Search Results (1,728)

To address the dilemma of ‘non-grain use of cultivated land’ and support China’s requisition–compensation balance policy, this study developed a multi-dimensional assessment framework integrating the production, ecological, and economic dimensions (3D evaluation model), using Shenyang City as a case study to demonstrate the framework’s operational application and policy relevance. Based on 34,704 Third National Land Survey (TNLS) parcels (27,408.39 ha), we applied the constraint factor assessment method and entropy-weighted composite index model. The results show that non-cultivated agricultural land (NCAL) is generally marginally suitable (citywide average score: 2.50/4), with highly suitable areas accounting for only 4.04% (1106.30 ha). These areas exhibit a triangular spatial pattern distributed across northeastern Faku County, central Sujiatun District, and southern Xinmin City. Sensitivity tests using equal weights and ±20% dimension-weight perturbations confirm that high-suitability area remains limited (3.37–5.63% under entropy-weight scenarios; 8.54% under equal weights). Primary limiting factors include severe organic matter deficiency (average 19 g/kg), shallow soil depth, unfavorable pH, land requiring engineering restoration (94%), and punctiform heavy metal contamination (7.53% of plots, 2065.05 ha as spatially excluded areas). Consequently, we propose a five-tier sequential restoration framework: (1) near-term priority recultivation of highly suitable areas; (2) mid-term topsoil reconstruction for moderately suitable areas; (3) medium-to-long-term topsoil stripping and thickening for low-suitability areas; (4) long-term soil amelioration and slope-to-terrace conversion for marginally suitable areas; and (5) strict prohibition of restoration in unsuitable areas. This study establishes a spatially explicit decision-making system integrating “evaluation–classification–sequencing”, and distinguishes technical suitability from economic, institutional, and policy feasibility, providing a decision-support framework for scientifically implementing the cultivated land requisition–compensation balance policy. Future empirical studies using post-restoration monitoring data are needed to test its predictive accuracy against observed restoration outcomes. Full article

Thorium (Th) is increasingly considered a promising fertile material for sustainable nuclear energy—which is not fissile itself, but convertible to fissile ²³³U—particularly as a by-product of rare earth element (REE) processing. This study develops a parametric techno-economic assessment (TEA) framework synthesizing published data from China, Russia, the USA, India, and Europe to establish the methodological foundation for evaluating thorium recovery economics from monazite ores and REE tailings under Central Asian conditions. Monazite typically contains 4–12% ThO₂, while tailings contain 0.1–3%, making secondary resources attractive for future recovery strategies. Particular attention is given to integration with uranium tailings and the application of advanced materials such as nanocomposite sorbents and carbon-based electrodes. Reported production costs of ThO₂ range from 50 to 500 USD/kg depending on process scale, feedstock quality, and co-production of REEs. The reviewed studies consistently show that coupling thorium recovery with REE processing improves economic feasibility. Modern approaches, including hybrid technologies and electrosorption systems, may reduce operational costs and improve process efficiency. Despite challenges related to capital investment, market uncertainty, and radioactive waste management, thorium continues to attract growing interest as a potential component of future nuclear fuel cycles and advanced reactor systems, including small modular reactors. To the best of the authors’ knowledge, this is the first parametric TEA framework structured around Central Asian conditions, combining literature-derived regional data, scenario-based process economics, and Monte Carlo sensitivity analysis within a single discounted cash flow structure. Full article

