Search Results (215)

Understanding the climate change impacts on the geographical distribution of plant species is vital for biodiversity conservation. Lycium ruthenicum, a second-grade protected plant in China, holds considerable medicinal and ecological value; however, its potential habitat distribution under climate change remains uncertain. By utilizing occurrence records and geographical and environmental data, we optimized a maximum entropy model and evaluated the current and future potential habitat suitability of L. ruthenicum in China. The main results were as follows: (1) The distribution of L. ruthenicum was primarily influenced by the precipitation of the warmest quarter, topsoil base saturation, precipitation seasonality, precipitation of the coldest quarter, and minimum temperature of the coldest month. (2) Under the current conditions, the potential suitable area of L. ruthenicum was approximately 2.25 × 10⁶ km² in China, predominantly distributed in Xinjiang, Qinghai, Gansu, Ningxia, and Inner Mongolia. (3) An obvious reduction in the predicted suitable area of L. ruthenicum was found under future climate scenarios, with the centroid primarily shifting northeastward. These findings highlight the potential vulnerability of this medicinally and ecologically important species and underscore the urgent need for targeted conservation strategies to ensure its long-term survival. Full article

After the original support system in the auxiliary transportation roadway of the northern wing of the Zhaoxian Mine failed, the extent of damage and deformation varied significantly across different sections of the drift. A single support method could not meet the engineering requirements. Therefore, this paper conducted research on the classification of roadway damage and zoning repair. The overall damage characteristics of the roadway are described by three indicators: roadway deformation, development of rock mass fractures, and water seepage conditions. These are further refined into nine secondary indicators. In summary, a rock mass damage combination weighting evaluation model based on the FAHP–entropy weight TOPSIS method is proposed. According to this model, the degree of damage to the roadway is divided into five grades. After analyzing the damage conditions and support requirements at each grade, corresponding zoning repair plans are formulated by adjusting the parameters of bolts, cables, channel steel beams, and grouting materials. At the same time, the reliability of partition repair is verified using FLAC3D 6.0 numerical simulation software. Field monitoring results demonstrated that this approach not only met the support requirements for the roadway but also improved the utilization rate of support materials. This provides valuable guidance for the design of support systems for roadways with similar heterogeneous damage. Full article

This paper investigates the nonlinear dynamics behavior and practical realization of a V/Heart-shape chaotic system. Nonlinear analysis contemporary tools, including bifurcation diagram, Lyapunov exponents, phase portraits, power spectral density (PSD) bicoherence, and spectral entropy (SE), are employed to investigate the system’s complex dynamical behaviors. To discover the system’s versatility, two case studies are presented by varying key system parameters, revealing various strange attractors. The system is modeled and implemented using an industrial-grade programmable logic controller (PLC) with structured text (ST) language, enabling robust hardware execution. The dynamics of the chaotic system are simulated, and the results are rigorously compared with experimental data from laboratory hardware implementations, demonstrating excellent agreement. The results indicate the potential usage of the proposed chaotic system for advanced industrial applications, secure communication, and dynamic system analysis. The findings confirm the successful realization of the V-shape and Heart-shape Chaotic Systems on PLC hardware, demonstrating consistent chaotic behavior across varying parameters. This practical implementation bridges the gap between theoretical chaos research and real-world industrial applications. Full article

