Search Results (2,259)

Rail-transit station areas concentrate commercial services, employment, and intensive land development, but their vitality is shaped by multiple built-environment conditions rather than rail accessibility alone. Focusing on 20 stations along the Osaka Metro Midosuji Line in Japan, this study uses Japanese chome units, which are small neighborhood-level address and statistical units, within an 800 m pedestrian catchment as analytical units and measures commercial-service agglomeration intensity, employment intensity, and commercial–employment composite vitality. The composite indicator measures the static co-concentration of commercial-service provision and employment carrying capacity, with pedestrian flow, consumption activity, and dwell time treated as separate dimensions of station-area vitality. Ten station-area environmental variables are examined using ordinary least squares (OLS), Lasso, Random Forest, Back-Propagation (BP) Neural Network, and extreme gradient boosting (XGBoost) models, with Shapley additive explanations (SHAP) applied to interpret variable contributions and nonlinear responses. Results show that nonlinear models generally outperform linear models. Development intensity, officially assessed land price, and network distance to the nearest metro station are the most influential variables, showing threshold, marginal, and non-monotonic effects. Split models indicate that commercial-service agglomeration is more sensitive to rail proximity and street-network conditions, whereas employment intensity is more associated with development intensity and land price. These findings support fine-grained station-area renewal and mixed-function planning. Full article

The existing arresting efficiency evaluation method overlooks corrosion defects in its formulation. If directly applied to evaluate and design arrestors for corroded sandwich pipes, it often leads to conservative evaluations of arresting efficiency and unreasonably designed arrestors. Based on this, this paper first verifies the reliability of numerical simulation results through physical experiments. On this basis, the influence of the structural parameters and material parameters of the arrestor on the arresting efficiency of the integral arrestor is analyzed. The results show that an increase in the length, thickness and material strength of the arrestor not only affects the arresting efficiency of the arrestor but also changes the arresting crossing mode, from parallel crossing to orthogonal crossing. A chart of arresting efficiency suitable for engineering design is proposed. Finally, a systematic comparison is conducted of different modeling methods. The results show that, considering both prediction accuracy and training efficiency, the Genetic Algorithm–Back Propagation (GA-BP) model significantly outperforms the empirical model, the Whale Optimization Algorithm–Back Propagation (WOA-BP) model, and the Particle Swarm Optimization–Back Propagation (PSO-BP) model. The average prediction error is only 6.56%, and 94.42% of the data error is less than 20%. The model provides a theoretical basis for the arrestor design and failure assessment of sandwich pipes with corrosion defects and has clear engineering guidance value. Full article

Intervertebral disk degeneration (IVDD) is widely recognized as a major contributor to discogenic low back pain (LBP), imposing a substantial burden on global public health and socioeconomic systems. Growing evidence confirms that disrupted redox homeostasis, excessive reactive oxygen species (ROS) accumulation, and oxidative stress act as major convergent mechanisms that propagate inflammatory cascades, nucleus pulposus cell dysfunction, and extracellular matrix degradation. Although conventional conservative therapies and surgical interventions are clinically effective in relieving macrostructural compression, they remain limited in resolving localized molecular dysregulation. In recent years, nanotechnology has emerged as a promising strategy for overcoming the limitations of traditional therapy for IVDD. This review provides an analysis of four categories of antioxidant nanotherapies for IVDD, including inorganic functional nanozymes, bioactive nanomaterials, stimuli-responsive nanosystems, and nanocomposite scaffolds. We elaborate on their mechanisms in scavenging excessive ROS, restoring redox equilibrium, protecting mitochondrial function, and ameliorating oxidative stress-induced degeneration. Integrating structural biomimicry with microenvironmental responsiveness enables the engineering of composite nanosystems with multi-pathway ROS-scavenging capabilities. Therefore, these platforms emerge as promising therapeutic strategies for arresting IVDD progression. Finally, we discuss the key obstacles to clinical translation. Overall, this review provides insights into the development of redox-targeted therapies. Full article

