Search Results (471)

Tea cultivation faces significant threats from red spider mite (RSM: Oligonychus coffeae) infestations, which reduce yields and economic viability in major tea-producing regions. Current automated detection methods rely on supervised deep learning models requiring extensive labeled data, limiting scalability for smallholder farmers. This article proposes AIM-Net (AI-based Infestation Mapping Network) by evaluating SwAV (Swapping Assignments between Views), a self-supervised learning framework, for classifying RSM infestation severity (Mild, Moderate, Severe) using a geo-referenced, field-acquired dataset of RSM infested tea-leaves, Cam-RSM. The methodology combines SwAV pre-training on unlabeled data with fine-tuning on labeled subsets, employing multi-crop augmentation and online clustering to learn discriminative features without full supervision. Comparative analysis against a fully supervised ResNet-50 baseline utilized 5-fold cross-validation, assessing accuracy, F1-scores, and computational efficiency. Results demonstrate SwAV’s superiority, achieving 98.7% overall accuracy (vs. 92.1% for ResNet-50) and macro-average F1-scores of 98.3% across classes, with a 62% reduction in labeled data requirements. The model showed particular strength in Mild_RSM-class detection (F1-score: 98.5%) and computational efficiency, enabling deployment on edge devices. Statistical validation confirmed significant improvements (p < 0.001) over baseline approaches. These findings establish self-supervised learning as a transformative tool for precision pest management, offering resource-efficient solutions for early infestation detection while maintaining high accuracy. Full article

Accurate precipitation forecasting remains a critical challenge due to the nonlinear and multifactorial nature of rainfall dynamics. This is particularly important in arid regions like Tamanghasset, where precipitation is the primary driver of agricultural viability and water resource management. This study evaluates the performance of several time series models for monthly rainfall prediction, including the autoregressive integrated moving average (ARIMA), Exponential Smoothing State Space Model (ETS), Seasonal and Trend decomposition using Loess with ETS (STL-ETS), Trigonometric Box–Cox transform with ARMA errors, Trend and Seasonal components (TBATS), and neural network autoregressive (NNAR) models. Historical monthly precipitation data from 1953 to 2020 were used to train and test the models, with lagged observations serving as input features. Among the approaches considered, the NNAR model exhibited superior performance, as indicated by uncorrelated residuals and enhanced forecast accuracy. This suggests that NNAR effectively captures the nonlinear temporal patterns inherent in the precipitation series. Based on the best-performing model, rainfall was projected for the year 2021, providing actionable insights for regional hydrological and agricultural planning. The results highlight the relevance of neural network-based time series models for climate forecasting in data-scarce, climate-sensitive regions. Full article

Geo-historical information deduced from geo-iconographical resources, derived from extensive research and the selection of cartographies and historical documents, enabled the investigation of the natural and anthropic transformations of the perifluvial area of the Po River in the Emilia-Romagna region (Italy). This territory, significant in terms of its historical, cultural, and environmental contexts, for centuries has been the scene of flood events. These have characterised the morphological and dynamic variability in the riverbed and relative floodplain. The close relationship between man and river is well documented: the interference induced by anthropic activity has alternated with the sometimes-damaging effects of river dynamics. The attention given to the fluvial region of the Po River and its main tributaries, in a peculiar lowland sector near Parma, is critical for understanding spatial–temporal changes contributing to current geo-hydrological risks. A GIS project outlined the geomorphological aspects that define the considerable variations in the course of the Po River (involving width reductions of up to 66% and length changes of up to 14%) and its confluences from the 16th to the 21st century. Knowledge of anthropic modifications is essential as a tool within land-use planning and enhancing community awareness in risk-mitigation activities and strategic management. This study highlights the importance of interdisciplinary geo-historical studies that are complementary in order to decode river dynamics in damaging flood events and latent hazards in an altered river environment. Full article

