize that the first ancestral "ante-cell" molecular structures, i.e., first RNAs and peptides which gradually transformed into real cells once the Earth had cooled sufficiently for organic molecules to appear there, have leaved traces in RNAs and genes of present cells. We propose a circular RNA which could have been one of these ancestral structures whose vestigial pentameric subsequences would mark the evolution from this key moment when the ante-cells have begun to join the living organisms. In particular, we propose that in present RNAs (ribosomal or messenger) playing an important role in the metabolism of current cells, we look for traces of the proposed primitive structure in the form of pentamers (or longer fragments) belonging to their nucleotide sequence. The result obtained can be summarized in the existence of a gradient of occurrence of such pentamers, with a high frequency for the most vital functions (protein synthesis, nucleic synthesis, cell respiration, etc.). This gradient is also visible between organisms, from the oldest (Archaea) to the most recent (Eukaryotes) in evolution of species.

Urbanization affects all elements of the pre-urban environment, including soils, hydrology, vegetation, and microclimate. Recently, Saudi Arabia has experienced rapid urbanization and growth. Thus, the country's biodiversity has been threatened. In Riyadh, beetle assemblages were assessed along a rural-suburban-urban gradient. A total of 2,791 individuals from 94 species belonging to seven families were collected at 15 sites along three different gradients of urbanization in Wadi Hanifa. Tenebrionidae dominated abundance (60.1%) and richness (38%). Beetle abundance, evenness, and diversity were not different among habitats; however, species richness was higher in rural habitats. DCA and CCA analyses showed distinct differences among sites along gradients. Urbanization intensity, soil variables, and land cover were significantly correlated with DCA axis 1, while elevation and flora were significantly correlated with DCA axis 2. The most critical operating environmental variables in Wadi Hanifa were buildings, elevation, soil organic carbon, litter cover, and litter depth, as well as plants such as Launaea capitata, Lycium shawii, Alhagi graecorum, and Heliotropium currasavicum. Ten species in our study are associated with urban habitats, six with suburban habitats, and seven with rural habitats. Consequently, expanding urban areas may negatively affect the richness and composition of beetles and may result in the loss of some native species.

The aim of this study was to evaluate implants survival rate, marginal bone loss, surgical and prosthetic complications of implants placed through sinus floor elevation and tilted implants en-gaged in basal bone in order to bypass maxillary sinus. 60 patients were enrolled for this study. According with residual bone height of posterior maxilla the sample was divided in three groups of 20 patients: Group A (lateral sinus floor elevation), Group B (transrectal sinus floor elevation) and Group C (tilted implants employed to bypass sinus floor). Follow-up visits were performed one week after surgery, at 3, 6 months and then once a year for next 4 years. The outcomes were implants survival rate, marginal bone loss and surgical and prosthetic complications. Although the Group A, B and C have demonstrated an implants survival rate of 83.3%, 86,7% and 98,3% respectively, the statistically analysis showed that there was no statistically significant difference between groups. Statistically significant differences between the groups were also not found con-cerning marginal bone loss, as recorded by intra-oral X-ray measurements during follow-up. About complications it wasn’t possible to perform a statistical analysis. To as to reduce potential surgical risks implants placement in basal bone should be preferred.

Digital Elevation Models (DEMs) are widely used in geographic and environmental studies. In the current work, the fusion of multi-source DEMs is investigated to improve the overall accuracy of public domain DEMs. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods – the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD) and nonlinear adaptive multi-scale decomposition (N-AMD) - are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D – 30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the ice cloud and land elevation satellite (ICESat) global land surface altimetry data as a reference. The vertical accuracy of the fused DEM shows significant improvements of 29.6% and 19.3% in a mountainous region and 27.4% and 15.5% in a low-relief region, compared to the SRTM-1 and AW3D30 respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill-merge-spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level-set method based on graph theory. The proposed level-set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level-set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine-resolution LiDAR-derived DEMs show that the raster-based level-set algorithm is much more efficient (~150 times faster) than the vector-based contour tree method. The proposed level-set algorithm has great potential for being applied to large-scale ecohydrological analysis and watershed modeling.

