Search Results (20,886)

The Yellow River Basin (YRB), a pivotal ecoregion in China, has long been plagued by a range of ecological problems, including water loss, soil erosion, and ecological degradation. Despite previous reports on the ecological environment of YRB, systematic studies on the multi-factor driving mechanism and the coupling between the ecological and hydrological systems remain scarce. In this study, with multi-source remote-sensing imagery and measured hydrological data, the random forest (RF) model and the geographical detector (GD) technique were employed to quantify the dynamic spatiotemporal changes in the ecological environment of YRB in 2000–2024 and identify the driving factors. The variables analyzed in this study included gross primary productivity (GPP), fractional vegetation cover (FVC), land use and cover change (LUCC), meteorological statistics, as well as runoff and sediment data measured at hydrological stations in YRB. The main findings are as follows: first, the GPP and FVC increased significantly by 37.9% and 18.0%, respectively, in YRB in 2000–2024; second, LUCC was the strongest driver of spatiotemporal changes in the ecological environment of YRB; third, precipitation and runoff contributed positively to vegetation growth, whereas the sediment played a contrary role, and the response of ecological variables to the hydrological processes exhibited a time lag of 1–2 years. This study is expected to provide scientific insights into ecological conservation and water resources management in YRB, and offer a decision-making basis for the design of sustainability policies and eco-restoration initiatives. Full article

Early and accurate prediction of mastitis is crucial in effective herd management and minimizing economic losses. This study investigated the effects of farm-specific factors on the accuracy of mastitis predictions by applying machine learning (ML) models to an automated milking system (AMS) and farm management data. We analyzed a large dataset consisting of 5.88 million observations over the period of 2019–2024 from four dairy farms in Germany. Six ML algorithms were applied to predict mastitis occurrence, with a focus on understanding how farm-specific factors like herd size, management practices, and farm environment may influence prediction accuracy. For training and testing on combined data, the accuracy, sensitivity and specificity ranged between 83 and 92%, 78 and 93% and 83 and 92%, respectively, with an area under curve (AUC) between 91 and 96%. However, under mixed-to-individual farm effects analysis, results exposed weaknesses in the generalization. Models adapted well to internal patterns when analyzing each individual farm separately, reaching very high AUCs of up to 98%, but the results were significantly different again when analyzed with a leave-one-out approach. The analysis determined that data from each farm carries variable underlying patterns, suggesting that a tailored approach to each farm’s unique characteristics might improve mastitis prediction through ML. Full article

Drilling fluid loss is a common and complex downhole problem that occurs during drilling in deep fractured formations, which has a significant negative impact on the exploration and development of oil and gas resources. Establishing a drilling fluid loss model for the quantitative analysis of drilling fluid loss is the most effective method for the diagnosis of drilling fluid loss, which provides a favorable basis for the formulation of drilling fluid loss control measures, including the information on thief zone location, loss type, and the size of loss channels. The previous loss model assumes that the drilling fluid is driven by constant flow or pressure at the fracture inlet. However, drilling fluid loss is a complex physical process in the coupled wellbore circulation system. The lost drilling fluid is driven by dynamic bottomhole pressure (BHP) during the drilling process. The use of a single-phase model to describe drilling fluids ignores the influence of solid-phase particles in the drilling fluid system on its rheological properties. This paper aims to model drilling fluid loss in the coupled wellbore–-fracture system based on the two-phase flow model. It focuses on the effects of well depth, drilling pumping rate, drilling fluid density, viscosity, fracture geometric parameters, and their morphology on loss during the drilling fluid circulation process. Numerical discrete equations are derived using the finite volume method and the “upwind” scheme. The correctness of the model is verified by published literature data and experimental data. The results show that the loss model without considering the circulation of drilling fluid underestimates the extent of drilling fluid loss. The presence of annular pressure loss in the circulation of drilling fluid will lead to an increase in BHP, resulting in more serious loss. Full article

Diabrotica virgifera virgifera Le Conte, 1868 (Coleoptera: Chrysomelidae), known as the western corn rootworm, is one of the most important alien insect pests affecting maize crops globally. It causes significant economic losses by feeding on the roots, which affects plant stability and nutrient absorption, as well as by attacking essential aerial organs (leaves, silk, pollen). Since its accidental introduction into Europe, the species has expanded its range across maize-growing regions, raising concerns about future distribution under climate change. This study aimed to estimate the risk of pest establishment across Europe over three future time frames (2034, 2054, 2074) based on geographic coordinates, climate data, and maize distribution. Spatial simulations were performed in QGIS using national centroid datasets, risk classification criteria, and temperature anomaly maps derived from Copernicus and ECA&D databases for 1992–2024. The results indicate consistently high risk in southern and southeastern regions, with projected expansion toward central and western areas by 2074. Risk zones showed clear spatial aggregation and directional spread correlated with warming trends and maize availability. The pest’s high reproductive potential, thermal tolerance, and capacity for human-assisted dispersal further support these predictions. The model emphasizes the need for expanded surveillance in at-risk zones and targeted policies in areas where D. v. virgifera has not yet established. Future work should refine spatial predictions using field validation, genetic monitoring, and dispersal modeling. The results contribute to anticipatory pest management planning and can support sustainable maize production across changing agroclimatic zones in Europe. Full article