With the rapid development of heterogeneous satellite networks integrating geostationary earth orbit (GEO) and low earth orbit (LEO) satellite systems, along with the significant growth in the number of satellite users, it is essential to consider frequency compatibility and coexistence between GEO and LEO systems, as well as to design effective system resource allocation strategies to achieve efficient utilization of system resources. However, existing beam-hopping (BH) resource allocation algorithms in LEO systems primarily focus on beam scheduling within a single time slot, lacking unified beam management across the entire BH cycle, resulting in low beam-resource utilization. Moreover, existing algorithms often employ iterative optimization across multiple resource dimensions, leading to high computational complexity and imposing stringent requirements on satellite on-board processing capabilities. In this paper, we propose a BH-based beam scheduling and resource allocation framework. The proposed framework first employs geographic isolation to protect the GEO system from the interference of the LEO system and subsequently optimizes beam partitioning over the entire BH cycle, time-slot beam scheduling, and frequency and power resource allocation for users within the LEO system. The proposed scheme achieves frequency coexistence between the GEO and LEO satellite systems and performs joint optimization of system resources across four dimensions—time, space, frequency, and power—with reduced complexity and a progressive optimization framework. Simulation results demonstrate that the proposed framework achieves effective suppression of both intra-system and inter-system interference via geographic isolation, while enabling globally efficient and dynamic beam scheduling across the entire BH cycle. Furthermore, by integrating the user-level frequency and power allocation algorithm, the scheme significantly enhances the total system throughput. The proposed progressive optimization framework offers a promising direction for achieving globally optimal and computationally tractable resource management in future satellite networks. Full article

This study was conducted in the northeast of the Tadla plain, within the Beni Mellal-Khenifra region of Morocco. The primary objective is to elucidate the geometric and hydrogeological characteristics of this aquifer by analyzing and interpreting data from deep boreholes as well as gravimetric and electrical measurements using GIS analysis. First, the regional gradient was established. Then, the initial data were extracted. Subsequently, based on the extracted data, a gravity map was created. The investigation of the Bouguer anomaly’s gravity map exposes the presence of a regional gradient, with values varying from −100 mGal in the South to −30 mGal in the North of the area. These Bouguer anomalies often correlate with exposed basement rock areas and variations in the thickness of sedimentary layers across the study area. The analysis of existing electrical survey and deep drilling data confirms the results of the gravimetry survey after applying different techniques such as horizontal gradient and upward extension on the gravimetric map. The findings enabled us to create a structural map highlighting the fault systems responsible for shaping the study area’s structure. The elaborated structural map serves as an indispensable geotectonic reference, facilitating the delineation of subsurface heterogeneities and providing a robust foundation for further hydrogeological assessments in the Tadla Plain. Full article

Geostationary Earth orbit (GEO) satellite communication systems have become increasingly significant in global communication networks and national strategic infrastructure, owing to their advantages of extensive coverage, high capacity, and robust reliability. Constructing accurate and reliable simulation systems is essential to support the design, evaluation, and optimization of GEO satellite communication systems. This article first reviews the current developments and application prospects of GEO satellite communication systems and highlights the critical role of simulation technologies in system design and performance assessment. Subsequently, a systematic analysis is conducted on two core modules of simulation systems, i.e., coverage analysis and resource management and scheduling. Moreover, this article provides a comprehensive comparison and evaluation of mainstream satellite communication simulation platforms and tools. This review aims to offer valuable insights and guidance for future research and applications in GEO satellite communication simulation, thereby promoting technological innovation and advancement in related fields. Full article

