Search Results (3,283)

Under China’s dual-carbon policy, medium-duty commercial vehicles (MDCVs)—widely used in urban distribution with high load fluctuation and long operating hours—are key to transportation energy conservation and emission reduction. Optimizing powertrain parameters and energy management is essential for fuel-cell MDCVs. However, traditional powertrain parameter selection relies on fixed thresholds and lacks optimization, while the equivalent consumption minimization strategy (ECMS) suffers from poor driving cycle adaptability despite addressing hydrogen consumption and online application challenges. To overcome these issues, this study proposes an innovative approach for fuel cell-powered MDCVs: a driving cycle model was constructed based on hydrogen consumption and fuel cell degradation rates. Subsequently, the powertrain system parameters were optimized, culminating in the development of an adaptive ECMS (A-ECMS). Specifically, the method includes: (1) a driving cycle construction approach analyzing driving cycle clustering’s impact on adaptive control parameters; (2) a powertrain parameter optimization method considering vehicle performance under synthetic driving cycles; and (3) an A-ECMS enhanced by a crayfish optimization algorithm (COA) to improve driving cycle adaptability. Simulations show that A-ECMS achieves hydrogen consumption close to the dynamic programming algorithm (DP) optimum, reducing consumption by 2.12% and 1.45% compared to traditional ECMS under synthetic and World Transient Vehicle Cycle (WTVC) cycles, significantly improving MDCV economy. Full article

Background: Burnout is a significant occupational risk among healthcare professionals, including community pharmacy staff, whose differing roles and responsibilities may influence burnout determinants. This study aimed to compare burnout levels and associated work characteristics between master pharmacists (MPs) and assistant pharmacists (APs) working in community pharmacies in Northeastern Bulgaria. Methods: A cross-sectional observational survey was conducted between November 2023 and December 2024 using an anonymous, self-administered online questionnaire completed by 221 MPs and 151 APs. Burnout was assessed using the Maslach Burnout Inventory—Human Services Survey for Medical Personnel, measuring emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA). Work characteristics were evaluated using items adapted from an internationally recognized European Commission guideline on occupational health and safety risks in the healthcare sector. Results: High levels of EE and DP were observed in both groups, with no statistically significant differences in mean burnout scores. Age and years of professional experience were not significantly associated with burnout. However, work environment factors differed: poor team communication and a negative workplace climate affected both groups, whereas lack of recognition and support was more influential for MPs, and physical workload and frequent interruptions were more prominent stressors for APs. Conclusions: Burnout is prevalent among community pharmacy professionals, with role-specific organizational factors shaping its determinants and highlighting the need for targeted preventive strategies. Full article

This paper proposes a fuzzy sliding mode controller optimized using particle swarm optimization (FSMC-PSO) for trajectory tracking of an autonomous underwater vehicle (AUV). Conventional sliding mode control (SMC) is well known for its robustness against external disturbances, unmodeled dynamics, and parameter uncertainties, ensuring stability under challenging operating conditions. In the proposed FSMC-PSO approach, fuzzy logic adaptively tunes the SMC parameters, while PSO optimizes the fuzzy output membership functions offline to improve tuning accuracy and overall control performance. During online operation, the optimized fuzzy system adaptively adjusts the SMC parameters with minimal computational cost. The effectiveness of the proposed method is evaluated through numerical simulations in the presence of random noise. Performance is assessed using standard tracking indices, including IAE, ITAE, ISE, ITSE, and RMSE. Comparative results show that FSMC-PSO achieves higher trajectory tracking accuracy, reduces steady-state and transient errors, and minimizes chattering compared to conventional SMC and SMC-PSO, as well as the super-twisting algorithm-based PSO (STA-PSO) controller.FSMC-PSO achieves up to an 86.58% reduction in ITAE and a 73.53% reduction in ITSE compared to classical SMC while also outperforming SMC-PSO and STA-PSO across all motion states (X, Y, and $\psi$). These results demonstrate the effectiveness of FSMC-PSO for high-precision and disturbance-resilient AUV trajectory tracking within the simulated scenarios. Full article

