Search Results (3,223)

Accurate prediction of road traffic crash severity is essential for developing data-driven safety strategies and optimizing resource allocation. This study presents a predictive modeling framework that utilizes Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbors (KNN) to estimate segment-level frequencies of fatalities, serious injuries, and slight injuries on Hungarian roadways. The model integrates an extensive array of predictor variables, including roadway geometric design features, traffic volumes, and traffic composition metrics. To address class imbalance, each severity class was modeled using resampled datasets generated via the Synthetic Minority Over-sampling Technique (SMOTE), and model performance was optimized through grid-search cross-validation for hyperparameter optimization. For the prediction of serious- and slight-injury crash counts, the Random Forest (RF) ensemble model demonstrated the most robust performance, consistently attaining test accuracies above 0.91 and coefficient of determination (R²) values exceeding 0.95. In contrast, for fatalities count prediction, the Gradient Boosting (GB) model achieved the highest accuracy (0.95), with an R² value greater than 0.87. Feature importance analysis revealed that heavy vehicle flows consistently dominate crash severity prediction. Horizontal alignment features primarily influenced fatal crashes, while capacity utilization was more relevant for slight and serious injuries, reflecting the roles of geometric design and operational conditions in shaping crash occurrence and severity. The proposed framework demonstrates the effectiveness of machine learning approaches in capturing non-linear relationships within transportation safety data and offers a scalable, interpretable tool to support evidence-based decision-making for targeted safety interventions. Full article

Lithium-ion batteries are extensively employed in electric vehicles (EVs) and energy storage systems (ESSs) owing to their high energy density, long cyclability, and cost-effectiveness. However, the use of flammable electrolytes makes them inherently susceptible to thermal runaway (TR), which can lead to ignition, explosion, and large-scale fires. Accordingly, early detection of defect internal conditions that precede thermal events is essential for ensuring battery safety. This study proposes a time-resolved partial discharge (TRPD)-based diagnostic method for identifying early electrical precursors of fire hazards in lithium-ion batteries. Both destructive (ex situ) and non-destructive (in situ) experiments were performed to collect defect signal data under physical deformation and accelerated degradation conditions. Through fast fourier transform (FFT) analysis of the acquired signals, specific frequency-domain characteristics associated with micro internal short circuits (MISC) were identified, particularly within the 3.9 MHz, 11.9 MHz, and 19 MHz bands. Defect signals were clearly distinguishable from background common-mode voltage (CMV) noise, confirming the diagnostic sensitivity of the proposed approach. The results demonstrate that the TRPD-based technique enables early recognition of latent insulation degradation and internal short-circuit phenomena before thermal runaway occurs. This work bridges the gap between conventional insulation monitoring and battery safety diagnostics, providing a scalable framework for integrating high-frequency signal analysis into EV and ESS battery management systems for fire prevention. Full article

Industrial electric motors powered by variable frequency drives (VFDs) offer better controllability as compared to the conventional sinusoid-fed motors. However, the switching transients of VFDs induce shaft voltage in electric motors, which can lead to bearing failure. This may cause the machine to shut down and pose a serious threat to the system’s reliability. Several shaft voltage mitigation strategies are suggested in the literature, including insulated bearings, grounding brushes, copper shields, and filters. Although mitigation strategies have been extensively studied, shaft voltage signal processing remains relatively underexplored. This review introduces diffusion models (DMs), a new generative learning technique, as an effective solution for processing shaft voltage signals. These models are good at reducing noise, handling uncertainty, and capturing complex patterns over time. DMs offer robust performance under dynamic conditions as compared to traditional machine learning (ML) and deep learning (DL) techniques. In summary, the review outlines the sources and causes of shaft voltage, its existing mitigation strategies, and the theory behind DMs for shaft voltage analysis. Thus, by combining insights from electrical engineering and artificial intelligence (AI), this work addresses an important gap in the existing literature and provides a strong path forward for improving the reliability of industrial motor systems. Full article

