Search Results (140)

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

Background/Objectives: While a preference for an equal distribution of health gains is common, there are situations where individuals may opt to concentrate health gains for a select few. This study investigates how distributive preferences, defined as societal valuations of alternative allocations of fixed total health benefits, vary with the magnitude of individual health gains. Methods: Using the person trade-off (PTO) method, we conducted an online survey with a nationally representative sample of Chinese adults (N = 500). The respondents evaluated five allocation programs differing in both individual health gain magnitude and number of beneficiaries. Distributive preferences are classified into five distinct types: diffusion, concentration, maximization, extreme egalitarianism and extreme inequality seeking. Threshold regression analysis identified critical transition points in preference patterns. Results: Non-maximizing tendencies were dominant (79% of the respondents). The health gain threshold was estimated to be 4.6 years (95% CI: [4.28, 4.85]): below this threshold, respondents tend to allocate smaller benefits to more patients (diffusion preference); above the threshold, people are inclined to allocate larger benefits to fewer patients (concentration preference). The income level and self-reported health status of the participants were identified as potential factors influencing distributive preferences. Conclusions: This study provides the first quantitative evidence from China that distributive preferences exhibit a non-linear shift based on the magnitude of health benefits. The identified 4.6-year threshold provides policymakers with an empirically based instrument to strike a balance between efficiency and the reduction in inequality in resource allocation. These findings advocate for incorporating social value weights into health technology assessments, especially for interventions that offer substantial individual benefits. Full article

Geotechnical slope failures—often precursors to catastrophic landslides and collapses—pose significant risks to mining operations and regional socioeconomic stability. Focusing on the Jiangte Xikeng lithium open-pit mine, this study integrates field reconnaissance, laboratory testing, and multi-physics numerical modeling to elucidate the mechanisms governing slope stability. Geological surveys and core analyses reveal a predominantly granite lithostratigraphy, bisected by two principal fault systems: the NE-striking F01 and the NNE-oriented F02. Advanced three-dimensional finite element simulations—accounting for gravitational loading, hydrogeological processes, dynamic blasting stresses, and extreme rainfall events—demonstrate that strain localizes at slope crests, with maximum displacements reaching 195.7 mm under blasting conditions. They indicate that differentiated slope angles of 42° for intact granite versus 27° for fractured zones are required for optimal stability, and that the integration of fault-controlled instability criteria, a coupled hydro-mechanical-blasting interaction model, and zonal design protocols for heterogeneous rock masses provides both operational guidelines for hazard mitigation and theoretical insights into excavation-induced slope deformations in complex metallogenic environments. Full article

A holistic scientific measurement of the internet traffic across all devices in Germany has quantified the extreme extent of digital monopolization. Due to the high level of concentration, provider pluralism and fair competition in the field of digital media have been systematically and intentionally abolished. As a result of the digital transformation, it can be assumed that the GAFA (the known acronym for Google, i.e., Alphabet, Amazon, Facebook, i.e., Meta, Apple) players will take control of the German media system in the coming years (due to comparable market structures, the situation will be similar in other Western democracies). From a German and a European perspective, it is the more alarming that this development can hardly be stopped on the basis of existing legislation. However, already the status quo is in striking contradiction to the anti-monopolistic principles of classic German media law. It is time for a fundamental debate and quick legislative actions to open the media markets again for competition and plurality and safeguard media freedom for the future. Full article

Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events. Full article

Shock attenuation may be a clinically feasible method to assess changes in lower extremity joint loading induced by gait modifications, such as decreasing foot strike angle (forefoot striking). The purpose of this study was to identify changes in lower extremity shock attenuation between habitual and forefoot strike gait conditions. Eighteen participants ran on a treadmill with their habitual gait and an instructed forefoot strike gait. Shock attenuation was measured with inertial measurement units as the ratio of proximal to distal peak resultant/vertical accelerations, with three sensor combinations: ankle to below/above knee (BK/A; AK/A) and AK/BK. Three participants were excluded who were habitual forefoot strikers or failed to decrease their foot strike angle by at least 5° in the forefoot strike condition. The results showed significantly greater resultant shock attenuation in the forefoot strike compared to the habitual condition for BK/A (mean Δ = 0.13, p = 0.004) and AK/A (mean Δ = 0.23, p = 0.007). No significant differences were found for AK/BK or vertical shock attenuation. These results suggest that shock attenuation may not reflect joint-specific loading changes that have been shown for forefoot striking (i.e., increased ankle/shank and decreased knee moments). However, it may capture changes in overall lower extremity loading (i.e., decreased vertical ground reaction forces). Full article