To achieve a significant breakthrough in the exploration of uranium resources in the Guangzitian area of northern Guangxi, China, an innovative combination of exploration methods was implemented at the peripheral regions of the Guangzitian uranium deposit under the guidance of the following principle: “exploring the edges and identifying the bottom, delving deep and un-covering blind spots”. This study introduces geo-electrochemical integrated technology for prospecting research at the peripheral areas of the Guangzitian deposit. By validating the technology’s effectiveness on known geological sections, distinct geo-electrochemical extraction anomalies were identified above recognized ore bodies. Simultaneously, soil ionic conductivity and thermally released mercury anomalies were observed, partially indicating the presence of concealed uranium deposits and fault structures. These findings demonstrate that geo-electrochemical integrated technology is effective in detecting buried uranium mineralization in this region. Subsequently, a geological-geoelectrical prospecting model was established through a systematic analysis of anomaly characteristics and metallogenic regularity, and it was subsequently applied to unexplored areas. As a result, one key anomaly verification zone, one Class A comprehensive anomaly zone, two Class B comprehensive anomaly zones, and one Class C comprehensive anomaly zone were identified within the unexplored research area. Drilling engineering validation was conducted in the No. Ι key anomaly verification zone, resulting in the discovery of an industrial-grade uranium ore body. This achievement not only provides critical technical support but also develops a robust theoretical foundation for future mineral exploration endeavors. Full article

This paper introduces a groundbreaking decentralized approach for real-time bus monitoring and geo-location, leveraging advanced geo-statistical and multivariate statistical methods. The proposed long short-term memory (LSTM) model predicts bus arrival times with confidence intervals and reconstructs missing positioning data, offering cities an accurate, resource-efficient tracking solution within typical infrastructure limits. By employing decentralized data processing, our system significantly reduces network traffic and computational load, enabling data sharing and sophisticated analysis. Utilizing the Haversine formula, the system estimates pessimistic and optimistic arrival times, providing real-time updates and enhancing the accuracy of bus tracking. Our innovative approach optimizes real-time bus tracking and arrival time estimation, ensuring robust performance under varying traffic conditions. This research demonstrates the potential of integrating advanced statistical techniques with decentralized computing to revolutionize public transit systems. Full article

Carbonaceous–siliceous–argillaceous rock-type uranium deposits, a major uranium resource in China, pose significant environmental risks due to heavy metal contamination. Geochemical investigations in the former Zoige uranium mine revealed elevated As, Cd, Cr, Cu, Ni, U, and Zn concentrations in soils and sediments, particularly at river confluences and downstream regions, attributed to leachate migration from ore bodies and tailings ponds. Surface samples exhibited high Cd bioavailability. The integrated BCR and mineral analysis reveals that Acid-soluble and reducible fractions of Ni, Cu, Zn, As, and Pb are governed by carbonate dissolution and Fe-Mn oxide dynamics via silicate weathering, while residual and oxidizable fractions show weak mineral-phase dependencies. Positive Matrix Factorization identified natural lithogenic, anthropogenic–natural composite, mining-related sources. Pollution assessments using geo-accumulation index and contamination factor demonstrated severe contamination disparities: soils showed extreme Cd pollution, moderate U, As, Zn contamination, and no Cr, Pb pollution (overall moderate risk); sediments exhibited extreme Cd pollution, moderate Ni, Zn, U levels, and negligible Cr, Pb impacts (overall extreme risk). USEPA health risk models indicated notable non-carcinogenic (higher in adults) and carcinogenic risks (higher in children) for both age groups. Ecological risk assessments categorized As, Cr, Cu, Ni, Pb, and Zn as low risk, contrasting with Cd (extremely high risk) and sediment-bound U (high risk). These findings underscore mining legacy as a critical environmental stressor and highlight the necessity for multi-source pollution mitigation strategies. Full article

In recent years, land subsidence in the Northern Anhui Plain has become increasingly pronounced, posing serious risks to infrastructure and groundwater management. However, quantitative assessments of its driving mechanisms remain limited. This study focuses on Bozhou, a typical resource-based city, and employs 186 Sentinel-1 SAR images and SBAS-based interferometric analysis to retrieve the spatiotemporal evolution of land subsidence from 2022 to 2024. Results show that the peak subsidence rate reaches 102 mm/year and has its major distribution in the central and eastern sectors of Bozhou. Temporally, the subsidence pattern shows an initial intensification followed by gradual stabilization. Furthermore, a GeoDetector-based analysis indicates that excessive groundwater extraction and coal mining are the dominant factors, with significant interactive enhancement effects. These findings provide crucial insights for the prevention and mitigation of land subsidence in resource-based cities. Full article

