Search Results (2,686)

The aim of the article is to determine the control range of a multiphase squirrel cage induction generator with more than three stator phases, operating in a wide range of driving speeds. The generator produces an output DC voltage using a multiphase converter operating as a PWM rectifier. The entire speed range is divided into intervals in which the sequence of stator phase voltages and, in effect, the number of pole pairs, is changed. In each interval, the output voltage is regulated by the frequency and amplitude of the stator voltages causing the highest possible power efficiency of the generator. The system can be scalar controlled or regulated using field orientation. Generator characteristics are calculated based on the set of steady-state equations derived from differential equations describing the multiphase induction machine. The calculation results are compared with simulations and with the steady-state measurement of the vector-controlled nine-phase generator. Recognizing the reliability of the obtained results, calculations are performed for a twelve-phase generator, obtaining satisfactory efficiency from 70% to 85% in the generator speed range from 0.2 to 1.0 of the assumed reference speed of 314 rad/s. The generator producing DC voltage can charge an electrical energy storage system or can be used directly to provide electrical power. This solution is not patented. Full article

Understanding the interplay between lightning activity and hydrometeor distribution is crucial for advancing knowledge of thunderstorm electrification processes. Using three-dimensional lightning mapping and dual-polarization radar observations, this study investigates the spatiotemporal correlations between low-frequency (LF) lightning sources and hydrometeors during a severe thunderstorm on 11 June 2014, in North Carolina, USA. The results reveal that lightning sources are predominantly observed above 6 km (near the −10 °C isotherm) and stabilize into a dual-peak vertical distribution as the storm progresses into its mature stage, with peaks located at 6–7 km (−10 °C to −15 °C) and 10–11 km (approximately −40 °C). Low-density graupel (LDG) and aggregates (AGs) dominate at lightning locations. Stronger updrafts lead to higher proportions of LDG and high-density graupel (HDG), and lower proportions of AG. LDG exhibits the strongest positive correlation with LF lightning sources, with a peak correlation coefficient of 0.65 at 9 km. During the vigorous development stage, HDG and hail (Ha) also show positive correlations with LF lightning sources, with peak correlation coefficients of 0.52 at 7 km and 0.42 at 8 km, respectively. As the storm reaches its mature phase, the correlation between LDG and lightning sources also displays a dual-peak vertical distribution, with peaks at 7–8 km and 13–14 km. Both the peak correlation coefficient and its corresponding height increase with the strengthening of updrafts, underscoring the critical role of updrafts in microphysical characteristics and driving electrification processes. Full article

High-viscosity stratified strata, characterized by complex geotechnical properties such as strong cohesion, low permeability, and pronounced layered structures, exhibit significant lateral friction resistance and high-end resistance during steel sheet pile installation. These factors substantially increase construction difficulty and may even cause structural damage. This study addresses two critical mechanical challenges during vibratory pile driving in Fujian Province’s hydraulic engineering project: prolonged high-frequency driving durations, and severe U-shaped steel sheet pile head damage in high-viscosity stratified soils. Employing the Coupled Eulerian–Lagrangian (CEL) numerical method, a systematic investigation was conducted into the penetration resistance, stress distribution, and damage patterns during vibratory pile driving under varying conditions of cohesive soil layer thickness, predrilled hole spacing, and aperture dimensions. The correlation between pile stress and penetration depth was established, with the influence mechanisms of key factors on driving-induced damage in high-viscosity stratified strata under multi-factor coupling effects elucidated. Finally, the feasibility of predrilling techniques for resistance reduction was explored. This study applies the damage prediction model based on the CEL method to U-shaped sheet piles in high-viscosity stratified formations, solving the problem of mesh distortion in traditional finite element methods. The findings provide scientific guidance for steel sheet pile construction in high-viscosity stratified formations, offering significant implications for enhancing construction efficiency, ensuring operational safety, and reducing costs in such challenging geological conditions. Full article