Anomalous transaction behaviors in cryptocurrency markets exhibit high concealment, substantial diversity, and strong cross-modal coupling, making traditional rule-based or single-feature analytical methods insufficient for reliable detection in real-world environments. To address the research focus, a data-centric multimodal anomaly detection framework integrating generative augmentation, latent distribution modeling, and dual-branch real-time detection is proposed. The method employs a generative adversarial network with feature-consistency constraints to mitigate the scarcity of fraudulent samples, and adopts a multi-domain variational modeling strategy to learn the latent distribution of normal behaviors, enabling stable anomaly scoring. By combining the long-range temporal modeling capability of Transformer architectures with the sensitivity of online clustering to local structural deviations, the system dynamically integrates global and local information through an adaptive risk fusion mechanism, thereby enhancing robustness and real-time detection capability. Experimental results demonstrate that the generative augmentation module yields substantial improvements, increasing the recall from $0.421$ to $0.671$ and the F1-score to $0.692$. In anomaly distribution modeling, the multi-domain VAE achieves an area under the curve (AUC) of $0.854$ and an F1-score of $0.660$, significantly outperforming traditional One-Class SVM and autoencoder baselines. Multimodal fusion experiments further verify the complementarity of the dual-branch detection structure, with the adaptive fusion model achieving an AUC of $0.884$, an F1-score of $0.713$, and reducing the false positive rate to $0.087$. Ablation studies show that the complete model surpasses any individual module in terms of precision, recall, and F1-score, confirming the synergistic benefits of its integrated components. Overall, the proposed framework achieves high accuracy and high recall in data-scarce, structurally complex, and latency-sensitive cryptocurrency scenarios, providing a scalable and efficient solution for deploying data-centric artificial intelligence in financial security applications. Full article

Companies need to allocate substantial effort and resources towards adapting to dynamic market trends and promptly meeting their customers’ evolving expectations in the online business context. Although e-commerce research has experienced significant growth over the past two decades, a comprehensive, systematic, and longitudinal analysis that maps the evolution of publications, academic collaboration patterns, influential actors and sources, thematic structures, and theoretical foundations of the field is still lacking. This gap limits a holistic understanding of the maturation, intellectual structure, and future research directions of e-commerce as an academic domain. Based on these premises, the primary objective of the present study is to analyse the landscape of e-commerce spanning the period from 2008 to 2024. By employing bibliometric analysis, we have identified the most prolific and influential authors and publications that have made notable contributions to the literature on e-commerce, as well as the collaborations between authors and countries within the same field. Furthermore, we have analysed the thematic map, research trends, and interconnections between research themes over the past 17 years, providing a dynamic summary of scientific topics of interest in the field of e-commerce and suggesting potential directions for future explorations. The results reveal the heterogeneity of themes associated with e-commerce. We found that research topics in this field have evolved alongside technological evolution and social changes. Some themes have persisted over the years, such as customer behaviour or trust, while others have either disappeared or transformed. For instance, research related to supporting e-commerce technologies has become more specific, focusing on topics such as artificial intelligence, deep learning, machine learning, metaverse or blockchain. From a social perspective, the impact of COVID-19 has resonated within the scientific community, becoming a significant focus of researchers around the world. This study serves as a comprehensive guide for professionals and researchers seeking to bridge current research topics with forthcoming developments in the field of e-commerce. Examining contributions and emerging trends reveals new perspectives on how technological progress interacts with the social and economic dimensions of e-commerce. Full article

Reliable state estimation is critical for the safe operation of second-life battery systems but is severely hindered by significant parameter heterogeneity arising from diverse historical aging conditions. Traditional static models struggle to adapt to such variability, while online identification methods are prone to divergence under dynamic loads. To overcome these challenges, this paper proposes a Dual-Anchor Adaptive Fusion Framework for robust State of Charge (SOC) estimation and State of Health (SOH) soft-sensing. Specifically, to establish a reliable physical baseline, an automated Dynamic Relaxation Interval Selection (DRIS) strategy is introduced. By minimizing the fitting Root Mean Square Error (RMSE), DRIS systematically extracts high-fidelity parameters to construct two “anchor models” that rigorously define the boundaries of the aging space. Subsequently, a residual-driven Bayesian fusion mechanism is developed to seamlessly interpolate between these anchors based on real-time voltage feedback, enabling the model to adapt to uncalibrated target batteries. Concurrently, a novel “SOH Soft-Sensing” capability is unlocked by interpreting the adaptive fusion weights as real-time health indicators. Experimental results demonstrate that the proposed framework achieves robust SOC estimation with an RMSE of 0.42%, significantly outperforming the standard Adaptive Extended Kalman Filter (A-EKF, RMSE 1.53%), which exhibits parameter drift under dynamic loading. Moreover, the a posteriori voltage tracking residual is compressed to ~0.085 mV, effectively approaching the hardware’s ADC quantization limit. Furthermore, SOH is inferred with a relative error of 0.84% without additional capacity tests. This work establishes a robust methodological foundation for calibration-free state estimation in heterogeneous retired battery packs. Full article

