Search Results (5,962)

A series of generalized fixed-bed physical model experiments were conducted to investigate the hydrodynamic effects of spur dike configuration and permeability. The study was carried out in a rectangular flume at a geometric scale of 1:40. A traditional impermeable spur dike, a novel impermeable spur dike with a curved geometry, and permeable spur dikes with varying porosities (p = 11.8%, 17.6%, and 23.2%) were systematically examined. Surface velocity and flow direction were measured using a large-scale surface flow field measurement system. Additionally, tracer-based experiments were conducted to characterize oil spill spreading pathways, areas, and rates. The results showed that the novel curved-profile spur dike alleviates upstream backwater effects and weakens downstream plunging flow compared to the conventional straight-profile spur dike, resulting in a more uniform surface flow structure. At low porosity (P = 11.8%), hydrodynamic behavior resembled that of impermeable structures. In contrast, at high porosity (P = 23.2%), upstream–downstream hydraulic connectivity was enhanced, and recirculation intensity was reduced. Regarding oil spill dispersion, spur dike promoted oil retention in the upstream region and lateral spreading around the spur dike head. The extent of the spreading area was strongly influenced by both the cross-sectional geometry and the porosity of the spur dike. Among the permeable cases, the largest spreading area was observed at an intermediate porosity (P = 17.6%). However, permeable spur dike generally exhibited smaller overall spreading areas compared to impermeable spur dike. Finally, an empirical model for predicting the oil spreading area was developed by incorporating flow velocity, water depth, and porosity. These findings provide a scientific basis for optimizing spur dike design and mitigating oil spill risks. Given the severe threat that oil pollution poses to aquatic environments, the retention capacity of spur dikes serves as a critical hydraulic barrier, thereby promoting environmental and ecological sustainability. Full article

Variable reluctance sensors are widely adopted for robust and contactless detection of motion in harsh and space-constrained environments. This paper presents a MEMS-based variable reluctance induction sensor for the noncontact characterization of rotating ferromagnetic targets, based on a micromachined planar micro-coil coupled with an external permanent magnet. The rotation of a ferromagnetic object modulates the magnetic circuit reluctance, generating a voltage signal across the micro-coil that encodes information on the target rotational speed, proximity, and cross-sectional shape. Sensor operation is investigated through a lumped-element magnetic–electrical circuit model and finite-element magnetostatic simulations, quantifying the effects of target diameter, distance, and angular position on the linked magnetic flux. Experimental validation is performed using rotating drill bits as representative targets and a dedicated high-gain, high-input-impedance front-end circuit to amplify the induced voltage. Measured results at fixed rotation frequency show periodic voltage waveforms whose amplitude and shape vary consistently with target geometry, proximity and speed. Reliable detection is achieved for rotational speeds up to 1500 rpm, for drill bit diameters as small as 5 mm, and at sensor-to-target distances up to 8 mm. These results demonstrate the potential of MEMS variable reluctance induction sensors for compact speed sensing and target shape detection. Full article

Given the increasing uncertainty in the global economy, enhancing urban economic resilience is now paramount to advancing coordinated regional development. As a key force reshaping factor allocation and economic structures, the digital economy’s role in empowering urban economic resilience holds significant theoretical and practical value, especially for closely linked riverine urban agglomeration. Using 2010–2022 panel data of 41 Yangtze River Delta cities, this paper constructs a multidimensional economic resilience evaluation system. Employing two-way fixed effects, instrumental variables, and Spatial Durbin Model, it systematically examines the impact mechanism, heterogeneity, and spatial spillover of the digital economy on urban economic resilience. The findings indicate the following: (1) The digital economy significantly enhances urban economic resilience. (2) Its empowering effect exhibits notable regional disparities, with metropolitan areas and core cities benefiting more substantially. (3) The digital economy strengthens surrounding cities through spatial spillovers, yet some peripheral cities remain trapped in “low–low” agglomeration. This paper offers important insights for global riverine urban agglomerations: promoting the digital economy should prioritize cross-regional governance, enhancing core cities’ leading role, and boosting targeted investment in digital infrastructure and innovation in peripheral areas. This approach is essential for narrowing regional differences and improving overall resilience and sustainability. Full article

