Search Results (6,050)

Accurate braking-distance prediction for heavy-duty multi-axle trucks remains challenging due to the large gross vehicle weight, tandem-axle interactions, and strong transient load transfer during emergency braking. Recent studies on tire–road friction estimation, commercial-vehicle braking control (EBS/AEBS), and weigh-in-motion (WIM) sensing have highlighted that unmeasured vertical-load dynamics and time-varying friction are key sources of prediction uncertainty. To address these limitations, this study proposes an integrated sensing–simulation–AI framework that combines real-time axle-load estimation, full-scale robotic braking tests, fused road-friction sensing, and physics-consistent machine-learning modeling. A micro-electro-mechanical systems (MEMS)-based load-angle sensor was installed on the leaf-spring panel linking tandem axles, enabling the continuous estimation of dynamic vertical loads via a polynomial calibration model. Full-scale on-road braking tests were conducted at 40–60 km/h under systematically varied payloads (0–15.5 t) using an actuator-based braking robot to eliminate driver variability. A forward-looking optical friction module was synchronized with dynamic axle-load estimates and deceleration signals, and additional scenarios generated in a commercial ASM environment expanded the operational domain across a broader range of friction, grade, and loading conditions. A gradient-boosting regression model trained on the hybrid dataset reproduced measured stopping distances with a mean absolute error (MAE) of 1.58 m and a mean absolute percentage error (MAPE) of 2.46%, with most predictions falling within ±5 m across all test conditions. The results indicate that incorporating real-time dynamic axle-load sensing together with fused friction estimation improves braking-distance prediction compared with static-load assumptions and purely kinematic formulations. The proposed load-aware framework provides a scalable basis for advanced driver-assistance functions, autonomous emergency braking for heavy trucks, and infrastructure-integrated freight safety management. All full-scale braking tests were carried out at approximately 60% of the nominal service-brake pressure, representing non-panic but moderately severe braking conditions, and the proposed model is designed to accurately predict the resulting stopping distance under this prescribed braking regime rather than to minimize the absolute stopping distance itself. Full article

This meta-analysis investigates the acute (immediate) pre–post changes in the modulation of physiological tremor in healthy adults following physical exercise, including resistance-based protocols. Physiological tremor is characterized by low-amplitude, high-frequency oscillations during posture or movement and reflects transient changes in neuromuscular control. Background/Objectives: Quantify the pooled effect of physical exercise on physiological tremor amplitude in healthy adults using magnitude-based metrics (RMS, peak power). A secondary objective was to synthesize evidence from acute resistance-based protocols separately. Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines and followed the methodological framework outlined in the Cochrane Handbook for Systematic Reviews of Interventions. Thirteen experimental studies met the inclusion criteria, with eleven included in the general exercise analysis and eight in the acute resistance-based subset. Results: Random-effects models revealed a moderate reduction in tremor amplitude following acute exercise (Hedges’ g = −0.42, p < 0.001). The resistance-based synthesis was restricted to acute single-session protocols only and indicated a directionally consistent reduction in tremor amplitude. Conclusions: These findings suggest that physical exertion is associated with transient suppression of physiological tremor amplitude. Acute single-session resistance-based exercise protocols showed a consistent direction of effect, although pooled estimates should be interpreted cautiously due to heterogeneity. Overall, physiological tremor may serve as a sensitive, non-invasive outcome measure reflecting short-term neuromuscular state. Full article

Heavy precipitation events are characterized by sudden onset, limited spatiotemporal scales, rapid evolution, and high disaster potential, posing long-standing challenges in weather forecasting. With the development of deep learning, an increasing number of researchers have leveraged its powerful feature representation and non-linear modeling capabilities to address the challenge of precipitation nowcasting. Despite recent advances in deep learning for precipitation nowcasting, most existing methods do not explicitly separate precipitation from non-precipitation regions. This often leads to the extraction of redundant or irrelevant features, thereby causing models to learn misleading patterns and ultimately reducing their predictive capability for heavy precipitation events. To address this issue, we propose a novel dynamic region attention (DRA) mechanism, and an improved model TPDTC-Net-DRA, based on our previously introduced TPDTC-Net. The proposed TPDTC-Net-DRA applies the DRA mechanism and incorporates its two key components: a dynamic region module and a weight control module. The dynamic region module generates a mask matrix that is applied to the feature maps, guiding the attention mechanism to focus only on precipitation areas. Meanwhile, the weight control module produces a location-sensitive weight matrix to direct the model’s attention toward regions with intense precipitation. Extensive experiments demonstrate that TPDTC-Net-DRA achieves superior performance for heavy precipitation, outperforming current state-of-the-art methods, and indicate that the proposed DRA mechanism exhibits strong generalization ability across diverse model architectures. Full article

