Search Results (460)

Novel high-entropy perovskite oxide powders were synthesized using a sol-gel process. The B-site contained five cations: chromium, cobalt, iron, manganese, and nickel. The B-site cations were present on an equiatomic basis. The A-site cation was lanthanum, with calcium doping. The amount of A-site doping varied from 0 to 30 at%, yielding a composition of La_1−xCa_x(Co_0.2Cr_0.2Fe_0.2Mn_0.2Ni_0.2)O_3−δ. The resulting perovskite powders were pressurelessly sintered in air at 1400 °C for 2 h. Sintered densities were measured, and the grain structure was imaged via scanning electron microscopy to investigate the effect of doping. Samples were cut and polished, and their resistance was measured at varying temperatures in air to obtain the electrical conductivity and the mechanism that governs it. Plots of electrical conductivity as a function of composition and temperature indicate that the increased configurational entropy of the perovskite materials has a demonstrable effect. Full article

Under the “dual carbon” goals and rapid clean energy development, power grids face challenges including rapid load growth, uneven power flow distribution, and limited transmission capacity. This paper proposes a novel FACTS device with fault tolerance and switchable topology that maintains power flow control over multiple lines during N-1 faults, enhancing grid safety and economy. The paper establishes a steady-state mathematical model based on additional virtual nodes and provides power flow calculation methods to accurately reflect the device’s control characteristics. An entropy-weighted TOPSIS method was employed to establish a quantitative evaluation system for assessing the grid performance improvement after FACTS device integration. To address interaction issues among multiple flexible devices, an optimization planning model considering th3e coordinated effects of UPFC and VSC-HVDC was constructed. Multi-objective particle swarm optimization obtained Pareto solution sets, combined with the evaluation system, to determine the optimal configuration schemes. Considering wind power uncertainty and fault risks, we propose a system-level coordinated operation strategy. This strategy constructs probabilistic risk indicators and introduces topology switching control constraints. Using particle swarm optimization, it achieves a balance between safety and economic objectives. Simulation results in the Jiangsu power grid scenarios demonstrated significant advantages in enhancing the transmission capacity, optimizing the power flow distribution, and ensuring system security. Full article

In this work, the thermodynamics of molecular transport between two compartments connected by a nanochannel is investigated through an analysis of internal energy and entropy changes, with a focus on how these changes depend on intermolecular interaction strength. When interactions are weak, resembling gas-like behavior, entropy dominates and favors configurations in which molecules are evenly distributed between the two compartments, despite an increase in internal energy. In contrast, strong interactions, characteristic of liquid-like behavior, lead to dominant energetic contributions that favor configurations with molecules localized in a single compartment, despite entropy loss. Intermediate interaction strengths yield comparable entropic and energetic contributions that cancel each other out, resulting in oscillatory behavior between evenly distributed and localized configurations, as observed in previous work. This thermodynamic analysis reveals energy–entropy compensation, in which entropic and energetic contributions offset each other across different interaction strengths; notably, this compensatory relationship exhibits a linear trend. These findings provide insight into the thermodynamic origins of molecular transport behavior and highlight fundamental parallels between molecular transport and molecular binding, the latter being particularly relevant to molecular recognition and drug design. Full article

We investigated the thermodynamic topology of quantum-corrected AdS-Reissner-Nordström black holes in Kiselev spacetime using non-extensive entropy formulation derived from Loop Quantum Gravity (LQG). Through systematic analysis, we examined how the Tsallis parameter $\lambda$ influences topological charge classification with respect to various equation of state parameters. Our findings revealed a consistent pattern of topological transitions: for $\lambda =0.1$, the system exhibited a single topological charge $(\omega =-1)$ with total charge $W=-1$, as $\lambda$ increased to 0.8, the system transitioned to a configuration with two topological charges $(\omega =+1,-1)$ and total charge $W=0$. When $\lambda =1$, corresponding to the Bekenstein–Hawking entropy limit, the system displayed a single topological charge $(\omega =+1)$ with $W=+1$, signifying thermodynamic stability. The persistence of this pattern across different fluid compositions—from exotic negative pressure environments to radiation—demonstrates the universal nature of quantum gravitational effects on black hole topology. Full article

