Search Results (786)

The global integration of digital technologies into energy systems constitutes a critical pathway for achieving sustainable and intelligent energy governance. This study evaluates the effectiveness of the energy digital transformation policies across eighteen major economies through a comprehensive four-dimensional framework, which encompasses policy objectives, intensity, instruments, and stakeholder engagement. Through the application of the entropy-weighted TOPSIS method, our comparative analysis identifies a distinct hierarchy in national policy performance. The first tier, including the United Kingdom, the United States, South Korea, Australia, China, and Germany, demonstrates high coherence, enforceable mechanisms, and multi-actor coordination. The second tier, comprising Saudi Arabia, France, Turkey, Russia, Canada, and India, exhibits partial alignment with notable strengths in selected dimensions yet significant gaps in enforceability or stakeholder integration. The third tier, featuring Italy, Brazil, Argentina, Mexico, Japan, and Indonesia, is characterized by fragmented approaches and aspirational goals lacking implementation specificity. Stakeholder inclusiveness emerges as the most influential dimension, accounting for 38.3% of total weighting and substantially accounting for variations in efficacy. Moreover, nonlinear threshold effects are identified, indicating that subcritical performance in any dimension leads to disproportionate declines in overall outcomes. These findings underscore that synergistic policy design, which entails balancing objectives, governance capacity, instruments, and actors, is indispensable for effective energy digitalization. Full article

Eutectic high-entropy alloys (EHEAs) combine the casting advantages of eutectic alloys with the comprehensive properties of high-entropy alloys, making them a research hotspot in the field of metallic materials. Among them, the AlCoCrFeNi_2.1 EHEA has attracted significant attention due to its excellent strength–toughness balance characteristics. In this study, alloy samples of AlCoCrFeNi_2.1Si_x (x = 0, 0.1, 0.2, 0.3) were prepared to investigate the regulatory effects of trace Si on its phase composition, microstructure, and mechanical properties. The results show that the base alloy AlCoCrFeNi_2.1 is composed of an FCC and BCC phase composition. With the increase in the Si content to x = 0.3, the CrSi₂ phase gradually precipitates in the alloy, and its microscopic morphology transforms from the regular lamellar to the dendrite and network structure. The introduction of Si significantly enhances the room-temperature microhardness, wear resistance, and yield strength of the alloy through the mechanisms of solid solution strengthening and second phase strengthening. However, an excessive addition leads to a decrease in ductility and toughness. This study reveals the role of Si in phase control and the strengthening and toughening mechanism of eutectic high-entropy alloys, providing experimental evidence and a theoretical reference for the design of high-performance silicon-modified high-entropy alloys. Full article

To alleviate the communication burden of wide-area protection and enhance the fault tolerance of multi-source criteria, this paper introduces an improved wide-area backup protection method based on multi-source information fusion. Initially, the variation characteristics of bus sequence voltages after a fault are utilized to screen suspected fault lines, thereby reducing communication traffic. Subsequently, four basic probability assignment functions are constructed using the polarity of zero-sequence current charge, the polarity of phase-difference current charge, and the starting signals of Zone II/III distance protection from the local and adjacent lines. The confidence of each probability function is evaluated using normalized information entropy, while consistency is analyzed via Gaussian similarity, enabling dynamic allocation of fusion weights. Additionally, a conflict adaptation factor is designed to adjust the fusion strategy dynamically, improving fault tolerance in high-conflict scenarios and mitigating the impact of abnormal single criteria on decision results. Finally, the fused fault probability is used to identify the fault line. Simulation results based on the IEEE 39-bus model demonstrate that the proposed algorithm can accurately identify fault lines under different fault types and locations and remains robust under conditions such as information loss and protection maloperation or failure. Full article