Precise short-term forecasting of wind power generation is indispensable for ensuring the security and economic efficiency of power grid operations. Nevertheless, the inherent non-stationarity and stochastic nature of wind power series present significant challenges for prediction accuracy. To address these issues, this paper proposes a dual-optimized hybrid deep learning framework combining Spearman correlation analysis, RIME-VMD, and TCN-BiGRU-SA. First, Spearman correlation analysis is employed to screen meteorological factors, eliminating redundant features and reducing model complexity. Second, an adaptive Variational Mode Decomposition (VMD) strategy, optimized by the RIME algorithm based on Minimum Envelope Entropy, decomposes the non-stationary wind power series into stable intrinsic mode functions (IMFs). Third, a hybrid predictor integrating Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and Self-Attention (SA) mechanisms is constructed to capture both local trends and long-term temporal dependencies. Furthermore, the RIME algorithm is utilized again to optimize the hyperparameters of the deep learning predictor to avoid local optima. The proposed framework is validated using full-year datasets from two distinct wind farms in Xinjiang and Gansu, China. Experimental results demonstrate that the proposed model achieves a Root Mean Square Error (RMSE) of 7.5340 MW on the primary dataset, significantly outperforming mainstream baseline models. The multi-dataset verification confirms the model’s superior prediction accuracy, robustness against seasonal variations, and strong generalization capability. Full article

Laser Doppler vibrometer (LDV) has the characteristics of long-distance, non-contact, and high sensitivity, and plays an increasingly important role in industrial, military, and security fields. Remote speech acquisition technology based on LDV has progressed significantly in recent years. However, unlike microphone receivers, LDV-captured signals have severe signal distortion, which affects the quality of the LDV-captured speech. This paper proposes a nested U-network with gated temporal convolution (TCNUNet) to enhance monaural speech based on LDV. Specifically, the network is based on an encoder-decoder structure with skip connections and introduces nested U-Net (NUNet) in the encoder to better reconstruct speech signals. In addition, a temporal convolutional network with a gating mechanism is inserted between the encoder and decoder. The gating mechanism helps to control the information flow, while temporal convolution helps to model the long-range temporal dependencies. In a real-world environment, we designed an LDV monitoring system to collect and enhance voice signals remotely. Different datasets were collected from various target objects to fully validate the performance of the proposed network. Compared with baseline models, the proposed model achieves state-of-the-art performance. Finally, the results of the generalization experiment also indicate that the proposed model has a certain degree of generalization ability for different languages. Full article

Social acceleration and recurrent structural shocks increase habitus–field mismatch, yet similar exposure does not produce uniform trajectories of daily well-being or suicidal distress. This paper asks how comparable structural strain can generate divergent, path-dependent outcomes and why suicidal vulnerability may persist after objective conditions improve. We develop a theory-building, concept-driven framework that integrates Bourdieu’s practice theory with social and behavioural scholarship on stress, anomie, and despair, and conceptualises these dynamics as social hysteresis. The regime-based model specifies two ideal-typical response orientations through which mismatch can stabilise: an anomic regime marked by shame, withdrawal, and inwardly directed harm, and a radicalising regime marked by grievance framing, moral indignation, and organised participation, without implying violent extremism. Represented through hysteresis loops, the framework implies multistability, asymmetric switching thresholds, and scarring, providing a mechanism for persistence and non-linearity in distress trajectories. The model derives testable expectations for longitudinal panel and experience-sampling designs and suggests that prevention and intervention design should combine reductions in mismatch with relational and institutional infrastructures that facilitate regime shifts and reopen the space of possibles. Full article

This study addresses the issue of missing basic data and insufficient accuracy in predicting runoff and non-point-source pollution in the Heilongjiang region of China using the Soil and Water Assessment Tool (SWAT) model. Based on the China Ground Climate Data Daily Dataset (V3.0) and SPAW soil characteristic calculation formula, and assisted by the Python V3.0 language for data processing and computation, new high-precision weather generators and soil attribute databases suitable for the Heilongjiang region of China were established. The weather generator is based on daily data and contains detailed meteorological parameters such as temperature, humidity, wind speed, rainfall, etc., used to characterize the periodic changes in meteorological elements. And the differences and fluctuations outside this change curve were also retained in the basic construction of the weather generator. The soil database covers various parameters, such as soil type, texture, structure, nutrient content, organic matter content, etc., enabling the SWAT model to better simulate hydrological and pollutant transport processes in the soil. Additionally, point-source input data, including various industrial and domestic wastewater discharge situations, were collected and organized to improve data quality. Furthermore, a series of agricultural management measures were developed based on the use of fertilizers and pesticides for simulation, providing an important basis for analyzing non-point-source pollution using the SWAT model. By comparing the different results of the simulation using optimized databases, it is shown that the above work improved the simulation accuracy of the SWAT model in predicting runoff and pollution load in Heilongjiang, China. The NSE of runoff simulation increased from 0.923 to 0.988, and the NSE of ammonia nitrogen and CBOD simulation increased from 0.852 and 0.758 to 0.930 and 0.902, respectively. It is expected that these efforts will provide strong data support for subsequent research and provide a theoretical basis for government decision-makers to build scientifically rigorous and effective pollution control strategies. Full article

