Search Results (24,899)

Conventional assessments of fault rupture propagation in overlying soil layers often rely on static or quasi-static analysis, neglecting the dynamic nature of fault displacement and inertial effects. This study develops a comprehensive simulation method for the entire process from rupture initiation to propagation under dynamic fault displacement. The method integrates a nonlinear elastic constitutive model based on the Hardin backbone curve with a non-uniform input technique for seismic waves on both sides of the fault using viscoelastic artificial boundaries. To demonstrate the distinct capabilities of this dynamic method, we conduct a comparative study on normal and reverse faulting driven by fault displacement time histories of identical magnitude but opposite sense. The simulations reveal that: (1) the fault displacement required for rupture initiation and propagation remains consistent between dynamic and quasi-static analyses; (2) crucially, the proposed method captures the transient dynamic response of fault rupture in the overlying soil. The study confirms that the proposed dynamic simulation framework is essential for resolving transient peak responses, oscillatory behavior, and deformation features associated with different faulting mechanisms, providing a more realistic tool for seismic risk assessment compared to conventional static approaches. Full article

As modern power systems increasingly depend on demand-side flexibility, accurately modeling building multi-energy demand under uncertainty has become essential for achieving reliable and flexible grid operation. This study proposes an agent-based framework to conduct uncertainty-aware modeling of building multi-energy demand and to assess demand-side flexibility under different demand response mechanisms. Firstly, an agent-based modeling framework is established to connect occupant activities, electrical appliance usage, and building thermal dynamics, characterizing the explicit relationship between Markovian behavioral uncertainties and multi-energy demands. Secondly, an integrated thermal load model is constructed based on a resistance–capacitance network, coupled with behavior-driven internal heat gains and building morphology-driven shading and radiative microclimate conditions. Then, the flexibility potential of electrical and thermal loads is quantified at both individual and aggregated scales. Finally, the demand response flexibilities of the multi-energy loads were assessed under price-based self-scheduling and incentive-based centralized optimization scenarios. The results demonstrate that the proposed approach effectively captures behavior-driven uncertainties and their impacts on the temporal pattern and magnitude of building energy demand, as well as on the resulting demand-side flexibility. In addition, the proposed demand response strategies effectively reduce electricity costs and achieve peak shaving and valley filling, while maintaining schedulable flexibility within acceptable operational limits. Full article

Despite decades of studies on symmetric impinging-jet atomization, the combined role of controlled pre-impingement asymmetry and viscosity in setting the instability pathways and droplet statistics of laminar microjets remains insufficiently quantified. The effects of pre-impingement jet-length difference and liquid viscosity on the flow morphologies, instability dynamics, and atomization behavior of laminar impinging microjets are investigated experimentally using high-speed imaging. By systematically varying the jet-length asymmetry and viscosity over a range of Weber numbers, the evolution of liquid-sheet motion and breakup is resolved from synchronized front- and side-view observations. Specifically, the scientific objective of this work is to elucidate how pre-impingement jet-length asymmetry and liquid viscosity jointly regulate the dynamical behavior of laminar impinging microjets, with particular emphasis on regime transitions of liquid-sheet morphologies, the coupling between upper-sheet oscillations and rim instabilities revealed by synchronized multi-view imaging and POD-based frequency analysis and the resulting droplet-size statistics. These aspects address physical questions that have not been systematically resolved in classical impinging-jet studies, which predominantly focus on symmetric configurations or performance-oriented atomization. With increasing Weber number, the flow undergoes a sequence of regime transitions, including merged-jet, liquid-chain, wavy-rim, fishbone, closed-rim, open-rim, and arc-shaped atomization states. The presence and extent of the closed-rim regime depend sensitively on both jet-length asymmetry and liquid viscosity. Increasing jet-length difference accelerates transitions between these regimes, whereas increasing liquid viscosity stabilizes the liquid sheet and shifts the onset of unsteady breakup to higher Weber numbers. Proper orthogonal decomposition is applied to time-resolved image sequences to extract dominant oscillatory modes and their characteristic frequencies. Within the fishbone regime, the oscillation frequency of rim deformation either coincides with that of the upper region of the liquid sheet or appears as its subharmonic, indicating period-doubling behavior under specific combinations of Weber number and jet-length asymmetry. These frequency characteristics govern the spatiotemporal organization of ligament formation and detachment along the sheet rim. In the arc-shaped atomization regime, droplet-size distributions follow a log-normal form, and at sufficiently high Weber numbers, the mean droplet diameter shows only a weak dependence on jet-length asymmetry. These findings provide microscale-regime guidance for tunable droplet formation in open microfluidic jetting and related small-scale multiphase flows. The innovation of this study lies in the systematic use of synchronized multi-view imaging combined with POD-based frequency analysis and droplet statistics to directly connect liquid-sheet oscillations, rim instability dynamics, and breakup organization under controlled geometric asymmetry and viscosity variations. This approach enables a unified physical interpretation of regime transitions and instability mechanisms that cannot be resolved from single-view observations or morphology-based classification alone. Full article