Ergonomic risk classification during manual lifting tasks is crucial for the prevention of workplace injuries. This study addresses the challenge of classifying lifting task risk levels (low, medium, and high risk, labeled as 0, 1, and 2) using multi-modal time-series data comprising raw facial landmarks and bio-signals (electrocardiography [ECG] and electrodermal activity [EDA]). Classifying such data presents inherent challenges due to multi-source information, temporal dynamics, and class imbalance. To overcome these challenges, this paper proposes a Multi-Adaptive Functional Neural Network (Multi-AdaFNN), a novel method that integrates functional data analysis with deep learning techniques. The proposed model introduces a novel adaptive basis layer composed of micro-networks tailored to each individual time-series feature, enabling end-to-end learning of discriminative temporal patterns directly from raw data. The Multi-AdaFNN approach was evaluated across five distinct dataset configurations: (1) facial landmarks only, (2) bio-signals only, (3) full fusion of all available features, (4) a reduced-dimensionality set of 12 selected facial landmark trajectories, and (5) the same reduced set combined with bio-signals. Performance was rigorously assessed using 100 independent stratified splits (70% training and 30% testing) and optimized via a weighted cross-entropy loss function to manage class imbalance effectively. The results demonstrated that the integrated approach, fusing facial landmarks and bio-signals, achieved the highest classification accuracy and robustness. Furthermore, the adaptive basis functions revealed specific phases within lifting tasks critical for risk prediction. These findings underscore the efficacy and transparency of the Multi-AdaFNN framework for multi-modal ergonomic risk assessment, highlighting its potential for real-time monitoring and proactive injury prevention in industrial environments. Full article

In this study, we introduce a novel interdisciplinary framework that applies concepts from statistical physics, specifically lattice-gas models, to the classical order lot-sizing problem in supply chain management. Traditional methods often rely on heuristic or deterministic approaches, which may fail to capture the inherently probabilistic and dynamic nature of decision-making across multiple periods. Drawing on structural parallels between inventory decisions and adsorption phenomena in physical systems, we constructed a mapping that represented order placements as particles on a lattice, governed by an energy function analogous to thermodynamic potentials. This formulation allowed us to employ analytical tools from statistical mechanics to identify optimal ordering strategies via the minimization of a free energy functional. Our approach not only sheds new light on the structural characteristics of optimal planning but also introduces the concept of configurational entropy as a measure of decision variability and robustness. Numerical simulations and analytical approximations demonstrate the efficacy of the lattice gas model in capturing key features of the problem and suggest promising avenues for extending the framework to more complex settings, including multi-item systems and time-varying demand. This work represents a significant step toward bridging physical sciences with supply chain optimization, offering a robust theoretical foundation for both future research and practical applications. Full article

The hydropower unit is the core of the hydropower station, and maintaining the safety and stability of the hydropower unit is the first essential priority of the operation of the hydropower station. However, the complex environment increases the probability of the failure of hydropower units. Therefore, aiming at the complex diversity of hydropower unit faults and the imbalance of fault data, this paper proposes a fault identification method based on modified fractional-order hierarchical fluctuation dispersion entropy (MFHFDE) and AdaBoost-stochastic configuration networks (AdaBoost-SCN). First, the modified hierarchical entropy and fractional-order theory are incorporated into the multiscale fluctuation dispersion entropy (MFDE) to enhance the responsiveness of MFDE to various fault signals and address its limitation of overlooking the high-frequency components of signals. Subsequently, the Euclidean distance is used to select the fractional order. Then, a novel method for evaluating the complexity of time-series signals, called MFHFDE, is presented. In addition, the AdaBoost algorithm is used to integrate stochastic configuration networks (SCN) to establish the AdaBoost-SCN strong classifier, which overcomes the problem of the weak generalization ability of SCN under the condition of an unbalanced number of signal samples. Finally, the features extracted via MFHFDE are fed into the classifier to accomplish pattern recognition. The results show that this method is more robust and effective compared with other methods in the anti-noise experiment and the feature extraction experiment. In the six kinds of imbalanced experimental data, the recognition rate reaches more than 98%. Full article