Trajectory prediction is critical for safe robot navigation, yet standard deep learning models predominantly rely on the Mean Squared Error (MSE) criterion. While effective under ideal conditions, MSE-based optimization is inherently fragile to non-Gaussian impulsive noise—such as sensor glitches and occlusions—common in real-world deployment. To address this limitation, this paper proposes MEE-LSTM, a robust forecasting framework that integrates Long Short-Term Memory networks with the Minimum Error Entropy (MEE) criterion. By minimizing Renyi’s quadratic entropy of the prediction error, our loss function introduces an intrinsic “gradient clipping” mechanism that effectively suppresses the influence of outliers. Furthermore, to overcome the convergence challenges of fixed-kernel information theoretic learning, we introduce a Silverman-based Adaptive Annealing (SAA) strategy that dynamically regulates the kernel bandwidth. Extensive evaluations on the ETH and UCY datasets demonstrate that MEE-LSTM maintains competitive accuracy on clean benchmarks while exhibiting superior resilience in degraded sensing environments. Notably, we identify a “Scissor Plot” phenomenon under stress testing: in the presence of 20% impulsive noise, the proposed model maintains a stable Average Displacement Error (ADE “≈” 0.51 m), whereas MSE baselines suffer catastrophic degradation (ADE > 2.1 m), representing a 75.7% improvement in robustness. This work provides a statistically grounded paradigm for reliable causal inference in hostile robotic perception. Full article

Accurate long-term temperature prediction is critical for the reliable operation of mass-produced electrical machines. However, due to the randomness inherent in the manufacturing process, machines with identical design parameters often exhibit distinct thermal properties. The aging of the insulation system can also lead to variation in thermal performance. Conventional lumped-parameter thermal network (LPTN) models with fixed parameters fail to account for these factors, thus leading to biased prediction results for long-term temperature forecasting of mass-produced machines. To enhance the robustness of LPTN models, this paper proposes a methodology for adaptive online parameter updating. Based on the mathematical formulation of LPTN, a fast Jacobian matrix calculation method for model prediction errors is developed, which avoids the time-consuming numerical computation process. To further alleviate the computational burden, key parameters with significant impacts on prediction errors are screened prior to each optimization iteration. These improvements collectively reduce computational resource requirements and enable real-time online implementation. Finally, experimental verification is conducted on a 10 kW permanent magnet machine. Comparative analyses against the numerical method and extended Kalman filter (EKF) demonstrate that the proposed method can be efficiently realized and is more effective in estimating the model parameters online. Full article

This study introduces an innovative approach for quantifying the technostress phenomenon, drawing on textual narratives from the firm’s annual report. Based on a dataset covering the Standard and Poor’s 500 (S&P 500) index firms, we analyze 2532 10-K annual reports and highlight the key contributors of technostress across six different dimensions of technostress using a combined score. A major advantage of the new six-dimensional scoring framework is that it offers a set of objective metric proxies to capture technostress without bias, utilizing a refined list of 42 key clues derived through factor analysis. Also, it adopts natural language processing, revealing hidden patterns and anomalies that indicate technostress. We further validate this framework by applying fixed-effect regression models to examine the impact of technostress on productivity. The main results imply that the four technostress dimensions presented in techno-risks, insecurity, uncertainty, and invasion negatively impact firms’ productivity. This framework offers practical implications for firms, allowing them to generate a rich profile concerning the degree of technostress associated with existing practices, highlighting the crucial need for advanced interventions, facilitating comparisons with other firms from the same or different industries, as well as cross-country comparisons. Full article

Background/Objectives: In the field of computer vision, prominent solutions often rely on transformers and convolutional neural networks (CNNs). Researchers frequently incorporate CNNs and transformers in developing image classification models. This study aims to introduce an innovative CNN model inspired by the Generative Pretrained Transformer (GPT) architecture and assess its image classification capabilities. Methods: This study utilized three distinct biomedical image datasets to evaluate the efficacy of the proposed GPTNeXt model. The datasets encompassed (i) Alzheimer’s disease (AD) magnetic resonance (MR) images, (ii) blood images, and (iii) lung cancer images. The choice of these datasets aimed to showcase the GPTNeXt model’s versatile classification performance. The GPTNeXt model and a deep feature engineering approach based on it were developed. In this deep feature engineering model, features were extracted from the global average pooling layer of GPTNeXt, and a novel deep feature extraction method was employed. This method extracted features from the entire image and generated nine fixed-size patches. To identify the most informative features, iterative neighborhood component analysis (INCA) was applied. The classification phase involved three shallow classifiers to produce classification results. Results: The GPTNeXt-based feature engineering model was applied to the three aforementioned biomedical image datasets, achieving classification accuracies exceeding 98% for all of them. Conclusions: This study demonstrates the high effectiveness of the proposed approach, as evidenced by the exceptional classification performance on the selected biomedical image datasets. Additionally, a lightweight CNN was introduced, showcasing outstanding classification performance. Full article