This work proposes a methodological approach applied to ephemeral gravel-bed streams to verify the change in the magnitude and frequency of hydrological events affecting the morphological dynamics and sediment budget in this type of channel. For the case study the Azohía Rambla, located in southeastern Spain, was chosen, emphasizing the research on two reference riverbed sections (RCRs): an upper one, with a predominance of erosion, and a middle one, where processes of incision, transport, and deposition converge. First, this approach focuses on relationships between peak discharges and sediment budgets during the period 2018 to 2022. For this purpose, water level measurements from pressure sensors, a One-Dimensional Hydrodynamic model, and findings from comparative analyses of high-resolution differential digital elevation models (HRDEM of Difference - HRDoD) based on SfM-MVS and LiDAR datasets were used. In a second phase the GeoWEPP model was applied to the period 1996-2022 in order to simulate runoff and sediment yield at the event scale for the watersheds draining into both RCRs. During the calibration phase a sensitivity analysis was carried out to detect the most influential parameters in the model and to confirm its capacity to simulate peak flow and sediment delivery in the area described above. Values of NS (Nash-Sutcliffe efficiency) and PBIAS (percent bias) equal to 0.86 and 7.81%, respectively, were found in the calibration period, while these indices were 0.81 and -4.1% in the validation period. Finally, different event class patterns (ECPs) were established for the monitoring period (2018-2022), according to flow stage and morphological channel adjustments (overtopping, bankfull and sub-bankfull, and half-sub-bankfull), and then retrospectively extrapolated to stages of the prior simulated period (1996-2018) from their typical sequences (PECPs). The results revealed a significant increase in the number of events and PECPs leading to lower bed incision rates and higher vertical accretion, which denotes a progressive increase in bed armoring and bank erosion processes.

Background: High coronary thrombus burden has been associated with unfavorable outcomes in patients with ST-segment elevation myocardial infarction (STEMI), the optimal management of which has not yet to be established. Methods: We evaluated the safety and efficacy of adjunctive catheter-directed thrombolysis (CDT) during primary percutaneous coronary intervention (PCI) in patients with STEMI and high thrombus burden. Results: Among the 1,849 consecutive patients with STEMI, 263 had high thrombus burden. Moreover, 41 patients received intracoronary infusion of tissue plasminogen activator during primary PCI (CDT group), whereas 222 did not receive (non-CDT group). No significant differences in bleeding complications and in-hospital and long-term mortalities were observed (9.8% vs. 7.2%, p=0.53; 7.3% vs. 2.3%, p=0.11; and 12.6% vs. 17.5%, p=0.84, CDT vs. non-CDT). In patients who underwent antecedent aspiration thrombectomy during PCI (75.6%; CDT group and 87.4%; non-CDT group), thrombolysis in myocardial infarction grade 2 or 3 flow rate after thrombectomy was significantly lower in the CDT group than in the non-CDT group (32.2% vs. 61.0%, p&lt;0.01). However, the final rates improved considerably without significant difference (90.3% vs. 97.4%, p=0.14). Conclusions: For STEMI patients with high thrombus burden, adjunctive CDT is safe and effective for improving coronary flow. CDT resulted in favorable coronary flow even after unsatisfactory aspiration thrombectomy.

Our study analyzes the ecology and distribution of fish communities related to the environmental variables of the Alto Madre de Dios River, an Andean-Amazon watershed of southern Peru, between 300 and 2811 m a.s.l. within the Manu Biosphere Reserve. We provide new ecological and diversity data for these highly unknown rivers and new data for palm swamp habitats. With electric fishing techniques, we collected a total of 1934 fish specimens belonging to 78 species, 42 genera and 15 families. To assess main patterns of diversity we combined SIMPER and ANOSIM with canonical correspondence analysis to obtain an overview of the community structure of fish and their distribution related to aquatic habitats. Our results show an important shift on fish diversity at 700 m a.s.l. separating headwater and middle-lowland communities. Electrofishing was a hindrance due to the depth, flow and low conductivity of the rivers, but also allowed us to capture fish not observed with other techniques. We also compared the use of elevation with slope as an alternative variable for statistical analysis. Our results show that slope offers a solid and equivalent explanation for fish distribution variability, avoids redundance, and instead of giving geographical data offers ecologically solid information.

Airborne distributed position and orientation system (ADPOS), which integrates multi-inertia measurement units(IMUs), data-processing computer and Global Navigation Satellite System (GNSS), serves as a key sensor in new higher-resolution airborne remote sensing, such as array SAR, multinode imaging loads and so on. ADPOS can provide multinode high accuracy spatio-temporal reference information to realize multinode motion compensation with the various state estimation methods such as Central Difference Kalman Filtering (CDKF), modifed CDKF. However, these methods focus on noise optimization based on known nonlinear models and its linear minimum variance estimation criterion, which cannot gain better estimation performance for high-dimensional nonlinear systems. For this reason, the variational optimization process based on conjugate gradient algorithm is proposed for the estimation of filtering update step, which is more efficient than linear minimum variance estimation criterion. Therefore, aiming at ADPOS with high-dimensional and nonlinearity, Central Difference Variational Filtering (CDVF) using variational optimization based on conjugate gradient algorithm for mean correction in the filtering update stage is devoted to advancing the estimation accuracy of multinode motion parameters. A real ADPOS flight test has been conducted, the experimental results show that the accuracy of the slave motion parameters has a distinct progress than the up to date CDKF, and the compensation performance is distinct.