The Internet of Things (IoT) holds transformative potential in fields such as power grid optimization, defense networks, and healthcare. However, the constrained processing capacities and resource limitations of IoT networks make them especially susceptible to cyber threats. This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. We conducted a comparative analysis of three widely used DL models—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (biLSTM)—across four benchmark IoT intrusion detection datasets: BoTIoT, CiCIoT, ToNIoT, and WUSTL-IIoT-2021. Each model was assessed under balanced and imbalanced dataset configurations and evaluated using three loss functions (cross-entropy, focal loss, and dual focal loss). By analyzing model efficacy across these datasets, we highlight the importance of generalizability and adaptability to varied data characteristics that are essential for real-world applications. The results demonstrate that the CNN trained using the cross-entropy loss function consistently outperforms the other models, particularly on balanced datasets. On the other hand, LSTM and biLSTM show strong potential in temporal modeling, but their performance is highly dependent on the characteristics of the dataset. By analyzing the performance of multiple DL models under diverse datasets, this research provides actionable insights for developing secure, interpretable IoT systems that can meet the challenges of designing a secure IoT system. Full article

Traditional perforated duct designs fail to resolve the energy consumption-uniformity conflict in semi-closed greenhouses. To address this, we develop a CFD-RSM-NSGA-II framework that simultaneously minimizes velocity non-uniformity (CV-v), pressure loss (ΔP), and temperature variation (CV-t). Key parameters—hole diameter (6–10 mm), spacing (30–70 mm), and inlet velocity (4–8 m/s)—are co-optimized. Model validation showed that the mean relative errors were 8.6% for velocity, 2.3% for temperature, and pressure deviations below 5 Pa, with the response surface model achieving an R² of 0.9831 (p < 0.0001). Larger hole diameters improved CV-v, while wider spacings led to a decrease in uniformity. Pressure loss followed an opposite trend. Temperature variation was mostly affected by inlet velocity. Sensitivity analysis revealed that hole diameter was the most influential factor, followed by spacing and velocity, with a significant interaction between diameter and spacing. Using entropy-weighted TOPSIS coupled with NSGA-II, the optimization identified an optimal configuration (hole diameter = 9.0 mm, spacing = 65 mm, velocity = 7.0 m/s). This solution achieved a 58.8% reduction in CV-v, a 10.8% decrease in ΔP, and a 5.2% improvement in CV-t, while stabilizing inlet static pressure at 72.8 Pa. Critically, it reduced power consumption by 17.4%—directly lowering operational costs for farmers. The “larger diameter, wider spacing” strategy resolves energy-uniformity conflicts, demonstrating how integrated multi-objective process control enables efficient greenhouse ventilation. Full article

