Search Results (7,593)

To address the issue of uneven load and premature failure in ball–column composite bearings caused by ring deformation, this study develops a mechanical analysis model, considering ring deformation based on flexible ring theory and rolling bearing design. It systematically examines radial deflection of the ring and how key parameters affect load distribution and stress. The results demonstrate that the elastic deformation of the collar redistributes the load, reduces the roller column’s load-carrying efficiency, and disrupts the optimal load distribution mode. Increasing the number of loaded rolling elements significantly improves the load uniformity, reduces the peak contact stress, and enhances the overall load-carrying performance. By optimizing the clearance matching across three bearings rows, a load-adaptive gradient bearing mechanism is realized by dynamically transferring, 70–90% of the heavy-load optimal distribution. These findings address the domestic research gaps and offer theoretical support for the performance prediction and optimal design of integrated ball–column composite bearings. Full article

The photonic spin Hall effect (PSHE) has emerged as a powerful metrological approach for precision measurements. Dynamic manipulation of PSHE through external stimuli could substantially expand its applications. In this work, we present a simple and active modulation scheme for PSHE in a surface plasmon resonance (SPR) structure by exploiting electric-field-tunable refractive indices of electro-optic materials. By applying an electric field, the enhancement of PSHE spin shifts is observed, and the dual-field control can further amplify these spin shifts through synergistic effects in this SPR structure. Notably, various operation modes of external electric field enable the real-time switching between two high-performance sensing functionalities (refractive index detection and angle measurement). Therefore, our designed PSHE sensor based on SPR structure with a simple structure of only three layers not only makes up for the complex structure in multi-functional sensors, but more importantly, this platform establishes a new paradigm for dynamic PSHE manipulation while paving the way for advanced multi-functional optical sensing technology. Full article

In this paper, we develop a dynamic pricing mechanism for a meal delivery platform that offers multiple transportation modes for order deliveries. We consider orders from heterogeneous customers who select their preferred delivery mode based on individual generalized cost (GC) functions, where GC captures the trade-off between price and delivery latency for each transportation option. Given the logistics of the underlying transportation network, the platform can utilize a pricing mechanism to guide customer choices toward delivery modes that optimize resource allocation across available transportation modalities. By accounting for variability in the latency and cost of modalities, such pricing aligns customer preferences with the platform’s operational objectives and enhances overall satisfaction. Due to the computational complexity of finding the optimal policy, we adopt a deep reinforcement learning (DRL) approach to design the pricing mechanism. Our numerical results demonstrate up to $143\%$ higher profits compared to heuristic pricing strategies, highlighting the potential of DRL-based dynamic pricing to improve profitability, resource efficiency, and service quality in on-demand delivery services. Full article

This review summarizes the recent advances in the application of nanomaterial coatings in optical fiber sensors, with a particular focus on deposition techniques and the research progress over the past five years in humidity sensing, gas detection, and biosensing. Benefiting from the high specific surface area, abundant surface active sites, and quantum confinement effects of nanomaterials, advanced thin-film fabrication techniques—including spin coating, dip coating, self-assembly, physical/chemical vapor deposition, atomic layer deposition (ALD), electrochemical deposition (ECD), electron beam evaporation (E-beam evaporation), pulsed laser deposition (PLD) and electrospinning, and other techniques—have been widely employed in the construction of functional layers for optical fiber sensors, significantly enhancing their sensitivity, response speed, and environmental stability. Studies have demonstrated that nanocoatings can achieve high-sensitivity detection of targets such as humidity, volatile organic compounds (VOCs), and biomarkers by enhancing evanescent field coupling and enabling optical effects such as surface plasmon resonance (SPR), localized surface plasmon resonance (LSPR), and lossy mode resonance (LMR). This paper first analyzes the principles and optimization strategies of nanocoating fabrication techniques, then explores the mechanisms by which nanomaterials enhance sensor performance across various application domains, and finally presents future research directions in material performance optimization, cost control, and the development of novel nanocomposites. These insights provide a theoretical foundation for the functional design and practical implementation of nanomaterial-based optical fiber sensors. Full article

