Search Results (26,044)

Soil moisture critically governs terrestrial energy and water cycles. Precise monitoring of soil water content is essential for precision agriculture, drought early warning, and water resource management. Ground-based observations offer limited spatial coverage, and satellite remote sensing generally lacks high spatial resolution. Unmanned aerial vehicle (UAV) remote sensing, which provides centimeter-level spatial detail, can effectively address this gap and has therefore attracted considerable attention in soil moisture inversion research. Using CiteSpace, we performed a bibliometric analysis of 97 Chinese papers from the China National Knowledge Infrastructure (CNKI) and 321 English papers from the Web of Science Core Collection (2014–2025). The field has expanded rapidly since 2018, with China occupying a leading role. Domestically, Northwest A&F University represents a major research cluster, while the Chinese Academy of Sciences leads internationally. Key research topics include UAVs, soil moisture, machine learning, hyperspectral sensing, canopy temperature, and precision agriculture. Research themes have progressed from reliance on vegetation indices and temperature data toward the integration of hyperspectral and thermal infrared measurements, and toward the use of machine learning approaches to improve inversion models and achieve more accurate estimations. This study delineates the classification and developmental context of a knowledge system for soil moisture inversion using UAV remote sensing. Current work concentrates on integrating multi-sensor data with machine learning, while future efforts will emphasize coupling physical mechanisms with deep learning. These findings offer researchers a clear view of the field’s frontiers and a basis for planning future studies. Full article

Private tutoring has become an increasingly popular approach for improving academic performance by providing individual or group support outside regular school hours to enhance student outcomes. In the context of mathematics tutoring, we introduce the eMath4All platform, designed to replicate traditional teaching methods through virtual tools for distance learning. Despite the growing prevalence of private tutoring, research on online tutoring platforms and their use in practice remains limited. Accordingly, this study explores the application of the eMath4All platform in two different private tutoring scenarios involving secondary school students from Romania. Study A examines group tutoring with five eighth-grade students preparing for a national examination over a three-month period, while Study B explores individual tutoring with ten students from various secondary education levels over a 12-month period. The paper analyzes how the key components of the eMath4All platform (such as the virtual whiteboard, mathematical editor, real-time audio–video communication, virtual library, assessment tool, and personal student profile) support tutoring activities. The platform is examined through a combination of platform usage data, descriptive analysis of student progression, and student-reported experience collected via questionnaires. The results of the exploratory study indicate consistent usage patterns, high engagement with platform features, and high usability ratings, highlighting the platform’s potential for supporting both individual and group mathematics tutoring. Full article

The retrofitting of existing stadiums with retractable roof systems presents a complex interdisciplinary challenge, requiring the reconciliation of aged structural capacity with modern performance demands. This paper investigates the engineering design and analysis of a new retractable roof system for the Ningbo (Yinzhou) Tennis Center, a facility originally completed in 2007 and now requiring an upgrade to host higher-tier WTA 500 events. The retrofit is further complicated by increased seismic design requirements and the need to preserve the existing structure. To address these constraints, this study proposes a novel, structurally independent roof system comprising 12 radially deployable units supported by an external single-layer spatial grid and lambda-shaped columns. A multidisciplinary approach integrates structural engineering, mechanical systems, and architectural technology. Key innovations include (1) the selection and detailed modeling of a rack-and-pinion drive mechanism, with a floating engagement design to accommodate dynamic load transfer; (2) a two-stage analytical framework employing both sub-assembly and integrated assembly finite element models to capture the unique mechanical behavior and coupling effects between the new and existing structures; (3) the strategic implementation of circumferential hoop cables to counteract uplift forces and redirect the internal force distribution in the supporting bifurcated columns; and (4) the validation of structural integrity through comprehensive static, stability, and seismic gap analyses, informed by wind tunnel testing. The results demonstrate that the proposed system satisfies all strength, stiffness, and stability criteria under multiple operational states (open, closed, and transitional) and meets the enhanced seismic fortification standards. This research provides a validated theoretical foundation and practical implementation guidelines for this specific stadium retrofit, demonstrating a viable pathway for extending the service life of aging sports infrastructure, with insights that may inform similar urban renewal projects under comparable conditions. Full article

