Search Results (442)

In the initial phase of large-scale disasters, delayed external relief supplies make scientific local emergency supply allocation crucial—not only for reducing casualties, but also for advancing sustainable disaster response, a key link in enhancing post-disaster resilience. Existing research mostly focuses on cross-regional material allocation while overlooking local challenges like low resource efficiency and unbalanced supply–demand dynamics. To tackle these limitations in the existing research, this study develops a multi-objective collaborative local emergency supply allocation model centered on sustainability. It uses an improved TOPSIS method to quantify the urgency of needs in disaster-stricken areas, prioritizing material distribution to vulnerable regions in line with the principle of “no vulnerable area left neglected in relief efforts”. The study also integrates the entropy weight method and analytic hierarchy process (AHP) to ensure rational indicator weighting, and designs a double-layer encoded genetic algorithm to obtain optimal allocation schemes that balance efficiency, fairness, and sustainability. Validated using the 2013 Ya’an Earthquake case study, the model outperforms traditional local allocation approaches: it boosts resource utilization efficiency by reducing material shortage rates, accelerates post-disaster recovery by shortening response times, and improves allocation fairness. Findings provide empirical support for the establishment of “local–external” collaborative rescue systems and sustainable disaster risk reduction frameworks. Empirical calculations using case-specific data and real-world estimates verify the model’s practical applicability: it meets the requirements for fair and rapid allocation needs, aligns with the goals of sustainable disaster management, and lowers the carbon footprint of relief operations by lessening reliance on long-distance external materials. Full article

This paper introduces an Improved Red-Billed Blue Magpie Optimizer (IRBMO) to enhance the Maximum Second-Order Cyclostationary Blind Deconvolution (CYCBD) method, which traditionally depends on manual, experience-based setting of its key parameters (filter length $L$ and cyclic frequency $\mathsf{\alpha }$). By adopting an Improved Envelope Spectrum Entropy (EK) as the fitness function, the IRBMO autonomously optimizes these parameters, eliminating the need for prior knowledge and improving its applicability in industrial settings. The Improved Red-Billed Blue Magpie algorithm is employed to adaptively optimize the penalty parameter and kernel function parameter of the support vector machine, thereby obtaining an optimal support vector machine model. By introducing fuzzy entropy theory, the feature vectors of filtered signals—processed by the Cyclostationary Blind Deconvolution method with optimal parameters—are extracted and used as input for the optimally parameterized support vector machine, achieving multi-fault classification for bogie bearings. The results show that the IRBMO-CYCBD method significantly enhances the periodic weak fault impulse components and improves the signal-to-noise ratio of the processed signal. Envelope spectrum analysis of the filtered signal allows for clear observation of shaft frequency components, as evidenced by the accurate identification of the 110 Hz fundamental frequency and its harmonic components at 220 Hz, 330 Hz, and 440 Hz in the spectrum. Simulation tests verify the efficacy of the IRBMO-CYCBD method in processing rolling bearing vibration signals under strong noise interference. Under laboratory conditions, simulation experiments were conducted by collecting vibration acceleration signals from rolling bearings in various states. The aforementioned method was applied for fault diagnosis, achieving a maximum diagnostic accuracy of 100%. Through repeated experiments, it was verified that this method meets the fault diagnosis requirements for rolling bearings in metro train bogies. Full article

High-entropy alloys (HEAs) have attracted significant attention due to their exceptional physical, chemical, and mechanical properties. The current development of HEAs primarily depends on time-consuming and costly trial-and-error approaches, which not only hinder the efficient exploration of new compositions but also result in unnecessary resource and energy consumption, thereby negatively affecting sustainable development and production. To address this challenge, this study introduces a machine learning-based methodology for predicting the yield strengths of various HEA compositions. The model was trained using 181 data points and achieved an R² performance score of 0.85. To further assess its reliability and generalization capability, the model was validated using external data not included in the collected dataset. The validation was performed across four categories: modified Cantor alloys, refractory HEAs, eutectic HEAs, and other HEAs. The predicted yield strength trends were found to align with the actual experimental trends, demonstrating the model’s robust performance across various categories of HEAs. The proposed machine learning approach is expected to facilitate the combinatorial design of HEAs, thereby enabling efficient optimization of compositions and accelerating the development of novel alloys. Moreover, it has the potential to serve as a guideline for sustainable alloy design and environmentally conscious production in future HEA development. Full article

In the spacetime of a charged rotating accelerated black hole, the dynamics equations of fermions and bosons are modified by Lorentz invariance violation (LIV). The correction effects of LIV on the quantum tunneling radiation of this black hole are investigated. New expressions for the quantum tunneling rate, Hawking temperature, and Bekenstein–Hawking entropy of this black hole, which depend on the charge parameter and acceleration parameter, are derived, incorporating LIV correction terms. The physical implications of these results are discussed in depth. Full article