Urban parks play a crucial role in promoting physical and mental health by providing green spaces for recreation, relaxation, and social interaction. However, access to these spaces is often constrained by the structure and performance of public transportation networks—particularly in megacities marked by spatial and social inequalities. This study evaluates equitable access to urban parks in Mexico City through the public transit system, using centrality-based metrics within a Geographic Information Systems (GIS) network analysis framework. Parks are categorized by size (small: 0.3–1 ha; medium: 1–4.5 ha; large: >4.5 ha), and three centrality measures—reach, gravity, and closeness—are applied to assess their accessibility via different transport modes: Metro, bus rapid transit (BRT), trolleybuses, public buses, and concessioned services. Results show that Metro stations are more connected to large parks, while BRT and trolleybus lines improve access to small and medium parks. Concessioned services, however, present fragmented and uneven coverage, reinforcing socio-spatial disparities in access to green infrastructure. The findings underscore the importance of integrated, multimodal transportation planning to enhance equitable access to parks—an essential component of urban health and well-being. By highlighting the spatial patterns of accessibility, this study contributes to designing healthier and more inclusive public spaces in the city, supporting policy frameworks that advance health equity and urban sustainability. Full article

In this paper, we propose a deep learning-based surrogate model for Multivariate Empirical Mode Decomposition (MEMD) using Long Short-Term Memory (LSTM) networks, aimed at efficiently extracting Intrinsic Mode Functions (IMFs) from electroencephalographic (EEG) signals. Unlike traditional data-driven methods, our approach leverages temporal sequence modeling to learn the decomposition process in an end-to-end fashion. We further enhance the decomposition targets by employing Noise-Assisted MEMD (NA-MEMD), which stabilizes mode separation and mitigates mode mixing effects, leading to better supervised learning signals. Extensive experiments on synthetic and real EEG data demonstrate the superior performance of the proposed LSTM surrogate over conventional feedforward neural networks and standard MEMD-based targets. Specifically, the LSTM trained on NA-MEMD outputs achieved the lowest mean squared error (MSE) and the highest signal-to-noise ratio (SNR), significantly outperforming the feedforward baseline, even when compared using the Power Spectral Density (PSD). These results confirm the effectiveness of combining LSTM architectures with noise-assisted decomposition strategies to approximate nonlinear signal analysis tasks such as MEMD. The proposed surrogate model offers a fast and accurate alternative to classical empirical methods, enabling real-time and scalable EEG analysis. Full article

Monkeypox virus (MPXV) experienced an unprecedented global outbreak in 2022, characterized by a significant departure from historical patterns: a rapid spread of the epidemic to more than 110 non-traditional endemic countries, with more than 90,000 confirmed cases; a fundamental shift in the mode of transmission, with human-to-human transmission (especially among men who have sex with men (MSM)) becoming the dominant route (95.2%); and genetic sequencing revealing a key adaptive mutation in a novel evolutionary branch (Clade IIb) that triggered the outbreak. These features highlight the significant evolution of MPXV in terms of host adaptation, transmission efficiency, and immune escape ability. The aim of this paper is to provide insights into the viral adaptive evolutionary mechanisms driving this global outbreak, with a particular focus on the role of immune escape (e.g., novel mechanisms of M2 proteins targeting the T cell co-stimulatory pathway) in enhancing viral transmission and pathogenicity. At the same time, we systematically evaluate the cross-protective efficacy and limitations of existing vaccines (ACAM2000, JYNNEOS, and LC16), as well as recent advances in novel vaccine platforms, especially mRNA vaccines, in inducing superior immune responses. The study further reveals the constraints to outbreak control posed by grossly unequal global vaccine distribution (e.g., less than 10% coverage in high-burden regions such as Africa) and explores the urgency of optimizing stratified vaccination strategies and facilitating technology transfer to promote equitable access. The core of this paper is to elucidate the dynamic game between viral evolution and prevention and control strategies (especially vaccines). The key to addressing the long-term epidemiological challenges of MPXV in the future lies in continuously strengthening global surveillance of viral evolution (early warning of highly transmissible/pathogenic variants), accelerating the development of next-generation vaccines based on new mechanisms and platforms (e.g., multivalent mRNAs), and resolving the vaccine accessibility gap through global collaboration to build an integrated defense system of “Surveillance, Research and Development, and Equitable Vaccination,” through global collaboration to address the vaccine accessibility gap. Full article