High-dynamic amusement ride conditions involving impacts, rapid rotations, and abrupt posture changes introduce severe motion artifacts that degrade vital sign quality and destabilize physiological state recognition. This study aims to develop an engineering-ready closed-loop framework for robust passenger monitoring and intelligent diagnosis. A multimodal sensing and modeling pipeline was designed to jointly leverage physiological signals such as heart rate and SpO₂ and kinematic measurements, including acceleration, angular rate, velocity, and attitude. Inertial and PPG signals were preprocessed into supervised samples through wavelet multiresolution denoising and coordinate frame unification, while a strapdown inertial navigation system was used to propagate a 12-channel physical quantity sequence. To ensure interpretability and standards compliance, constraints from GB 8408-2018 were translated into executable threshold rules, enabling standards-driven auto-labeling and rule-based early warning. Building on this foundation, three learning modules were developed: a fusion model for high-dynamic heart rate estimation, a CNN–LSTM dynamic-threshold-enhanced network TAPNet for rapid kinematic anomaly screening, and an attention-augmented hybrid model HS-BANet integrating one-dimensional residual blocks, bidirectional LSTM, and multi-head attention for fine-grained arrhythmia classification. Experimental results demonstrated accurate and consistent heart rate estimation with RMSE of 1.18 bpm on HSSH-I and 1.24 bpm on the independent HSSH-II set, strong agreement with training and testing correlations of 0.9928 and 0.9865, and near-zero bias in Bland–Altman analysis. TAPNet achieved 96.9% validation accuracy and 98.2% test accuracy for kinematic anomaly recognition, maintaining robust generalization under class imbalance. HS-BANet enabled multi-class identification of PVC, PAC, VT, SVT, and AF, achieving an accuracy of 92.37%, an F1-score of 86.87%, a precision of 88.45%, a sensitivity of 88.14%, and a specificity of 89.42%. Overall, the proposed two-stage multimodal closed-loop—fast, interpretable early warning based on physical quantity thresholds followed by fine-grained diagnosis from physiological signals—supports stable feature extraction and reliable decision-making under strong motion artifacts and non-stationary dynamics, balancing responsiveness and diagnostic credibility, while showing potential for practical safety early warning and future deployment-oriented operational support in amusement ride scenarios. Full article

Efficient and financially sustainable social protection systems are essential under conditions of economic instability and increasing social demand. However, traditional administrative models are often characterized by high operational costs, procedural complexity, and delayed benefit delivery. This study examines the role of digitalization, process automation, and AI-driven administrative solutions in reducing administrative expenses while enhancing the sustainability and resilience of social protection systems. An integrated Automation Index is developed using standardized proxy indicators that reflect reductions in operational and transaction costs associated with digital and automated technologies. To assess future trajectories of administrative expenses, scenario-based modelling is applied under three digital transformation paths—baseline, moderate, and intensive. Administrative efficiency is estimated using a translog Stochastic Frontier Analysis (SFA) framework. The results indicate that digitalization and automation significantly reduce administrative costs only when supported by favorable institutional conditions, including decentralized governance, effective inter-agency coordination, and clearly regulated administrative procedures. Under the intensive digital transformation scenario, administrative expenses decline substantially relative to the baseline, while system responsiveness and beneficiary coverage improve. In contrast, weak institutional environments limit the efficiency gains of technological solutions. The study concludes that AI agents and automated systems should be viewed not as substitutes for human decision-making but as tools for optimizing administrative architectures. This transition from resource-intensive to technology-intensive models is particularly important for developing countries seeking sustainable social protection under constrained fiscal conditions. Full article