Neural machine translation (NMT) performance is strongly influenced by tokenization strategies, particularly for morphologically rich languages such as Arabic. Despite the importance of tokenization, there is a lack of controlled, reproducible studies examining its impact under low-resource conditions, which limits our understanding of how different methods affect translation quality and training dynamics. This paper presents a controlled experimental study analyzing the impact of different tokenization methods on English → Arabic (EN → AR) translation using a Tiny Transformer model under low-resource conditions. The study aims to provide a systematic and reproducible comparison that isolates the effect of tokenization choices under fixed modeling and training constraints. Experiments are conducted with identical architecture, training steps, decoding procedure, and evaluation pipeline to ensure reproducibility. Translation quality is assessed using multiple metrics including BLEU, ChrF++, TER, and BERTScore, revealing substantial divergences and demonstrating empirically, in the context of low-resource Arabic NMT, that BLEU alone is insufficient for reliable evaluation. While the limitations of BLEU are known in general, our results provide new evidence showing that, under low-resource conditions and across different tokenization strategies, reliance on BLEU can lead to misleading conclusions about translation quality. Training dynamics are analyzed using TensorBoard, linking tokenization strategies to differences in convergence, saturation, and stability. For validation, a small-scale English → German (EN → DE) experiment confirms that the Tiny Transformer setup reproduces expected behavior. The contribution of this work lies in establishing controlled empirical evidence and practical insights, rather than absolute performance gains, for low-resource Arabic NMT. Our results provide controlled evidence that tokenization choice critically affects both translation quality and optimization dynamics, offering practical guidance for low-resource Arabic NMT research. Overall, byte-pair encoding (BPE) achieves the strongest balance across surface-level and semantic metrics under controlled low-resource conditions (BLEU: 8.57, ChrF++: 18.56, TER: 97.38, BERTScore-F1: 0.785). Character-level tokenization yields higher semantic similarity than subword-based methods, as reflected by BERTScore, but remains weaker in structural fidelity and surface-form accuracy, while SentencePiece exhibits intermediate behavior, favoring semantic adequacy over exact n-gram matching. These results confirm that tokenization choice critically influences both evaluation outcomes and optimization behavior, and that BLEU alone is insufficient for assessing Arabic translation quality. Full article

Background: Endometrial cancer (EC) represents one of the most prevalent gynecological malignancies worldwide, with increasing incidence rates attributed to rising obesity, metabolic syndrome, and demographic aging. Recent evidence suggests that dyslipidemia, including elevated triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and reduced high-density lipoprotein cholesterol (HDL-C), may have a significant role in the pathogenesis of EC through inflammatory, oxidative stress, and hormonal mechanisms. Objective: This meta-analysis aims to systematically evaluate the association between serum lipid biomarkers and endometrial cancer risk by synthesizing quantitative data from observational studies. Methods: We conducted a comprehensive search of five electronic databases (PubMed, Web of Science, Scopus, EMBASE, and Cochrane) to identify studies examining lipid biomarkers in patients with EC compared to healthy controls. After screening 639 articles and applying rigorous inclusion/exclusion criteria, six studies were selected for final analysis. The standardized mean differences (SMD) were calculated with 95% confidence intervals using random-effects and fixed-effects models, considering heterogeneity assessed by the I² statistic. Publication bias was evaluated using funnel plots and Egger’s regression test. Results: The meta-analysis revealed significantly elevated TG levels in EC patients compared to controls (SMD +0.87, 95% CI [+0.65, +1.10]), markedly reduced HDL-C levels (SMD −0.92, 95% CI [−1.15, −0.69]), and increased LDL-C levels (SMD +0.74, 95% CI [+0.50, +0.98]). The heterogeneity was moderate to substantial, with an I² ranging from 49% to 62%. Subgroup analyses demonstrated stronger associations in Type I EC and obese patients (BMI > 30 kg/m²). Conclusions: This meta-analysis establishes a significant association between dyslipidemia and endometrial cancer risk, with elevated triglycerides and LDL-C conferring increased risk while HDL-C appears protective. These findings support the integration of lipid profiling into EC risk assessment protocols and suggest the potential preventive value of lipid-modulating interventions. Further studies are needed to establish causality and evaluate therapeutic applications. Full article