This study investigates how laser power–scan speed combinations influence densification, surface quality, and mechanical performance of Ti-6Al-4V parts fabricated by Powder Bed Fusion–Laser Beam/Metal (PBF-LB/M) on a DMG MORI LASERTEC 30 SLM (2nd generation) system. A parametric matrix was explored by varying laser power (150–400 W) and scan speed (0.9–1.4 m·s⁻¹) at constant layer thickness and hatch spacing, deliberately omitting contour exposure to isolate core scan effects. A stable processing window was identified (250–300 W; 0.9–1.0 m·s⁻¹) corresponding to ~50–60 J·mm⁻³ volumetric energy density (VED) achieved at 99.5% with residual porosity of 0.1–0.3%. In this regime, as-built roughness measured Ra = 4–6 µm on top surfaces and Ra = 15–17 µm on side surfaces. Mechanical testing in the as-built showed ultimate tensile strength (UTS) = 1150–1180 MPa and offset yield strength (YS_0.2) = 955–994 MPa, with elongation up to 6.7%. Hardness increased from 220 HV to 360 HV as densification improved. Notably, similar VED values derived from distinct power–speed combinations resulted in divergent outcomes, confirming that VED alone does not uniquely predict quality. Comparative benchmarks from the literature data highlight the performance achieved. The resulting process–property map provides a practical reference for parameter optimization, reproducibility evaluation, and transferability across platforms. Full article

Taking a metal structural part blanking workshop as the application background, this study addresses the challenges of high material variety, long crane feeding travel caused by heterogeneous line-side storage layouts, and frequent machine stoppages due to the limited feeding capacity of a single overhead crane. To this end, an integrated machine–crane dual-resource scheduling model is developed by explicitly considering line-side storage locations. The objective is to minimize the maximum waiting time among all machine tools. Under constraints of material assignment, processing sequence, and the crane’s single-task execution and travel requirements, the storage positions of materials in line-side buffers are jointly optimized. To solve the problem, a genetic algorithm with fitness-value-based crossover is proposed, and a simulated-annealing acceptance criterion is embedded to suppress premature convergence and enhance the ability to escape local optima. Comparative experiments on randomly generated instances show that the proposed algorithm can significantly reduce the maximum waiting time and yield more stable results for medium- and large-scale cases. Furthermore, a simulation based on real production data from an industrial enterprise verifies that, under limited feeding capacity, the proposed method effectively shortens material-waiting time, improves equipment utilization, and enhances production efficiency, demonstrating its effectiveness. Full article