Genomic organization and its comparative analysis throughout all major kingdoms of life are extensively studied across multiple scales, ranging from individual gene-level analyses to system-wide investigations. This work introduces a novel framework for characterizing genetic architecture through a new integral genomic parameter. We propose the concept of a multidimensional Gene Space to enable holistic quantification of genome organization principles. Gene Space—a multidimensional space based on the frequencies of nucleotide tokens, such as individual nucleotides, codons, or codon pairs. We demonstrate that in this space, genes from each of the studied microorganism species occupy a limited region, and individual genes from different species can be effectively separated with more than 95% accuracy. Consequently, a specific Genome Subspace can be defined for each species, which constrains the organism’s evolutionary pathways, thereby determining the constraints on gene optimization for these species. Further in-depth analysis is required to test if it is true for other organisms as well. The Gene Space framework offers a novel and powerful approach for genome analysis at the most basic levels, with promising applications in comparative genomics, evolutionary biology, and gene optimization. Full article

With the evolution of semiconductor devices, power electronics systems are trending towards higher frequencies and greater integration, leading to increasingly severe electromagnetic interference (EMI) issues. As an effective means of suppressing EMI, EMI filters have been extensively researched consequently. Over the past few decades, research on EMI filters has yielded a wealth of valuable achievements. However, the existing literature lacks a comprehensive and systematic collation of different EMI filters. In order to fill this gap, this work presents a thorough survey of EMI filters. According to their principles and implementation methods, these EMI filters can be broadly categorized into three types: Passive EMI Filters (PEFs), Active EMI Filters (AEFs) and integrated electromagnetic EMI filters (IEFs). Based on the review of the principles for each category, this paper analyzes their respective advantages, drawbacks, and development status. Through organization and categorization, this work aims to provide a reference for researchers and designers. Full article

Transportation is the highest emitting sector in the US, and electrifying transportation is an effective way to reduce emissions. However, electrification efforts have typically focused on battery electric vehicles (BEVs); but extended-range EVs (EREVs), EVs with a backup gasoline generator, could play a major role. Nonetheless, reducing transportation-related costs and carbon emissions hinges on understanding how an EREV’s range and charging profile affect electric miles driven and, by extension, emission savings. This study evaluates the distribution of vehicle miles traveled (VMT) between electric and gasoline modes for EREVs across electric range (25–150 miles) and charging frequency scenarios. Using 2023 U.S. trip data by distance and monthly VMT benchmarks, we apply a dynamic mean-distance estimation method to match observed totals and allocate VMT to EV or gasoline power based on trip length. We explore different charging, efficiency, and cost scenarios. Our results show, at current average efficiencies, that EREVs with a 50-mile range (13.7 kWh battery) could electrify 73.3% of national VMT, while 150-mile range EVs could electrify 86.8% illustrating that there are diminishing returns at higher ranges. We also compute corresponding carbon emissions savings using national fuel economy and emissions factors. Results highlight the nonlinear trade-offs between range and emissions reduction. Findings suggest that expanding the EREV range significantly boosts electrification potential up to 100 miles but offers marginal gains beyond. However, if users charge infrequently, larger range EVs are needed to maintain the benefits of vehicle electrification. Our results imply that policymakers and manufacturers should prioritize moderate range EREVs for households who frequently charge (e.g., homeowners) and long range BEVs for infrequent users (e.g., apartment dwellers). Full article