The development of cost-effective and reliable railway monitoring technologies is crucial for the maintenance of modern infrastructure. Embedding sensors into rail pads has emerged as a promising approach for monitoring wheel–track interactions, but the successful implementation of these systems requires a robust framework for signal data acquisition and analysis. This study validates a custom-designed External Signal Acquisition Device (ESAD) for use with smart rail pads, comparing its performance against a high-precision commercial analog module. While the commercial module delivers exceptional accuracy, its high cost, bulky size, and complex installation requirements limit its practicality for large-scale railway applications. Laboratory-scale and full-scale experiments simulating real-world railway conditions demonstrated that the custom ESAD performs comparably to the commercial module. During simulated train passages, the ESAD showed reduced signal dispersion as load and train speed increased, confirming its ability to provide reliable calibration data. Moreover, the device maintained over 95% reliability in analyzing load-to-signal linearity, ensuring consistent and dependable performance in both laboratory and field settings. However, the ESAD does have limitations, including slightly lower resolution for low frequencies and potential sensitivity to extreme environmental conditions, which may affect its performance in specific scenarios. These findings highlight the ESAD’s potential to strike a balance between cost and functionality, making it a viable solution for widespread railway monitoring applications. This research contributes to the advancement of affordable and efficient railway monitoring technologies, fostering the adoption of preventive maintenance practices and enhancing overall infrastructure performance. Full article

Current trends in the aerospace and UAV sectors emphasize integrating Artificial Intelligence (AI) technologies into the design process. AI technologies necessitate extensive data to capture the non-linearities in fluid phenomena. To address these needs, this work focuses on automating the data aggregation process for fixed-wing platforms, ranging from Micro–Mini to HALE-Strike UAVs, as classified by NATO. Specifically, this paper presents a framework for automating the tedious tasks required for geometry generation, mesh generation, and solution setup in a commercial Computational Fluid Dynamics (CFD) solver, for any arbitrary wing within the aforementioned design space. By combining various well-established open-source suites and commercial software via Python scripting, the preprocessing steps up to the solution require only a few minutes on a typical laptop workspace. Despite the rapid geometry acquisition, mesh generation, and solution setup through the pipeline, the guidelines and common practices for subsonic external flow simulations are still strictly followed. This results in solutions with a deviation of merely sub 5% from those of an experienced designer, even for the extremes of the flight envelope. The proposed framework significantly reduces design iteration times, enabling more efficient and innovative UAV development. Additionally, the framework’s ability to accumulate high-quality data for machine learning enhances predictive modeling and optimization capabilities across UAV design practices. Full article

The growing penetration of renewable energy sources (RES) has exacerbated operational flexibility deficiencies in modern power systems under time-varying conditions. To address the limitations of existing flexibility management approaches, which often exhibit excessive conservatism or risk exposure in managing supply–demand uncertainties, this study introduces a data-driven distributionally robust optimization (DRO) framework for power system scheduling. The methodology comprises three key phases: First, a meteorologically aware uncertainty characterization model is developed using Copula theory, explicitly capturing spatiotemporal correlations in wind and PV power outputs. System flexibility requirements are quantified through integrated scenario-interval analysis, augmented by flexibility adjustment factors (FAFs) that mathematically describe heterogeneous resource participation in multi-scale flexibility provision. These innovations facilitate the formulation of physics-informed flexibility equilibrium constraints. Second, a two-stage DRO model is established, incorporating demand-side resources such as electric vehicle fleets as flexibility providers. The optimization objective aims to minimize total operational costs, encompassing resource activation expenses and flexibility deficit penalties. To strike a balance between robustness and reduced conservatism, polyhedral ambiguity sets bounded by generalized moment constraints are employed, leveraging Wasserstein metric-based probability density regularization to diminish the probabilities of extreme scenarios. Third, the bilevel optimization structure is transformed into a solvable mixed-integer programming problem using a zero-sum game equivalence. This problem is subsequently solved using an enhanced column-and-constraint generation (C&CG) algorithm with adaptive cut generation. Finally, simulation results demonstrate that the proposed model positively impacts the flexibility margin and economy of the power system, compared to traditional uncertainty models. Full article