Against the backdrop of rapid urbanization and climate change, energy burden inequity arises between existing and new residential buildings due to generational differences in building envelopes. This study develops a demand-side energy burden equity assessment framework based on energy simulations of typical existing and new apartments in representative cities across China’s five major climate zones. The framework integrates multi-climate conditions, long-term evolution under different Shared Socioeconomic Pathways, and adaptable retrofit implications. Results indicate that demand-side energy burden inequity is widespread but structurally heterogeneous across climate zones, with the largest disparity observed in heating-dominated regions (up to 95.69 kWh/m² in Harbin). Under future warming, three scaling pathways emerge: convergence in heating-dominated regions (up to −27%), divergence in cooling-dominated and mixed regions (up to +382%), and offsetting effects driven by heating–cooling structural shifts in cold regions (up to −5%). Retrofit analysis shows that combined envelope upgrades achieve substantial inequity reduction (88–152%), though with diminishing marginal returns, while single targeted measures already yield high benefits in cooling-dominated and mild regions (74% and 83%, respectively). The findings provide differentiated and forward-looking evidence to support equity-oriented interventions in urban residential retrofitting and policy design. Full article

Due to the rapid proliferation and evolution of the Internet of Things (IoT) in industrial and smart city applications, concerns over sensitive data security have become increasingly prominent. This is especially true in resource-constrained “cloud–terminal” centralized architectures, where ensuring privacy protection for downlink data and implementing fine-grained access control have become critical. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) serves as an effective solution due to its fine-grained access control capability. Nevertheless, conventional CP-ABE approaches face notable limitations when deployed in these practical settings, including the lack of an efficient and lightweight client-side revocation mechanism, excessive decryption overhead on terminal devices, and the practical difficulty in balancing security with performance. To address these issues, this paper proposes LOR-A2ABE, a Lightweight, Outsourced, and Revocable Anonymous Attribute-Based Encryption scheme. The scheme achieves lightweight client-side revocation through partial updates by embedding version numbers and timestamps into keys and ciphertexts via hash mapping. Furthermore, it employs outsourcing to offload the majority of computations to the cloud, allowing client-side decryption with only constant, low-complexity operations, thereby significantly reducing the computational burden on resource-constrained terminals. Considering the practical context where client devices are typically resource-limited sensors or microcontrollers and downlink data often require real-time processing, our scheme adopts a practical security model optimized for IoT constraints. This model prioritizes forward security and efficient revocation—the most critical requirements for operational IoT systems—while maintaining provable security under the Decisional Linear (DLIN) assumption within a bounded collusion model, achieving IND-CPA security and anonymity. Theoretical analysis and experimental simulations show that LOR-A2ABE incurs acceptable and controllable overhead in the key issuance and encryption phases, while outperforming most existing schemes in decryption and revocation efficiency, making it particularly suitable for “cloud–terminal” centralized IoT environments where terminal devices are resource-constrained and require frequent decryption operations. Full article

Introduction: Resting-state EEG (rsEEG) is a scalable window onto trait-like “executive readiness,” but findings have been fragmented by task impurity on the executive-function (EF) side and heterogeneous EEG pipelines. This review synthesizes rsEEG features that reliably track EF in healthy samples across development and aging and evaluates moderators such as cognitive reserve. Materials and methods: Following PRISMA 2020, we defined PECOS-based eligibility (human participants; eyes-closed/eyes-open rsEEG; spectral, aperiodic, connectivity, topology, microstate, and LRTC features; behavioral EF outcomes) and searched MEDLINE/PubMed, Embase, PsycINFO, Web of Science, Scopus, and IEEE Xplore from inception to 30 August 2025. Two reviewers were screened/double-extracted; the risk of bias in non-randomized studies was assessed using the ROBINS-I tool. Sixty-three studies met criteria (plus citation tracking), spanning from childhood to old age. Results: Across domains, tempo, noise, and wiring jointly explained EF differences. Faster individual/peak alpha frequency (IAF/PAF) related most consistently to manipulation-heavy working may and interference control/vigilance in aging; alpha power was less informative once periodic and aperiodic components were separated. Aperiodic 1/f parameters (slope/offset) indexed domain-general efficiency (processing speed, executive composites) with education-dependent sign flips in later life. Connectivity/topology outperformed local power: efficient, small-world-like alpha networks predicted faster, more consistent decisions and higher WM accuracy, whereas globally heightened alpha/gamma synchrony—and rigid high-beta organization—were behaviorally sluggish. Within-frontal beta/gamma coherence supported span maintenance/sequencing, but excessive fronto-posterior theta coherence selectively undermined WM manipulation/updating. A higher frontal theta/beta ratio forecasts riskier, less adaptive choices and poorer reversal learning for decision policy. Age and reserve consistently moderated effects (e.g., child frontal theta supportive for WM; older-adult slow power often detrimental; stronger EO ↔ EC connectivity modulation and faster alpha with higher reserve). Boundary conditions were common: low-load tasks and homogeneous young samples usually yielded nulls. Conclusions: RsEEG does not diagnose EF independently; single-band metrics or simple ratios lack specificity and can be confounded by age/reserve. Instead, a multi-feature signature—faster alpha pace, steeper 1/f slope with appropriate offset, efficient/flexible alpha-band topology with limited global over-synchrony (especially avoiding long-range theta lock), and supportive within-frontal fast-band coherence—best captures individual differences in executive speed, interference control, stability, and WM manipulation. For reproducible applications, recordings should include ≥5–6 min eyes-closed (plus eyes-open), ≥32 channels, vigilant artifact/drowsiness control, periodic–aperiodic decomposition, lag-insensitive connectivity, and graph metrics; analyses must separate speed from accuracy and distinguish WM maintenance vs. manipulation. Clinical translation should prioritize stratification and monitoring (not diagnosis), interpreted through the lenses of development, aging, and cognitive reserve. Full article