Understanding the coupled and coordinated development of China’s urban and rural basic education systems is crucial for fostering their interaction and synergistic growth. Using China’s provincial panel data from 2011 to 2023, this study measures the coupled and coordinated development level of urban–rural basic education (CCD-URBE) via the entropy weight method, G1-method and coupling coordination degree model. On this basis, the Dagum Gini coefficient decomposition method, traditional and spatial Markov chain models, as well as convergence test models are employed for empirical research. The results show that: (1) During the study period, the CCD-URBE across the nation and the four major regions improves significantly. Both intra-regional and inter-regional disparities show a consistent downward trend. Inter-regional disparities are the main source of the overall disparities, and the contribution rate of transvariation density to the overall disparities exhibits the most significant increase. (2) The CCD-URBE demonstrates strong stability, as most regions tend to maintain their original CCD-URBE grades. Meanwhile, neighborhood grades moderate the local transition probability significantly. Neighborhoods with high CCD-URBE promote the upward improvement of the local CCD-URBE, while those with low CCD-URBE inhibit it. (3) The CCD-URBE across the nation and the four major regions shows obvious trends of σ-convergence, absolute β-convergence, and conditional β-convergence. The central region, which has lower CCD-URBE, exhibits higher convergence speed. Based on these findings, targeted policy implications are derived. Full article

We aimed to investigate the utility of peritumoral edema-derived radiomic features from magnetic resonance imaging (MRI) image weights and fused MRI sequences for enhancing the performance of machine learning-based glioma grading. The present study utilized the Multimodal Brain Tumor Segmentation Challenge 2023 (BraTS 2023) dataset. Laplacian Re-decomposition (LRD) was employed to fuse multimodal MRI sequences. The fused image quality was evaluated using the Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. A comprehensive set of radiomic features was subsequently extracted from peritumoral edema regions using PyRadiomics. The Boruta algorithm was applied for feature selection, and an optimized classification pipeline was developed using the Tree-based Pipeline Optimization Tool (TPOT). Model performance for glioma grade classification was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC) parameters. Analysis of fused image quality metrics confirmed that the LRD method produces high-quality fused images. From 851 radiomic features extracted from peritumoral edema regions, the Boruta algorithm selected different sets of informative features in both standard MRI and fused images. Subsequent TPOT automated machine learning optimization analysis identified a fine-tuned Stochastic Gradient Descent (SGD) classifier, trained on features from T₁Gd+FLAIR fused images, as the top-performing model. This model achieved superior performance in glioma grade classification (Accuracy = 0.96, Precision = 1.0, Recall = 0.94, F1-Score = 0.96, AUC = 1.0). Radiomic features derived from peritumoral edema in fused MRI images using the LRD method demonstrated distinct, grade-specific patterns and can be utilized as a non-invasive, accurate, and rapid glioma grade classification method. Full article

This study quantifies ship-type performance indicators by training intelligent agents to evaluate and score vessels. The Analytic Hierarchy Process (AHP) is then applied to assess the internal consistency of the collected data, ensuring its authenticity and validity. Subsequently, the entropy weight method is employed to objectively determine the significance of each indicator in ship-type decision-making. Finally, COSCO (China COSCO Shipping Corporation Limited) Shipping’s capacity gap reflects the results of the methodology: the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) technique ranks all feasible ship-type combinations, presenting their relative merits through quantitative results. A standardized grading system is further proposed to evaluate these combinations systematically. Ultimately, the 10 most suitable solutions are identified—none achieving the theoretical maximum rating of Grade 10—demonstrating room for improvement in vessel performance. Full article

Amidst the global energy structure transition and intensification of climate warming, the temperature control targets of the Paris Agreement and China’s “dual carbon” goals have driven the rapid development of building-integrated photovoltaics (BIPVs). However, solar cells in BIPV systems may produce exhaust gases that affect local urban air quality if exposed to extreme environmental conditions such as high temperatures during operation. In this study, eight air quality monitoring points were established around the BIPV system at Shenyang Jianzhu University as the experimental group, along with one additional air quality monitoring point serving as a control group. The concentrations of four air pollutant indicators (PM_2.5, PM₁₀, SO₂, and NO₂) were monitored continuously for 14 days. The weight of each indicator was calculated using the principle of information entropy, and the air quality evaluation grades were determined by combining the homomorphic inverse correlation function. The Entropy-Weighted Set Pair Analysis model was applied to evaluate the air quality of the BIPV system at Shenyang Jianzhu University. The results indicated that due to the high concentrations of SO₂ and NO₂, the Air Quality Index (AQI) grade at Shenyang Jianzhu University was classified as “light pollution.” Corresponding recommendations were proposed to promote the sustainable development of urban BIPV. Simultaneously, the evaluation results of the Entropy-Weighted Set Pair Analysis model were similar to those obtained using other methods, demonstrating the feasibility of this evaluation model for assessing the impact on air quality. Full article