There is a growing trend towards vehicles powered by alternative energy sources due to the environmental pollution caused by fossil fuel vehicles. Electric vehicles (EVs) are thought to make a significant contribution to reducing environmental pollution. This study presents a performance comparison of field-oriented control (FOC) and finite control set-based model predictive current control (FCS-MPCC) methods for controlling PMSM motors, which are commonly preferred for EV applications. A multilevel ANPC inverter topology, which has a higher-quality power flow than classical two-level inverters, was preferred to power the PMSM. While the classical FOC method has a fixed switching frequency by including cascaded PI controllers and a pulse width modulation (PWM) modulator, the FCS-MPCC method determines a variable frequency-switching signal that minimizes the cost function by predicting the future current behavior of the PMSM using the mathematical model of the system. The performance comparison of FOC and FCS-MPCC methods was carried out by conducting real-time experimental studies. Both control algorithms were analyzed under variable speed and load conditions using the same motor and drive structure. Performance analysis of FOC and FCS-MPCC control algorithms was carried out in terms of speed tracking, torque, current, and harmonics. According to the results obtained, the total harmonic distortion (THD) value of the stator current was 7.03% in the FOC method, while it was 22.19% in the FCS-MPCC method. Furthermore, a comparative analysis was conducted on the dynamic performance of the two methods in different scenarios using the mean absolute error (MAE), root mean square error (RMSE), integral absolute error (IAE), integrated time absolute error (ITAE), and integral squared error (ISE) criteria. The FCS-MPCC method was observed to be superior in different speed scenarios according to these criteria. In terms of processor load, it was calculated as 17.09% in the FOC method and 63.75% in the FCS-MPCC method. This study is important for determining the control strategy of PMSMs used in EV drives. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

Autonomous navigation of unmanned surface vehicles (USVs) in complex obstacle scenarios remains challenging due to redundant perception inputs, unstable value estimation, and inefficient policy convergence. To address these problems, this paper proposes LDA-D3QN, an improved deep reinforcement learning method for USV autonomous navigation. The proposed method constructs a compact navigation state representation by combining target-related information with local obstacle features, allowing the agent to retain key decision-making information while reducing unnecessary environmental redundancy. Based on this representation, an enhanced value-learning framework is developed to improve the stability of navigation decisions in cluttered environments. Moreover, a reward-guided and staged training strategy is introduced to help the agent gradually adapt to increasingly complex navigation tasks. The proposed method was evaluated on a Unity–ROS–MATLAB integrated simulation platform. Experimental results show that LDA-D3QN achieves superior overall navigation performance compared with several representative reinforcement learning algorithms. Specifically, the proposed method achieves a final training success rate of 91.4%, outperforming PPO (82.3%), Dueling DQN (78.5%), Double DQN (79.8%), and Rainbow DQN (86.5%). Additional tests in complex multi-obstacle and multi-target scenarios further demonstrate that the learned policy can generate safe, stable, and effective navigation behaviors. Preliminary validation using real-USV sensor data also confirms the feasibility of the LiDAR and GPS data processing procedures, providing a basis for future closed-loop autonomous navigation experiments and multi-sensor fusion deployment. Full article

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications. Full article

In UAV-based remote sensing, accurate and efficient image mosaicing is crucial for achieving real-time monitoring. Traditional cloud-centric processing paradigms, however, face core scientific challenges such as high latency, bandwidth bottlenecks, and limited autonomy, making them inadequate for dynamic, real-time scenarios. To address these issues, this paper proposes an edge-computing-enabled UAV image mosaicing system. The system consists of a UAV remote sensing platform and an edge computing terminal, with the core being our novel B-SIFT-ILS algorithm. The algorithm first uses geographic coordinates for unified registration, constructs a Gaussian scale space for multi-resolution representation, and then precisely locates extrema in the Difference of Gaussian (DoG) space using a 3D quadratic function. A BANSAC algorithm is subsequently employed to refine feature points and extract stable SIFT features, and finally, Iterative Least Squares (ILS) are used to achieve seamless mosaicing. Experimental results demonstrate that, compared with classical RANSAC, the proposed method achieves superior feature sampling accuracy (rotation: 0.879, translation: 0.877) and lower latency. The ILS-based smoothing stage effectively eliminates noise and ghosting without introducing gradient reversal, performing comparably to deep learning methods while significantly outperforming direct averaging and Gaussian approaches. On the NVIDIA Jetson Orin NX edge terminal, a single processing instance requires only 1124 ms, highlighting its strong potential for real-time, low-latency, and autonomous mosaicing tasks. Future research will focus on extending the approach to non-planar terrains and implementing adaptive parameter tuning for the BANSAC algorithm. Full article