High-quality growth of the construction industry and an improved human settlements environment are essential to sustainable urbanization. Existing studies have paid limited systematic attention to the spatial and temporal dynamics of the coordinated development between the construction industry and human settlements, as well as the underlying factors driving regional disparities. This gap restricts the formulation of precise, differentiated sustainable policies tailored to regions at different development stages and with varying resource endowments. Southern China, characterized by pronounced spatial heterogeneity and unique development trends, offers a natural laboratory for examining the spatio-temporal interaction between these two dimensions. Using panel data for 15 southern provinces (2013–2022), we applied the entropy method, coupling coordination model, Dagum Gini coefficient, spatial trend surface analysis, gravity model, and grey forecasting to evaluate current conditions and predict future trends. The main findings are as follows. (1) The coupling coordination degree rose steadily, forming a stepped spatial pattern from the southwest through the center to the southeast. (2) The coupling coordination degree appears obvious polarization effect, presenting a spatial linkage pattern with Jiangsu-Shanghai-Zhejiang, Hubei-Hunan-Jiangxi, and Sichuan-Chongqing as the core of the three major clusters. (3) The overall Dagum Gini coefficient declined, but intra-regional disparities persisted: values were highest in the southeast, moderate in the center, and lowest in the southwest; inter-regional differences dominated the total inequality. (4) Forecasts for 2023–2027 suggest further improvement in the coupling coordination degree, yet spatial divergence will widen, creating a configuration of “eastern leadership, central catch-up acceleration, and differentiated southwestern development.” This study provides an evidence base for policies that foster high-quality construction sector growth and enhance the living environment. The findings of this study indicate that policymaking should prioritize promoting synergistic regional development, enhancing the radiating and driving role of core regions, and establishing a multi-level coordinated governance mechanism to bridge regional disparities and foster more balanced and sustainable development. Full article

Ecological security patterns (ESPs) are fundamental to safeguarding regional ecological integrity and enhancing human well-being. Consequently, research on conservation and restoration in critical regions is vital for ensuring ecological security and optimizing territorial ecological spatial configurations. Focusing on the Henan section of the Yellow River Basin, this study established the regional ESP and conservation–restoration framework through an integrated approach: (1) assessing four key ecosystem services—soil conservation, water retention, carbon sequestration, and habitat quality; (2) identifying ecological sources based on ecosystem service importance classification; (3) calculating a comprehensive resistance surface using the entropy weight method, incorporating key factors (land cover type, NDVI, topographic relief, and slope); (4) delineating ecological corridors and nodes using Linkage Mapper and the minimum cumulative resistance (MCR) theory; and (5) integrating ecological functional zoning to synthesize the final spatial conservation and restoration strategy. Key findings reveal: (1) 20 ecological sources, totaling 8947 km² (20.9% of the study area), and 43 ecological corridors, spanning 778.24 km, were delineated within the basin. Nineteen ecological barriers (predominantly located in farmland, bare land, construction land, and low-coverage grassland) and twenty-one ecological pinch points (primarily clustered in forestland, grassland, water bodies, and wetlands) were identified. Collectively, these elements form the Henan section’s Ecological Security Pattern (ESP), integrating source areas, a corridor network, and key regional nodes for ecological conservation and restoration. (2) Building upon the ESP and the ecological baseline, and informed by ecological functional zoning, we identified a spatial framework for conservation and restoration characterized by “one axis, two cores, and multiple zones”. Tailored conservation and restoration strategies were subsequently proposed. This study provides critical data support for reconciling ecological security and economic development in the Henan Yellow River Basin, offering a scientific foundation and practical guidance for regional territorial spatial ecological restoration planning and implementation. Full article

The location-allocation of nursing homes was examined by combining the entropy weight evaluation model and robust allocation model. The data of the elderly in Xuhui District in 2024 after the pandemic were used in this study. We constructed an evaluation index system by establishing the evaluation index principle of nursing homes’ location. Secondly, the polyhedral uncertainty set was used to predict the number of critical patients, and a model of robust configuration with capacity limitation and time constraints was constructed to minimize costs. The entropy weight method provided empirical results for the selection of nursing homes, and the robust configuration model ensured timely medical treatment. The feasibility and robustness of the mathematical model and solution method were verified, and the performance and advantages of the uncertain model over the deterministic model were proved. Full article