To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a Multi-scale Feature Complementary Aggregation Module (MFCAM), designed to mitigate the loss of small target information as network depth increases. By integrating channel and spatial attention mechanisms with multi-scale convolutional feature extraction, MFCAM effectively locates small objects in the image. Furthermore, we introduce a novel neck architecture termed Gated Activation Convolutional Fusion Pyramid Network (GAC-FPN). This module enhances multi-scale feature fusion by emphasizing salient features while suppressing irrelevant background information. GAC-FPN employs three key strategies: adding a detection head with a small receptive field while removing the original largest one, leveraging large-scale features more effectively, and incorporating gated activation convolutional modules. To tackle the issue of positive-negative sample imbalance, we replace the conventional binary cross-entropy loss with an adaptive threshold focal loss in the detection head, accelerating network convergence. Additionally, to accommodate diverse application scenarios, we develop multiple versions of SRTSOD-YOLO by adjusting the width and depth of the network modules: a nano version (SRTSOD-YOLO-n), small (SRTSOD-YOLO-s), medium (SRTSOD-YOLO-m), and large (SRTSOD-YOLO-l). Experimental results on the VisDrone2019 and UAVDT datasets demonstrate that SRTSOD-YOLO-n improves the mAP@0.5 by 3.1% and 1.2% compared to YOLO11n, while SRTSOD-YOLO-l achieves gains of 7.9% and 3.3% over YOLO11l, respectively. Compared to other state-of-the-art methods, SRTSOD-YOLO-l attains the highest detection accuracy while maintaining real-time performance, underscoring the superiority of the proposed approach. Full article

The Larimichthys crocea represents a critically important economic marine species in China’s East Yellow Sea. However, its populations have experienced significant decline due to overexploitation. Despite implemented conservation measures—including stock enhancement, spawning ground protection, and seasonal fishing moratoria—the recovery of yellow croaker resources remains markedly slow. To address this, our study employed the Maximum Entropy (MaxEnt) model to evaluate and characterize the habitat selection patterns of Larimichthys crocea, thereby providing a theoretical foundation for scientifically informed stock enhancement and resource recovery strategies. Species occurrence data were compiled from field surveys conducted during April and November (2019–2023), supplemented with records from the GBIF database and peer-reviewed literature. Concurrent environmental variables, including primary productivity, current velocity, depth, temperature, salinity, silicate, nitrate, phosphate, and pH, were obtained from the Copernicus and NOAA databases. After rigorous screening, 136 distribution points (April) and 369 points (November) were retained for analysis. The model performance was robust, with an AUC (Area Under the Curve) value of 0.935 for April (2019–2023) and 0.905 for November (2019–2023), indicating excellent predictive accuracy (AUC > 0.9). April (2019–2023): Nitrate, salinity, phosphate, and silicate were identified as the primary environmental factors influencing habitat suitability. November (2019–2023): Silicate, salinity, nitrate, and primary productivity emerged as the dominant drivers. Spatially, Larimichthys crocea exhibited high-density distributions in offshore regions of Zhejiang and Jiangsu, particularly near the Yangtze River estuary. Populations were also associated with island-reef systems, forming continuous distributions along Zhejiang’s offshore waters. In Jiangsu, aggregations were concentrated between Nantong and Yancheng. This study delineates habitat suitability zones for Larimichthys crocea, offering a scientific basis for optimizing stock enhancement programs, designing targeted conservation measures, and establishing marine protected areas. Our findings enable policymakers to develop sustainable fisheries management strategies, ensuring the long-term viability of this ecologically and economically vital species. Full article

Smaller data environments with expert systems are generally driven by the need for interpretable reasoning frameworks, such as fuzzy rule-based systems (FRBS), which cannot often quantify epistemic uncertainty during decision-making. This study proposes a novel Layered Entropy Model (LEM) comprising three semantic layers: Membership Function Entropy (MFE), Rule Activation Entropy (RAE), and System Output Entropy (SOE). Shannon entropy is applied at each layer to enable granular diagnostic transparency throughout the inference process. The approach was evaluated using both synthetic simulations and a real-world case study on the PIMA Indian Diabetes dataset. In the real data experiment, the system produced sharp, fully confident decisions with zero entropy at all layers, yielding an Epistemic Confidence Index (ECI) of 1.0. The proposed framework maintains full compatibility with conventional Type-1 FRBS design while introducing a computationally efficient and fully interpretable uncertainty quantification capability. The results demonstrate that LEM can serve as a powerful tool for validating expert knowledge, auditing system transparency, and deployment in high-stakes, small-data decision domains, such as healthcare, safety, and finance. The model contributes directly to the goals of explainable artificial intelligence (XAI) by embedding uncertainty traceability within the reasoning process itself. Full article