Introduction: The vulnerable status of endemic Iberian fish like A. iberus and V. hispanica, together with limited knowledge of their reproductive biology, makes understanding temperature effects on hatching essential for defining their thermal limits and guiding conservation in a warming climate. Objectives: The main goals of this study were (i) to describe the embryonic development from fertilization to hatching in both species, and (ii) to evaluate the effect of temperature (20, 25, and 30 °C) on the embryonic development of both species, analyzing their future viability in the face of future climate scenarios. Methodology: Eggs at the earliest embryonic stage were evenly distributed among Petri dishes under controlled laboratory conditions. All dishes contained pH-controlled water with methylene blue to reduce fungal development. The hatching success of A. iberus and V. hispanica eggs was assessed across three temperature treatments (T20, T25, T30). Results: At 20 °C and 25 °C, A. iberus embryos achieved 100% hatching, with no failed eclosions. At 30 °C, however, hatching success declined to 33%, largely due to embryo mortality associated with fungal growth favored by higher temperatures. V. hispanica showed an even stronger temperature-dependent response: hatching was 100% at 20 °C, decreasing to 50% at 25 °C, and only 14% at 30 °C. These results indicate that V. hispanica embryos exhibit lower thermal tolerance compared to A. iberus. Conclusion: The data obtained in this study have helped to improve the limited knowledge available on the reproductive biology of these species, complementing existing information on their reproductive cycle. The experiments conducted at different temperatures have also provided valuable insights into how future climate change scenarios may affect their early stages, particularly during sensitive developmental periods. This information is essential for predicting potential impacts of climate change and can be used to guide the development of new conservation and management measures aimed at reducing threats and improving the long-term conservation prospects of these species, especially in vulnerable or changing habitats. Full article

WiFi channel state information (CSI) has become a compelling sensing modality for contactless human activity recognition. However, differences in datasets, preprocessing protocols and model configurations make consistent comparison and reproducibility challenging. This study presents a unified baseline evaluation of four widely adopted deep learning architectures: multilayer perceptron (MLP), convolutional neural network (CNN), gated recurrent unit (GRU) and a hybrid CNN–GRU model across multiple publicly available CSI datasets encompassing a range of sensing tasks. We harmonize the datasets, implement a standardized preprocessing and training pipeline to reduce experimental inconsistencies and support controlled within-dataset comparisons of model behavior. Evaluations include single-person activity recognition, fall-risk estimation, multiperson occupancy classification and localization-aware activity recognition, representing progressively higher temporal and spatial complexity. Our results show dataset-dependent trends: CNNs provide an efficient accuracy–complexity trade-off in several structured activity scenarios, whereas GRUs are advantageous when temporal dynamics are more prominent, although with greater training and inference costs. In contrast, MLPs generally underperform due to limited capacity to capture spatial and temporal dependencies. Confusion matrix analysis reveals that dynamic behaviors and low-motion states remain challenging to distinguish, underscoring the importance of temporal modeling. By releasing the complete experimental pipeline and benchmarking results, this work establishes a reproducible reference framework for the research community and highlights directions for future investigation, including cross-dataset generalization, hybrid model design and lightweight deployment strategies. Full article