Short-Term Residential Load Forecasting (STRLF) is a core task in smart grid dispatching and energy management, and its accuracy directly affects the economy and stability of power systems. Current mainstream methods still have limitations in addressing issues such as complex temporal patterns, strong stochasticity of load data, and insufficient model interpretability. To this end, this paper proposes an explainable and efficient forecasting framework named KAN+Transformer, which integrates Kolmogorov–Arnold Networks (KAN) with Transformers. The framework achieves performance breakthroughs through three innovative designs: constructing a Reversible Mixture of KAN Experts (RMoK) layer, which optimizes expert weight allocation using a load-balancing loss to enhance feature extraction capability while preserving model interpretability; designing an attention-guided cascading mechanism to dynamically fuse the local temporal patterns extracted by KAN with the global dependencies captured by the Transformer; and introducing a multi-objective loss function to explicitly model the periodicity and trend characteristics of load data. Experiments on four power benchmark datasets show that KAN+Transformer significantly outperforms advanced models such as Autoformer and Informer; ablation studies confirm that the KAN module and the specialized loss function bring accuracy improvements of 7.2% and 4.8%, respectively; visualization analysis further verifies the model’s decision-making interpretability through weight-feature correlation, providing a new paradigm for high-precision and explainable load forecasting in smart grids. Collectively, the results demonstrate our model’s superior capability in representing complex residential load dynamics and capturing both transient and stable consumption behaviors. By enabling more accurate, interpretable, and computationally efficient short-term load forecasting, the proposed KAN+Transformer framework provides effective support for demand-side management, renewable energy integration, and intelligent grid operation. As such, it contributes to improving energy utilization efficiency and enhancing the sustainability and resilience of modern power systems. Full article

Bi-doped low-silver Sn-Ag-Cu solders are increasingly gaining attention in advanced electronic packaging due to their cost-effectiveness and enhanced mechanical properties. However, the thermo-mechanical reliability mechanisms of such modified solders, particularly Sn-0.3Ag-0.7Cu-3Bi (SAC0307-3Bi) within Ball Grid Array (BGA) assemblies, remain insufficiently understood. To address this gap, this research proposes a comprehensive assessment framework integrating constitutive parameter calibration with finite element analysis (FEA) to accurately characterize the mechanical behavior and fatigue durability of SAC0307-3Bi solder joints under cyclic thermal loads. The Anand viscoplastic parameters were first calibrated via the Norton creep law and virtual tensile tests. Subsequently, a 3D quarter-symmetry model was constructed to replicate thermal cycling conditions between 25 °C and 125 °C. Simulation data reveal a strong correlation between stress concentration and the Distance to Neutral Point (DNP), pinpointing the chip-side interface of the corner joint as the critical failure site. Moreover, creep strain was observed to accrue in a “step-wise” pattern, predominantly during the heating and cooling ramps, reflecting distinct temperature sensitivity. Utilizing the Syed model, the fatigue life was estimated at approximately 2239 cycles. These insights serve as a crucial benchmark for designing robust packages using Bi-doped, low-silver lead-free solders. Full article