Dry reforming of methane and ethanol is a promising catalytic process for the conversion of carbon dioxide and hydrocarbon feedstocks into synthesis gas (H₂/CO), which serves as a key platform for the production of fuels and chemicals. Over the past decade, substantial progress has been achieved in the design of catalysts with enhanced activity and stability under the demanding conditions of these strongly endothermic reactions. This review summarizes the latest developments in catalyst systems for DRM and EDR, including Ni-based catalysts, perovskite-type oxides, MOF-derived materials, and high-entropy alloys. Particular attention is given to strategies for suppressing carbon deposition and preventing metal sintering, such as oxygen vacancy engineering in oxide supports, rare earth and transition metal doping, strong metal–support interactions, and morphological control via core–shell and mesoporous architectures. These approaches have been shown to improve coke resistance, maintain metal dispersion, and extend catalyst lifetimes. The review also highlights emerging concepts such as multifunctional hybrid systems and innovative synthesis methods. By consolidating recent findings, this work provides a comprehensive overview of current progress and future perspectives in catalyst development for DRM and EDR, offering valuable guidelines for the rational design of advanced catalytic materials. Full article

The rapid development of contemporary artificial intelligence and Internet of Things (IoT) technologies has given rise to the emerging paradigm of the AIoT (Artificial Intelligence of Things), which is profoundly impacting human life and driving the digital transformation of industries and society. The AIoT not only enhances product functionality and convenience but also accelerates the achievement of the United Nations Sustainable Development Goals (SDGs). However, the widespread adoption of these technologies still poses challenges related to social inclusivity, particularly regarding insufficient accessibility for elderly users, which may exacerbate the digital divide and social inequality, contradicting SDG 10 (reducing inequality). This study integrates AIoT product development processes based on sustainability and universal design principles using methods such as Quality Function Deployment, the Analytic Hierarchy Process, the Scenario Method, the Entropy Weight Method, and Fuzzy Comprehensive Evaluation. The features of this process include ease of operation and high flexibility, making it suitable for cross-departmental product development while prioritizing the needs of diverse age groups throughout the development process. The research findings indicate that the AIoT product concepts proposed can meet the needs of diverse users, contributing to the realization of age-friendly products. This study provides a reference point for future AIoT product development, promoting the inclusive and sustainable development of smart technology. Full article

Magnetocardiography (MCG) offers a noninvasive method for early screening and precise localization of cardiovascular diseases by measuring picotesla-level weak magnetic fields induced by cardiac electrical activity. However, in unshielded magnetic environments, geomagnetic disturbances, power-frequency electromagnetic interference, and physiological/motion artifacts can significantly overwhelm effective magnetocardiographic components. To address this challenge, this paper systematically constructs an integrated denoising framework, termed “AOA-VMD-WT”. In this approach, the Arithmetic Optimization Algorithm (AOA) adaptively optimizes the key parameters (decomposition level K and penalty factor α) of Variational Mode Decomposition (VMD). The decomposed components are then regularized based on their modal center frequencies: components with frequencies ≥50 Hz are directly suppressed; those with frequencies <50 Hz undergo wavelet threshold (WT) denoising; and those with frequencies <0.5 Hz undergo baseline correction. The purified signal is subsequently reconstructed. For quantitative evaluation, we designed performance indicators including QRS amplitude retention rate, high/low frequency suppression amount, and spectral entropy. Further comparisons are made with baseline methods such as FIR and wavelet soft/hard thresholds. Experimental results on multiple sets of measured MCG data demonstrate that the proposed method achieves an average improvement of approximately 8–15 dB in high-frequency suppression, 2–8 dB in low-frequency suppression, and a decrease in spectral entropy ranging from 0.1 to 0.6 without compromising QRS amplitude. Additionally, the parameter optimization exhibits high stability. These findings suggest that the proposed framework provides engineerable algorithmic support for stable MCG measurement in ordinary clinic scenarios. Full article