A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As Typhoon Gaemi (05 W) was moving poleward into an unfavorable environment north of 20° N, a sharp westward turn to cross Taiwan was a challenge to forecast. The pre-Yagi (12 W) westward turn across Luzon Island, re-formation, and then extremely rapid intensification prior to striking Hainan Island were challenges to forecast. The slow intensification of Bebinca (14 W) after moving poleward across 20° N into an unfavorable environment was better forecast by the ECEPS than by the Joint Typhoon Warning Center (JTWC), which consistently over-predicted the intensification. An early westward turn south of 20° N by Kong-Rey (23 W) leading to a long westward path along 17° N and then a poleward turn to strike Taiwan were all track forecasting challenges. Four-dimensional COAMPS-TC Dynamic Initialization analyses utilizing high-density Himawari-9 atmospheric motion vectors are proposed to better define the TC intensities, vortex structure, and unfavorable environment for diagnostic studies and as initial conditions for regional model predictions. In Part 2 study of selected 2024 season TCs that started north of 20° N, more challenging track forecasts and slow intensification rates over an unfavorable TC environment will be documented. Full article

Lizards are ideal models for investigating animal adaptations to climate change, given their sensitivity to temperature and their significance in physiological ecology. In this study, the effects of temperature on the thermal biology and locomotor performance of two sympatric desert lizards, Eremias roborowskii and Phrynocephalus axillaris, were examined. We analyzed morphological differences, the relationship between environmental temperatures (T_e) and selected body temperatures (T_sel), and locomotor performance across varying T_e. We also assessed the critical thermal maximum (CT_max) and active body temperature (T_b) to evaluate current thermal conditions. Our results indicate that E. roborowskii’s T_sel line intersected isotherm at 27.37 °C is higher than P. axillaris (27.04 °C), and the difference in correlation coefficients between the T_sel line and isotherm indicates that P. axillaris exhibits a superior physiological thermoregulatory capacity, exhibiting less dependence on T_e. Locomotor performance assessments showed P. axillaris and E. roborowskii displayed distinct strengths in sprint speed, number of pauses, and maximum distance movement. Eremias roborowskii demonstrated better endurance with fewer pauses and a more consistent length of continuous movement at higher T_e, while P. axillaris exhibited a faster sprint speed (0.8355 vs. 0.8157 m/s at 30 °C) and greater movement distance (78.53 vs. 89.82 cm at 32 °C). These differences may be attributable to variations in body size and ecological strategies, as E. roborowskii is an ambush lizard, whereas P. axillaris is an active striking type, which suggests that there is a balanced relationship between endurance and speed. Our study provides critical insights into the convergent evolution and ecological adaptation of two sympatric lizard species in extreme desert ecosystems. Full article

Multimodal brain–computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic features from functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- and fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We propose a novel method of personalizing rehabilitation by aligning each patient’s specific abilities with the treatment options available. We collected 160 single trials of motor imagery using the multimodal BCI from 10 healthy participants. We identified a confounding effect of respiration in the fNIRS signal data collected. Hence, we propose to incorporate a breathing sensor to synchronize motor imagery (MI) cues with the participant’s respiratory cycle. We found that implementing this respiration synchronization (RS) resulted in less dispersed readings of oxyhemoglobin (HbO). We then conducted a clinical trial on the personalized multimodal BCI-SR for stroke rehabilitation. Four chronic stroke patients were recruited to undergo 6 weeks of rehabilitation, three times per week, whereby the primary outcome was measured using upper-extremity Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores on weeks 0, 6, and 12. The results showed a striking coherence in the activation patterns in EEG and fNIRS across all patients. In addition, FMA and ARAT scores were significantly improved on week 12 relative to the pre-trial baseline, with mean gains of 8.75 ± 1.84 and 5.25 ± 2.17, respectively (mean ± SEM). These improvements were all better than the Standard Arm Therapy and BCI-SR group when retrospectively compared to previous clinical trials. These results suggest that personalizing the rehabilitation treatment leads to improved BCI performance compared to standard BCI-SR, and synchronizing motor imagery cues to respiration increased the consistency of HbO levels, leading to better motor imagery performance. These results showed that the proposed multimodal BCI-SR holds promise to better engage stroke patients and promote neuroplasticity for better motor improvements. Full article