This paper proposes a novel, dual-output, hybrid-clamped, quasi-five-level inverter (DO-HC-FLI) topology, capable of providing two independent AC voltage outputs with adjustable frequency and amplitude. Derived from a dual-output, active, neutral-point-clamped, three-level inverter, the proposed topology introduces three additional switches per phase to create dynamic switching paths. This expands the available range of DC-side voltage outputs and significantly improves the utilization rate of the DC–link voltage. Additionally, by adopting an asymmetric DC–link voltage configuration, the output line voltage levels of the conventional four-level inverter are increased to a number comparable to that of a five-level inverter. The front-end stage employs a hybrid series-parallel architecture, integrating dual Buck circuits with DC power sources. This configuration supplies the subsequent inverter stage with DC voltage levels at an optimal asymmetric ratio. In conjunction with a dual-output space vector pulse width modulation (SVPWM) strategy, the proposed system can collaboratively optimize the output voltage level characteristics of the inverter stage. Furthermore, a comprehensive analysis and comparison with other multilevel inverters are presented to demonstrate the superiority of the proposed topology. Finally, simulations and experiments are conducted to validate the effectiveness and feasibility of the proposed topology and modulation strategy. Full article

Herein, a cobalt–molybdenum bimetallic oxide precursor was synthesized via a hydrothermal route, followed by a phosphidation strategy in a tube furnace to produce a CoMoP cocatalyst. Subsequently, a CoMoP/BiVO₄ composite photoanode was successfully constructed by loading the CoMoP cocatalyst onto the surface of an electrodeposited BiVO₄ film using a drop-casting method. A suite of analytical tools such as TEM, XRD, and XPS was utilized to comprehensively examine the material morphology and crystalline features, verifying that CoMoP was effectively anchored on the BiVO₄ surface with intimate interfacial contact. Photoelectrochemical (PEC) performance testing indicated that the composite photoanode achieved optimal performance with a 200 µL loading of the CoMoP dispersion (2 mg/mL). Under front-side illumination, the photocurrent density of the CoMoP/BiVO₄ composite photoelectrode reached a photocurrent density of 2.8 mA/cm² at 1.23 V (vs. RHE), which is approximately 3.1 times higher than that of unmodified BiVO₄ (0.9 mA/cm²). Under back-side illumination, the composite photoanode generated 3.5 mA/cm², representing a 2.3-fold improvement over the 1.5 mA/cm² recorded for bare BiVO₄. The bandgap energy of BiVO₄ was determined to be approximately 2.44 eV based on UV–vis absorption spectra and the corresponding Tauc plot. Owing to its metallic nature, CoMoP exhibits strong broadband absorption in the visible-light region and does not display an intrinsic semiconductor bandgap behavior. Combined with photoluminescence (PL) spectroscopy and PEC results, it was demonstrated that the CoMoP loading effectively promoted interfacial charge separation and transport while accelerating water oxidation kinetics. These results demonstrate that the CoMoP/BiVO₄ system serves as an advanced semiconductor material with excellent performance for photoelectrocatalytic water splitting. Full article