Medical image classification is a fundamental task in medical image analysis and underpins a wide range of clinical applications, including dermatological screening, retinal disease assessment, and malignant tissue detection. In recent years, diffusion models have demonstrated promising potential for medical image classification owing to their strong representation learning capability. However, existing diffusion-based classification methods often rely on oversimplified prior modeling strategies, which fail to adequately capture the intrinsic multi-scale semantic information and contextual dependencies inherent in medical images. As a result, the discriminative power and stability of feature representations are constrained in complex scenarios. In addition, fixed noise injection strategies neglect variations in sample-level prediction confidence, leading to uniform perturbations being imposed on samples with different levels of semantic reliability during the diffusion process, which in turn limits the model’s discriminative performance and generalization ability. To address these challenges, this paper proposes a Confidence-Guided Adaptive Diffusion Network (CGAD-Net) for medical image classification. Specifically, a hybrid prior modeling framework is introduced, consisting of a Hierarchical Pyramid Context Modeling (HPCM) module and an Intra-Scale Dilated Convolution Refinement (IDCR) module. These two components jointly enable the diffusion-based feature modeling process to effectively capture fine-grained structural details and global contextual semantic information. Furthermore, a Confidence-Guided Adaptive Noise Injection (CG-ANI) strategy is designed to dynamically regulate noise intensity during the diffusion process according to sample-level prediction confidence. Without altering the underlying discriminative objective, CG-ANI stabilizes model training and enhances robust representation learning for semantically ambiguous samples.Experimental results on multiple public medical image classification benchmarks, including HAM10000, APTOS2019, and Chaoyang, demonstrate that CGAD-Net achieves competitive performance in terms of classification accuracy, robustness, and training stability. These results validate the effectiveness and application potential of confidence-guided diffusion modeling for two-dimensional medical image classification tasks, and provide valuable insights for further research on diffusion models in the field of medical image analysis. Full article

Independent directors play a critical role in overseeing company management, safeguarding the interests of both the company and its shareholders, and ensuring that decisions made by the board are scientific, rational, and fair. Directors with industry expertise bring greater experience and knowledge to their roles, enabling them to prevent short-sighted decision-making while preserving their professional reputations. This research empirically examines whether the industry expertise trait of independent directors can inhibit the irregularities of the companies they serve, using a fixed-effects model that controls for industry, company, and year, with Chinese A-share-listed companies from 2003 to 2023 as the observational sample. Endogeneity issues are addressed by using the Heckman two-stage model and the propensity score matching (PSM) model. The findings reveal that (1) independent directors with industry expertise significantly mitigate corporate violations; and (2) their influence primarily stems from improvements in the quality of information disclosure, enhancements to internal control systems, and the resolution of principal–agent conflicts. Further analysis indicates that the restraining effect of independent directors with industry expertise is particularly pronounced in environments characterized by low institutional ownership and fewer analysts, highlighting their stronger supervisory role in such contexts. Full article

The accurate remote sensing retrieval of chlorophyll-a (Chla) concentrations in highly turbid estuarine waters remains challenging due to complex optical conditions. In this study, a small sample machine learning-based retrieval framework tailored for limited training samples was developed for the Pearl River Estuary (PRE) by integrating Sentinel-3 OLCI satellite imagery with long-term fixed-station Chla observations from the Hong Kong Environmental Protection Department. Normalized remote sensing reflectance features derived from multiple OLCI spectral bands were used as model inputs, and the performance of support vector regression (SVR) and a back propagation neural network (BPNN) was evaluated and compared with those of traditional second-order polynomial models. The results show that SVR achieves the best overall performance on both training and independent testing datasets, with a higher accuracy, smaller systematic bias, and more stable generalization capability, demonstrating its effectiveness in capturing complex nonlinear relationships under limited sample conditions. Specifically, for the training and testing datasets, the correlation coefficients between SVR-predicted and measured Chla reach 0.88 and 0.78, RMSEs are 1.75 and 1.23 mg/m³, and biases are −0.29 and 0 mg/m³, respectively. The retrieval results further reveal the clear spatiotemporal patterns of Chla concentration in the PRE, characterized by a west–high and east–low spatial distribution and pronounced seasonal migration. Elevated Chla concentrations occur mainly in the lower estuary during summer, retreat toward the upper estuary in winter, and shift to the middle estuary during spring and autumn. This study provides a practical methodological reference for the operational remote sensing monitoring of water quality in optically complex and highly turbid estuarine environments. Full article