Cabled underwater information networks (CUINs) are a focal point and priority in the field of global marine science and technology. Reliability and economic viability are among the primary constraints on the large-scale deployment of such networks. The remote power supply system for grid-like CUINs is the component with the highest technical risk, exerting a significant impact on both network reliability and economic feasibility. This paper designs and constructs a minimal model and a basic model of a constant-current remote power supply system (CCRPSS) for grid-like CUINs. Through simulation modeling and analysis, the system’s capability to handle faults in a single underwater unit or multiple underwater units in different power supply link segments (PSLSs) is validated, and the impact of underwater unit faults on the system’s operational state is analyzed. Based on this, a descriptive method for determining the power supply reliability (PSR) of observation equipment (OE) is proposed, and the variation patterns of this reliability across different power supply links (PSLs) are derived. Building on this foundation, a constrained engineering design method for the grid-like CCRPSS is proposed. This method aims to deploy a larger number of secondary nodes (SNs) at a lower cost. By integrating constraints including the PSR of OE for each PSL, the open-circuit and short-circuit fault rates of underwater units, and the allowable number of SNs per PSLS, it optimizes the system engineering design problem. This approach yields an optimal solution for the number of longitudinally and transversely deployed SNs as well as the reliability requirements for each underwater unit. Case simulation results validate the descriptive method for the PSR of OE and the variation patterns of such reliability, thereby confirming the feasibility of the constrained engineering design approach. The research findings presented in this paper can provide theoretical references for the reliability analysis, scale design, and long-term planning of CUINs and their remote power supply systems. Full article

Never has the analysis of bus travel times been so essential to transit planning: travelers complain about a decline in service quality, urban congestion is on the rise, and public transport companies struggle with a structural driver shortage. This research paper aims to address the urgent need to explore new tools to increase commercial speed on transit lines while avoiding costly, potentially inefficient technological investments. A data-driven, cost-neutral, and replicable methodological framework is proposed to provide a first-order estimation of the potential benefits of Transit Signal Priority (TSP) at signalized intersections. The approach is based on Automatic Vehicle Monitoring (AVM) data analysis, which is underpinned by a lean network representation logic built from origin/destination pairs of stops located upstream and downstream of signalized intersections. Bus travel inter-times across network arcs are compared between peak and off-peak periods through a two-level analytical process that progressively refines the estimation of recoverable delay. The methodology is applied to the urban bus network of Pavia (Italy), operated by Autoguidovie S.p.A. (one of the most important Local Public Transport companies in Italy), with a specific focus on the high-frequency PV3 line. Results indicate a potential reduction of up to approximately 6 h and 45 min of operating time per day at the line level (−13.5% of total driving time), and up to 2 min per trip along a 2 km corridor (−6% along the single corridor selected). The procedure integrates both infrastructural and operational perspectives, supporting preliminary decision-making on TSP implementation using only data already collected by transit agencies. Full article

Achieving sustainable development requires moving beyond pollution metrics to holistic measures, such as the load capacity factor (LCF), which balances ecological demand and supply. While recent studies have provided important insights into the determinants of LCF in OECD countries, further research is needed to incorporate additional determinants and updated estimation approaches. This study addresses this gap by examining the impacts of financial innovation, forestry, urbanization, population, and economic growth on the LCF in Organization for Economic Cooperation and Development (OECD) economies from 1990 to 2023. Using second-generation panel econometric methods, including tests for cross-sectional dependence, slope heterogeneity, second-generation unit roots, and cointegration techniques, this paper confirms a stable long-run relationship among the variables. The core analysis applies the method of moments quantile regression to uncover the heterogeneous effects across the LCF distribution. The results indicate that financial innovation consistently enhances the ratio of biocapacity to ecological footprint. In contrast, economic growth and urbanization exert significant negative pressure on the LCF, whereas population size shows a uniformly detrimental effect. Forestry has a positive but less pronounced influence. Robustness checks using fully modified ordinary least squares, dynamic ordinary least squares, and panel-corrected standard errors confirm these results. The present study concludes that targeted financial innovation and stringent urban demographic policies support OECD nations in improving ecological balance and reducing ecological deficits. Full article