As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions. Full article

This research assesses how land-cover transitions from 2012 to 2022 have impacted the value of ecosystem services in Denton County, Texas. Using remote sensing and spatial analysis, this study quantitatively links land-use change to its ecological and economic consequences. Full-county Landsat data were analyzed in ArcGIS Pro through supervised classification and categorical change detection. To quantify the impact of these changes, an accuracy assessment was performed, and a benefit-transfer method using both global and Texas-specific coefficients was applied to estimate the change in Ecosystem Service Value (ESV). Results revealed a complex dynamic: while the county experienced significant urban expansion, it also saw substantial greening as large areas of bare land transitioned to vegetation. However, this greening was not enough to offset the economic impact of losing high-value ecosystems. The analysis shows a net loss in total ESV over the decade, estimated between USD 24 million and USD 95 million per year, primarily driven by the significant reduction of water bodies. This study provides a replicable framework for policymakers to assess the environmental trade-offs of development and highlights the critical importance of preserving existing high-value ecosystems alongside urban greening initiatives. Full article

Background: Vision loss due to chronic eye diseases remains a significant public health challenge. Early detection through screening programs may reduce the burden of vision loss. This study aimed to assess the detection rate of eye diseases (glaucoma, AMD, and diabetic retinopathy), including those newly detected during opportunistic screening and ophthalmological consultations within the local government health policy program “Good Vision for Mazovians” in Mazovia, Poland. Material and methods: This study is a retrospective analysis of medical data from the registry of the Ophthalmology Department of the Międzylesie Specialist Hospital in Warsaw, which implemented the local government preventive program “Good Vision for Mazovians. Data from 1812 individuals (aged 18–92 years) participating in the “Good Vision for Mazovians” preventive program were analyzed. Results: Most participants were female (59.7%), aged over 60, and took medications regularly (62.7%). Excluding subjects with prior diagnosis of eye conditions, the detection rate was 38 suspected cases (3.8%) of glaucoma cases, 84 suspected cases of AMD (4.6%), and 21 suspected cases of diabetic retinopathy (1.2%). Most participants had not visited an ophthalmologist in the past two years (58.6%), reported low or average knowledge of eye health, had difficulty accessing ophthalmology services in their region (57%), and identified long waiting times for appointments as the main barrier to care (83.5%). Conclusions: Opportunistic screening for eye diseases in populations with limited access to eye care should be considered as a method for detecting common causes of irreversible visual impairment, particularly AMD. Older adults and individuals without higher education appear to face the greatest barriers to accessing ophthalmology services and may benefit the most from targeted opportunistic screening initiatives. Full article

Forecasting electricity consumption is one of the most important scientific and practical tasks in the field of electric power engineering. The forecast accuracy directly impacts the operational efficiency of the entire power system and the performance of electricity markets. This paper proposes algorithms for source data preprocessing and tuning XGBoost models to obtain the most accurate forecast profiles. The initial data included hourly electricity consumption volumes and meteorological conditions in the power system of the Republic of Tatarstan for the period from 2013 to 2025. The novelty of the study lies in defining and justifying the optimal model training period and developing a new evaluation metric for assessing model efficiency—financial losses in Balancing Energy Market operations. It was shown that the optimal depth of the training dataset is 10 years. It was also demonstrated that the use of traditional metrics (MAE, MAPE, MSE, etc.) as loss functions during training does not always yield the most effective model for market conditions. The MAPE, MAE, and financial loss values for the most accurate model, evaluated on validation data from the first 5 months of 2025, were 1.411%, 38.487 MWh, and 16,726,062 RUR, respectively. Meanwhile, the metrics for the most commercially effective model were 1.464%, 39.912 MWh, and 15,961,596 RUR, respectively. Full article