Background/Objectives: A 68-year-old man presented with progressive walking difficulty that developed into spastic paraplegia. This condition was a long-term consequence of a lightning strike injury sustained at the age of 22 years. His symptoms progressively deteriorated, eventually requiring double crutches for ambulation at approximately 40 years of age. A physical evaluation prior to hybrid assistive limb (HAL) training revealed a T10 level neurological injury and an American Spinal Cord Injury Association impairment scale grade D. Here, we aimed to evaluate the therapeutic effects of novel gait training with an HAL in this patient with chronic and progressive neural damage caused by a lightning strike. Methods: The HAL training program is composed of two sections. In the first section, one month of gait training with HAL was conducted across 10 sessions, with 2–3 sessions weekly. The second section followed 6 months later. A final evaluation was performed three months after the second section. Results: Electromyographic and kinematic evaluation showed that the HAL gait training inhibited abnormal antagonistic muscle activation in his lower extremities, especially after the first section. Our results collectively indicate that the repeated HAL gait training improved the gait pattern of this patient. Conclusions: Our results suggest that HAL gait training may improve the gait pattern in patients with delayed progressive spastic paraplegia, as observed in this case. In addition, a longer intervention period is recommended to facilitate better adaptation to HAL training. Hence, neurorehabilitation with an HAL could be an innovative treatment approach for delayed progressive spastic paraplegia. Full article

While extreme oceanic phenomena can often be accurately predicted, sudden abnormal waves along the shore (surge) are often difficult to foresee; therefore, an immediate sensing system was developed to monitor sudden and extreme events to take necessary actions to prevent further risks and damage. Real-time images from coastal surveillance video and meteorological data were used to construct a warning model for incoming waves using long short-term memory (LSTM) machine learning. This model can predict the wave magnitude that will strike the destination area seconds later and issue an alarm before the surge arrives. The warning model was trained and tested using 110 h of historical data to predict the wave magnitude in the destination area 6 s ahead of its arrival. If the forecasting wave magnitude exceeds the threshold value, a warning will be issued, indicating that a surge will strike in 6 s, alerting personnel to take the necessary actions. This configuration had an accuracy of 60% and 88% recall. The proposed prediction model could issue a surge alarm 5 s ahead with an accuracy of 90% and recall of 80%. For surge caused by a typhoon, this approach could offer 10 s of early waring with recall of 76% and an accuracy of 74%. Full article

The Solanaceae family occupies a significant position, and the study of resistance genes within this family is extremely valuable. Therefore, our goal is to examine disease resistance genes based on the high-quality representative genomes of Solanaceae crops, and to develop corresponding Simple Sequence Repeat (SSR) molecular markers. Among nine representative Solanaceae species, we identified 819 NBS-LRR genes, which were further divided into 583 CC-NBS-LRR (CNL), 54 RPW8-NBS-LRR (RNL), and 182 TIR-NBS-LRR (TNL) genes. Whole genome duplication (WGD) has played a very important role in the expansion of NBS-LRR genes in Solanaceae crops. Gene structure analysis showed the striking similarity in the conserved motifs of NBS-LRR genes, which suggests a common ancestral origin, followed by evolutionary differentiation and amplification. Gene clustering and events like rearrangement within the NBS-LRR family contribute to their scattered chromosomal distribution. Our findings reveal that the majority of NBS-LRR family genes across all examined species predominantly localize to chromosomal termini. The analysis indicates the significant impact of the most recent whole genome triplication (WGT) on the NBS-LRR family genes. Moreover, we constructed Protein–Protein Interaction (PPI) networks for all 819 NBS-LRR genes, identifying 3820 potential PPI pairs. Notably, 97 genes displayed clear interactive relationships, highlighting their potential role in disease resistance processes. A total of 22,226 SSRs were detected from all genes of nine Solanaceae species. Among these SSRs, we screened 43 NBS-LRR-associated SSRs. Our study lays the foundation for further exploration into SSR development and genetic mapping related to disease resistance in Solanaceae species. Full article