In this paper, a wireless network architecture is considered that combines double intelligent reflecting surfaces (IRSs), energy harvesting (EH), and non-orthogonal multiple access (NOMA) with cooperative relaying (C-NOMA) to leverage the performance of non-line-of-sight (NLoS) communication mainly and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase shift configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behavior and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication compared with conventional systems, thereby facilitating green communication systems. Full article

This paper presents a human-centered, AI-driven framework for font design that reimagines typography generation as a collaborative process between humans and large language models (LLMs). Unlike conventional pixel- or vector-based approaches, our method introduces a Continuous Style Projector that maps visual features from a pre-trained ResNet encoder into the LLM’s latent space, enabling zero-shot style interpolation and fine-grained control of stroke and serif attributes. To model handwriting trajectories more effectively, we employ a Mixture Density Network (MDN) head, allowing the system to capture multi-modal stroke distributions beyond deterministic regression. Experimental results show that users can interactively explore, mix, and generate new typefaces in real time, making the system accessible for both experts and non-experts. The approach reduces reliance on commercial font licenses and supports a wide range of applications in education, design, and digital communication. Overall, this work demonstrates how LLM-based generative models can enhance creativity, personalization, and cultural expression in typography, contributing to the broader field of AI-assisted design. Full article

Non-destructive estimation of leafy vegetable fresh weight is crucial for precision management in both greenhouse and open-field production. However, mutual occlusion between plants in dense canopies poses a significant challenge to image-based estimation accuracy. This study systematically investigates the potential of deep learning-based image inpainting methods to reconstruct occluded regions in RGB lettuce images, thereby improving input data quality for downstream weight estimation models. Three state-of-the-art inpainting models—Vision Transformer-based Denoising Autoencoder (ViT-DAE), Aggregated Contextual–Transformation Generative Adversarial Network (AOT-GAN), and a conditional Diffusion Model (CDM)—were implemented and evaluated. A dataset comprising 503 individual lettuce images with artificially generated random occlusions was used for training and testing. Performance was assessed using pixel-level metrics (PSNR, SSIM) and, more importantly, by evaluating the fresh weight estimation accuracy (R², NRMSE, MAPE) of a pre-trained CNN model (CNN_284) using the inpainted images. Results indicated that AOT-GAN achieved the best overall performance, with an SSIM of 0.9379 and an R² of 0.8480 for weight estimation after inpainting under single-direction occlusion, closely matching the performance using original non-occluded images (R² = 0.8365). In complex multi-direction occlusion scenarios, AOT-GAN demonstrated superior robustness, maintaining an R² of 0.7914 and an MAPE of 12.02% for weight prediction, significantly outperforming the other models. This study demonstrates that advanced inpainting techniques, particularly AOT-GAN, can effectively mitigate the impact of occlusion, enhancing the reliability of vision-based leafy vegetable biomass estimation in practical production. Full article

A principled, model-agnostic framework for structural feature extraction in time series is presented, grounded in topological data analysis (TDA). The motivation stems from two gaps identified in the literature: First, compact and interpretable representations that summarise the global geometric organisation of trajectories across scales remain scarce. Second, a unified, task-agnostic protocol for evaluating structure preservation against established non-topological families is still missing. To address these gaps, time-delay embeddings are employed to reconstruct phase space, sliding windows are used to generate local point clouds, and Vietoris–Rips persistent homology (up to dimension two) is computed. The resulting persistence diagrams are summarised with three transparent descriptors—persistence entropy, maximum persistence amplitude, and feature counts—and concatenated across delays and window sizes to yield a multiscale representation designed to complement temporal and spectral features while remaining computationally tractable. A unified experimental design is specified in which heterogeneous, regularly sampled financial series are preprocessed on native calendars and contrasted with competitive baselines spanning lagged, calendar-driven, difference/change, STL-based, delay-embedding PCA, price-based statistical, signature (FRUITS), and network-derived (NetF) features. Structure preservation is assessed through complementary criteria that probe spectral similarity, variance-scaled reconstruction fidelity, and the conservation of distributional shape (location, scale, asymmetry, tails). The study is positioned as an evaluation of representations, rather than a forecasting benchmark, emphasising interpretability, comparability, and methodological transparency while outlining avenues for adaptive hyperparameter selection and alternative filtrations. Full article