Water management in arid regions, such as Arizona, is critical due to increasing demands from the urban, agricultural, and recreational sectors. In this study, Finite element analysis software COMSOL Multiphysics (COMSOL 6.3) is used to quantify water demands in Chandler, Arizona. Evapotranspiration from vegetation and pools is studied. Factors are divided into environmental (temperature, humidity, wind speed) and soil-related properties (moisture content, hydraulic conductivity), which are modeled and used to estimate annual water losses. This study represents the first comprehensive investigation of the usage across several main categories at Arizona. Results indicate that pools contribute 61% of surface water evaporation. Annual water demand in Chandler for 2024 peaks at 425,000 m³ in June, with irrigation for vegetation dominating consumption. Validation against experimental data confirms model accuracy. This simulation work aims to provide scalable insights for water management in arid urban environments. Based on the simulation, various solutions were proposed to reduce water consumption and minimize water loss. Some active measures include the optimization of irrigation time and frequency based on dynamic and real-time environmental conditions. The proposed solution can help minimize the water consumption while maintaining the water demands for plant life sustenance. Other passive measures include the modification of localized environmental conditions to reduce water evaporation. In particular, it was found that fence installation can significantly change the water vapor flow and distribution close to the water surface and suppress the water evaporation by simply lowering the wind speed right above the water surface. A logical takeaway is that evaporation would also decrease when pools are built with deeper water surfaces. Full article

In laser wireless power transmission systems, the powersphere serves as a spherical enclosed receiver that performs photoelectric conversion, achieving uniform light distribution within the cavity through infinite internal light reflection. However, in practical applications, the high level of light absorption displayed by photovoltaic cells leads to significant disparities in light intensity between directly irradiated regions and reflected regions on the inner surface of the powersphere, resulting in poor light uniformity. One approach aimed at addressing this issue uses a spectroscope to split the incident beam into multiple paths, allowing the direct illumination of all inner surfaces of the powersphere and reducing the light intensity difference between direct and reflected regions. However, experimental results indicate that light transmission through lenses introduces power losses, leading to improved uniformity but reduced output power. To address this limitation, this study proposes a method that utilizes multiple incident laser beams combined with a centrally positioned spherical reflector within the powersphere. A wireless power transmission system model was developed using optical simulation software, and the uniformity of the intracavity light field in the system was analyzed through simulation. To validate the design and simulation accuracy, an experimental system incorporating semiconductor lasers, spherical mirrors, and a powersphere was constructed. The data from the experiments aligned with the simulation results, jointly confirming that integrating a spherical reflector and distributed incident lasers enhances the uniformity of the internal light field within the powersphere and improves the system’s efficiency. Full article

In recent years, telecom fraud remains prevalent in many regions, severely impacting people’s daily lives and causing substantial economic losses. However, previous research has mainly relied on expert knowledge for feature engineering, which lags behind and struggles to adapt to the continuously evolving patterns of fraud effectively. In addition, the extreme imbalance in fraud amounts within real communication data hinders the development of deep learning methods. In response, we propose a feature transformation method to represent users’ communication behavior as comprehensively as possible, and develop a convolutional neural network (CNN) with a Focal Loss function to identify rare fraudulent activities in highly imbalanced data. Experimental results on a real-world dataset show that, under conditions of severe class imbalance, the proposed method significantly outperforms existing approaches in two key metrics: recall (0.7850) and AUC (0.8662). Our work provides a new approach for telecommunication fraud detection, enabling the effective identification of fraudulent numbers. Full article

The quality of mangoes is a crucial factor in both domestic and commercial markets that directly influences consumer satisfaction and economic value. Traditional methods of checking mango quality often involve destructive techniques, which lead to the loss of the fruit in the testing process. This study presents an advanced approach that could predict the quality of mangoes using advance non-destructive methods leveraging machine learning algorithms to predict quality parameters such as ripeness, sweetness and overall freshness without damaging the fruit. In this research, a dataset consisting of various mango samples was collected, with attributes including color, texture, size, weight and acidity levels. Sensors, such as pH sensors (for acidity) and e-nose sensors (for aroma and sweetness detection), were used to gather data, while a combination of machine learning models such as Decision Tree, K-Nearest Neighbors (KNN), and Automated Machine Learning (AutoMLP), Naive Bayes were applied to predict the mangoes’ quality. The accuracy of each model was measured based on its ability to classify mangoes as fresh, ripe, or rotten. The results determine that the AutoMLP model performs the best out of the traditional models, achieving an accuracy of 98.46%, making it the most suitable model for mango quality prediction. The research explains the significance of feature extraction methods, model optimization, and sensor data pretreatment in reaching a high prediction accuracy. Full article