Magnetic Barkhausen noise (MBN) represents a powerful non-destructive testing and material characterization methodology enabling quantitative assessment of microstructural features, mechanical properties, and stress states in ferromagnetic materials. This comprehensive review synthesizes recent advances spanning theoretical foundations, sensor design, signal processing methodologies, and industrial applications. The physical basis rooted in domain wall dynamics and statistical mechanics provides rigorous frameworks for interpreting MBN signals in terms of grain structure, dislocation density, phase composition, and residual stress. Contemporary instrumentation innovations including miniaturized sensors, multi-parameter systems, and high-entropy alloy cores enable measurements in challenging environments. Advanced signal processing techniques—encompassing time-domain analysis, frequency-domain spectral methods, time–frequency transforms, and machine learning algorithms—extract comprehensive material information from raw Barkhausen signals. Deep learning approaches demonstrate superior performance for automated material classification and property prediction compared to traditional statistical methods. Industrial applications span manufacturing quality control, structural health monitoring, railway infrastructure assessment, and predictive maintenance strategies. Key achievements include establishing quantitative correlations between material properties and stress states, with measurement uncertainties of ±15–20 MPa for stress and ±20 HV for hardness. Emerging challenges include standardization imperatives, characterization of advanced materials, machine learning robustness, and autonomous system integration. Future developments prioritizing international standards, physics-informed neural networks, multimodal sensor fusion, and wireless monitoring networks will accelerate industrial adoption supporting safe, efficient engineering practice across diverse sectors. Full article

In order to effectively reveal the nonlinear characteristics of a dish concentrating solar thermal power system (DCSTPS) under pulsating wind-induced loads, a fluid simulation model of the DCSTPS was established, and the simulated pulsating winds were developed via the user-defined function (UDF) combined with the autoregressive (AR) model using MATLAB (R2015b). And based on the fluid simulation calculations of the DCSTPS, the time-range data of the relevant wind vibration coefficients under different working conditions were obtained. The research results show the following: (1) When the altitude angle α is 0° or 180° due to the azimuth angle β = 0°, the maximum values of their drag coefficient C_x, lateral force coefficient C_y, and lift coefficient C_z are similar, and the maximum of rolling moment coefficient C_Mx is significantly smaller than the values at the other two angles; the maximum of the pitch moment coefficient C_My and maximum of the azimuth moment coefficient C_Mz are significantly larger than the values of the other two angles. (2) The increase in altitude angle α leads to a reduction in the drag coefficient C_x, an increase in the lift force coefficient C_z, and an increase of the pitch moment C_Mx. Moreover, an improved phase space delay reconstruction method was developed to calculate the delay time, Lyapunov exponent, and Kolmogorov entropy of the DCSTPS, and the research results show that (1) the maximum Lyapunov exponent and Kolmogorov entropy of the DCSTPS are greater than zero under the action of pulsating wind; (2) the action of pulsating wind will cause increases in the maximum Lyapunov exponent and Kolmogorov entropy of the DCSTPS and will accelerate the divergence speed of the DCSTPS trajectory; and (3) the time for the DCSTPS to enter the chaotic state will be shortened, while the time of entering a chaotic state and degree of subsequent chaotic states will be significantly affected by relevant wind vibration coefficients but without regularity. Full article

Amidst the accelerating trend of population aging, addressing regional disparities in basic elderly care services (BECS for short) and identifying the key obstacles to their development have become crucial prerequisites for development. Taking urgent transformation measures is indispensable for enhancing the quality of fundamental senior care provisions and advancing the attainment of the United Nations Sustainable Development Goals (SDGs for short) by 2030. However, the extant literature does not have a sufficient understanding of the evolution of differences, spatial correlations, and sources of obstacles. Therefore, this paper takes the period from 2021 to 2023 as the investigation period and comprehensively applies the entropy weight method, Dagum Gini coefficient, kernel density estimation, Moran Index, and obstacle degree model to conduct a systematic analysis of BECS in China. Quantitative results obtained from the research demonstrate that the level of BECS in China follows the pattern of eastern > western > central > northeastern regions. The overall difference slightly increases, and the differences within and between regions vary. The kernel density estimation results are highly consistent with the current landscape of the level of BECS in China, and the spatial correlation and aggregation characteristics are obvious. It was also found that the main obstacles in the quasi-measurement layer (including the indicator layer) were concentrated in the dimension of welfare subsidies. Based on this, a policy combination proposal is put forward in terms of strengthening the construction of a multi-subject supply network, promoting the cross-regional coordinated development of human, financial, and material factors, and enhancing the government’s governance capacity, with the aim of increasing Chinese contributions to improving the level of BECS and achieving the United Nations 2030 Sustainability Goals on schedule. Full article