A novel reinforcement learning deep deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in the attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the studied quadrotor is derived and the attitude control problem is described by formulas. Second, a sliding mode controller including its sliding mode surface and reaching law is selected for the nonlinear dynamic system, and the stability of the designed SMC system is supported by Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adjust the switching control gain adaptively. During the training process, the input signals of agent are the actual and desired attitude angles, and the output action is the time-varying control gain. Finally, the above trained agent is applied to the SMC as a parameter regulator, to implement the adaptive adjustment of the switching control gain related to reaching law, and the simulation results verify the robustness and effectiveness of the proposed DDPG-SMC method.

There has been considerable research on reconstructing 3D shapes from single-view images; however, preserving the detailed information of the input image remains difficult. In this paper, we propose the application of a gradient map to train a network, aimed at improving the visual quality of fine-grained details such as the thin and tiny components of generated shapes. Each gradient map was created from the original voxel data, and each value represented the amount of information per volume. Here, the gradient map was defined by several methods that mathematically quantify and represent the detailed structure of an object. By applying this map to the loss function in training, we could induce the network to intensively train partial details, such as thin and narrow parts. We demonstrated that the detailed information was well-recovered when a weight that is proportional to the gradient value was applied to the loss. Furthermore, it is expected that our method will contribute to the development of 3D technologies related to the construction of virtual space for simulation and new customer experience.

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14% ± 1.51% and 98.45% ± 1.22% with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

Soil erosion is the major factor that has contributed to the reduction of soil fertility in the highlands of Ethiopia. This factor was reduced by soil and water conservation (SWC) practices. Hence, the study evaluated the effects of SWC practices and slope gradient on soil physicochemical properties in the Agemi watershed of northwestern Ethiopia. Farmland treated by SWC such as stone-faced soil bund (SFSB) and soil bund (SB), untreated farmland and slope gradient were identified in the study area. About 27 soil samples were collected from the treated farmland and untreated farmland with three slope classes, including lower (5-10%), middle (10-15%) and upper slopes (>15%). The soil physicochemical properties were analyzed by two ways analysis of variance (ANOVA) using SAS 9.2. The results of the study showed that the content of sand, clay, total porosity, pH, soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), exchangeable cations such as Ca²⁺, Mg²⁺, K⁺ and cation exchange capacity (CEC) were significantly affected (p<0.01) by SWC practices and untreated farmland with slope gradients. However, the content of silt, percent of base saturation (PBS) and exchangeable Na were statistically insignificant (p>0.05). Farmland treated with SFSB confirmed higher mean values of soil physicochemical properties than SB and untreated farmland. Moreover, the lower total porosity (TP) and higher bulk density (BD) were recorded on untreated farmland and upper slopes. This study concludes that farmland treated with SFSB improved soil physicochemical properties than farmland treated with SB followed by untreated farmland. Therefore, SFSB should be implemented on farmland to improve soil fertility in the study area.

The Hertzian contact theory, as well as most of the other classical theories of normal and tangential contact, provides displacements and the distribution of normal and tangential stress components directly in the contact surface. However, other components of the full stress tensor in the material may essentially influence the material behaviour in contact. Of particular interest are principal stresses and the equivalent von Mises stress, as well as the gradient of the hydrostatic pressure. For many engineering and biomechanical problems, it would be important to find these stress characteristics at least in the contact plane. In the present paper, we show that the complete stress state in the contact plane can be easily found for axially symmetric contacts under very general assumptions. We provide simple explicit equations for all stress components and the normal component of the gradient of hydrostatic pressure in the form of one-dimensional integrals.

Primary percutaneous coronary intervention (pPCI) has revolutionized the prognosis of ST-segment ele-vation myocardial infarction (STEMI) and is the gold standard treatment. As a result of its success, the number of pPCI centres has expanded worldwide. Despite decades of advancements, clinical outcomes in STEMI patients have plateaued. Out-of-hospital car-diac arrest and cardiogenic shock remain a major cause of high in-hospital mortality, whilst the growing burden of heart failure in long-term STEMI survivors presents a growing problem. Many elements aiming to optimize STEMI treatment are still subject to debate or lack sufficient evidence. This review provides an overview of the most contentious current issues in pPCI in STEMI patients, with an emphasis on unresolved questions and persistent challenges.