Background/Objectives: Digital technologies and data-driven approaches are rapidly transforming healthcare practice and enabling more personalized and preventive care. As personal health data becomes increasingly embedded in healthcare systems, understanding how future healthcare professionals interpret these developments is essential for shaping responsive health education. This study aims to identify the factors that students in health-related programs consider important regarding the increasing use of personal health data in healthcare. Methods: An exploratory qualitative focus group study was conducted between March 2024 and July 2025 across five higher education institutions in Australia, Canada, and the Netherlands. Seven focus groups were conducted with forty students from health-related programs, including nursing, public health, occupational therapy, and social work. Participants discussed the use of personal health data in healthcare and reflected on short fictional future scenarios designed to stimulate discussion about possible developments in data-driven healthcare. Data were analyzed using reflexive thematic analysis using ATLAS.ti. Results: Three overarching domains were identified: (1) personalization and prevention, (2) data quality and ethical considerations, and (3) organizational implications and conditions. Students described personal health data as a powerful tool for personalization, prevention, and informed decision-making. At the same time, they raised concerns about data reliability, overreliance on automated systems, patient anxiety, potential dehumanization of care, privacy risks, and emerging inequalities related to access to and representation within data systems. Overall, students appeared neither purely techno-optimistic nor technophobic, but articulated nuanced ethical, cultural, and professional tensions surrounding data-driven care. Conclusions: Preparing future healthcare professionals for data-driven healthcare requires integrating critical data literacy, ethical reflection, interdisciplinary collaboration and opportunities to critically engage with the societal and professional implications of data-driven technologies into health professional education, while ensuring that organizational conditions support the responsible use of personal health data. Full article

As artificial intelligence (AI) becomes increasingly important in rural revitalization, building consensus among multiple stakeholders and developing participatory digital co-creation platforms has grown increasingly urgent. However, existing large language model (LLM) systems predominantly adopt a one-shot generation paradigm, making it challenging to accurately capture villagers’ cultural aspirations and frequently resulting in a significant disconnect between design outputs and community expectations. This situation reveals deficiencies in progressive deliberation mechanisms and cultural controllability. To address these issues, this study proposes a multimodal Participatory Landscape Demand Generation (PLDG) system to enhance AI-generated dialogue controllability, facilitate effective cultural translation in sensitive rural contexts, and promote sustainable development where landscape design both drives and reflects rural revitalization. The system leverages LLMs to simulate stakeholder participatory interactions in village landscape design scenarios. Using culturally distinctive Chinese villages as case studies, the research conducts multi-role simulated dialogues, multimodal semantic extraction, and iterative consensus-building, and evaluates the resultant data to generate landscape design proposals. The results indicate that the PLDG system significantly improves participation efficiency among diverse design stakeholders and enhances the sustainability of design decisions. Compared to conventional methods, metrics such as cultural compatibility, villager participation, and design innovation show substantial improvements. These findings demonstrate the considerable potential of human-AI collaboration in future rural planning. This study introduces the Culture Constraint-Driven Rural Landscape AI Collaborative Design Framework (PLDG), validating its practical efficacy in identifying culturally sensitive elements, ensuring cultural congruence, facilitating community participation, and fostering design innovation. Consequently, it provides a reusable, iterative operational tool for the digital renewal of sustainable rural landscapes. Full article

Digital twin technology is becoming a core enabler for the intelligent transformation of civil engineering. This review adopts an integrated mixed-method design that combines a reproducible bibliometric protocol with structured content analysis to connect macro-level knowledge evolution with domain-specific engineering implementation. Based on the Web of Science Core Collection, the study analyzes publication trends, collaboration patterns, highly cited studies, keyword co-occurrence, network centrality, and citation bursts, and then reviews application status and technical pathways across five thematic areas: intelligent construction, bridge engineering, tunnel engineering, smart water conservancy, and other infrastructure. Key findings include: rapid growth in publication volume after 2021, three dominant keyword clusters (model/system construction, structural health monitoring and sensing, and AI-enabled optimization/decision-making), and an evolution of research frontiers from concept introduction to engineering scenario deepening and further to three-dimensional reconstruction, knowledge fusion, and intelligent decision-making. The content analysis shows differentiated technical pathways across sub-domains and identifies data heterogeneity/interoperability as the most urgent bottleneck because it constrains model updating, cross-platform integration, and engineering-scale deployment. Future directions should focus on data standardization, hybrid modeling, platform interoperability, artificial intelligence empowerment, and full-lifecycle cross-system coordination. This review provides a quantitatively supported panoramic reference for digital twin research in civil engineering. Full article