Search Results (643)

Hand exoskeletons are used in rehabilitation together with serious games to enhance patient experience and, possibly, therapy outcomes. To achieve good engagement, a realistic virtual representation of hand motion is needed; however, the relationship between exoskeleton joint motion and anatomical finger kinematics is rarely obtained using low-cost procedures. This work introduces a mechanical redesign and modeling pipeline that utilizes temporary sensors to identify the exoskeleton–finger mapping, enabling qualitatively realistic virtual hand motion driven solely by the existing on-board sensor. A recently developed hand exoskeleton prototype was redesigned to host two temporary rotary encoders aligned with the MetaCarpoPhalangeal (MCP) and Proximal InterPhalangeal (PIP) joints, in addition to the actuation encoder. Healthy subjects wore the modified device and performed full flexion–extension cycles. Encoder trajectories were processed; then each cycle was approximated by a third-order polynomial in the normalized actuation angle, and a group-level model was obtained by averaging coefficients across valid cycles. Finally, the encoder-based reconstructions of MCP and PIP motion were evaluated against measurements from a gold-standard optical motion capture system. Results indicate that the proposed polynomial model enables joint-angle estimation with sufficient accuracy for interactive rehabilitation scenarios, supporting its use to drive smooth virtual hand motion from the on-board exoskeleton encoder alone. Full article

This paper proposes a closed-form feedback control framework for rear- and front-wheel-drive car-like vehicles, aiming to solve trajectory tracking and collision avoidance tasks operating in complex environments. This method constructs novel risk assessment functions that incorporate motion information, enabling accurate risk assessment and reducing the conservatism in collision avoidance. Consequently, the proposed framework can effectively handle various on-road situations, including lane-following and obstacle avoidance, parking maneuvers, and navigation through intersections. Lyapunov-based analysis proves the stability of the designed closed-form control scheme. Simulation results in various typical scenarios demonstrate that the proposed method can achieve safe, stable, and smooth trajectory tracking, with improved performance metrics such as reduced tracking error and control effort, verifying its feasibility and effectiveness. Full article

To address the challenges of strong electromechanical coupling, nonlinear friction, and poor disturbance rejection in semi-direct-drive scraper conveyor systems under complex coal mining conditions, this paper aims to propose a high-performance drive control strategy that balances dynamic response speed with steady-state operational smoothness. First, an integrated electromechanical coupling dynamic model incorporating Permanent Magnet Synchronous Motor (PMSM) vector control and the time-varying meshing stiffness of a two-stage planetary gear train is established. Subsequently, a Sliding Mode Control (SMC) strategy optimized with a saturation boundary layer is designed and compared with traditional Proportional-Integral (PI) control under multiple operating conditions. Time-frequency domain analysis indicates that SMC significantly enhances the dynamic stiffness of the drive system. Under sudden load change conditions, the speed recovery time is shortened by approximately 76%, and the steady-state error is reduced by 37% compared to PI control. Microscopic characteristic evaluation based on FFT and Total Variation (TV) metrics reveals that SMC achieves active disturbance rejection through spectral broadening of the electromagnetic torque. Crucially, the steady-state cumulative control effort of SMC is equivalent to that of PI, implying no additional mechanical stress burden, while the equivalent dynamic transmission force fluctuation in the mechanical chain is reduced by about 3%. The study confirms that the proposed strategy successfully achieves a synergistic optimization of “macroscopic rapid response” and “microscopic smooth operation,” providing a theoretical basis for the high-precision control of heavy-duty underground transmission equipment. Full article