Interferometry Synthetic Aperture Radar (InSAR) is an advanced remote sensing technique for studying the earth's surface topography and deformations. It is used to generate high-quality Digital Elevation Models (DEMs). DEMs are a crucial and primary input to various topographical quantification and modelling applications. The quality of input DEMs can be further improved using fusion methods, which combine multi-sensor or multi-temporal datasets intelligently to retrieve the best information amongst the input data. This research study is based on developing a Neural Network based fusion approach for improving InSAR based DEMs in plain and hilly terrains. The study areas comprise of relatively plain terrain from Ghaziabad and hilly terrain of Dehradun and their surrounding regions. The training dataset consists of DEM elevations and derived topographic attributes like slope, aspect, topographic position index (TPI), terrain ruggedness index (TRI), and vector roughness measure (VRM) in different land use land cover classes of the study areas. The spaceborne altimetry ICESat-2 ATL08 photon data is used as a reference elevation. A Feed Forward Neural Network with backpropagation algorithm is trained based on the prepared training samples. The trained model produces fused DEMs by learning the relationship between the input and target samples. This is used to predict elevations in the test areas. The accuracy of results from the models are assessed with TanDEM-X 90 m DEM. The fused DEMs show significant improvement in terms of RMSE over the input DEMs with improvement factor of 94.65 % in plain area and 82.62 % in hilly area. The study concludes that the ANN with its universal approximation property is able to significantly improve the fused DEM.

In southern South America, zonal flora, defined by macroclimatic conditions, is strongly influenced by an aridity filter near 29°S. Cushion bogs, high-altitude wetlands along the Andes, display homogeneous flora with cushion species dominance, contrasting with zonal vegetation. Despite being influenced by microclimatic conditions, these ecosystems experience varied environmental effects. This study identifies environmental filters affecting bog communities along a broad-scale latitudinal gradient from 15°S to 42°S. We analyzed 420 bogs and 284 species across three macroclimatic regions with distinct summer, winter, and transitional arid rainfall regimes. Using variance partitioning and membership-based regionalization models, we examined the impacts of climatic, edaphic, and spatial variables on beta diversity. We also assessed species' niche overlap and the influence of environmental filters on the communities' phylogenetic diversity. Results show that species turnover and niche overlap vary with macroclimatic differences, delineating three distinct regions. Notably, phylogenetic clustering in the driest part of the gradient (23°S —24°S) highlights the impact of the environmental filter. Aridity and temperature variations at a broad scale serve as environmental filters shaping the composition of bog communities across southern South America.

In this work, we explore the extreme searching of multidimensional functions by natural gradient descent based on Dirichlet and generalized Dirichlet distributions. The natural gradient is based on describing multidimensional surface with probability distributions, which allows us to reduce changing the accuracy of gradient and step-size. In this article, we propose an algorithm of natural gradient descent based on Dirichlet and generalized Dirichlet distributions. We demonstrate that the natural gradient descent with step-size adaptation with Dirichlet and generalized Dirichlet distributions has higher accuracy and does not take a large number of iterations for minimizing test functions than gradient descent and Adam.

Gradient based methods are popularly used in training neural networks and can be broadly categorized into first and second order methods. Second order methods have shown to have better convergence compared to first order methods, especially in solving highly nonlinear problems. The BFGS quasi-Newton method is the most commonly studied second order method for neural network training. Recent methods have shown to speed up the convergence of the BFGS method using the Nesterov’s acclerated gradient and momentum terms. The SR1 quasi-Newton method though less commonly used in training neural networks, are known to have interesting properties and provide good Hessian approximations when used with a trust-region approach. Thus, this paper aims to investigate accelerating the Symmetric Rank-1 (SR1) quasi-Newton method with the Nesterov’s gradient for training neural networks and briefly discuss its convergence. The performance of the proposed method is evaluated on a function approximation and image classification problem.