Reconstructing high-resolution images of anisotropic targets in microwave imaging remains a challenging problem due to the strong directionality of electromagnetic responses and the inherent nonlinearity of the inverse scattering process. To address these issues, we propose a novel Perceptual Generative Adversarial Network (PGAN) enhanced with a Self-Attention mechanism for anisotropic electromagnetic imaging. The perceptual loss encourages the preservation of high-level structural features, while the Self-Attention module enables the model to capture long-range dependencies and directional correlations that are critical in representing anisotropic material distributions. This joint architecture is trained to refine coarse permittivity estimates obtained from conventional Back-Propagation Schemes (BPSs). Numerical simulations and validation using measured experimental data demonstrate that the proposed method achieves improved reconstruction accuracy and structural similarity compared with the PGAN without SA and U-Net. In particular, PGAN with SA reduces the Root Mean Square Error (RMSE) by 15.1% and improves the Structural Similarity Index Measure (SSIM) by 3.8%, confirming its effectiveness in recovering fine-scale details and enhancing reconstruction quality. These results suggest that the proposed framework offers a promising solution for robust and high-resolution electromagnetic imaging in geophysical and remote sensing applications. Full article

Reliable prediction of the factor of safety (Fs) of circular-failure soil slopes is critical to geotechnical practice. Data-driven models developed on small slope-stability datasets are, however, prone to overfitting, data leakage, and optimistic bias, which can lead to overestimated predictive performance. This study presents a small-sample-oriented, leakage-aware support vector regression (SVR) framework with a radial basis function (RBF) kernel for Fs prediction. A database of 80 published circular-failure slope cases was compiled, and six predictors were adopted: soil unit weight, slope height, pore pressure ratio, cohesion, internal friction angle, and slope angle. To improve reliability under limited-data conditions, preprocessing, hyperparameter tuning, and performance evaluation were all embedded within a repeated nested cross-validation framework. The proposed SVR model was benchmarked against the back-propagation neural network (BPNN) and radial basis function neural network (RBFNN) models under identical validation partitions and evaluation settings. The results indicated that SVR achieved the best predictive performance among the three candidate models. For case-level illustration, a single representative hold-out split was reported in addition to the repeated nested cross-validation results, on which the SVR model attained an R² of 86.56%, an RMSE of 0.07497, an MAE of 0.0666, and an MRE of 5.29%. In this test subset, all SVR predictions exhibited relative errors below 10%, indicating more stable predictive behaviour than the benchmark models. The main contribution of this study is thus a validated SVR framework for small-sample conditions. Full article

With the advancement of China’s carbon peaking and carbon neutrality targets and the low-carbon upgrading of the construction industry, steel fiber recycled aggregate concrete (SFRAC) has attracted increasing attention as a sustainable construction material due to its advantages in resource recycling and enhanced mechanical performance. However, its compressive strength is influenced by multiple interacting factors, making accurate prediction challenging when using conventional empirical or regression-based methods. To enhance predictive performance, a compressive strength database was established based on published experimental data. The input layer included seven mixture parameters: water content, cement content, fine aggregate content, natural coarse aggregate content, recycled coarse aggregate content, steel fiber content, and superplasticizer dosage, with the 28-day compressive strength serving as the output variable. Using this database, four prediction models were developed, including a back-propagation (BP) neural network and three optimized variants—GA–BP, PSO–BP, and GA–PSO–BP, optimized by genetic algorithm (GA) and particle swarm optimization (PSO)—were developed. Their performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Among the four models, GA–PSO–BP produced the best predictive performance, with a best-run R² of 0.9308 on the validation set, exceeding the BP, GA–BP, and PSO–BP neural networks by 0.0642, 0.0326, and 0.0512, respectively. Over 10 independent runs, it attained an average R² of 0.8822 and consistently delivered the lowest RMSE and MAE with small standard deviations, confirming its superior predictive accuracy and stability. These findings suggest that integrating GA and PSO can effectively enhance the predictive accuracy and stability of the BP neural network, thereby providing a dependable reference for compressive strength prediction and mix proportion optimization of steel fiber recycled aggregate concrete. Full article

Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature vector, implicitly assuming a single dominant driver such as precipitation, even though atmospheric moisture demand, radiation and wind-mediated evapotranspiration co-determine drought onset; (ii) wavelet preprocessing is typically applied to the full series, introducing future-information leakage that violates the operational causality requirement of forecasting; and (iii) most architectures predict a single horizon and provide no causal attribution explaining when, where and which climatic variables initiated the event. This study proposes WaveDroughtNet, a multi-modal, multi-horizon deep-learning framework that addresses these limitations through five integrated components: (a) a strictly causal Daubechies-4 wavelet decomposition computed in a rolling fashion; (b) six modality-specific encoders with stochastic modality dropout (p = 0.15); (c) cross-modal multi-head attention with four heads; (d) a four-layer temporal convolutional network (TCN) backbone with dilation factors yielding a 240-step receptive field; and (e) a post hoc DroughtOriginTracer that combines temporal attention, modal-attribution and inter-district propagation scans. The Standardised Precipitation Evapotranspiration Index (SPEI), used as the supervisory target, is computed following the canonical Vicente-Serrano formulation. water balance $D=P-\mathrm{P}\mathrm{E}\mathrm{T}$ (Hargreaves PET) at a 4-week (≈1-month) timescale, fitted with a three-parameter log-logistic distribution via L-moments, validated by Kolmogorov–Smirnov goodness-of-fit testing ($\alpha =0.05$) per district, and standardised through the inverse-normal cumulative distribution function. Trained on 18,304 weekly district records from NASA POWER reanalysis (2014–2025) covering all 32 districts of Tamil Nadu, India, WaveDroughtNet uses only 256,869 parameters and produces, in a single forward pass, four forecasts (1 week, 1 month, 3 months, 1 year). On the held-out 2024 test partition ($N=1728$), the model attains weighted $F1=0.9221$ and $R^2=0.8512$ at the 1-week horizon, and weighted $F1=0.8498$ and $R^2=0.6812$ at the 1-year horizon. Diebold–Mariano tests confirm that WaveDroughtNet significantly outperforms naive persistence, seasonal naive, LSTM, ConvLSTM and a vanilla Transformer at the 3-month and 1-year horizons (p < 0.001). The DroughtOriginTracer successfully back-projects 15 Coimbatore events to causal origins 29–41 weeks prior to onset. We explicitly acknowledge three limitations that constrain operational deployment in its current form—zero severe events in the 2024 test partition ($F{1}_{severe}$ = 0.000), static inter-district modelling, and absence of vegetation-index supervision—and propose concrete mitigation pathways in the Discussion. Full article

The turbine-driven coaxial boiler feed pump (TD-BFP) speed regulation system is a core auxiliary machine in thermal power generating units. Its complex physical characteristics, including strong square-law nonlinearity, multivariable coupling, and large inertia, pose significant challenges for conventional fixed-parameter PID controllers, which often suffer from severe regulation lag, integral windup, and high-frequency oscillation during wide-range operating condition transitions. To address these issues, an improved adaptive PID control strategy based on a Back Propagation (BP) neural network is proposed in this paper. Specifically, to overcome the negative control gradient loss caused by the square-law resistance in the physical model, a sign-preserving mapping logic ($u\mid u\mid$) is innovatively designed. Furthermore, a dynamic anti-integral windup mechanism with physical boundary constraints and a first-order inertial filtering algorithm is introduced. Comprehensive simulation experiments on the Matlab/Simulink platform under high-load step operating conditions (3683 r/min and 1104 t/h) reveal that the proposed algorithm achieves millisecond-level, zero-overshoot tracking. Quantitative evaluations demonstrate that, compared with the traditional PID controller, the proposed method reduces the Root Mean Square Error (RMSE) by 88.29% and the Integral of Absolute Error (IAE) by 93.75%, achieving a near-perfect goodness of fit ($R^2$) of 0.9998. Additionally, the Total Variation (TV) of the control command is substantially decreased. These results convincingly demonstrate that the proposed controller perfectly balances extremely high dynamic fitting accuracy with reduced mechanical wear, presenting exceptional engineering application value for the localization transformation of power plant control systems. Full article

Hereditary damping and fractional attenuation are widely used to model wave propagation in complex media, but the variational and spectral origin of the corresponding nonlocal-in-time operators is often left implicit. In this work, we derive such operators from a minimal conservative field–reservoir model. A real scalar field is coupled locally to a continuum of harmonic reservoir modes, which are then eliminated exactly. The resulting reduced dynamics is a causal wave equation with a memory-friction term acting on the field velocity. The memory kernel is generated by the reservoir coupling spectrum through a cosine-transform relation, establishing a direct spectrum-to-kernel correspondence. This relation provides both a physical interpretation of hereditary damping and a practical admissibility criterion: macroscopic attenuation and dispersion arise from the delayed back-action of unresolved internal modes, while physically admissible kernels are constrained by the non-negativity of the underlying spectral density. The framework unifies several standard damping regimes. A broadband reservoir recovers the Markovian locally damped wave equation, reservoirs with a finite characteristic time generate finite-memory relaxation and frequency-dependent dispersion, and scale-free reservoir spectra produce power-law memory kernels. In the latter case, the hereditary damping operator reduces to a Caputo-type fractional derivative, showing that fractional wave attenuation can emerge as an effective reduced dynamics rather than being postulated phenomenologically. We further analyze dispersion, attenuation, causality, stability, and admissibility conditions in terms of the reservoir spectrum. The main contribution of the work is therefore to provide a variational and spectral derivation of hereditary and fractional wave damping, linking the structure of unresolved reservoir modes to macroscopic nonlocal wave dynamics. Full article