High energy-consuming industrial parks are characterized by high base-load-to-peak-valley ratios, overlapping production cycles, and megawatt-scale step changes, which significantly complicate short-term load forecasting. To tackle these challenges, this study proposes a novel forecasting framework that combines hierarchical multimodal decomposition with a hybrid deep learning architecture. First, Maximal Information Coefficient (MIC) analysis is applied to identify key input features and eliminate redundancy. The load series is then decomposed in two stages: seasonal-trend decomposition uses the Loess (STL) isolates trend and seasonal components, while variational mode decomposition (VMD) further disaggregates the residual into multi-scale modes. This hierarchical approach enhances signal clarity and preserves temporal structure. A parallel neural architecture is subsequently developed, integrating an Informer network to model long-term trends and a bidirectional gated recurrent unit (BiGRU) to capture short-term fluctuations. Case studies based on real-world load data from a typical industrial park in northeastern China demonstrate that the proposed model achieves significantly improved forecasting accuracy and robustness compared to benchmark methods. These results provide strong technical support for fine-grained load prediction and intelligent dispatch in high energy-consuming industrial scenarios. Full article

Electrical networks face operational challenges from power quality-affecting disturbances. Since disturbance signatures directly affect classifier performance, optimized feature selection becomes critical for accurate power quality assessment. The pursuit of robust feature extraction inevitably constrains the dimensionality of the discriminative feature set, but the complexity of the recognition model will be increased and the recognition speed will be reduced if the feature vector dimension is too high. Building upon the aforementioned requirements, in this paper, we propose a feature extraction framework that combines improved symplectic geometric mode decomposition, refined generalized multiscale quantum entropy, and refined generalized multiscale reverse dispersion entropy. Firstly, based on the intrinsic properties of power quality disturbance (PQD) signals, the embedding dimension of symplectic geometric mode decomposition and the adaptive mode component screening method are improved, and the PQD signal undergoes tri-band decomposition via improved symplectic geometric mode decomposition (ISGMD), yielding distinct high-frequency, medium-frequency, and low-frequency components. Secondly, utilizing the enhanced symplectic geometric mode decomposition as a foundation, the perturbation features are extracted by the combination of refined generalized multiscale quantum entropy and refined generalized multiscale reverse dispersion entropy to construct high-precision and low-dimensional feature vectors. Finally, a double-layer composite power quality disturbance model is constructed by a deep extreme learning machine algorithm to identify power quality disturbance signals. After analysis and comparison, the proposed method is found to be effective even in a strong noise environment with a single interference, and the average recognition accuracy across different noise environments is 97.3%. Under the complex conditions involving multiple types of mixed perturbations, the average recognition accuracy is maintained above 96%. Compared with the existing CNN + LSTM method, the recognition accuracy of the proposed method is improved by 3.7%. In addition, its recognition accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results show that the proposed strategy can accurately classify and identify various power quality interferences and that it is better than traditional methods in terms of classification accuracy and robustness. The experimental results of the simulation and measured data show that the combined feature extraction methodology reliably extracts discriminative feature vectors from PQD. The double-layer combined classification model can further enhance the model’s recognition capabilities. This method has high accuracy and certain noise resistance. In the 30 dB white noise environment, the average classification accuracy of the model is 99.10% for the simulation database containing 63 PQD types. Meanwhile, for the test data based on a hardware platform, the average accuracy is 99.03%, and the approach’s dependability is further evidenced by rigorous validation experiments. Full article