Unmanned Aerial Vehicles (UAVs) are integral components of future 6G networks, offering rapid deployment, enhanced line-of-sight communication, and flexible coverage extension. However, UAV communications in low-altitude environments face significant challenges, including rapid link variations due to attitude instability, severe signal blockage by urban obstacles, and critical sensitivity to transmitter–receiver alignment. While traditional planar reconfigurable intelligent surfaces (RIS) show promise for mitigating these issues, they exhibit inherent limitations such as angular sensitivity and beam squint in wideband scenarios, compromising reliability in dynamic UAV scenarios. To address these shortcomings, this paper proposes and evaluates a spherical-cap reflective intelligent surface (ScRIS) specifically designed for dynamic low-altitude communications. The intrinsic curvature of the ScRIS enables omnidirectional reflection capabilities, significantly reducing sensitivity to UAV attitude variations. A rigorous analytical model founded on Generalized Sheet Transition Conditions (GSTCs) is developed to characterize the electromagnetic scattering of the curved metasurface. Three distinct 1-bit RIS unit cell coding arrangements, namely alternate, chessboard, and random, are investigated via numerical simulations utilizing CST Microwave Studio and experimental validation within a mechanically stirred reverberation chamber. Our results demonstrate that all tested ScRIS coding patterns markedly enhance electromagnetic field uniformity within the chamber and reduce the lowest usable frequency (LUF) by approximately 20% compared to a conventional metallic spherical reflector. Notably, the random coding pattern maximizes phase entropy, achieves the most uniform scattering characteristics and substantially reduces spatial field autocorrelation. Furthermore, the combined curvature and coding functionality of the ScRIS facilitates simultaneous directional focusing and diffuse scattering, thereby improving multipath diversity and spatial coverage uniformity. This effectively mitigates communication blind spots commonly encountered in UAV applications, providing a resilient link environment despite UAV orientation changes. To validate these findings in a practical context, we conduct link-level simulations based on a reproducible system model at 3.5 GHz, utilizing electromagnetic scale invariance to bridge the fundamental scattering properties observed in the RC to the application band. The results confirm that the ScRIS architecture can enhance link throughput by nearly five-fold at a 10 km range compared to a baseline scenario without RIS. We also propose a practical deployment strategy for urban blind-spot compensation, discuss hybrid planar-curved architectures, and conduct an in-depth analysis of a DRL-based adaptive control framework with explicit convergence and complexity analysis. Our findings validate the significant potential of ScRIS as a passive, energy-efficient solution for enhancing communication stability and coverage in multi-band 6G networks. Full article

Automatic modulation recognition (AMR) plays a critical role in intelligent wireless communication systems, particularly under conditions with a low signal-to-noise ratio (SNR) and complex channel environments. To address these challenges, this paper proposes a three-branch fusion network that integrates complementary features from the time, frequency, and spatial domains to enhance classification performance. The model consists of three specialized branches: a multi-channel convolutional branch designed to extract discriminative local features from multiple signal representations; a bidirectional long short-term memory (BiLSTM) branch capable of capturing long-range temporal dependencies; and a vision transformer (ViT) branch that processes constellation diagrams to exploit global structural information. To effectively merge these heterogeneous features, a path attention module is introduced to dynamically adjust the contribution of each branch, thereby achieving optimal feature fusion and improved recognition accuracy. Extensive experiments on the two popular benchmarks, RML2016.10a and RML2018.01a, show that the proposed model consistently outperforms baseline approaches. These results confirm the effectiveness and robustness of the proposed approach and highlight its potential for deployment in next-generation intelligent modulation recognition systems operating in realistic wireless communication environments. Full article

The integration of molten salt thermal energy storage (TES) into coal-fired power units offers a viable strategy to improve operational flexibility. However, existing studies have predominantly employed steady-state models to quantify the extension of the unit’s load range, while failing to adequately capture dynamic performance. To address this gap, this study utilizes a validated dynamic model of a molten salt TES-integrated power unit to investigate its dynamic characteristics during frequency regulation. The results indicate that molten salt TES exhibits significant asymmetry between its charging and discharging processes in terms of both the speed and magnitude of the power response. Moreover, under load step scenarios, the TES-integrated unit increases its ramp rate from 1.5% to 8.6% P_N/min during load decrease, and from 1.5% to 6.3% P_N/min during load increase. Under load ramping scenarios, molten salt TES reduces the integral of absolute error (IAE) to 0.15–0.25 MWh, significantly lower than the 3.21–4.59 MWh of the standalone unit. Additionally, in response to actual AGC commands, molten salt TES reduces non-compliant operation time from 729 s to 256 s and decreases the average power deviation by 33.6%. These improvements also increase the ancillary service revenue by 37.7%, from CNY 3364 to CNY 4632 per hour. Full article