The wetland in the Yarlung Zangbo River Basin is an important part of the ecological barrier zone of the Qinghai–Tibet Plateau and exerts a significant influence on the climate. To elucidate the evolutionary characteristics and potential causes of wetlands in the Yarlung Zangbo River Basin against the background of “warming-humidification” of the plateau, this study focused on the spatial–temporal changes of wetlands in the Yarlung Zangbo River from 1990 to 2020 and simultaneously discussed the contribution of natural factors to these wetland changes. The data used in this study encompassed meteorological observation, the Digital Elevation Model (DEM), land use remote sensing monitoring, the vegetation index and other relevant data, and the methods used were mainly hydrological analysis, landscape change dynamic analysis and GeoDetector. The research findings indicated the following: (1) The wetland area in the Yarlung Zangbo River Basin exhibits significant fluctuations. The wetland area increased steadily from 1990 to 2005, followed by a slight decline after 2005, reflecting the changing process of “humidification–drought–humidification–drought”. Nevertheless, the overall trend over the 30 years has been an increase in wetland area (a total increase of 14.92%), primarily driven by the conversion of forest and grassland. (2) The wetlands in the Yarlung Zangbo River Basin are mainly distributed in the lower river basin, especially in the Niyang River basin and the Yigong–Parlung Zangbo basin. The spatial distribution of these wetlands remained relatively stable over the 30 years studied. (3) The driving factor analysis results showed that the three main natural factors leading to the increase and reduction in wetland area include vegetation cover change, precipitation and evapotranspiration. Vegetation cover change contributed the most to the increase in wetlands in the Yarlung Zangbo River basin, and evapotranspiration played a decisive role in the reduction in wetland area. This study provided valuable perspectives for wetlands, water resources and ecosystem assessments in the Yarlung Zangbo River Basin and the broader Qinghai–Tibet Plateau region. Full article

As an important target for deep marine shale gas exploration, shale reservoirs near denudation areas have enormous resource potential. Based on the impression method, the sedimentary paleogeomorphology near the denudation area is identified as three units: the first terrace, the second terrace, and the third terrace. At the second terrace where Well Z212 is located, the thickness of the Longmaxi Formation first section is only 0.8 m, and the continuous thickness of the target interval is only 4.3 m, making it a typical thin shale reservoir. By integrating petrology, mineralogy and the seismic method, the thin shale reservoir is characterized. Compared to shale reservoirs far away from the denudation area, the Well Z212 (near denudation area) production interval (Wufeng Formation first section) has high porosity (6%–10%), moderate TOC (3%–4%), a high carbonate mineral content (10%–35%), and a high gas content (>7 m³/t). The correlation between the total porosity of shale and the density of high-frequency laminations is the strongest, indicating that the silt laminations have a positive effect on pore preservation. There is a significant positive correlation between carbonate content and the volume of mesopores and macropores, as well as the porosity of inorganic pores. It is suggested that carbonate minerals are the main carrier of inorganic pores in Well Z212, and the pores are mainly composed of mesopores and macropores. Under the condition of being far away from the fault zone, even near the denudation area, it has good shale gas preservation characteristics. The key development technologies consist of integrated geo-steering technology, acidification, and volume fracking technology. Based on geological characteristics, the fracturing process optimization of Well Z212 has achieved shale reservoir stimulation. Full article