This study introduces an improved X-shaped re-entrant auxetic structure designed to enhance mechanical performance by incorporating diamond-shaped elements into the re-entrant hexagonal configuration. Using a validated numerical model, the resistance of sandwich beams with the proposed core under localized impulsive loading is explored. The results reveal that local compression and global shear deformation dominate the response. The study further examines the effects of cell arrangement, geometric parameter, inclined gradient distribution, and cell construction on structural behavior. The X-direction arrangement of cells significantly enhances deformation control, improving deflection by dissipating impact energy. Increasing the angle α enhances mechanical properties and reduces residual deflection. Various inclined gradient distribution designs notably affect performance: positive gradients improve energy absorption, while negative gradients alter deformation mode. Under the same conditions, the proposed sandwich beam outperforms the conventional re-entrant hexagonal sandwich beam in terms of impact resistance. This research offers valuable insights for the design of explosion-resistant metamaterial sandwich structures. Full article

This study systematically investigates the influence of varying corrosion severity on the bearing capacity of K-shaped circular-section joints, with explicit consideration of weld line positioning. Four full-scale circular-section joint specimens with clearance gaps were designed to simulate localized corrosion through artificially introduced perforations, and axial static loading tests were performed to assess the degradation of structural performance. Experimental results indicate that the predominant failure mode of corroded K-joints manifests as brittle fracture in the weld-affected zone, attributable to the combined effects of material weakening and stress concentration. The enlargement of corrosion pit dimensions induces progressive deterioration in joint stiffness and ultimate bearing capacity, accompanied by increased displacement at failure. A refined finite element model was established using ABAQUS. The obtained load–displacement curve from the simulation was compared with the experimental data to verify the validity of the model. Subsequently, a parametric analysis was conducted to investigate the influence of multiple variables on the residual bearing capacity of the nodes. Numerical investigations indicate that the severity of corrosion exhibits a positive correlation with the reduction in bearing capacity, whereas web-chord members with smaller inclination angles demonstrate enhanced corrosion resistance, when θ is equal to 30 degrees, K_s decreases from approximately 0.983 to around 0.894. Thin-walled joints exhibit accelerated performance deterioration compared to thick-walled configurations under equivalent corrosion conditions. Furthermore, increased pipe diameter ratios exacerbate corrosion-induced reductions in structural efficiency, when the corrosion rate is 0.10, β = 0.4 corresponds to K_s = 0.98, and when β = 0.7, it is approximately 0.965. and distributed micro-pitting results in less severe capacity degradation than concentrated macro-pitting over the same corrosion areas. Full article

In this paper, a novel enhancement-mode β-Ga₂O₃-based FinFET structure with a gate formed by the NiO/β-Ga₂O₃ heterojunction named HJ-FinFET has been proposed, and the excellent performance of the device has also been demonstrated. The primary operational mechanism of this structure involves integrating p-type NiO on both sides of the fin-shaped channel, which forms p-n junctions with β-Ga₂O₃. The depletion regions thus generated are utilized to establish electron channels, enabling enhancement-mode operation. The reverse p-NiO/n-Ga₂O₃ heterojunction diode is integrated to reduce the reverse free-wheeling loss. Compared with the conventional devices, the threshold voltage of the HJ-FinFET is greatly improved, and normally off operation is realized, showing a positive threshold voltage of 2.14 V. Meanwhile, the simulated breakdown voltage of the HJ-FinFET reaches 2.65 kV with specific on-resistance (R_on,sp) of 2.48 mΩ·cm² and the power figure of merit (PFOM = BV²/R_on,sp) reaches 2840 MW/cm², respectively. In addition, the influence of the doping concentration of the heterojunction layer constituting the gate, the doping concentration of the drift layer, and the channel width on the electrical characteristics of the devices were focused on. This structure provides a feasible idea for high-performance β-Ga₂O₃-based FinFET. Full article