With the electrification of generator sets, electric locomotives, new energy vehicles, and other industries, AC motors subject bearings to an electric field environment, leading to galvanic corrosion due to the use of variable frequency power supply drives. The phenomenon of bearing discharge breakdown in subway traction motors is a critical issue in understanding the relationship between shaft current strength and the extent of bearing damage. This paper analyzes the mechanism of impulse discharge that leads to galvanic corrosion damage in bearings at a microscopic level and conducts electric thermal coupling simulations of the traction motor bearing discharge breakdown process. It examines the temperature rise associated with lubricant film discharge breakdown during the dynamic operation of the bearing and investigates how breakdown channel parameters and operational conditions affect the temperature rise in the micro-region of bearing lubrication. Ultimately, the results of the electric thermal coupling simulation are validated through experimental tests. This study revealed that in an electric field environment, the load-bearing area of the outer ring experiences significantly more severe corrosion damage than the inner ring, whereas non-bearing areas remain unaffected by electrolytic corrosion. When the inner ring reaches a speed of 4500_rpm, the maximum widths of electrolytic corrosion pits for the outer and inner rings are measured at 89 um and 51 um, respectively. Additionally, the highest recorded temperatures for the breakdown channels in the outer and inner rings are 932 °C and 802 °C, respectively. Furthermore, as the inner ring speed increases, both the width of the electrolytic corrosion pits and the temperature of the breakdown channels rise. Specifically, at inner ring speeds of 2500_rpm, 3500_rpm, and 4500_rpm, the widths of the electrolytic pits in the outer ring raceway load zone were measured at 34 um, 56 um, and 89 um, respectively. The highest temperatures of the lubrication film breakdown channels were recorded as 612 °C, 788 °C, and 932 °C, respectively. This study provides a theoretical basis and data support for the protective and maintenance practices of traction motor bearings. Full article

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

Given the severe global traffic safety challenges—including threats to human lives and socioeconomic impacts—this study analyzes visual behavior to promote sustainable transportation, improve road safety, and reduce resource waste and pollution caused by accidents. Four typical road sections, namely, turning, straight ahead, uphill, and downhill, were selected, and the eye movement data of 23 drivers in different driving stages were collected by aSee Glasses eye-tracking device to analyze the visual gaze characteristics of the drivers and their transfer patterns in each road section. Using Markov chain theory, the probability of staying at each gaze point and the transfer probability distribution between gaze points were investigated. The results of the study showed that drivers’ visual behaviors in different road sections showed significant differences: drivers in the turning section had the largest percentage of fixation on the near front, with a fixation duration and frequency of 29.99% and 28.80%, respectively; the straight ahead section, on the other hand, mainly focused on the right side of the road, with 31.57% of fixation duration and 19.45% of frequency of fixation; on the uphill section, drivers’ fixation duration on the left and right roads was more balanced, with 24.36% of fixation duration on the left side of the road and 25.51% on the right side of the road; drivers on the downhill section looked more frequently at the distance ahead, with a total fixation frequency of 23.20%, while paying higher attention to the right side of the road environment, with a fixation duration of 27.09%. In terms of visual fixation, the fixation shift in the turning road section was mainly concentrated between the near and distant parts of the road ahead and frequently turned to the left and right sides; the straight road section mainly showed a shift between the distant parts of the road ahead and the dashboard; the uphill road section was concentrated on the shift between the near parts of the road ahead and the two sides of the road, while the downhill road section mainly occurred between the distant parts of the road ahead and the rearview mirror. Although drivers’ fixations on the front of the road were most concentrated under the four road sections, with an overall fixation stability probability exceeding 67%, there were significant differences in fixation smoothness between different road sections. Through this study, this paper not only reveals the laws of drivers’ visual behavior under different driving environments but also provides theoretical support for behavior-based traffic safety improvement strategies. Full article

Introduction: Sexual minority men (SMM) are at increased risk for psychosocial stressor exposure, substance use, and poor mental health relative to heterosexual men. While the burden of mental health is growing in the United States, among SMM these trends are increasing at a greater rate, driving health disparities. Methods: Framed within a minority stress framework, these analyses examine how stressors explain substance use and poorer mental health over time. Participants were asked questions on stressor exposure (stigma, discrimination, internalized homophobia, perceived stress), mental health (anxiety, depression, PTSD), and substance use (alcohol to intoxication, club drugs, poly club drugs) over 36 months among 528 SMM in NYC. Results: Perceived stress increased frequency of all substance use, whereas discrimination decreased days of club and poly club drug use. Depression severity predicted increased days of club drug and poly club drug use. PTSD severity predicted increased days of club drug and poly club drug use. Conclusion: We are able to expand on the literature with granular substance use data to highlight associations with stressors and mental health. These findings support an increased need for systematic policy solutions and public health interventions to address drivers of substance use disparities among young SMM. Full article