Buckypaper (BP), a flexible and porous material, exhibits photovoltaic properties when exposed to light. In this study, we have employed Radio Frequency (RF) Sputtering of Zinc Oxide (ZnO) followed by rapid thermal annealing to enhance the photovoltaic response of BP. We investigated the impact of various sputtering parameters, such as the gas flow ratio of argon to oxygen and deposition time, on the morphology, composition, resistivity, and photovoltaic characteristics of ZnO-modified BP. Additionally, the photovoltaic performance of the samples under different illumination modes and wavelengths was compared. It was found that optimal sputtering conditions—argon to oxygen flow ratio of 1:2, deposition time of 20 minutes, and power of 100 watts—resulted in a ZnO film thickness of approximately 45 nanometers. After annealing at 400°C for 10 minutes, the ZnO-modified BP demonstrated a significant increase in photocurrent and photovoltage, along with a reduction in resistivity, compared to unmodified BP. Moreover, under gradient illumination, the ZnO-modified BP exhibited a photovoltage enhancement of 14.70 times and a photocurrent increase of 13.86 times, compared to uniform illumination. Under blue light, it showed a higher photovoltaic response than under other colors. The enhancement in photovoltaic response is attributed to the formation of a Schottky junction between ZnO and BP, an increased carrier concentration gradient, and an expanded light absorption spectrum. Our results validate that ZnO sputtering followed by annealing is an effective method for modifying BP for photovoltaic applications such as solar cells and photodetectors.

The study aimed to evaluate implant success and bone height variations after transcrestal sinus floor elevation (tSFE) using the osseodensification technique (OD) combined with beta-tricalcium phosphate (β-TCP), as bone graft material and simultaneous implant placement. Thirteen patients requiring the placement of one implant in the upper posterior maxilla, with a residual bone height of

Over the last decades, an important number of regional and global Digital Elevation Models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs require to be expressed in the same Coordinate Reference System (CRS), sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention), and with heights relative to the same Vertical Reference System (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements, or disparities, may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect or curvature. Therefore, the control of DEM planimetric accuracy is certainly the most important task to perform at first before any comparison. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy, by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have been already computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been performed in a 11x11 grid with a 0.1-pixel step in both directions. The Copernicus DEM GLO-30 has been interpolated by a “classical” bicubic to achieve these 11x11 displacements. This validation method has been applied in four 10 km x 10 km DEMIX tiles showing different height distributions. Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with with typical errors (e_b) ranging from 12 to 20 % of the pixel size (worst case in Croatia).These errors in displacement retrievals are not constant in the 11x11 grid and the overall error E_b depends on the roughness (altimetry variations) encountered in the different tiles. The second idea of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d=1 of the interpolated point) on the accuracy of the displacement retrieval. By considering E_b as a quality indicator, tests have been performed in the four DEMIX tiles making the bicubic parameter vary between -1.5 and 0.0 by step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four E_b minimal values. This BBC parameter b* is low for flat areas (around -0.95) and higher in mountainous areas (around -0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r=0.717). The logarithmic regression formula estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC.

Objective: To explore the application effect of Atropine injection combined with nursing intervention on the interventional treatment of PATENTS with ST-segment elevation myocardial infarction. Methods: A total of 60 patients with ST-segment elevation myocardial infarction treated in our hospital from January 2022 to January 2023 were selected as the research objects. According to different nursing methods, they were divided into conventional group (n = 30) and systematic group (n = 30), both groups were treated with percutaneous coronary intervention (PCI). The conventional group received routine nursing before and after operation. On the basis of routine nursing, systematic nursing cooperation was given in the system group. The time of opening infarct vessels, the incidence of total adverse reactions during interventional treatment, mortality, success rate of treatment, improvement of cardiac function and length of hospital stay were compared between the two groups. Results: The time of opening infarct vessel in system group was significantly better than that in routine group (P < 0.05). The total adverse reaction rate in system group was significantly lower than that in routine group (P < 0.05); The mortality of routine group was significantly higher than that of system group (P < 0.05). The success rate of system group was significantly higher than that of routine group (P < 0.05); The number of patients with improved cardiac function and length of hospital stay in system group were significantly better than those in routine group (P < 0.05). Conclusion: Good intraoperative cooperation and nursing is an important guarantee for patients with ST-segment elevation myocardial infarction, which can effectively reduce the occurrence of adverse reactions during interventional treatment, improve the success rate of treatment, and reduce the death of patients during interventional treatment.