Changing risk dynamics and the demand for more personalized, technology-driven services have spurred innovation in insurance through Insurtech, reshaping how insurance is supplied, purchased, and managed. This paper systematically reviews the impact of Insurtech on insurance inclusion, guided by the PRISMA-P protocol. The review finds strong evidence that Insurtech enhances insurance inclusion by lowering transaction costs, improving accessibility, and broadening market participation. These effects are most visible in short-term insurance, where digital platforms and tailored products reach previously underserved populations. Beyond this primary finding, the review highlights how insurance inclusion is conceptualized and measured in the literature. Quantitative measures typically include penetration rates, density, and the proportion of households with insurance coverage, while broader indices account for availability, usage, and accessibility of insurance services. Qualitative approaches often emphasize mismatches between the products offered and those needed, particularly for vulnerable groups. Similarly, studies of Insurtech adopt both demand-side indicators (such as product uptake and coverage per user) and supply-side measures (including patents, capital inflows, and innovation outputs). These insights suggest that fostering Insurtech development, while addressing regulatory, access, and equity concerns, can significantly improve insurance inclusion and narrow protection gaps. Full article

Digital technologies such as big data are reshaping resource allocation, raising interest in whether and how heterogeneous science and technology innovation (STI) policies can help unlock urban carbon lock-in. Using panel data for 286 prefecture-level cities in China from 2009 to 2023, this paper examines the relationship between heterogeneous STI policy intensity—classified as supply-side, demand-side, complementary-factor, and institutional-reform policies—and urban carbon unlocking efficiency. We develop a mechanism-based framework and empirically assess (i) the moderating roles of digital infrastructure, science and technology finance, and government green attention, and (ii) spatial spillover effects using spatial econometric models. The results show that all four policy types show a significant positive association with local carbon unlocking efficiency, with institutional-reform policies exhibiting the strongest association. When the four types are included jointly, only supply-side and demand-side policies retain statistically significant direct associations. Heterogeneity analyses indicate that demand-side, complementary-factor, and institutional-reform policies are more strongly associated with efficiency gains in low-pollution cities, whereas supply-side and demand-side policies have a stronger association in high energy-consuming cities. Mechanism analysis reveals that regional digital infrastructure exerts a selective moderating effect on the relationship between heterogeneous sci-tech innovation policies and urban carbon emission reduction efficiency. It positively reinforces the effectiveness of supply-side, demand-side, and institutional reform-oriented policies, while its interaction with complementary policies is statistically insignificant. Technology finance and government green policies function as a “resource catalyst” and an “institutional guarantee” respectively, significantly enhancing the correlation between heterogeneous sci-tech innovation policies and urban carbon emission reduction efficiency. Finally, carbon unlocking efficiency displays significant spatial dependence: the intensity of supply-side and institutional-reform policies is positively associated with carbon unlocking efficiency in neighboring cities, while complementary-factor policies exhibit a negative spatial association. Overall, the findings provide empirical evidence to inform the design and coordination of heterogeneous STI policy portfolios aimed at improving urban carbon unlocking efficiency. Full article

As integrated circuits face increasingly stringent demands regarding power consumption, area, and stability, integrating novel spintronic devices with computing architectures has become a crucial direction for breaking through traditional computing paradigms. In the paper, switching mechanism of Magnetic Tunnel Junctions (MTJs) under the synergistic effect of Voltage-Controlled Magnetic Anisotropy (VCMA) and the Spin Hall Effect (SHE) is investigated. VCMA-assisted switching SHE-MTJ device is adopted, and a macrospin approximation model is established based on the Landau-Lifshitz-Gilbert (LLG) equation to systematically analyze its dynamic characteristics. The research demonstrates that applying VCMA voltage pulses with appropriate amplitude and width can significantly reduce the required spin Hall current density and pulse width for switching, thereby effectively minimizing ohmic losses and Joule heating. Furthermore, by incorporating a thermal fluctuation field, voltage-controlled SHE-MTJ device with stochastic switching behavior can be constructed, obtaining an approximately sigmoidal voltage-probability response curve. This provides an ideal physical foundation for stochastic computing and neuromorphic computing. Based on the above established fundamental discovery, an in-memory computing architecture supporting binarized Convolutional Neural Networks (CNNs) is proposed and designed in the paper. Combined with the lightweight network SqueezeNet, this architecture achieves a Top-1 recognition accuracy of 72.49% on the CIFAR-10 dataset, with a parameter count of only 1.25 × 10⁶. This work offers a feasible spintronic implementation scheme for low-power, high-energy-efficiency edge-side intelligent chips. Full article