Recent evidence indicates that both epithelial-to-mesenchymal transition (EMT) and its reverse process, mesenchymal-to-epithelial transition (MET), are key mechanisms driving breast cancer (BC) metastasis. During EMT, epithelial BC cells acquire mesenchymal traits that enhance motility, invasiveness, and resistance to therapy. A deeper understanding of EMT regulation may therefore unveil novel therapeutic targets to limit disease progression. In this study, we analyzed the expression of key EMT-associated proteins, namely Vimentin, E-cadherin, Cytokeratin-18, and alpha-smooth muscle actin, in a cohort of 95 BC tissue samples and observed marked intra- and inter-tumoral heterogeneity. Notably, we found positive correlations between epithelial and mesenchymal markers, supporting the presence of hybrid epithelial/mesenchymal phenotypes and substantial cellular plasticity, which may contribute to BC heterogeneity. High heterogeneity in marker expression was also detected between tumor tissues and matched adjacent normal tissues. The unexpected complexity uncovered at the protein level prompted us to question whether single markers or limited proteomic panels are sufficient to capture the EMT landscape in BC. Through integrative bioinformatics, we defined a novel EMT gene signature significantly associated with prognosis. Functional enrichment revealed pathways related to extracellular matrix organization, proteoglycans, and intercellular communication, emphasizing the dynamic bidirectional crosstalk between BC cells and the tumor microenvironment. Moreover, we identified a gene cluster linked to cancer stem cell-like features, which may be clinically relevant for patient risk stratification. Overall, our findings underscore the complexity of EMT regulation in BC and introduce a new EMT signature with potential prognostic and therapeutic relevance. Full article

Glide-snow avalanches pose a major challenge for operational forecasting and local avalanche authorities. Although their key prerequisite, a moist interface between the snowpack and smooth ground, is well known, predicting the timing of glide-snow avalanches remains difficult. We analyzed five seasons of avalanche monitoring data in the Planneralm area of Styria, Austria. Glide-snow avalanche activity in the study area follows typical temporal patterns, with the highest release probability in the early afternoon and peak activity from mid-March to mid-April. Using meteorological data and avalanche observations as input, we trained machine-learning models to predict hours with glide-snow avalanche release. The most significant predictors were the mean air temperature of the preceding 48h, the day of the winter season, the hour of the day, and the decrease in snow height. The combination of those variables suggests a longer-term predisposition toward glide-snow avalanche release, as well as short-term driving factors. Our decision tree model correctly identified the vast majority of avalanche hours (recall 90%) at the cost of a moderate false alarm rate (15%). Our model could support operational glide-snow avalanche forecasting by identifying hours with elevated glide-snow potential that warrant increased attention and may require warnings or temporary closures by local authorities. 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

Distributed drive electric vehicles (DDEVs) offer remarkable advantages in handling stability owing to the independent torque and steering control of each wheel. Traditional in-dependent strategies have the disadvantages of slow response speed and unsmooth control interval switching. To overcome the performance tradeoffs of traditional independent strategies, this study proposes an integrated control approach combining four-wheel steering (4WS) and direct yaw moment control (DYC) to achieve coordinated multiobjective optimization. Based on phase-plane theory, the vehicle’s stable domain is divided using a double line method, and speed-dependent control regions and weights are designed to enable smooth switching between control modes. Simulation results demonstrate that, in high-adhesion conditions, compared with the DYC-only strategy, the integrated system reduces the maximum sideslip angle by about 77.8% and the cost function peak by 22.4%. Moreover, it decreases the maximum rear-wheel steering angle by 38.4% and maximum sideslip angle by about 15.4% compared with 4WS-only strategy. Under low-adhesion conditions, compared with the DYC-only strategy, the integrated system reduces the maximum sideslip angle by about 21.1% and the cost function peak by 37.6%. Additionally, the integrated system decreases the maximum rear-wheel steering angle by 60.2% and maximum sideslip angle by about 64.3% compared with 4WS-only strategy. Full article

This paper presents a systematic approach for designing an electric motor drive system (EMDS) for a permanent magnet DC motor to achieve precise speed regulation using a classical PID controller. Smooth voltage trajectory planning based on Bézier curves is employed to mitigate high voltage and current peaks during step speed transitions, improving dynamic performance, reducing electrical stress, and making the control system physically realizable. A comparative evaluation of inverting buck–boost, positive buck–boost, and quadratic DC–DC converters is conducted using the same motor and controller, enabling the identification of the most suitable controller–converter pairing. Experimental results demonstrate that, with an appropriate converter topology and voltage trajectory, peak voltages and currents are significantly reduced, resulting in a smoother control action and reliable speed regulation without the need for complex control schemes. Full article