Background: According to newly published studies, patients with Coronavirus Disease 2019 (COVID-19) have significantly lower serum 25-hydroxyvitamin D 25(OH)D levels than those without the SARS-CoV-2 infection. Vitamin D insufficiency may be a predictor of poor prognosis in COVID-19 patients. Vitamin D supplementation is related to improved clinical outcomes in terms of intensive care unit (ICU) admission and death, particularly in individuals with moderate-to-severe forms of COVID-19 requiring hospitalization. Materials and methods: This placebo-controlled five-day study was performed on 100 hospitalized COVID-19 patients with vitamin D insufficiency randomized into two groups. Vitamin D in the form of a sublingual sprayable microemulsion (LYL love your life® sunD3 LYLmicro™) was given three times daily after breakfast, lunch, and dinner (daily dose 12,000 IU) to 52 patients with blood vitamin D levels below 30 ng/ml. 48 patients in the control group received a placebo spray in the same daily regiment. Results: We observed a statistically significant increase by 8.6 ng/ml in median vitamin D level after five days of high-dose vitamin D3 spray therapy. 90.2% of subjects reached normal serum levels of vitamin D. There was a strong correlation between changes in vitamin D and C-reactive protein (CRP) levels in individuals with moderately severe disease (p<0.05), while mild and severe cases showed no statistical significance comparing case and control groups. There were no statistically significant changes in ferritin and interleukin-6 (IL-6) levels. Conclusions: In this study, we demonstrated the possibility to rapidly increase circulating levels of vitamin D just in 5 days of high-dose intervention and found a statistically significant decrease of CRP levels in patients with moderately severe COVID-19. We were not able to detect statistically significant changes in ferritin and IL-6 levels during the 5-day vitamin D intervention.

Background: Post-operative pain and swelling are frequently observed after sinus lift procedures. The aim of the present study was a clinical evaluation of swelling and pain of two sinus lift different flaps technique measured through visual analogue scale (VAS), verbal rating scale (VRS) and infrared thermal imaging (i.e., thermography). Methods: A randomized controlled trial was conducted in fifteen patients treated for a total of 30 sinus randomly allotted into two groups. In patients of Group I, a triangular flap was performed, while in Group II modified triangular flap without anterior release was performed. Postoperative pain was scored by VAS and inflammation was recorded by VRS at 2, 4, 6 and 14 days. The facial temperature was recorded before and after sinus augmentation, at 2, 4, 6 day intervals for the first week and at 14 day, to check the course of healing. Results: In group I the pain intensity was recorded at 2 days after the surgery with a score of 38.67 ± 6.4 mm. Swelling was higher at 2 and 4 day, and it was absent at day 6. Facial temperature difference before and after the procedure was 3 (4.737 °C ± 0.37). In Group II the pain was lower than in Group I (p < 0,05). Swelling scored 2 on the first and second day, and it was reduced at day 4. After the second day the difference of temperature reduced significantly compared to day of surgery (0.77 °C), at 2 and 4 days no difference was registered. Conclusion: In conclusion based on the outcome of the present study, modified triangular flap can be used with success in sinus augmentation procedures and is to be preferred to trapezoidal flap.

Climate change and development have increased urban flooding, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information, are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

In this paper, a novel nonlocal strain gradient anisotropic elastic shell model is developed to analyse the vibrations of simply supported single‒walled carbon nanotubes (SWCNTs). Sanders‒Koiter shell theory is used to obtain the strain‒displacement relationships. Eringen nonlocal elasticity theory and Mindlin strain gradient theory are adopted to derive the constitutive equations, where the anisotropic elastic constants are expressed via Chang molecular mechanics model. The complex variable method is used to analytically solve the equations of motion and to obtain the natural frequencies of SWCNTs. First, the anisotropic elastic shell model is validated via comparisons with the results of molecular dynamics simulations reported in the literature. Then, the effect of nonlocal and material parameters on the natural frequencies of SWCNTs with different geometries and wavenumbers is analysed. From the numerical simulations it is obtained that the natural frequencies decrease with increasing nonlocal parameter, while they increase with increasing material parameter. Moreover, the decrease of natural frequencies with increasing SWCNT radius is exponential as the material parameter increases, while it is linear as the nonlocal parameter increases. Finally, as the number of waves increases, the natural frequencies linearly vary with increasing nonlocal parameter, while they exponentially increase with increasing material parameter.

Intelligent transportation systems (ITS) have recently evolved rapidly, which requires development of highly trustworthy and effective communication technologies for uses, including vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication; where Orthogonal Frequency Division Multiplexing (OFDM) is regarded as a strong candidate and highly popular option technique among these methods. However, the movement of vehicles introduces Doppler frequencies which produce inter-carrier interference (ICI), that is frequently occurs in V2X channels. This interference has the potential to compromise the integrity of subcarrier orthogonality within OFDM, leading to lower-quality communication and an increased likelihood of data transmission errors. When employing channels with doubly dispersive fading, OFDM necessitates the usage of a complex equalization based on the minimum mean-square error (MMSE) equalizer, which requires channel matrix inversion. Several low-complexity equalizers for OFDM have been developed and are based on band factorization, time domain LSQR (Least-Square QR) iterative computing, and banded minimum mean squared error (BMMSE). This paper proposes Conjugate Gradient Least Squares (CGLS), which is a novel iterative computation algorithm integrated with nonlinear equalizers. The suggested nonlinear equalization technique determines the trade-off between computations and performance. According to simulation data, the suggested nonlinear equalizer performs better than the current BMMSE and linear CGLS algorithms across doubly dispersive fading channels.