Public participation in planning, though a foundational democratic principle, faces persistent implementation challenges across diverse planning systems. This paper examines participatory planning practice in Ireland and Serbia—two countries representing distinct planning traditions (discretionary and conformance-based, respectively) yet confronting shared structural pressures. Through comparative analysis of four local land use planning instruments (the Development Plan and Local Area Plan in Ireland; the Municipal Spatial Plan and General Regulation Plan in Serbia), the study investigates how institutional design and legislative frameworks shape the depth and quality of participatory practice. Methodologically, the research triangulates statutory regulations, public hearing documentation, and non-statutory participation records across two planning scales (county/municipal and local/sub-municipal). A four-dimensional analytical framework—informing, consultation, collaboration, and monitoring—guides the systematic comparison of participatory mechanisms across the selected cases. Findings reveal that, while both systems remain predominantly at the informing and consultation levels, critical differences emerge in how participation is structured and documented in institutional practice. Ireland’s discretionary system enables multi-channel information dissemination, feedback-oriented consultation, and non-statutory collaborative experimentation beyond legal minimums. Serbia’s conformance-based system confines participation largely to statutory procedures, with objection-based consultation and limited collaborative mechanisms, though distinctive features, such as the public hearing session, provide direct opportunities for deliberation absent in the Irish context. The study contributes to European comparative planning scholarship by demonstrating that participatory depth is shaped less by the formal existence of legal provisions than by the interplay between institutional design, procedural arrangements, transparency, and responsiveness. Full article

This paper provides a systematic review of recent applications of NLP methods for analyzing traffic crash reports, with a focus on estimating crash severity, crash duration, and crash causation. The review covers prior research using probabilistic topic modeling methods such as LDA, STM, and hierarchical Dirichlet processes in addition to research using transformer-based language models, which include encoder-based models like BERT and PubMedBERT as well as decoder-based models like GPT, GPT2, ChatGPT, GPT-3, and LLaMA. The review starts with a systematic literature selection process with predefined inclusion criteria. We categorize the reviewed studies into the following application areas: crash severity prediction, risk factor identification in crashes, and road safety analysis. The results show several complementary advantages of using different NLP techniques to achieve different analytical goals. Topic models allow for interpretable and exploratory pattern discovery, while encoder models are well-suited for structured prediction problems. Decoder models have the additional flexibility to perform zero-shot and few-shot reasoning, which makes them useful for reasoning about under-sampled or under-reported data. Across the literature, hybrid methods that combine text and structured data outperform individual methods in terms of prediction accuracy and broad applicability. Challenges across the literature include class imbalance, lack of standardization in preprocessing and evaluation methods, and the tradeoff between prediction accuracy and interpretability of prediction models. These findings highlight the importance of aligning model selection with data availability and operational constraints, pointing toward future research directions in hybrid modeling frameworks, standardized evaluation protocols, and real-world deployment of NLP-driven traffic safety systems. Full article