The nominal compositions of Fe₆₅Co_10−xNi_10−xCr₁₅Si_2x (x = 1, 2, and 3 at.%) medium-entropy alloys (MEAs) were designed and fabricated by vacuum arc melting. Their microstructure, hardness, and mechanical properties were systematically characterized. Corrosion behavior was evaluated in 3.5 wt.% NaCl solution by potentiodynamic polarization and electrochemical impedance spectroscopy. The investigated MEAs exhibit a dual-phase microstructure composed of face-centered cubic (FCC) and body-centered-cubic (BCC) phases. With increasing Si content, yield strength and ultimate tensile strength increase, while uniform elongation decreases. Hardness also increases with increasing Si content. For the x = 3 MEA, the yield strength, ultimate tensile strength, and hardness of are ~518 MPa, ~1053 MPa, and 262 ± 4.8 HV, respectively. The observed strengthening can be primarily attributed to solid solution strengthening effect by Si. Polarization curves indicate that the x = 3 MEA exhibits the best corrosion resistance with the lowest corrosion current density ((0.401 ± 0.19) × 10⁻⁶ A × cm⁻²) and corrosion rate ((4.65 ± 0.19) × 10^–2 μm × year⁻¹)). Equivalent electric circuit analysis suggests the formation of a stable passive oxide film on the MEAs. This conclusion is supported by the capacitive behavior, high impedance values (> 10⁴ Ω cm²) at low frequencies, and phase angles within a narrow window of 80.05°~80.64° in the medium-frequency region. The passive-film thickness was calculated and the corrosion morphology was analyzed by SEM. These results provide a reference for developing high-strength, corrosion-resistant, medium-entropy alloys. Full article

Understanding the shear behavior of loess–concrete interfaces is essential for foundation design in collapsible loess regions, yet the pore-scale mechanisms remain unclear. This study investigates the relationship between interface shear strength and loess microstructure at different burial depths. Direct shear tests were conducted on undisturbed loess samples under stress conditions simulating in situ confinement. High-resolution SEM images were analyzed via Avizo to quantify pore area ratios at multiple scales, fractal dimensions, and directional probability entropy. Pearson correlation, principal component analysis (PCA), and hierarchical cluster analysis (HCA) were employed to statistically interpret the microstructure–mechanics relationship. Results show that interface shear strength increases significantly with depth (35.2–258.4 kPa), primarily due to reduced total porosity and macropore content, increased small and micropore fractions, and enhanced isotropy of pore orientation. Fractal dimension negatively correlates with strength, indicating that compaction-induced boundary regularization enhances particle contact and shear resistance, while entropy positively correlates with strength, reflecting structural homogenization and isotropic pore orientation. PCA and HCA further confirm that small and micropores are the dominant contributors to interface resistance. This study provides a quantitative framework linking microstructural evolution to mechanical performance, offering new insights for optimizing pile–soil interface design in loess areas. Full article

How to rationally design composition of alloys with desired properties has always been an open and challenging question, especially for high-entropy alloy (HEA) which has huge selections of composition due to the feature of multi-principal elements. Although great efforts have been made in the past decades, such as approaches based on thermo-kinetic analysis and simulations, strategies to quick determine the optimal HEA composition remain lacking. In this study, based on the effective estimations of elastic modulus of alloys from compositions, we proposed a strategy to design intrinsically strong, ductile, and low-weight refractory HEA (RHEA) compositions. First, the Young’s moduli of three RHEAs were experimentally measured using uniaxial tensile test and impulse excitation of vibration (IEV) test. Then, the present results, combining with the data of elastic moduli of ~130 HEAs in literature, were utilized to validate the prediction of elastic moduli from compositions of HEAs. Finally, based on the property maps that containing 38,326 compositions, a novel RHEA was designed and experimentally tested, exhibiting superior strength, ductility, and low density compared to the equimolar NbMoTaVW alloy. This study provides a new strategy for developing HEAs and contributes to the development of new refractory HEAs with desired properties. Full article