Deep foundation pit construction faces significant safety challenges—including frequent accidents and severe disaster consequences—due to inherent complexity and uncertainty. Conventional risk assessment methods cannot adequately evaluate these complex engineering systems. This study introduces the concept of resilience to analyze safety issues during the deep foundation pits construction process and develops a safety resilience evaluation model based on the extension cloud model theory. First, based on the characteristics of the deep foundation pit construction process and the four stages of safety resilience, a safety resilience curve for deep foundation pit construction is plotted. Then, using multi-text analysis, an evaluation indicator list for deep foundation pit construction safety resilience is constructed, comprising 4 primary indicators and 24 secondary indicators. Next, based on the extension cloud model theory, the IF-AHP and entropy weight methods are combined to calculate the cloud membership degrees, systematically constructing a safety resilience evaluation model for deep foundation pit construction. Taking the Nanchang HH Center deep foundation pit project as an example, the model’s effectiveness and accuracy are validated. The results indicate that the safety resilience level of this deep foundation pit project is Grade IV, consistent with the actual engineering conditions, thereby validating the scientific validity of this method. This study innovatively applies the concepts of safety resilience and the extension cloud model to deep foundation pit construction assessment, providing a suitable method for evaluating safety risks in deep foundation pit construction projects. The model assists decision-makers in appropriate risk classification and scientific risk prevention strategies, enhances the safety management system for deep foundation pit construction, and even promotes the sustainable development of the construction industry. Full article

The current green building assessment system suffers from issues such as insufficient coverage of smart indicators, significant biases in subjective weighting, and weak dynamic adaptability, which restrict the scientific promotion of green buildings. This study focuses on the gaps in the quantitative assessment of smart technologies in China’s green building evaluation standards (such as the current Green Building Evaluation Standard). While domestic standards are relatively well-established in traditional dimensions like energy conservation and environmental protection, there are fragmentation issues in the assessment of smart technologies such as the Internet of Things (IoT) and BIM. Moreover, the coverage of smart indicators in non-civilian building fields is significantly lower than that of international systems such as LEED and BREEAM. This study determined the basic framework of the evaluation indicator system through the Delphi method. Drawing on international experience and contextualized within China’s (GB/T 50378-2019) standards, it systematically integrated secondary indicators including “smart security,” “smart energy,” “smart design,” and “smart services,” and constructed dual-drive evaluation dimensions of “greenization + smartization.” This elevated the proportion of the smartization dimension to 35%, filling the gap in domestic standards regarding the quantitative assessment of smart technologies. In terms of research methods, combined weighting using the Analytic Hierarchy Process (AHP) and entropy weight method was adopted to balance subjective and objective weights and reduce biases (the resource conservation dimension accounted for 39.14% of the combined weights, the highest proportion). By integrating the grey clustering model with the whitening weight function to handle fuzzy information, evaluations were categorized into four grey levels (D/C/B/A), enhancing the dynamic adaptability of the system. Case verification showed that Project A achieved a comprehensive evaluation score of 5.223, with a grade of B. Among its indicators, smart-related ones such as “smart energy” (37.17%) and “smart design” (37.93%) scored significantly higher than traditional indicators, verifying that the system successfully captured the project’s high performance in smart indicators. The research results indicate that the efficient utilization of resources is the core goal of green buildings. Especially under pressures of energy shortages and carbon emissions, energy conservation and resource recycling have become key priorities. The evaluation system constructed in this study can provide theoretical guidance and technical support for the promotion, industrial upgrading, and sustainable development of green buildings (including non-civilian buildings) under the dual-carbon goals. Its characteristic of “dynamic monitoring + smart integration” forms differentiated complementarity with international standards, making it more aligned with the needs of China’s intelligent transformation of buildings. Full article