This article develops a method for the early detection of low-intensity DDoS attacks based on a three-factor vector metric and implements an applied hybrid neural network traffic analysis system that combines preprocessing stages, competitive pretraining (SOM), a radial basis layer, and an associative Grossberg output, followed by gradient optimisation. The initial tools used are statistical online estimates (moving or EWMA estimates), CUSUM-like statistics for identifying small stable shifts, and deterministic signature filters. An algorithm has been developed that aggregates the components of fragmentation, reception intensity, and service availability into a single index. Key features include the physically interpretable features, a hybrid neural network architecture with associative stability and low computational complexity, and built-in mechanisms for adaptive threshold calibration and online training. An experimental evaluation of the developed method using real telemetry data demonstrated high recognition performance of the proposed approach (accuracy is 0.945, AUC is 0.965, F1 is 0.945, localisation accuracy is 0.895, with an average detection latency of 55 ms), with these results outperforming the compared CNN-LSTM and Transformer solutions. The scientific contribution of this study lies in the development of a robust, computationally efficient, and application-oriented solution for detecting low-intensity attacks with the ability to integrate into edge and SOC systems. Practical recommendations for reducing false positives and further improvements through low-training methods and hardware acceleration are also proposed. Full article

A parallel-stirred tank bioreactor system on a 10 mL-scale automated with a liquid handling station introduces significant benefits in bioprocess analysis and design regarding preserving time, cost, and workload, thereby enabling quick generation of bioprocess results that can be easily scaled up. Although up-to-date approaches enable the online analysis of individual reactors for pH, dissolved oxygen (DO), and optical density (OD), the automated calibration of a new online laser-based infrared OD sensor device and noise reduction are still required. Among the extensive research on the full-data smoothing tools, the Savitzky–Golay (Savgol) filter was determined as the most effective one. Scattered and transmitted online light values were successfully aligned with the reference at-line OD values measured at 600 nm by the liquid handler with a step time of a few hours. The growth of an engineered Gluconobacter oxydans designed for specific whole-cell oxidations has been investigated in two parallel batch process setups with varied sugar types at varying sugar concentrations, combinations of sugars, and altered concentrations of complex media. Simulation of real-time smoothing was applied with a Kalman filter. Rapid adaptation was observed within a few upcoming data points by altering the parameters for the estimation of the noise in the signal. For almost all tested reaction conditions, a successful alignment of the simulation of real-time smoothed online OD with at-line values was achieved. The best growth condition was determined in the presence of 120 g L⁻¹ glucose and 30 g L⁻¹ fructose with the tripled peptone concentration. Under these conditions, OD₆₀₀ increased by 109%, from 2.1 to 4.4, compared to the reference process. Full article

To address the increasing demands for faster response and higher control accuracy in the braking systems of electric and intelligent vehicles, a novel brake-by-wire actuation unit and its braking force control methods are proposed. The braking unit employs a permanent-magnet linear motor as the driving actuator and utilizes the lever-based force-amplification mechanism to directly generate the caliper force. Compared with the “rotary motor and motion conversion mechanism” configuration in other electromechanical braking systems, the proposed scheme significantly simplifies the force-transmission path, reduces friction and structural complexity, thereby enhancing the overall dynamic response and control accuracy. Due to the strong nonlinearity, time-varying parameters, and significant thermal effects of the linear motor, the braking force is prone to drift. As a result, achieving accurate force control becomes challenging. This paper proposes a model-free adaptive control method based on compact-form dynamic linearization. This method does not require an accurate mathematical model. It achieves dynamic linearization and direct control of complex nonlinear systems by online estimation of pseudo partial derivatives. Finally, the proposed control method is validated through comparative simulations and experiments against the fuzzy PID controller. The results show that the model-free adaptive control method exhibits significantly faster braking force response, smaller steady-state error, and stronger robustness against external disturbances. It enables faster dynamic response and higher braking force tracking accuracy. The study demonstrates that the proposed brake-by-wire scheme and its control method provide a potentially new approach for next-generation high-performance brake-by-wire systems. Full article