Against the backdrop of the carbon peaking and neutrality (“dual-carbon”) goals and evolving new-type power system dispatch, the share of pumped-storage hydropower (PSH) in power systems continues to increase, imposing stricter requirements on units for higher cycling frequency, greater operational flexibility, and rapid, stable startup and shutdown. Focusing on the entire hot-standby-to-generation transition of a PSH plant, a full-flow-path three-dimensional transient numerical model encompassing kilometer-scale headrace/tailrace systems, meter-scale runner and casing passages, and millimeter-scale inter-component clearances is developed. Three-dimensional unsteady computational fluid dynamics are determined, while the surge tank free surface and gaseous phase are captured using a volume-of-fluid (VOF) two-phase formula. Grid independence is demonstrated, and time-resolved validation is performed against the experimental model–test operating data. Internal instability structures are diagnosed via pressure fluctuation spectral analysis and characteristic mode identification, complemented by entropy production analysis to quantify dissipative losses. The results indicate that hydraulic instabilities concentrate in the acceleration phase at small guide vane openings, where misalignment between inflow incidence and blade setting induces separation and vortical structures. Concurrently, an intensified adverse pressure gradient in the draft tube generates an axial recirculation core and a vortex rope, driving upstream propagation of low-frequency pressure pulsations. These findings deepen our mechanistic understanding of hydraulic transients during the hot-standby-to-generation transition of PSH units and provide a theoretical basis for improving transitional stability and optimizing control strategies. Full article

In the context of privacy-preserving face recognition systems, entropy plays a crucial role in determining the efficiency and security of computational processes. However, existing schemes often encounter challenges such as inefficiency and high entropy in their computational models. To address these issues, we propose a privacy-preserving face recognition method based on the Face Feature Coding Method (FFCM) and symmetric homomorphic encryption, which reduces computational entropy while enhancing system efficiency and ensuring facial privacy protection. Specifically, to accelerate the matching speed during the authentication phase, we construct an N-ary feature tree using a neural network-based FFCM, significantly improving ciphertext search efficiency. Additionally, during authentication, the server computes the cosine similarity of the matched facial features in ciphertext form using lightweight symmetric homomorphic encryption, minimizing entropy in the computation process and reducing overall system complexity. Security analysis indicates that critical template information remains secure and resilient against both passive and active attacks. Experimental results demonstrate that the facial authentication efficiency with FFCM classification is 4% to 6% higher than recent state-of-the-art solutions. This method provides an efficient, secure, and entropy-aware approach for privacy-preserving face recognition, offering substantial improvements in large-scale applications. Full article

High-entropy alloys (HEAs) are a class of multi-principal element materials composed of five or more elements in near-equimolar ratios. This unique compositional design generates high configurational entropy, which stabilizes simple solid solution phases and reduces the tendency for intermetallic compound formation. Unlike conventional alloys, HEAs exhibit a combination of properties that are often mutually exclusive, such as high strength and ductility, excellent thermal stability, superior corrosion and oxidation resistance. The exceptional mechanical performance of HEAs is attributed to mechanisms including lattice distortion strengthening, sluggish diffusion, and multiple active deformation pathways such as dislocation slip, twinning, and phase transformation. Advanced characterization techniques such as transmission electron microscopy (TEM), atom probe tomography (APT), and in situ mechanical testing have revealed the complex interplay between microstructure and properties. Computational approaches, including CALPHAD modeling, density functional theory (DFT), and machine learning, have significantly accelerated HEA design, allowing prediction of phase stability, mechanical behavior, and environmental resistance. Representative examples include the FCC-structured CoCrFeMnNi alloy, known for its exceptional cryogenic toughness, Al-containing dual-phase HEAs, such as AlCoCrFeNi, which exhibit high hardness and moderate ductility and refractory HEAs, such as NbMoTaW, which maintain ultra-high strength at temperatures above 1200 °C. Despite these advances, challenges remain in controlling microstructural homogeneity, understanding long-term environmental stability, and developing cost-effective manufacturing routes. This review provides a comprehensive and analytical study of recent progress in HEA research (focusing on literature from 2022–2025), covering thermodynamic fundamentals, design strategies, processing techniques, mechanical and chemical properties, and emerging applications, through highlighting opportunities and directions for future research. In summary, the review’s unique contribution lies in offering an up-to-date, mechanistically grounded, and computationally informed study on the HEAs research-linking composition, processing, structure, and properties to guide the next phase of alloy design and application. Full article