An extended operational space kinematics and dynamics formulation is presented for the control of redundant non-serial compound robotic manipulators. A broad spectrum of high-load-capacity non-serial manipulators used in earth moving, material handling, and construction applications is addressed. Departing from conventional approaches that rely on Jacobian pseudoinverses and local null-space projections, a globally valid, differential-geometry-based, multi-valued inverse kinematic mapping is defined at the configuration level, with the explicit self-motion parameterization of manipulator redundancy. The formulation yields coupled second-order ordinary differential equations of manipulator dynamics on the product space of task variables and self-motion coordinates. This enables the direct integration of system dynamics with control strategies, such as model predictive control or feedback design, while maintaining task constraint compliance. The methods presented are validated through the simulation and control of a non-serial compound material loader manipulator with multiple degrees of redundancy, demonstrating advantages in generality, numerical accuracy, and trajectory smoothness. Full article

In shallow-water high-speed mobile acoustic channels, severe non-uniform Doppler effects pose significant challenges to traditional equalization methods based on linear and time-invariant channel assumptions. Existing approaches typically rely on inverse compensation strategies, which are inadequate for handling path-dependent nonlinear Doppler distortions and fail to accurately reflect the underlying physical propagation process. To address these limitations, this paper proposes a forward reference-sample equalization (FRSE) method. Based on estimated channel parameters, forward channel modeling is performed for all possible transmitted symbols to generate a reference-sample matrix that is consistent with channel-induced distortions. At the receiver, a least-squares decision criterion is employed to match the received signal with the closest reference sample, thereby enabling reliable demodulation. Simulation results demonstrate that, at a high relative speed of 30 kn and a signal-to-noise ratio (SNR) of 8 dB, the proposed method achieves a bit error rate (BER) of $1.75\times {10}^{-4}$, significantly outperforming conventional equalization methods. Furthermore, sea trial experiments validate the robustness of the proposed approach in real shallow-water environments. By avoiding signal inversion, FRSE achieves improved detection reliability and strong robustness against non-uniform Doppler effects, highlighting its potential for practical underwater acoustic communication applications. Full article

The scorpion family Buthidae, renowned for its neurotoxin-rich venoms, dominates toxinology, while non-buthid venoms remain largely unexplored. Here, we present a comprehensive proteomic and biochemical characterization of the Amazonian chactid scorpion Brotheas amazonicus venom (BamazV), with emphasis on molecular complexity, proteolytic processing, and peptide diversity. Using an integrative venomics approach that combines molecular mass-based fractionation, reversed-phase chromatography, high-resolution mass spectrometry, N-terminal sequencing, and functional and immunological analyses, we reveal an unexpectedly complex venom profile enriched in high-molecular-weight components and extensively processed peptides, with more than 40 venom peptides sequenced by MS/MS and Edman degradation. The data provide evidence for non-canonical proteolytic events, including the generation of peptides from precursor regions not classically associated with mature venom components. In contrast to the venom of Tityus serrulatus, BamazV displays a “hydrolase-rich, neurotoxin-poor” profile, featuring a catalytically active Group III phospholipase A₂ (BamazPLA₂), a highly active hyaluronidase, metalloproteases, low-mass peptides, and potassium channel toxins. Our results suggest a hydrolytic prey-subjugation strategy, and limited cross-reactivity with commercial antivenom highlighted its distinct structural landscape. Overall, this study advances the understanding of venom evolution and proteolytic diversification in underexplored scorpion lineages, positioning B. amazonicus as a valuable model for investigating alternative venom strategies and identifying novel biotechnological scaffolds. Full article

This study examines how the design and interaction features of AI-powered fintech chatbots shape the customer experience of Generation Z users by integrating the Stimulus-Organism-Response framework with dual-process perspectives. Two cross-sectional surveys were conducted in Türkiye. Study 1 (n = 166) examines the effect of social presence, interactivity, visual appeal, design originality, and usability on perceived competence and perceived warmth, which, in turn, shape the customer experience. Social presence and design originality significantly increased perceived competence (β = 0.47, p < 0.001), while visual appeal enhanced perceived warmth (β = 0.32, p < 0.001). Together, competence and warmth explained a substantial proportion of customer experience (R² ≈ 0.60). Usability and interactivity showed no significant effects. Study 2 (n = 195) replicated these findings with trained users and introduced task complexity as a moderator. Under high task complexity, usability and interactivity became significant predictors of competence, which emerged as the primary driver of customer experience, whereas the influence of warmth diminished. Non-normal data distributions justified the use of Partial Least Squares Structural Equation Modeling. Overall, the findings suggest a shift from heuristic to systematic processing as fintech tasks become more complex, highlighting the growing importance of competence-based evaluations in fintech chatbot interactions. Full article