Landslides are one of the most common natural hazards in China, and the efficient screening of important factors is crucial for landslide susceptibility mapping. Taking the Zigui–Badong section of the Three Gorges Reservoir Area (TGRA) as the study area, this research initially selected 25 evaluation factors based on topography, geology, hydrology, remote sensing images, and previous studies. Thirteen key factors were obtained through analysis. Three machine learning models—RF, DT, and XGBoost—were then used for landslide susceptibility mapping, with SHAP and LIME employed to interpret the models. Finally, a scoring method was used to rank the six sets of results and compare them with those from the traditional AUC-based Recursive Feature Elimination (AUC-RFE) method. The results showed that the core factor sets screened by interpretable methods outperformed those from AUC-RFE. To further obtain accurate core factor sets, two additional interpretable methods—PI and Explainable Boosting Machine (EBM)—were integrated, ultimately identifying a core factor set consisting of eight factors including Elevation, Slope Height, and Aspect. This set achieved an AUC value of 0.931, only 0.003 lower than that of the 13 filtered factors. The screening method proposed in this paper can significantly improve the efficiency of factor acquisition, reduce the difficulty of factor acquisition, and provide a new approach for the selection of key factors in landslide susceptibility assessment. Full article

The increasing complexity of next-generation wireless systems challenges the scalability and generalization capabilities of traditional model-driven physical layer (PHY) design, which relies on analytically derived channel models and optimization frameworks. This paper presents a comprehensive survey and critical review of deep learning (DL) architectures enabling the transition toward AI-native PHY design. A unified optimization perspective is developed in which all PHY tasks—including channel estimation, channel state information (CSI) feedback, massive MIMO processing, signal detection, channel coding, beamforming, resource allocation, and semantic-aware transmission—are formulated under a common empirical risk minimization (ERM) framework. Neural architectures such as autoencoders, convolutional and recurrent networks, transformers, and reinforcement learning models are examined through their underlying optimization formulations, loss functions, training methodologies, and representation learning mechanisms. The review compares model-driven and AI-native approaches in terms of performance metrics, computational complexity, robustness, generalization capability, and practical deployment constraints, including hardware limitations, energy efficiency, and real-time feasibility. The analysis highlights the conditions under which AI-native architectures provide adaptability and performance improvements while identifying trade-offs in complexity, latency, and interpretability. The study concludes by outlining prioritized research directions toward fully adaptive and self-optimizing wireless communication systems. Full article

Blood glucose level (BGL) prediction, by providing early warnings regarding unsatisfactory glycaemic control and maximising the amount of time BGL remains in the target range, can contribute to minimising both acute and chronic complications related to diabetes. This paper aims to provide an overview of data-driven approaches for BGL prediction in type 1 diabetes mellitus (T1DM). This review summarises different aspects of developing and evaluating data-driven prediction models, including model strategy, model input, prediction horizon, and prediction performance. It also examines applications of recent artificial intelligence (AI) techniques, including deep learning, transfer learning, ensemble learning, and causal analysis in the management of T1DM. Recent studies indicate that machine learning approaches often outperform classical time-series forecasting models in BGL prediction, particularly when using multivariate inputs. These findings also highlight the potential of advanced AI methods to improve prediction accuracy. Moreover, applying appropriate statistical analyses is essential to enable valid comparisons between different BGL prediction models, especially given the considerable inter-individual variability among people with T1DM. The development of efficient methods for integrating affecting variables into BGL prediction requires further research. Given the promising performance of advanced AI techniques and the rapid growth of AI innovation, continued exploration of cutting-edge AI strategies will be crucial for further improving BGL prediction models. Full article

Driven by the iteration of digital technologies and the upgrading of residents’ health consumption demands, smart food packaging has developed rapidly and is widely applied across various food categories. However, issues such as consumer cognitive biases and insufficient acceptance hinder its market penetration. This paper constructs a chained mediation model based on the Theory of Planned Behavior (TPB) and Perceived Value Theory, employing PLS-SEM and IPMA methods to validate multiple research hypotheses. It innovatively integrates multiple theories to establish an interdisciplinary research framework, overcoming the limitations of single theories. The analysis, combined with IPMA, clarifies the priority of each variable, addressing existing research gaps. The results indicate that the four perceptual factors of smart food packaging significantly and positively influence the three core constructs of TPB, with experiential factors exerting the strongest drive on individual needs. The TPB constructs significantly and positively affect perceived value, perceived trust, and self-efficacy, with the drive of individual needs being most prominent. Perceived trust has the strongest influence on healthy eating behavior. IPMA analysis reveals that perceived value (PV) is a key area for improvement, while individual needs (IN) and self-efficacy (SEHB) are key areas of strength. This study elucidates the internal mechanisms through which smart food packaging influences consumers’ healthy eating behaviors, providing theoretical and practical support for enterprises to optimize design and guide healthy consumption. Full article