Silicon carbide (SiC) single crystals are extensively utilized in various fields due to their exceptional properties, such as a wide bandgap and a high breakdown threshold. Nevertheless, the intrinsic high hardness of SiC creates significant challenges for contact machining. This study investigates the surface damage characteristics and underlying mechanisms involved in processing both high-purity silicon carbide (HP-SiC) and nitrogen-doped silicon carbide (N-SiC) crystals using fundamental-frequency nanosecond pulsed lasers. This study establishes a laser-induced damage threshold (LIDT) testing platform and employs the internationally standardized 1-ON-1 test method to evaluate the damage characteristics of HP-SiC and N-SiC crystals under single-pulse laser irradiation. Experimental results indicate that N-SiC crystals exhibit superior absorption characteristics and a lower LIDT compared with HP-SiC crystals. Subsequently, a defect analysis model was established to conduct a theoretical examination of defect information across various types of SiC. Under fundamental-frequency nanosecond pulsed laser irradiation, N-SiC crystals demonstrate a lower average damage threshold and a broader defect damage threshold distribution than their HP-SiC counterparts. By integrating multi-dimensional analytical methods—including photothermal weak absorption mechanisms and damage morphology analysis—the underlying damage mechanisms of the distinct SiC forms were comprehensively elucidated. Moreover, although N-SiC crystals show weaker photothermal absorption properties, they exhibit more pronounced absorption and damage response processes. These factors collectively account for the different laser damage resistances observed in the two types of silicon carbide crystals, implying that distinct processing methodologies should be employed for nanosecond pulsed laser treatment of different SiC crystals. This paper elucidates the damage characteristics of various SiC materials induced by near-infrared nanosecond pulsed lasers and explores their underlying physical mechanisms. Additionally, it provides reliable data and a comprehensive mechanistic explanation for the efficient removal of these materials in practical applications. Full article

Transportation panoptic perception (TPP) is a fundamental capability for both on-board and roadside monitoring systems. In this paper, we propose an end-to-end lightweight multitask model, MT-TPPNet, which jointly performs three tasks: object detection, drivable area segmentation, and lane line segmentation. To accommodate task differences while sharing a common backbone, we introduce the Asymmetric Projection with Expanded-value (APEX) mechanism, which integrates attention mechanisms with different biases to enhance performance across various tasks. We further propose the Selective Channel–Spatial Coupling (${\mathrm{SC}}^2$) mechanism, which injects complementary frequency-band information into the channel-spatial coupled features. Additionally, by using a unified loss function to simultaneously handle detection and segmentation tasks, we eliminate the need for task-specific customizations, improving both training stability and deployment flexibility. Extensive experiments on self-collected field data and public benchmarks from roadway and railway scenarios demonstrate that MT-TPPNet consistently outperforms strong baselines in terms of mAP, mIoU, and FPS. In particular, MT-TPPNet achieves a mAP50 of 83.2% for traffic object detection, a mIoU of 91.6% for drivable-area segmentation, and an IoU of 28.9% for lane-line segmentation, demonstrating the effectiveness of the proposed approach. Full article