This article examines a hybrid generation of cryptographic keys, whose novelty lies in the fusion of a multiscale subkey generation with prime sieve and subkeys inspired by quantum mechanics. It combines number theory with techniques emulated and inspired by quantum mechanics, also based on two demons capable of dynamically modifying the cryptographic model. The integration is structured through the JDL. In fact, a specific information fusion model is used to improve security. As a result, the resulting key depends not only on the individual components, but also on the fusion path itself, allowing for dynamic and cryptographically agile configurations that remain consistent with quantum mechanics-inspired logic. The proposed approach, called quantum and prime information fusion (QPIF), couples a simulated quantum entropy source, derived from the numerical solution of the Schrödinger equation, with a multiscale prime number sieve to construct multilevel cryptographic keys. The multiscale sieve, based on recent advances, is currently among the fastest available. Designed to be compatible with classical computing environments, the method aims to contribute to cryptography from a different perspective, particularly during the coexistence of classical and quantum computers. Among the five key generation algorithms implemented here, the ultra-optimised QRNG offers the most effective trade-off between performance and randomness. The results are validated using standard NIST statistical tests. This hybrid framework can also provide a conceptual and practical basis for future work on PQC aimed at addressing the challenges posed by the quantum computing paradigm. Full article

This paper presents an enhanced hub location model tailored to port–hinterland logistics planning, grounded in the Capacitated Multiple-Allocation Hub Location Problem (CMAHLP). The formulation incorporates nonlinear cost structures, hub-specific operating costs, adaptive capacity constraints, and a feasibility condition based on the Social Net Present Value (NPVsocial) to support the design of intermodal freight networks under asymmetric spatial and socio-environmental conditions. The empirical case focuses on Spain, leveraging its strategic position between Asia, North Africa, and Europe. The model includes four major ports—Barcelona, Valencia, Málaga, and Algeciras—as intermodal gateways connected to the 47 provinces of peninsular Spain through calibrated cost matrices based on real distances and mode-specific road and rail costs. A Genetic Algorithm is applied to evaluate 120 scenarios, varying the number of active hubs (4, 6, 8, 10, 12), transshipment discounts (α = 0.2 and 1.0), and internal parameters. The most efficient configuration involved 300 generations, 150 individuals, a crossover rate of 0.85, and a mutation rate of 0.40. The algorithm integrates guided mutation, elitist reinsertion, and local search on the top 15% of individuals. Results confirm the central role of Madrid, Valencia, and Barcelona, frequently accompanied by high-performance inland hubs such as Málaga, Córdoba, Jaén, Palencia, León, and Zaragoza. Cities with active ports such as Cartagena, Seville, and Alicante appear in several of the most efficient network configurations. Their recurring presence underscores the strategic role of inland hubs located near seaports in supporting logistical cohesion and operational resilience across the system. The COVID-19 crisis, the Suez Canal incident, and the persistent tensions in the Red Sea have made clear the fragility of traditional freight corridors linking Asia and Europe. These shocks have brought renewed strategic attention to southern Spain—particularly the Mediterranean and Andalusian axes—as viable alternatives that offer both geographic and intermodal advantages. In this evolving context, the contribution of southern hubs gains further support through strong system-wide performance indicators such as entropy, cluster diversity, and Pareto efficiency, which allow for the assessment of spatial balance, structural robustness, and optimal trade-offs in intermodal freight planning. Southern hubs, particularly in coordination with North African partners, are poised to gain prominence in an emerging Euro–Maghreb logistics interface that demands a territorial balance and resilient port–hinterland integration. Full article

This study investigates the resilience of service supply chains and its role in promoting sustainable regional development in China. Based on data from 31 provinces between 2017 and 2023, we develop a multidimensional evaluation framework grounded in the structure, relationship, and subject model. Using entropy weighting, fuzzy-set qualitative comparative analysis, necessary condition analysis, and OLS regression, we identify three dominant configurations: cost-adaptive, cost-growth, and technology-driven. Among them, the cost-adaptive path remains statistically significant when tested against updated 2023 data. The findings reveal persistent regional disparities and demonstrate how context-specific strategies can shape service resilience under institutional and market variations. This study contributes to supply chain sustainability research by integrating recent empirical evidence with configurational analysis, offering practical policy insights for balancing efficiency, adaptability, and inclusive development. Full article