Purpose: Prostate cancer remains the 2nd leading cancer killer of men, yet it is also a disease with a high rate of overtreatment. Diffusion weighted imaging (DWI) has shown promise as a reliable, grade-sensitive imaging method, but it is limited by low image quality. Currently, DWI image quality is directly related to low gradient ampli-tudes, since weak gradients must be compensated with long echo times. Methods: We propose a new type of MRl accessory, an "inside-out" and nonlinear gradient, whose sole purpose is to deliver diffusion encoding to a region of interest. Performance was simulated in OPERA and the resulting fields were used to simulate DWI with two compartment and kurtosis models. Experiments with a nonlinear head gradient prove the accuracy of DWI and ADC maps diffusion encoded with nonlinear gradients. Results: Simulations validated thermal and mechanical safety while showing a 5 to 10-fold increase in gradient strength over prostate. With these strengths, lesion CNR in ADC maps approximately doubled for a range of anatomical positions. Proof-of-principle experiments show that spatially varying b-values can be corrected for accurate DWI and ADC. Conclusions: Dedicated nonlinear diffusion encoding hardware could improve prostate DWI.

Producing complex shaped tungsten carbide cobalt (WC-Co) tools by classical technologies is difficult and often impossible due to their high hardness and brittle fracture behavior. Additive Manufacturing (AM) is a suitable technology for creating complex structures and simultaneously shortening expensive machining processes. Binder Jetting (BJ) is an innovative AM technology that offers several advantages over laser-based processes, for example low manufacturing costs and high build-up rates. Binders with nanoparticle additives have already proven to be effective in in- creasing the packing density of the powder bed and improving the sintering properties. Addition- ally, they offer the possibility of selectively changing the material composition in the part. This paper presents a concept for the use of nanoparticles to generate gradients in the green compact, which leads to a cobalt gradient in the part after sintering. The possibility of introducing particles locally into complex structures allows local modification of the material properties.

Temperature is one of the most important range-limiting factors for many seaweeds. Driven by the recent climatic changes, rapid northward shifts of species’ distribution ranges can potentially modify the phylogeographic signature of Last Glacial Maximum such as increased genetic diversity at lower latitudes. To explore this question in detail in the kelp Saccharina latissima, we used microsatellites and double digest RAD-sequencing derived SNPs on S. latissima sporophytes sampled within 11 sites spanning the entire European Atlantic latitudinal range of this species. In addition, we checked for statistical correlation between genetic marker allele frequencies and three environmental proxies (sea surface temperature, salinity, and water turbidity). Our findings revealed that genetic diversity was significantly higher for the northern localities compared to the southern ones in contrast to the expected phylogeographic pattern. This suggests that the southernmost S. latissima populations are negatively affected by the recent climatic changes but also that the recolonization of S. latissima range following the LGM may have occurred from northerly refugia. Seven SNPs and 12 microsatellite alleles were found to be significantly associated with at least one of the three environmental variables. We discuss the potential adaptive functions of the genes associated with the outlier markers and the importance of these markers for successful conservation and aquaculture strategies for S. latissima in this age of rapid global change.

Over the past 150 million years, the Chilean Atacama Desert has been transformed into one of the most inhospitable landscapes by geophysical changes, which makes it an ideal Mars analog that has been explored for decades. However, two heavy rainfalls that occurred in the Atacama in 2015 and 2017 provide a unique opportunity to study the response of resident extremophiles to rapid environmental change associated with excessive water and salt shock. Here we combine mineral/ salt composition measurements, amendment cell culture experiments, and next-generation sequencing analyses to study the variations in salts and microbial communities along a latitudinal aridity gradient of the Atacama Desert. In addition, we examine the reshuffling of Atacama microbiomes after the two rainfall events by comparing with previous researches. Analysis of microbial community composition revealed that soils within the southern arid desert were consistently dominated by Actinobacteria, Proteobacteria, Acidobacteria, Planctomycetes, Chloroflexi, Bacteroidetes, Gemmatimonadetes, and Verrucomicrobia. Intriguingly, the hyperarid microbial consortia exhibited a similar pattern to the more southern desert. Salts at the shallow subsurface were dissolved and leached down to a deeper layer, challenging indigenous microorganisms with the increasing osmotic stress. Microbial viability was found to change with aridity and rainfall events. This study sheds light on the structure of xerophilic, halophilic, and radioresistant microbiomes from the hyperarid northern desert to the less arid southern transition region, as well as their response to changes in water availability. Our findings may infer similar events that happened on the wetter early Mars.