Plane geometry problem solving has been a long-term challenge in mathematical reasoning and symbolic artificial intelligence. With the continued advancement of automated methods, the need for large-scale datasets and rigorous evaluation frameworks has become increasingly critical for benchmarking and guiding system development. However, existing resources often lack sufficient scale, systematic difficulty modeling, and quantifiable, process-based evaluation metrics. To address these limitations, we propose FGeo-Eval, a comprehensive evaluation system for plane geometry problem solving, and introduce the FormalGeo30K dataset, an extended dataset derived from FormalGeo7K. The evaluation system includes a problem completion rate metric PCR to assess partial progress, theorem weight computation to quantify knowledge importance, and a difficulty coefficient based on reasoning complexity. By analyzing problem structures and solution dependencies, this system enables fine-grained difficulty stratification and objective performance measurement. Concurrently, FormalGeo30K expands the dataset to 30,540 formally annotated problems, supporting more robust model training and evaluation. Experimental results demonstrate that the proposed metrics effectively evaluate problem difficulty and assess solver capabilities. With the augmented dataset, the average success rate across all difficulty levels for the FGeo-HyperGNet model increases from 77.43% to 85.01%, while the average PCR increases from 88.57% to 91.79%. These contributions provide essential infrastructure for advancing plane geometry reasoning systems, offering standardized benchmarks for model development and guiding optimization of geometry-solving models. Full article

Background: Depressive symptoms and cognitive decline are common in older adults. The aims of this study were (i) to assess the frequency of depressive symptoms and cognitive impairment in users of municipal home care services and (ii) to explore factors that may pertain to seeking in-depth neuropsychiatric diagnostic workup, if recommended. Methods: The study was mainly conducted in low-resource areas of south-western Greece. The Geriatric Depression Scale (GDS-15), the Mini-Mental State Examination (MMSE) and the Clock Drawing Test (CDT) were employed. The study included the tracking of whether participants sought medical consultation within 12 months after receiving the recommendation for further neuropsychiatric diagnostic workup. Results: The study encompassed 406 individuals. Cognitive deficits were detected in 312 (76.84%) study participants, of whom only 82 (26.28%) had received the diagnosis of a mental or neurological disorder. Depressive symptoms were detected in 236 (58.27%) individuals, of whom only 18 (4%) had received the diagnosis of a mental or neurological disorder. Only just over a third of individuals consulted physicians. Reluctance towards in-depth neuropsychiatric workup mainly derived from a lack of insight and fears related to COVID-19. Previously diagnosed neuropsychiatric disorders slightly correlated with the decision to consult a physician. Conclusions: Developing pragmatic cognitive and mental healthcare services to address the needs of older people with disabling chronic disorders who live in low-resource settings is urgently needed. Full article

Geoethics, which addresses the ethical, social, and cultural dimensions of geoscientific activities, is essential for fostering responsible human engagement with the Earth, particularly within frameworks such as UNESCO Global Geoparks (UGGps). UGGps play a critical role in safeguarding geological heritage and advancing sustainable regional development. This study introduces the Geoethical Awareness Scale (GAS), a 32-item instrument developed across 16 thematic axes, designed to assess geoethical awareness. We analyzed responses from n = 798 residents across nine Hellenic UGGps using Exploratory and Confirmatory Factor Analyses, retaining items with factor loadings of ±0.30 or higher. Six factors emerged: (1) geological heritage conservation and sustainable georesource use, (2) community engagement and collaborative governance, (3) sustainability through geoenvironmental education, (4) environmental challenges and risk adaptation, (5) sustainable geotourism, and (6) climate awareness and ecosystem resilience. Collectively, these factors explained 60.12% of the variance, with Cronbach’s alpha values demonstrating acceptable to excellent reliability. Structural Equation Modeling confirmed the scale’s validity, with fit indices indicating acceptable model adequacy. Incremental indices suggested moderate alignment, while parsimony-adjusted metrics supported a balance between model complexity and fit. Overall, the GAS demonstrated generalizability and sufficient sample robustness. Correlation analyses highlighted the role of geoeducation, organizational involvement, and direct experience in fostering pro-geoconservation attitudes. While perceptions of sustainable development and ecosystem resilience varied geographically across UGGps, community engagement and governance remained consistent, likely reflecting standardized policy frameworks. GAS offers a valuable tool for assessing geoethical awareness and underscores the importance of targeted geoeducation and participatory governance in promoting ethical geoscientific practices within UGGps and similar socioecological systems. Full article