This paper proposes a hardware–software co-design for adaptive lossless compression based on Hybrid Arithmetic–Huffman Coding, a table-driven approximation of arithmetic coding that preserves near-optimal compression efficiency while eliminating the multiplicative precision and sequential bottlenecks that have traditionally prevented arithmetic coding deployment in resource-constrained embedded systems. The compression pipeline is partitioned as follows: flexible software on the processor core dynamically builds and adapts the prefix coding (usually Huffman Coding) frontend for accurate probability estimation and binarization; the resulting binary stream is fed to a deeply pipelined systolic hardware accelerator that performs binary arithmetic coding using pre-calibrated finite state transition tables, dedicated renormalization logic, and carry propagation mitigation circuitry instantiated in on-chip memory. The resulting implementation achieves compression ratios consistently within 0.4% of the theoretical entropy limit, multi-gigabit per second throughput in 28 nm/FinFET nodes, and approximately 68% lower energy per compressed byte than optimized software arithmetic coding, making it ideally suited for real-time embedded vision, IoT sensor networks, and edge multimedia applications. Full article

Climate change accelerates biodiversity loss, threatening ecosystems worldwide. Using predictive models, such as the maximum entropy model (Maxent), allows us to identify changes in species distribution and guide conservation strategies. This study aims to model the current and future distribution of Anthemis pedunculata subsp. Atlantica and Anthemis pedunculata subsp. pedunculata in Mediterranean regions through MaxEnt modeling with bioclimatic predictors. Using the MaxEnt algorithm, we combine bioclimatic variables and 49 occurrence locations of Anthemis pedunculata subsp. pedunculata and 13 occurrence locations of Anthemis pedunculata subsp. atlantica. The future distribution of the species is projected using MIROC6 model simulations under emission scenario SSP5-8.5 for 2030 and 2050. The current model predicted approximately 99,330,066 ha as a suitable habitat for Anthemis pedunculata subsp. pedunculata. Projections for the future range exhibited a gradual increase in the suitable area in 2030 by 144,365,562 ha and 2050 by 147,335,265 ha. The current model predicted approximately 201,179,880 ha as a suitable habitat for Anthemis pedunculata subsp. atlantica. Projections for the future range exhibited a gradual enhancement of the suitable area in 2030 by 213,898,608 ha and 2050 by 229,357,062. Our results provide further evidence of the negative impact of climate change on these endemic species and emphasize the importance of their conservation. This study provides information that could strengthen the protection of these species and identify potential protection areas. Full article

The rapid advancement of machine learning (ML) has ushered in a new era for materials science, particularly in the design and understanding of high-entropy alloys (HEAs). As a class of compositionally complex materials, HEAs have greatly benefited from the predictive power and computational efficiency of ML techniques. Recent years have witnessed remarkable expansion in the scope and sophistication of ML applications to HEAs, spanning from phase formation prediction to property and microstructure modeling. These developments have significantly accelerated the discovery and optimization of novel HEA systems. This review provides a comprehensive overview of the current progress and emerging trends in applying ML to HEA research. We first discuss phase prediction methodologies, encompassing both pure ML frameworks and hybrid physics-informed models. Subsequently, we summarize advances in ML-driven prediction of HEA properties and microstructural features. Further sections highlight the role of ML in exploring vast compositional spaces, guiding the design of high-performance HEAs, and optimizing existing alloys through data-driven algorithms. Finally, the challenges and limitations of current approaches are critically examined, and future directions are proposed toward interpretable models, mechanistic understanding, and efficient exploration of the HEA design space. Full article

Reliability evaluation of power distribution systems is computationally intensive, as standard Monte Carlo simulations require extensive sampling to accurately estimate rare event-based indices like SAIDI and SAIFI. This paper introduces a multi-objective cross-entropy approach for reliability evaluation of power distribution systems, aiming to accelerate reliability evaluation by optimizing importance sampling reference parameters. The multi-objective approach aims to optimize a set of objective functions related to systemic and load point reliability indices. A deduction of an analytical solution for the optimization of reference parameters of the cross-entropy method is developed, taking into account the standard hypotheses used in reliability assessments. The proposed method has been validated on a real 181-node Brazilian distribution feeder. Results show that the proposed approach can accelerate the convergence of estimates for reliability indices in comparison with the crude Monte Carlo approach and the single-objective CE method. Full article

FeCoNiCrAl and FeCoNiCrAlScY high-entropy coatings were fabricated via electron beam physical vapor deposition. The microstructure and short-term isothermal oxidation behavior of the coatings were compared. Sc and Y inhibited coating element diffusion to the superalloy substrate and formed co-precipitated phases during coating manufacturing. The Sc/Y co-doped coating exhibited accelerated phase transformation from θ- to α-Al₂O₃ as compared to the undoped one. The effect mechanism associated with the nucleation of α-Al₂O₃ was discussed. The preferential formation of Sc/Y-rich oxides promoted the nucleation of α-Al₂O₃ beneath them, and the θ-α phase evolution process was directly skipped, which suppressed the rapid growth of θ-Al₂O₃ and the initial formation of cracks in the alumina film and provided the FeCoNiCrAl high-entropy coating with an improved oxidation property in the early oxidation stage. Full article