The graded management of structured sensitive data has become a key challenge in data security governance, particularly amid digital transformation in sectors such as government, finance, and healthcare. The existing methods suffer from limited generalization, low efficiency, and reliance on static rules. This paper proposes PPM-SACG, a privacy preference matrix-based model for sensitive attribute classification and grading. The model adopts a three-stage architecture: (1) composite sensitivity metrics are derived by integrating information entropy and group privacy preferences; (2) domain knowledge-guided clustering and association rule mining improve classification accuracy; and (3) mutual information-based hierarchical clustering enables dynamic grouping and grading, incorporating high-sensitivity isolation. Experiments using real-world vehicle management data (50 attributes, 3000 records) and user privacy surveys verify the method’s effectiveness. Compared with existing approaches, PPM-SACG doubles computational efficiency and supports scenario-aware deployment, offering enhanced compliance and practicality for structured data governance. Full article

This study integrates EEG technology with the VIKOR-GRA model to construct a quantitative method for assessing emotional responses to rural landscapes. Taking 94 scenes from Xiedian Ancient Village in Macheng City, Hubei Province, as the research objects, arousal (Arousal) and valence (Valence) were calculated based on the power ratio of α and β frequency bands. The entropy weight method was employed to determine weights and compute group utility value (S), individual regret value (R), and compromise solution (Q). The results indicate that 16 scenes had Q values > 0.75 (Grade IV), reflecting poor emotional experiences, with significantly lower arousal (−2.15 ± 0.38) and valence (−0.87 ± 1.02). Vegetation morphology and water visibility were identified as the primary limiting factors, while graphic symbols and historical culture exhibited strong positive feedback. Optimization strategies are proposed, providing a quantifiable technical pathway for the renewal of rural heritage landscapes. Full article

Digital livestock farming faces a critical deployment challenge: bridging the gap between cutting-edge AI algorithms and practical implementation in resource-constrained agricultural environments. While deep learning models demonstrate exceptional accuracy in laboratory settings, their translation to operational farm systems remains limited by computational constraints, connectivity issues, and user accessibility barriers. Dairy DigiD addresses these challenges through a novel edge-cloud AI framework integrating YOLOv11 object detection with DenseNet121 physiological classification for cattle monitoring. The system employs YOLOv11-nano architecture optimized through INT8 quantization (achieving 73% model compression with <1% accuracy degradation) and TensorRT acceleration, enabling 24 FPS real-time inference on NVIDIA Jetson edge devices while maintaining 94.2% classification accuracy. Our key innovation lies in intelligent confidence-based offloading: routine detections execute locally at the edge, while ambiguous cases trigger cloud processing for enhanced accuracy. An entropy-based active learning pipeline using Roboflow reduces the annotation overhead by 65% while preserving 97% of the model performance. The Gradio interface democratizes system access, reducing technician training requirements by 84%. Comprehensive validation across ten commercial dairy farms in Atlantic Canada demonstrates robust performance under diverse environmental conditions (seasonal, lighting, weather variations). The framework achieves mAP@50 of 0.947 with balanced precision-recall across four physiological classes, while consuming 18% less energy than baseline implementations through attention-based optimization. Rather than proposing novel algorithms, this work contributes a systems-level integration methodology that transforms research-grade AI into deployable agricultural solutions. Our open-source framework provides a replicable blueprint for precision livestock farming adoption, addressing practical barriers that have historically limited AI deployment in agricultural settings. Full article