The influence of social media personalities has grown significantly, especially among youth audiences who spend substantial time on platforms like TikTok. The emergence and popularity of different types of social media influencers accelerated during the COVID-19 pandemic in many countries, including South Africa. In turn, this period also saw a surge in youth audiences following these influencers. This rapid growth of influencer followings among young people is largely driven by specific types of content that resonate with them, thus encouraging continued engagement. However, the benefits that these young followers gain from engaging with various influencers and the factors driving their preferences for specific influencers remain underexplored, particularly within the context of South African students within higher education. Therefore, this study explores the types of social media influencers most followed by university students at a South African University and investigates the key factors that drive their preferences. A structured online questionnaire was distributed, gathering both multiple-choice and open-ended responses from students. The data were analyzed using categorical frequency counts and thematic analysis. The data highlight how students actively turn to influencers as emotional anchors, role models, and sources of practical guidance. Their engagement reflects a deep need for connection, inspiration, and identity formation in a challenging academic and social environment. These patterns show that influencer content is not just entertainment but plays a critical developmental role. Understanding these motivations helps educators, policymakers, and brands to align better with youth values. The significance of these results lies in how influencer content is now coming in to fill the emotional, cultural, and educational gaps left by traditional systems among the young South African university students in this modern era. Full article

Osteosarcoma (OS), the most common primary bone cancer of mesenchymal origin in children and young adolescents, remains a challenge due to metastasis and resistance to chemotherapy. It displays severe aneuploidy and a high mutation frequency which drive tumor initiation and progression; however, recent studies have highlighted the role of epigenetic modifications as a key driver of OS pathogenesis, independent of genetic mutations. DNA and RNA methylation, histone modifications and non-coding RNAs are among the major epigenetic modifications which can modulate the expression of oncogenes. Abnormal activity of these mechanisms contributes to gene dysregulation, metastasis and immune evasion. Therapeutic targeting against these epigenetic mechanisms, including inhibitors of DNA and RNA methylation as well as regulators of RNA modifications, can enhance tumor suppressor gene activity. In this review, we examine recent studies elucidating the role of epigenetic regulation in OS pathogenesis and discuss emerging drugs or interventions with potential clinical utility. Understanding of tumor- specific epigenetic alterations, coupled with innovative therapeutic strategies and AI-driven biomarker discovery, could pave the way for personalized therapies based on the molecular profile of each tumor and improve the management of patients with OS. Full article

Background/Objectives: This study aims to investigate the effects of caffeinated chewing gum on maximal strength, muscular power, and neural drive to the prime movers during bench press and back squat in resistance-trained men. Methods: Sixteen resistance-trained males participated in a double-blind, randomized trial, chewing either caffeinated gum (4 mg/kg) or placebo gum on two separate occasions, seven days apart. After chewing for 5 min, participants performed a maximal strength test followed by muscular power assessments at 25%, 50%, 75%, and 90% of their one-repetition maximum (1RM), completing with 3, 2, 1, and 1 repetition (s), respectively, for bench press and back squat. Surface electromyography data were recorded for each repetition. Results: Caffeinated gum did not significantly improve one-repetition maximum (1RM) for bench press (p > 0.05), but increased mean frequency (MF) and median frequency (MDF) in anterior deltoid, pectoralis major, and biceps brachii (all p < 0.05) compared to placebo. For back squat, 1RM increased with caffeinated gum, along with higher MF and MDF in vastus medialis (all p < 0.05). Caffeinated gum also improved mean and peak velocities, and mean and peak power outputs at 25–75% 1RM during the bench press (all p < 0.05), along with elevated MDF in pectoralis major and biceps brachii (all p < 0.05). Similar improvements were seen in mean and peak velocities during the back squat at 25–90% 1RM (all p < 0.05), along with higher MF and MDF in vastus medialis and increased normalized root mean square activity in gluteus maximus (all p < 0.05). Conclusions: Caffeinated chewing gum (4 mg/kg) enhanced muscular power (25–75% 1RM) in the bench press and improved maximal strength and muscular power (25–90% 1RM) in the back squat by increasing muscle recruitment in resistance-trained men. Full article

Terahertz (THz) frequencies, spanning from 0.1 to 1 THz, are poised to play a pivotal role in the development of future 6G wireless communication systems. These systems aim to utilize photonic technologies to enable ultra-high data rates—on the order of terabits per second—while maintaining low latency and high efficiency. In this work, we present a novel photonic method for generating sub-THz vector signals within the THz band, employing a semiconductor optical amplifier (SOA) and phase modulator (PM) to create an optical frequency comb, combined with in-phase and quadrature (IQ) modulation techniques. We demonstrate, both through simulation and experimental setup, the generation and successful transmission of a 0.1 THz vector. The process involves driving the PM with a 12.5 GHz radio frequency signal to produce the optical comb; then, heterodyne beating in a uni-traveling carrier photodiode (UTC-PD) generates the 0.1 THz radio frequency signal. This signal is transmitted over distances of up to 30 km using single-mode fiber. The resulting 0.1 THz electrical vector signal, modulated with quadrature phase shift keying (QPSK), achieves a bit error ratio (BER) below the hard-decision forward error correction (HD-FEC) threshold of ${3.8 \times 10}^{-3}$. To the best of our knowledge, this is the first experimental demonstration of a 0.1 THz photonic vector THz wave based on an SOA and a simple PM-driven optical frequency comb. Full article