This study employs first-principles calculations combined with the Special Quasirandom Structure (SQS) technique to investigate the impact of three interstitial elements C, N, and O, on the mechanical properties and stacking fault energy (SFE) of NiCoCr medium-entropy alloys. The results indicate that non-metallic O, C, and N tend to occupy octahedral interstitial sites, which can effectively release stress concentration and enhance the strength and deformability of the material. Differential charge density analysis shows that the dissolution of C, N, and O significantly alters the surrounding electronic environment, strengthening the interaction between solute atoms and metal atoms, thereby hindering dislocation glide and increasing the strength and hardness of the material. Elastic property analysis indicates that NiCoCr alloys doped with C, N, and O exhibit good ductility and anisotropic characteristics. Furthermore, the study of stacking fault energy reveals that the doping with C, N, and O can significantly increase the stacking fault energy of NiCoCr alloys, thereby optimizing their mechanical properties. These findings provide theoretical evidence for the design of advanced high-entropy alloys that combine high strength with good ductility. Full article

The classification of very-low-frequency and low-frequency (VLF/LF) lightning-radiation electric-field waveforms is of paramount importance for lightning-disaster prevention and mitigation. However, traditional waveform classification methods suffer from the complex characteristics of lightning waveforms, such as non-stationarity, strong noise interference, and feature coupling, limiting classification accuracy and generalization. To address this problem, a novel framework is proposed for VLF/LF lightning-radiated electric-field waveform classification. Firstly, an improved Kalman filter (IKF) is meticulously designed to eliminate possible high-frequency interferences (such as atmospheric noise, electromagnetic radiation from power systems, and electronic noise from measurement equipment) embedded within the waveforms based on the maximum entropy criterion. Subsequently, an attention-based multi-fusion convolutional neural network (AMCNN) is developed for waveform classification. In the AMCNN architecture, waveform information is comprehensively extracted and enhanced through an optimized feature fusion structure, which allows for a more thorough consideration of feature diversity, thereby significantly improving the classification accuracy. An actual dataset from Anhui province in China is used to validate the proposed classification framework. Experimental results demonstrate that our framework achieves a classification accuracy of 98.9% within a processing time of no more than 5.3 ms, proving its superior classification performance for lightning-radiation electric-field waveforms. Full article

An experiment with a two interaction modalities (traditional: touch; novel: touch–speech) × two task complexities (low: visual single task; high: audio–visual dual task) within-subjects design was conducted to observe alterations in crew performance (including task performance, subjective workload, and eye responses) in a typical planning task-based on a high-fidelity special vehicle simulation platform. The results revealed that (1) compared to the traditional interaction modality, the novel interaction modality significantly improved task performance, reduced subjective workload, increased mean peak saccade velocity, and decreased fixation entropy; (2) under high task complexity, subjective workload, mean pupil diameter, and the nearest neighbor index showed significant increases, while no significant decline in task performance was observed; (3) no interaction effect of crew performance was observed between interaction modality and task complexity. The foregoing results imply that (1) the novel interaction modality incorporating speech input exhibits advantages over the traditional touch-based modality in terms of enhancing task performance (over 45% improvement) and reducing cognitive workload; (2) leveraging dual-channel audio–visual information processing facilitates the maintenance of task performance under high task complexity and multi-tasking demands; (3) eye movement characteristics may serve as informative indicators for evaluating the benefits of speech-based interaction and the effectiveness of cognitive resource allocation under high-complexity task conditions. The results can provide a basis for the design of the display and control interface in special vehicles. Full article