Existing risk assessment approaches for soft rock tunnels in fault-fractured zones typically employ single weighting schemes, inadequately integrate subjective and objective weights, and fail to define clear risk. This study proposes a risk-grading methodology that integrates an enhanced game theoretic weight-balancing algorithm with an optimized cloud entropy extension cloud model. Initially, a comprehensive indicator system encompassing geological (surrounding rock grade, groundwater conditions, fault thickness, dip, and strike), design (excavation cross-section shape, excavation span, and tunnel cross-sectional area), and support (support stiffness, support installation timing, and construction step length) parameters is established. Subjective weights obtained via the analytic hierarchy process (AHP) are combined with objective weights calculated using the entropy, coefficient of variation, and CRITIC methods and subsequently balanced through a game theoretic approach to mitigate bias and reconcile expert judgment with data objectivity. Subsequently, the optimized cloud entropy extension cloud algorithm quantifies the fuzzy relationships between indicators and risk levels, yielding a cloud association evaluation matrix for precise classification. A case study of a representative soft rock tunnel in a fault-fractured zone validates this method’s enhanced accuracy, stability, and rationality, offering a robust tool for risk management and design decision making in complex geological settings. Full article

The large-scale integration of volatile and intermittent renewables necessitates greater flexibility in the power system. Improving this flexibility is key to achieving a high proportion of renewable energy consumption. In this context, the scientific selection of energy storage technology is of great significance for the construction of new power systems. From the perspective of life cycle cost analysis, this paper conducts an economic evaluation of four mainstream energy storage technologies: lithium iron phosphate battery, pumped storage, compressed air energy storage, and hydrogen energy storage, and quantifies and compares the life cycle cost of multiple energy storage technologies. On this basis, a three-dimensional multi-energy storage comprehensive evaluation indicator system covering economy, technology, and environment is constructed. The improved grade one method and entropy weight method are used to determine the comprehensive performance, and the fuzzy comprehensive evaluation method is used to carry out multi-attribute decision-making on the multi-energy storage technology in the source, network, and load scenarios. The results show that pumped storage and compressed air energy storage have significant economic advantages in long-term and large-scale application scenarios. With its fast response ability and excellent economic and technical characteristics, the lithium iron phosphate battery has the smallest score change rate (15.2%) in various scenarios, showing high adaptability. However, hydrogen energy storage technology still lacks economic and technological maturity, and breakthrough progress is still needed for its wide application in various application scenarios in the future. Full article

Rizhao Reservoir, Shandong Province, China, as a key regional water supply hub, provides water for domestic, industrial, and agricultural uses in and around Rizhao City by intercepting runoff, which plays a central role in guaranteeing water supply security and supporting regional development. This study systematically collected 66 surface water samples to elucidate the hydrochemical characteristics within the reservoir area, identify the principal influencing factors, and clarify the sources of dissolved ions, aiming to enhance the understanding of the prevailing water quality conditions. A systematic analysis of hydrochemical facies, solute provenance, and governing processes in the study area’s surface water was conducted, employing an integrated mathematical and statistical approach, comprising Piper trilinear diagrams, correlation analysis, and ionic ratios. Meanwhile, the entropy weight-based water quality index (EWQI) and irrigation water quality evaluation methods were employed to assess the surface water quality in the study area quantitatively. Analytical results demonstrate that the surface water system within the study area is classified as freshwater with circumneutral to slightly alkaline properties, predominantly characterized by Ca-HCO₃ and Ca-Mg-SO₄-Cl hydrochemical facies. The evolution of solute composition is principally governed by rock–water interactions, whereas anthropogenic influences and cation exchange processes exert comparatively minor control. Dissolved ions mostly originate from silicate rock weathering, carbonate rock dissolution, and sulfate mineral dissolution processes. Potability assessment via the entropy-weighted water quality index (EWQI) classifies surface waters in the study area as Grade I (Excellent), indicating compliance with drinking water criteria under defined boundary conditions. Irrigation suitability analysis confirms minimal secondary soil salinization risk during controlled agricultural application, with all samples meeting standards for direct irrigation use. Full article