The ergodicity and ageing phenomena in a vibrational motor system driven by a periodic external force are investigated. Within the tailored parameter regime, the amplitude and frequency demonstrate contrasting effects on ergodicity. An increase of amplitude induces a transition from non-ergodic to ergodic behavior, whereas a higher driving frequency leads to a transition from ergodic to non-ergodic dynamics. These transitions are attributed to the enhanced ability of larger amplitudes to overcome potential energy barriers and the improved responsiveness of the system to external variations at lower frequencies. Moreover, pronounced ageing effects are observed at low amplitudes or high frequencies. These findings offer new insights into the intrinsic dynamical mechanisms of vibrational motor systems and provide a theoretical foundation for predicting their long-term operational performance. Full article

To address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise convolution block attention) module. The main contributions are twofold: (1) A DCBA module is introduced to extract multi-scale features—including prominent, local, and average information—from grayscale images reconstructed from vibration signals across different domains; and (2) a two-stream network architecture is designed to learn complementary feature representations from time-domain and time–frequency-domain signals, which are fused through fully connected layers to improve diagnostic accuracy. Experimental results demonstrate that the proposed method achieves high recognition accuracy under various working speeds, loads, and road surfaces. Comparative studies with SENet, ECANet, CBAM, and single-stream 2DCNN models confirm its superior performance and robustness. The integration of DCBA with dual-domain feature learning effectively enhances fault feature extraction under complex operating conditions. Full article

Automotive NVH (Noise, Vibration, and Harshness) performance significantly impacts driving comfort and traffic safety. Vehicles exhibiting superior NVH characteristics are more likely to achieve consumer acceptance and enhance their competitiveness in the marketplace. In the development of automotive NVH performance, traditional vibration reduction methods have proven to be mature and widely implemented. However, due to constraints related to size and weight, these methods typically address only high-frequency vibration control. Consequently, they struggle to effectively mitigate vehicle body and component vibration noise at frequencies below 200 Hz. In recent years, acoustic metamaterials (AMMs) have emerged as a promising solution for suppressing low-frequency vibrations. This development offers a novel approach for low-frequency vibration control. Nevertheless, conventional design methodologies for AMMs predominantly rely on empirical knowledge and necessitate continuous parameter adjustments to achieve desired bandgap characteristics—an endeavor that entails extensive calculations and considerable time investment. With advancements in machine learning technology, more efficient design strategies have become feasible. This paper presents a tandem neural network (TNN) specifically developed for the design of AMMs. The trained neural network is capable of deriving both the bandgap characteristics from the design parameters of AMMs as well as deducing requisite design parameters based on specified bandgap targets. Focusing on addressing low-frequency vibrations in the back frame of automobile seats, this method facilitates the determination of necessary AMMs design parameters. Experimental results demonstrate that this approach can effectively guide AMMs designs with both speed and accuracy, and the designed AMMs achieved an impressive vibration attenuation rate of 63.6%. Full article

This study designed and validated a dual-component beat-frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) gas detection system utilizing time-division multiplexing (TDM). By applying TDM to drive distributed feedback lasers, the system achieved the simultaneous detection of acetylene and methane. Its key innovation lies in exploiting the transient response of the quartz tuning fork (QTF) to acquire gas concentrations while concurrently capturing the QTF resonant frequency and quality factor in real-time. Owing to the short beat period and rapid system response, this approach significantly reduces time-delay constraints in time-division measurements, eliminating the need for periodic calibration inherent in conventional methods and preventing detection interruptions. The experimental results demonstrate minimum detection limits of 5.69 ppm for methane and 0.60 ppm for acetylene. Both gases exhibited excellent linear responses over the concentration range of 200 ppm to 4000 ppm, with the R² value for methane being 0.996 and for acetylene being 0.997. The system presents a viable solution for the real-time, calibration-free monitoring of dissolved gases in transformer oil. Full article