In order to protect the high-sensitivity optical lens of the “magnetic field and velocity field imager” in extreme deep space environments, this paper proposes a new type of dual redundant planetary differential lens cover drive mechanism. In view of the critical vulnerability that traditional single-motor direct drive is prone to sudden mechanical jamming and catastrophic single-point failure (SPF) in severe tasks such as Jupiter exploration, this study constructs a “dual input single output (DISO)” rigid decoupling architecture from the perspective of physical topology. Through theoretical analysis and kinematic modeling, the adaptive decoupling mechanism of the two-degree-of-freedom (2-DOF) system under unilateral mechanical stalling is revealed. Dynamic analysis shows that in the nominal dual-motor synergy mode, the system shows a significant “kinematic load-sharing effect”, thus greatly reducing the sliding friction and gear wear rate. In addition, under the severe dynamic fault injection scenario (maximum gravity deviation and sudden jam superposition of a single motor), the cold standby motor is activated and the dynamic takeover is quickly performed. The high-fidelity transient simulation based on ADAMS verifies that although the fault will produce transient global torque spikes and pulsed internal gear contact forces at the moment, all extreme dynamic loads remain well within the structural safety margin. The output successfully achieved a smooth transition, which is characterized by a non-zero-crossing velocity recovery. This research provides an innovative theoretical basis and a practical engineering paradigm for the design of high-reliability fault-tolerant mechanisms in deep space exploration. Full article

Small settlements in Serbia are confronted with long-term processes of depopulation, ageing, and migration, characterised by pronounced spatial and structural heterogeneity. This raises questions about the effectiveness of uniform development policies and underscores the need for a differentiated, place-based approach. The aim of this paper is to identify the demographic heterogeneity of small settlements (with fewer than 100 inhabitants) and to analyse its implications for decentralised territorial development. The research is based on the analysis of 1302 settlements in Serbia, using 26 demographic, socio-economic, and geographical indicators. The methodological framework is based on principal component analysis and cluster analysis, complemented by nonparametric tests and logistic regression. The results indicate pronounced population ageing, low labour potential, and a clear spatial polarisation between accessible and peripheral settlements. Four clearly differentiated types of small settlements are identified. It is concluded that demographic heterogeneity represents a key determinant of development capacity, indicating the need for territorially sensitive and differentiated development policies. In this context, decentralisation and tailored development models may contribute to the revitalisation and long-term sustainability of rural areas. Full article

For high-voltage power transmission, the surface voltage gradient (SVG) of the conductor plays a crucial role in meeting corona performance requirements. The SVG is greatly impacted by the smoothness of the conductor’s surface. Under identical conditions, the SVG of smooth, round conductors differs from that of stranded conductors with the same outer radius. This paper uses Finite Element Analysis (FEA) to study the effect of different stranded conductor geometries and three-phase line topologies with stranded conductor bundles on the SVG. Although industry standards and the scientific literature often rely on simplified smooth-cylinder approximations, this research demonstrates that surface irregularities significantly increase electrical stress compared to idealized smooth surfaces. Through simulating various three-phase configurations, the study reveals a nearly constant field enhancement factor across diverse stranded designs. These results enable us to apply formulas developed for smooth conductors to more realistic power line applications involving stranded conductor bundles. Consequently, this FEA approach offers engineers a precise, versatile method for designing high-voltage transmission lines. The findings presented here facilitate a deeper understanding of the SVG surrounding stranded conductors, particularly with regard to its influence on corona phenomena. Full article