The controlled synthesis of long hydrophobic blocks in amphiphilic block copolymers remains challenging in living anionic ring-opening polymerization (LAROP), particularly when competing effects such as back-biting and solubility limitations are involved. In this study, we investigated the temperature-dependent growth of poly(allyl glycidyl ether) (PAGE) blocks in PEO-b-PAGE block copolymers synthesized via LAROP using potassium naphthalenide as a co-initiator. Systematic variation in reaction parameters revealed that reaction temperature plays a significant role in governing effective PAGE block extension and dispersity control. Lower temperatures facilitated the formation of longer PAGE blocks with dispersities below 1.1 and DP values approaching targeted compositions, whereas elevated temperatures limited block growth. A group method of data handling (GMDH) polynomial neural network was employed as an auxiliary tool to prioritize influential variables within the experimental design matrix. The GMDH-guided analysis consistently identified temperature as the most influential variable, in agreement with experimental observations. These results provide quantitative insight into the temperature-controlled propagation behavior of PAGE in LAROP systems and offer a practical framework for improving block copolymer synthesis under kinetically and thermodynamically constrained conditions. Full article

On 29 July 2025, an Mw 8.8 megathrust earthquake occurred offshore of the southeastern Kamchatka Peninsula, ranking among the ten largest earthquakes worldwide since 1900. Due to observational limitations, the rupture characteristics of large earthquakes along the Kamchatka subduction zone and the north–south contrast in earthquake magnitudes remain poorly understood. In this study, we combine InSAR data, GNSS displacements, and teleseismic waveforms to investigate the spatiotemporal evolution of the 2025 mainshock by constructing a curved fault geometry with along-strike and downdip variations and applying finite-fault inversion together with back-projection analysis. The inversion results show that the mainshock was characterized by unilateral rupture propagating from northeast to southwest, with a rupture length of about 560 km, a duration of about 200 s, and dominant slip concentrated at depths of 15–30 km, with a peak slip of about 10 m. Slip was weak during the initial nucleation stage near the hypocenter, whereas the main slip patch was located within a strongly locked region in the southern segment, and the rupture accelerated rapidly after entering that region. The back-projection results indicate that high-frequency radiation mainly migrated southwestward and was concentrated along the boundaries of the large-slip region and possible structural segmentation zones. These results indicate that the rupture behavior of the 2025 mainshock was jointly controlled by curved megathrust geometry and along-strike locking heterogeneity. The north–south contrast in earthquake size along the Kamchatka subduction zone may result from the combined effects of stronger locking and smoother megathrust geometry in the south, versus more complex fault geometry and submarine tectonic features in the north. This study provides new constraints on rupture processes, seismic cycle behavior, and regional seismic hazard along the Kamchatka subduction zone, and offers important implications for understanding the mechanisms and magnitude potential of future great earthquakes in the Kamchatka region. Full article

To enhance the accuracy, stability, and interpretability of residential building cost prediction models, and thereby provide a reliable basis for project investment decision-making. This study takes Sichuan Province as the research area and develops an improved sparrow search algorithm (ISSA). The performance of the Genetic Algorithm (GA), Wolf Pack Algorithm (WPA), Sparrow Search Algorithm (SSA), and ISSA was first evaluated and compared using benchmark test functions. Subsequently, nine prediction models, including Back Propagation Neural Network (BP), GA-BP, WPA-BP, SSA-BP, ISSA-BP, Random Forest (RF), ISSA-RF, Extreme Gradient Boosting (XGBoost), and ISSA-XGBoost, were established for comparative analysis. Finally, SHapley Additive exPlanations (SHAP) were employed to rank the key factors affecting construction cost. The results show that: (1) The ISSA algorithm demonstrates excellent convergence accuracy, stability and speed on benchmark test functions. (2) The ISSA-BP model achieved an average coefficient of determination (R²) of 0.9773, an average root mean square error (RMSE) of 39.2339, an average mean absolute error (MAE) of 17.0973, an average mean absolute percentage error (MAPE) of 0.6293, and an average mean bias error (MBE) of 9.1583. Compared with the other models, ISSA-BP exhibited the best overall predictive performance. (3) SHAP analysis indicates that indicators such as total building area and structure type have the greatest impact on project cost, while roof form and roof waterproofing have the least influence. This study can serve as a reference for refining and intelligently managing construction project costs. Full article