Background: Foam rolling is a popular self-myofascial release (SMR) technique, yet empirical evidence regarding its long-term impact on cycling endurance remains inconclusive. This study investigated the effects of chronic SMR on cardiorespiratory capacity, metabolic kinetics, and mechanical performance in road cyclists. Methods: We conducted a six-month randomized controlled trial (RCT) with 32 male recreational cyclists. Both an intervention group (IG) and a control group (CG) followed a standardized training protocol. The IG additionally applied a Blackroll^® foam roller immediately after cycling training sessions. Outcomes included maximum oxygen uptake (VO₂max), submaximal heart rate, lactate slope, and relative mechanical power (W/kg) at aerobic and anaerobic thresholds. Data were analyzed using linear mixed-effects models (LMM), with age included as a fixed-effect covariate to control for baseline imbalances between groups. Effect sizes were determined via marginal and conditional R². Additionally, model robustness was verified through Shapiro–Wilk tests and Q–Q plots of conditional residuals. Results: No significant effects were observed for VO₂max or submaximal heart rate. In contrast the IG demonstrated significant improvements in metabolic kinetics, evidenced by a reduced lactate slope (p = 0.004). Furthermore, foam rolling yielded a statistically significant positive effect on relative mechanical performance at both the aerobic (p = 0.031) and anaerobic (p = 0.007) lactate thresholds. Sensitivity analyses confirmed that these effects were independent of the age difference between groups. Conclusions: Foam rolling did not enhance all endurance-related variables but showed positive effects on metabolic kinetics and mechanical performance. While it did not shift systemic cardiorespiratory limits, SMR appeared to optimize performance through improved metabolic economy and mechanical efficiency, suggesting it is a valuable supplemental tool for recovery and long-term performance maintenance in cycling. Full article

The widespread connectivity of the Industrial Internet of Things (IIoT) improves the efficiency and functionality of connected devices. However, it also raises serious concerns about cybersecurity threats. Implementing an effective intrusion detection system (IDS) for IIoT is challenging due to heterogeneous data, high feature dimensionality, class imbalance, and the risk of data leakage during evaluation. This paper presents a leakage-safe hybrid intrusion detection framework that combines text-based and numerical network flow features in an IIoT environment. Each network flow is converted into a short text description and encoded using a frozen Large Language Model (LLM) called the Bidirectional Encoder Representations from Transformers (BERT) model to obtain fixed semantic embeddings, while numerical traffic features are standardized in parallel. To improve class separation, class prototypes are computed in Principal Component Analysis (PCA) space, and cosine similarity scores for these prototypes are added to the feature set. Class imbalance is handled only in the training data using the Synthetic Minority Over-sampling Technique (SMOTE). A Random Forest (RF) is used to select the top features, followed by a Histogram-based Gradient Boosting (HGB) classifier for final prediction. The proposed framework is evaluated on the Edge-IIoTset and ToN_IoT datasets and achieves promising results. Empirically, the framework attains 98.19% accuracy on Edge-IIoTset and 99.15% accuracy on ToN_IoT, indicating robust, leakage-safe performance. Full article

We examine the effects from small, spatially localized permanent charges on ionic transport in narrow membrane channels. Our analysis is based on a one-dimensional steady-state Poisson–Nernst–Planck (PNP) model involving two oppositely charged ion species with constant diffusion coefficients under electroneutral boundary conditions. In the framework of geometric singular perturbation theory, the steady PNP system is reformulated as a fast–slow dynamical system amenable to boundary-layer analysis. In the limit of vanishing permanent charge, the solution exhibits a singular structure with sharp boundary-layer segments and smooth bulk segments across regions of piecewise constant charge. Assuming the permanent charge strength Q is small, we carry out a regular perturbation expansion about $Q=0$ and derive explicit first-order corrections to each ion’s flux. Closed-form expressions are obtained for both the leading-order (zero-charge) fluxes and the $O\left(Q\right)$ flux corrections, revealing how even a small fixed charge can modulate the magnitude of individual ionic fluxes as a function of the applied transmembrane voltage and boundary concentration asymmetry. These results elucidate how permanent charge enhances or inhibits specific ionic flows, thereby influencing channel selectivity. Overall, our analysis provides clear asymptotic formulas and highlights the broader relevance of this perturbative approach to electro-diffusive transport modeling in biophysical systems. Full article