To address trajectory tracking of cable-driven continuum robots (CDCRs) in the presence of obstacles, this paper proposes an integrated control framework that combines online trajectory replanning, obstacle avoidance, and tracking control. The control system consists of two modules. The first is a trajectory replanning controller developed on an improved model predictive control (IMPC) framework. The second is a trajectory-tracking controller that integrates an adaptive disturbance observer with a fast non-singular terminal sliding mode control (ADO-FNTSMC) strategy. The IMPC trajectory replanning controller updates the trajectory of the CDCRs to avoid collisions with obstacles. In the ADO-FNTSMC strategy, the adaptive disturbance observer (ADO) compensates for uncertain dynamic factors, including parametric uncertainties, unmodeled dynamics, and external disturbances, thereby enhancing the system’s robustness and improving trajectory tracking accuracy. Meanwhile, the fast non-singular terminal sliding mode control (FNTSMC) guarantees fast, stable, and accurate trajectory tracking. The average tracking errors for IMPC-ADO-FNTSMC, MPC-FNTSMC, and MPC-SMC are 1.185 cm, 1.540 cm, and 1.855 cm, with corresponding standard deviations of 0.035 cm, 0.057 cm, and 0.078 cm in the experimental results. Compared with MPC-FNTSMC and MPC-SMC, the IMPC-ADO-FNTSMC controller reduces average tracking errors by 29.96% and 56.54%. Simulation and experimental results demonstrate that the designed two-module controller (IMPC-ADO-FNTSMC) achieves fast, stable, and accurate trajectory tracking in the presence of obstacles and uncertain dynamic conditions. Full article

Amid the uncertainties of the post-pandemic era, there has been a notable rise in information addiction among individuals, which may function as a coping mechanism in response to perceived situational threats. Family cohesion can function as a protective factor against internet addiction. However, the mechanism by which family cohesion mitigates internet addiction remains largely undiscovered. The study aimed to reveal the role of family cohesion in increasing information addiction behavior and the mediating effects of mindfulness and anxiety in this epidemic. A total of 1043 college students completed an online questionnaire including the Family Adaptability and Cohesion Evaluation Scale (FACESIII), State-Trait Anxiety Inventory (STAI), Mindful Attention Awareness Scale (MAAS), and Information Addiction Scale. (1) Family cohesion and information addiction exhibited a negative correlation; (2) mindfulness and anxiety functioned as mediators within this association; (3) stronger family cohesion was predictive of reduced information addiction behavior through a chain mediating effect, whereby mindfulness negatively predicted anxiety. These findings substantiate the study’s theoretical framework, underscoring the interconnected nature of information addiction during crises. Full article

The development of highly dynamic bipedal robots demands sensing capable of capturing key contact-related signals in real time, particularly the Center of Pressure (CoP). CoP is fundamental for locomotion control and state estimation and is also of interest in biomedical applications such as gait analysis and lower-limb assistive devices. To enable reliable CoP acquisition under dynamic walking, this paper presents a foot-mounted measurement system and an online self-calibration method that adapts sensor scale and bias parameters during locomotion using both external foot sensors and the robot’s proprioceptive measurements. We demonstrate an online self-calibration pipeline that updates foot-sensor scale and bias parameters during a walking experiment on a NAO-V5 platform using a sliding window optimization. The reported results indicate improved within-trial consistency relative to an offline-calibrated reference baseline under the tested walking conditions. In addition, the framework reconstructs a digitized estimate of the vertical ground reaction force (vGRF) from load-cell readings; due to ADC quantization and the discrete offline calibration dataset, the vGRF signal may exhibit stepwise behavior and should be interpreted as a reconstructed (digitized) quantity rather than laboratory-grade continuous force metrology. Overall, the proposed sensing-and-calibration pipeline offers a practical solution for dynamic CoP acquisition with low-cost hardware. Full article