Background: Polyphenols from Sorbus aucuparia L. (rowanberry) fruits are valuable bioactive compounds, yet their efficient extraction remains a challenge. Ultrasound-assisted extraction (UAE) offers a promising technique to enhance yield, but optimization of parameters is necessary. Methods: UAE was performed using a VC750 processor (20 kHz) at ultrasound intensities of 1.3, 7.65, and 14 W/cm² in pulsed mode (2 s on, 4 s off). Sonication times of 5, 10, and 15 min (total extraction times: 15, 30, 45 min) and ethanol concentrations of 30%, 60%, and 90% were tested. Selected polyphenols (gallic acid, neochlorogenic acid, chlorogenic acid, vanillic acid, epicatechin, trans-ferulic acid, rutin, quercetin, cinnamic acid) were quantified using HPLC. Response Surface Methodology (RSM) was applied for process optimization. Results: High-quality predictive models were obtained, particularly for neochlorogenic acid. Ethanol concentration exerted the strongest influence on extraction efficiency for most of the studied polyphenols, whereas extraction time showed no significant effect. Conclusions: Ethanol concentration is a key factor in maximizing polyphenol yield from S. aucuparia fruits using UAE. These findings may guide selective extraction strategies for phenolic compounds at early stages of food and nutraceutical processing. Full article

This systematic review examines the methodologies and applications of integrated risk indicators in positron emission tomography (PET) radiopharmaceutical production, focusing on occupational, technological, and environmental risks. Conducted in accordance with PRISMA 2020 guidelines and utilizing the Ryyan software 2023 for article screening, the review synthesizes findings from 70 studies published between 2020 and 2025 in English and Spanish, including articles, conference papers, and reviews. The review was registered on PROSPERO (CRD420251078221). Key disciplines contributing to risk assessment frameworks include environmental science, occupational health and safety, civil engineering, mining engineering, maritime safety, financial/economic risk, and systems engineering. Predominant risk assessment methods identified are probabilistic modeling (e.g., Monte Carlo simulations), machine learning (e.g., neural networks), multi-criteria decision-making (e.g., AHP and TOPSIS), and failure mode and effects analysis (FMEA), each offering strengths, such as uncertainty quantification and systematic hazard identification, alongside limitations like data dependency and subjectivity. The review explores how frameworks from other industries can be adapted to address PET-specific risks, such as radiation exposure to workers, equipment failure, and waste management, and how studies integrate these factors into unified risk indicators using weighted scoring, probabilistic methods, and fuzzy logic. Gaps in the literature include limited stakeholder engagement, lack of standardized frameworks, insufficient real-time monitoring, and under-represented environmental risks. Future research directions propose developing PET-specific tools, integrating AI and IoT for real-time data, establishing standardized frameworks, and expanding environmental assessments to enhance risk management in PET radiopharmaceutical production. This review highlights the interdisciplinary nature of risk assessment and the critical need for comprehensive, tailored approaches to ensure safety and sustainability in this field. Full article

A fast terminal sliding mode control based on a fixed-time sliding surface is proposed for a permanent magnet arc motor (PMAM), effectively improving speed response, control accuracy, and disturbance rejection capability. Due to its piecewise structure and advanced logarithmic characteristics, a PMAM is subject to high-frequency disturbances. Additionally, it is also influenced by external disturbances. To address this, a sliding mode reaching law that combines terminal terms, linear terms, and switching terms is designed to reduce chattering and enhance robustness. Furthermore, to improve the convergence speed of the sliding mode and disturbance rejection ability, a novel fixed-time converging sliding surface based on a variable exponent terminal term is introduced. Numerical simulations verify the convergence and disturbance rejection capabilities of the proposed sliding surface. Stability based on the Lyapunov theorem is strictly proven. Experimental results validate the effectiveness and superiority of the proposed algorithm. Full article