Object detection in wide-field-of-view (wide-FoV) unmanned aerial vehicle (UAV) imagery for Internet of Things (IoT) remote sensing applications requires accurate recognition of tiny objects under severe background redundancy and extreme scale variation. As the field of view expands, conventional dense detectors tend to waste substantial computation on non-informative regions, while feature downsampling and static receptive fields often cause the dilution of foreground information and scale confusion. To address these issues, we propose MFRC-Det, a unified framework built upon two complementary principles: mask-guided feature routing and adaptive context modeling. Specifically, a Superpixel-Masking Generator (SP-Masker) is introduced to estimate an image-space soft foreground prior by comparing Simple Linear Iterative Clustering (SLIC) superpixel histograms with a peripheral background reference, propagating the resulting scores on a superpixel adjacency graph, and projecting the refined region-level scores back to a pixel-level routing mask. Guided by these priors, a Greedy-Cutter (G-Cutter) converts dense feature maps into compact, foreground-focused patches without repeated backbone evaluation on cropped image regions, thereby reducing redundant background computation while preserving local structural coherence. On top of the retained regions, an Adaptive Receptive-field Selection Network (ARSNet) aggregates multi-scale contextual responses from several learnable receptive-field candidate branches. ARSNet predicts spatial selection weights conditioned on the input features, allowing each location to emphasize a suitable receptive-field response for object representation. Experimental results on VisDrone-DET and UAVDT demonstrate that MFRC-Det achieves competitive detection accuracy with favorable computational efficiency. Specifically, MFRC-Det obtains 36.1% AP, 60.4% $A{P}_{50}$, and 38.5 FPS on VisDrone-DET and 21.3% AP, 36.8% $A{P}_{50}$, and 37.4 FPS on UAVDT. These results validate the effectiveness of mask-guided feature routing and adaptive context modeling for wide-FoV UAV object detection and suggest their potential value for computation-efficient aerial perception in IoT remote sensing applications. Full article

In this article, a data-driven adaptive neural network additional damping controller (DDANN-ADC) is proposed to regulate rotor-side converters of a DFIG-based power system to suppress sub-synchronous control interaction (SSCI). Firstly, a back propagation (BP) intermediate variable observer is designed to construct a dynamic model of DFIG-based wind farms based on real-time input–output measurement data. Subsequently, a modified cost function is developed for a BP online controller to generate a target control law, thereby contributing additional damping to the DFIG-based power system. The proposed DDANN-ADC can effectively utilize limited data generated during the control process to achieve online system identification and precise control of the system. Then, the stability of DFIG-based power system under the proposed DDANN-ADC is demonstrated with the Lyapunov function. Finally, simulation results reveal that the proposed DDANN-ADC methodology outperforms the traditional method with better adaptability and robustness under different operational conditions. Full article

Controlled spacecraft reentries from interplanetary trajectories provide rare, well-characterized hypersonic sources for advancing seismoacoustic observation techniques. Here we present seismic observations of the OSIRIS-REx sample return capsule (SRC) reentry on 24 September 2023, recorded by 16 three-component nodal seismometers deployed near Eureka, Nevada, at ground distances of 7–20 km from the capsule trajectory. Air-to-ground coupled signals are detected at all stations, exhibiting impulsive onsets consistent with ballistic shock arrivals from the descending Mach cone. We characterize the seismic wavefield through signal amplitude, period, waveform cross-correlation, and array processing. Signal periods decrease systematically with increasing distance from the trajectory within the airport array, indicating that higher-frequency content becomes more prominent at greater offsets, opposite to expectations from geometric spreading and atmospheric absorption. Seismic array processing identifies frequency-dependent back-azimuth variations whose origin remains unresolved; possible contributing factors include source geometry, scattering by fine-scale layered structure in the stratosphere, and near-surface effects. These observations document a spatially complex seismic wavefield from a well-characterized hypersonic line source and provide constraints for future modeling of atmospheric propagation and air-to-ground coupling. Full article