The present study investigates the influence of alternating current (AC) frequency on the formation and properties of calcium-, phosphorus-, and selenium-containing micro-arc oxidation (MAO) coatings on high-purity magnesium. Coatings were produced at 50–400 Hz in a phytic-acid-based electrolyte containing Ca, P, and Se precursors, and their structure, chemistry, and functional performance were systematically evaluated. Surface morphology, analyzed by SEM and optical profilometry, revealed frequency-dependent features: lower frequencies (50 Hz) promoted thicker, rougher coatings with extensive cracking, whereas intermediate frequencies (100–200 Hz) yielded more uniform, porous surfaces. The CaPSe_100 specimen exhibited the most homogeneous topography (lowest S10z and SD) combined with the highest porosity (28.4%), strong hydrophilicity, and the greatest selenium incorporation (1.30 wt.%). Hydrogen evolution testing in Hanks’ solution demonstrated a drastic improvement in corrosion resistance following MAO treatment: the degradation rate of bare Mg (5.50 mm/year) was reduced to 0.012 mm/year for the CaPSe_100 coating—well below the clinical tolerance threshold for biodegradable implants. This outstanding performance is attributed to the synergistic effect of a uniform oxide barrier, optimized porosity, and homogeneous surface morphology. The results highlight the potential of frequency-controlled AC-MAO processing as a route to tailor magnesium surfaces for multifunctional, corrosion-resistant biomedical applications. Full article

The accuracy of machine learning models in plant disease detection significantly relies on large volumes of knowledge-based labeled data; the acquisition of annotation remains a significant bottleneck in domain-specific research such as plant disease detection. While unsupervised learning alleviates the need for labeled data, its effectiveness is constrained by the intrinsic separability of feature clusters. These limitations underscore the need for approaches that enable supervised early disease detection without extensive annotation. To this end, we propose a self-supervised learning (SSL) framework for the early detection of soybean’s sudden death syndrome (SDS) using hyperspectral data acquired from an unmanned aerial vehicle (UAV). The methodology employs a novel distance-based spectral pairing technique that derives intermediate labels directly from the data. In addition, we introduce an adapted contrastive loss function designed to improve cluster separability and reinforce discriminative feature learning. The proposed approach yields an 11% accuracy gain over agglomerative hierarchical clustering and attains both classification accuracy and F1 score of 0.92, matching supervised baselines. Reflectance frequency analysis further demonstrates robustness to label noise, highlighting its suitability in label-scarce settings. Full article

Bladder and urothelial carcinoma are marked by profound genomic diversity. Using a large, multi-institutional dataset, we performed comprehensive genomic profiling of 4631 tumor samples from 4050 individuals. A retrospective analysis of bladder and urothelial cancer was performed using the AACR Project GENIE database. Demographic associations, mutation frequencies, copy number changes, and survival correlations were analyzed with a p-value < 0.05. Frequent mutations were identified in TP53, TERT, KDM6A, KMT2D, ARID1A, and FGFR3. Mutation frequencies varied by sex and race, with specific alterations enriched in female and Asian patients. Distinct patterns of co-occurrence, including TP53 with RB1, and mutual exclusivity, including TP53 with FGFR3 or KDM6A, revealed distinct molecular subtypes. This study highlights the extensive heterogeneity of bladder cancer, and our findings emphasize the clinical importance of molecular stratification and support the need for further mechanistic and prospective studies to inform the development of targeted therapies. Full article

The γ-rhythm plays a key role in coordinating the activity of the major brain systems and facilitating higher-level neurological processes. Several pathological conditions are associated with impaired generation or regulation of γ-oscillations. Modulating the γ-rhythm using periodic signals is considered a potential way to halt and/or treat such neurodegenerative processes. Despite the extensive knowledge gained in this field over the last 70 years, a unified theory linking the effectiveness of γ-stimulation to the characteristics of the stimulus and the stimulated remains elusive. In this review, we conducted a quantitative analysis of these relationships. The γ-stimulation effectiveness depends on species, age, frequency, and stimulus type. Here, we found with our analysis that experiments using white light were more effective than red and infrared. The range of effective central frequencies depends on age. We also showed that AD patients and mouse models respond differently to γ-stimulation, so the careful selection of study subjects is essential when assessing therapeutic potential. This review also provides an overview of the mechanisms of γ-stimulation and makes recommendations for optimizing the method based on these mechanisms. Our findings may be useful to understanding -stimulation mechanisms, planning future experiments for research groups and identifying potential therapeutic γ-stimulation regimens. Full article