In response to the deterioration of path-tracking accuracy and driving stability encountered by intelligent vehicles under dynamically varying operating conditions, a multi-objective optimization strategy integrating soft actor-critic (SAC) reinforcement learning with variable-parameter Model Predictive Control (MPC) is proposed in this paper to achieve online adaptive adjustment of path-tracking controller parameters. Based on a three-degree-of-freedom vehicle dynamics model, a linear time-varying (LTV) MPC controller is constructed to jointly optimize the front wheel steering angle. An SAC agent is developed utilizing the actor-critic framework, with a comprehensive reward function designed around tracking accuracy and control smoothness to enable online tuning of the MPC weighting matrices (lateral error weight, heading error weight, and steering control weight) as well as the prediction horizon parameter, thereby realizing adaptive balance between tracking accuracy and stability under different operating conditions. Based on the simulation results, it can be concluded that under normal operating conditions, the proposed integrated SAC-MPC control scheme demonstrates superior tracking performance, with the maximum absolute lateral error and mean lateral error reduced by 44.9% and 67.2%, respectively, and the maximum absolute heading error reduced by 23.5%. When the system operates under nonlinear conditions during the transitional phase, the proposed control scheme not only enhances tracking accuracy—evidenced by reductions of 43.4% and 23.8% in the maximum absolute lateral error and maximum absolute heading error, respectively—but also significantly improves system stability, as indicated by a 20.7% reduction in the sideslip angle at the center of gravity. Experimental validation further confirms these findings. The experimental results reveal that, compared with the fixed-parameter MPC, the maximum absolute value and mean value of the lateral error are reduced by approximately 36.2% and 78.1%, respectively; the maximum absolute heading angle error is decreased by 24.3%; the maximum absolute yaw rate is diminished by 19.6%; and the maximum absolute sideslip angle at the center of gravity is reduced by 30.8%. Full article

This study addresses the challenges of real-time performance, safety, and trajectory smoothness in robot navigation by proposing an innovative variable-horizon model predictive control (MPC) scheme that utilizes evolutionary algorithms. To effectively adapt to the complex and dynamic conditions during navigation, a constrained multi-objective evolutionary algorithm is used to tune the control parameters precisely. The optimized parameters are then used to dynamically adjust the MPC’s prediction horizon online. To further enhance the system’s real-time performance, warm start and multiple shooting techniques are introduced, significantly improving the computational efficiency of the MPC. Finally, simulation and real-world experiments are conducted to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed control scheme exhibits excellent navigation performance in differential-drive robot models, offering a novel solution for intelligent mobile robot navigation. Full article

In complex decision-making environments, the uncertainty and hesitancy of evaluation information, coupled with differences among evaluators, lead to asymmetric characteristics in decision information and preferences. Traditional methods struggle to effectively handle scenarios where interval uncertainty and hesitant information coexist, nor can they suppress asymmetric biases caused by extreme evaluations or imbalanced information distributions. To address this, this paper proposes a Symmetric Perception Decision Framework based on credibility-based interval hesitant fuzzy information. First, a Robust Credibility-Based Interval Hesitant Fuzzy Score Function (R-CHFSF) is constructed. This function quantifies asymmetric information by integrating interval width, distribution dispersion, and hesitancy characteristics. An adaptive penalty mechanism is introduced to suppress unreasonable asymmetric amplification effects caused by anomalous intervals or extreme evaluations. Second, the R-CHFSF is embedded into DEMATEL and TODIM methods to construct an integrated model combining causal analysis and ranking decisions, forming a closed-loop decision mechanism that simultaneously regulates information asymmetry and preference asymmetry. Empirical analysis using online movie reviews demonstrates that this framework effectively suppresses interference from excessively asymmetric evaluations, enhances the robustness and interpretability of ranking results, and validates its effectiveness in asymmetry regulation and decision stability. Full article