This study assessed the periodic fluctuations among microbial biomass Carbon (C), Nitrogen (N) and Phosphorus (P), and the consequences of variations in altitude and abiotic factors on the soil microbial biomass (C, N and P) in a temperate mixed-oak pine forest of Central Himalaya. This research was directed at three forest stands along an altitudinal gradient. Samples were collected in triplicates, seasonally from each selected site and microbial C, N and P were determined through the fumigation extraction method. Microbial biomass C, N and P decreased significantly (P<0.01, correlation coefficient -0.985, -0.963, -0.948, respectively) with increasing altitude, while the rainy season showed the highest values, and winter season revealed the least values. Microbial biomass C, N and P showed positive correlation with silt particles, water holding capacity, bulk density, porosity, soil moisture, organic C, total N and P, and negative correlations with sand particles and soil pH. The microbial biomass C showed strong associations with soil microbial N (r=0.80, P<0.01) and P (r=0.89, P<0.01) contents, while the soil microbial biomass N and P also showed strong positive correlation (r=0.92, P< 0.01). Soil microbial biomass was greatly influenced by the altitude and abiotic variables whereas, weakly by temporal variation. The microbial C: N ratio indicated that fertility of soil is influenced by the species assemblage. Our findings suggest that high microbial biomass and low C: N ratio during rainy season could be considered as a strategy to conserve nutrients by temperate mixed-oak pine forest ecosystem.

Oil spillage over a sea or ocean’s surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data has been used efficiently for the detection of oil spills due to its operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, look-alikes, ships and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification; and introduce to use a new loss function, namely ‘gradient profile’ (GP) loss, which is in fact the constituent of the more generic Spatial Profile loss proposed for image translation problems. For the purpose of training, testing and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, look-alikes, ships and land class is 96.00%, 60.87%, 74.61% and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F1 scores for CNNs as well as ViTs based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images.

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. Experimental results demonstrate that our affine motion estimation algorithm reduces encoding time by approximately 15%-35% while maintaining video bitrate and quality.

Purposes: To provide abdominal contrast-enhanced MR image synthesis, we developed an image gradient regularized multi-modal multi-discrimination sparse-attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring. Methods: With IRB approval, 165 abdominal MR studies from 61 liver cancer patients were retrospectively solicited from our institutional database. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast-enhanced (T1ce) images. The GRMM-GAN synthesis pipeline consists of a sparse attention fusion network, an image gradient regularizer (GR), and a generative adversarial network with multi-discrimination. The studies were randomly divided into 115 for training, 20 for validation, and 30 for testing. The two pre-contrast MR modalities, T2 and T1pre images, were adopted as inputs in the training phase. The T1ce image at the portal venous phase was used as an output. The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural-similarity-index (SSIM), and mean-squared-error (MSE). A Turing test and experts’ contours evaluated the image synthesis quality. Results: The proposed GRMM-GAN model achieved a PSNR of 28.56, an SSIM of 0.869, and an MSE of 83.27. The proposed model showed statistically significant improvements in all metrics tested with p-values <0.05 over the state-of-the-art model comparisons. The average Turing test score was 52.33%, close to random guessing, supporting the model's effectiveness for clinical application. In the tumor-specific region analysis, the average tumor contrast-to-noise ratio (CNR) of the synthesized MR images was not statistically significant from the real MR images. The average DICE from real vs. synthetic images was 0.90, compared to the inter-operator DICE of 0.91. Conclusion: We demonstrated the function of a novel multi-modal MR image synthesis neural network, GRMM-GAN, for T1ce MR synthesis based on pre-contrast T1 and T2 MR images. GRMM-GAN shows promise for avoiding repeated contrast injections during radiation therapy treatment.

The response of a single tin oxide nanowire was collected at different temperatures to create a virtual array of sensors working as a nano-electronic nose. The single nanowire, acting as a chemiresistor, was first tested with pure ammonia and then used to determine the freshness status of trout fish (Oncorhynchus mykiss) in a rapid and non-invasive way. The gas sensor reacts to total volatile basic nitrogen, detecting the freshness status of the fish samples in less than 30 seconds. The sensor response at different temperatures correlates well with the total viable count (TVC), demonstrating that it is a good (albeit indirect) way of measuring the bacterial population in the sample. The nano-electronic nose is able to classify the samples according to their degree of freshness, but also to quantitatively estimate the concentration of microorganisms present. The system was tested with samples stored at different temperatures, managing to classify them perfectly (100%) and estimating their log(TVC) with an error lower than 5%.