Environmental degradation and soil desertification are among the most severe environmental issues of recent decades worldwide. Over time, these processes have led to increasingly extreme and highly dynamic climatic conditions. In Brazil, the Northeast Region is characterized by semi-arid and arid areas that exhibit high climatic variability and are extremely vulnerable to environmental changes and pressures from human activities. The application of geotechnologies and geographic information system (GIS) modeling is essential to mitigate the impacts and pressures on the various ecosystems of Northeastern Brazil (NEB), where the Caatinga biome is predominant and critically threatened by these factors. In this context, the objective was to map and assess the spatiotemporal patterns of land use and land cover (LULC), detecting significant trends of loss and gain, based on surface reflectance data and precipitation data over two decades (2000–2019). Remote sensing datasets were utilized, including Landsat satellite data (LULC data), MODIS sensor data (surface reflectance product) and TRMM data (precipitation data). The Google Earth Engine (GEE) software was used to process orbital images and determine surface albedo and acquisition of the LULC dataset. Satellite data were subjected to multivariate analysis, descriptive statistics, dispersion and variability assessments. The results indicated a significant loss trend over the time series (2000–2019) for forest areas (Z_MK = −5.872; Tau = −0.958; p < 0.01) with an annual loss of −3705.853 km² and a total loss of −74,117.06 km². Conversely, farming areas (agriculture and pasture) exhibited a significant gain trend (Z_MK = 5.807; Tau = 0.947; p < 0.01), with an annual gain of +3978.898 km² and a total gain of +79,577.96 km², indicating a substantial expansion of these areas over time. However, it is important to emphasize that deforestation of the region’s native vegetation contributes to reduced water production and availability. The trend analysis identified an increase in environmental degradation due to the rapid expansion of land use. LULC and albedo data confirmed the intensification of deforestation in the Northern, Northwestern, Southern and Southeastern regions of NEB. The Northwestern region was the most directly impacted by this increase due to anthropogenic pressures. Over two decades (2000–2019), forested areas in the NEB lost approximately 80.000 km². Principal component analysis (PCA) identified a significant cumulative variance of 87.15%. It is concluded, then, that the spatiotemporal relationship between biophysical conditions and regional climate helps us to understand and evaluate the impacts and environmental dynamics, especially of the vegetation cover of the NEB. Full article

Improving the isotropic magnetic properties of FeSi electrical steels has traditionally focused on enhancing their crystallographic texture and microstructural morphology. Strengthening the cube texture within a ferritic matrix of optimal grain size is known to reduce core losses and increase magnetic induction. However, conventional cold rolling followed by annealing remains insufficient to optimise the magnetic performance of thin FeSi strips fully. This study explores an alternative approach based on grain boundary migration driven by temperature gradients combined with deformation gradients, either across the sheet thickness or between neighbouring grains, in thin, weakly deformed non-oriented (NO) electrical steel sheets. The concept relies on deformation-induced grain growth supported by rapid heat transport to promote the preferential formation of coarse grains with favourable orientations. Experimental material consisted of vacuum-degassed FeSi steel with low silicon content. Controlled deformation was introduced by temper rolling at room temperature with 2–40% thickness reductions, followed by rapid recrystallisation annealing at 950 °C. Microstructure, texture, and residual strain distributions were analysed using inverse pole figure (IPF) maps, kernel average misorientation (KAM) maps, and orientation distribution function (ODF) sections derived from electron backscattered diffraction (EBSD) data. This combined thermomechanical treatment produced coarse-grained microstructures with an enhanced cube texture component, reducing coercivity from 162 A/m to 65 A/m. These results demonstrate that temper rolling combined with dynamic annealing can surpass the limitations of conventional processing routes for NO FeSi steels. Full article

Wildfire events pose significant threats to global ecosystems, with Greece experiencing substantial economic losses exceeding EUR 1.7 billion in 2023 alone, generating immediate financial burdens while contributing to atmospheric carbon dioxide emissions and accelerating climate change effects. This study presents a group of classification models for Greece wildfires utilizing historical datasets spanning 2017 to 2021, incorporating satellite-derived remote sensing data, topographical characteristics, and meteorological observations through a multimodal methodology that integrates satellite imagery processing with traditional numerical data analysis techniques. The framework encompasses multiple deep learning architectures, specifically implementing four standalone models comprising two convolutional neural networks optimized for spatial image processing and long short-term memory networks designed for temporal pattern recognition, extending classification approaches by incorporating visual satellite data alongside established numerical datasets to enable the system to leverage both spatial visual patterns and temporal numerical trends. The implementation employs an ensemble methodology that combines individual model classifications through systematic voting mechanisms, harnessing the complementary strengths of each architectural approach to deliver enhanced predictive capabilities and demonstrate the substantial benefits achieved through multimodal data integration for comprehensive wildfire risk assessment applications. Full article