Glutamine is a known regulator of vascular smooth muscle cell (VSMC) function, but the molecular pathways underlying this response remain incompletely understood. This study investigated how glutamine metabolism influences VSMC behavior and identified the responsible enzymes and metabolites. Glutamine deprivation markedly reduced VSMC proliferation, migration, and collagen synthesis, while modestly decreasing viability. Pharmacological inhibition of glutaminase-1 (GLS1) or aminotransferases (AT) similarly suppressed these cellular functions, whereas inhibiting glutamate dehydrogenase 1 (GLUD1) had no effect. Metabolite analysis revealed that glutamine deprivation or AT inhibition, but not GLUD1 inhibition, reduced intracellular α-ketoglutarate (αKG) concentrations, establishing AT as the primary enzyme converting glutamine-derived glutamate to αKG. To identify which metabolite drives VSMC responses, glutamine-starved cells were supplemented with various glutamine-derived molecules. The cell-permeable αKG analog dimethyl-αKG significantly restored VSMC proliferation, migration, collagen synthesis, and survival, while ammonia only enhanced viability, demonstrating αKG’s primary role in mediating glutamine-dependent functions. These findings establish that glutamine metabolism via the GLS1-AT-αKG pathway is a critical driver of VSMC activation and survival. Targeting this glutamine-αKG metabolic axis through GLS1 inhibition, AT blockade, or downstream αKG disruption offers a compelling therapeutic strategy for ameliorating fibroproliferative vascular diseases, including atherosclerosis, post-angioplasty restenosis, and pulmonary hypertension. Full article

Asthma is a heterogeneous disease that varies in clinical presentation, severity, and underlying biology but consistently involves airway remodeling (AR) and airway hyperresponsiveness (AHR), which is characterized by excessive airway narrowing in response to various stimuli. Airway smooth muscle (ASM) cells are primary contributors to airway hyperresponsiveness and bronchoconstriction. This review focuses on ASM cells and their role in asthma. We discuss the mechanisms by which ASM mediates AHR, increases airway thickness, and contributes to AR. Signaling through G protein-coupled receptors (GPCRs) regulates many ASM functions, including contraction, growth, and the synthetic activities that drive airway inflammation and remodeling. GPCR-dependent calcium flux serves as a key signaling axis controlling the contractile responses of ASM. Here we provide a comprehensive summary of the major GPCRs as well as other non-GPCRs identified in ASM cells. GPCR-induced calcium mobilization, downstream signaling and how it has been linked to specific ASM functions are also discussed. Furthermore, we highlight the clinical significance of targeting GPCRs in asthma therapy as well as recent development of novel therapeutics in the management of asthma. Thus, this review provides a comprehensive overview of airway smooth muscle in the context of asthma pathophysiology. Full article

Vascular smooth muscle cells (VSMCs) in the tunica media are essential for maintaining the structure and function of the arterial wall. These cells regulate vascular tone and contribute to vasculogenesis and angiogenesis, particularly during development. Proper control of VSMC differentiation ensures the correct size and patterning of vessels. Dysregulation of VSMC behaviour in adulthood, however, is linked to serious cardiovascular diseases, including aortic aneurysm, coronary artery disease, atherosclerosis and pulmonary hypertension. VSMCs are characterised by their phenotypic plasticity, which is the capacity to transition from a contractile to a synthetic, dedifferentiated state in response to environmental cues. This phenotypic switch plays a central role in vascular remodelling, a process that drives the progression of many vascular pathologies. Epigenetic mechanisms, which are defined as heritable but reversible changes in gene expression that do not involve alterations to the DNA sequence, have emerged as key regulators of VSMC identity and behaviour. These mechanisms include DNA methylation, histone modifications, chromatin remodelling, non-coding RNA and RNA modifications. Understanding how these epigenetic processes influence VSMC plasticity is crucial to uncovering the molecular basis of vascular development and disease. This review explores the current understanding of VSMC biology, focusing on epigenetic regulation in health and pathology. Full article

This work presents a hierarchical deep reinforcement learning (DRL) framework based on Soft Actor–Critic (SAC) for the autonomy of rock-breaking machinery in surface mining comminution processes. The proposed approach explicitly integrates mobile navigation and hydraulic manipulation as coupled subprocesses within a unified decision-making architecture, designed to operate under the unstructured and highly uncertain conditions characteristic of open-pit mining operations. The system employs a hysteresis-based switching mechanism between specialized SAC subagents, incorporating automatic entropy tuning to balance exploration and exploitation, twin critics to mitigate value overestimation, and curriculum learning to manage the progressive complexity of the task. Two coupled subsystems are considered, namely: (i) a tracked mobile machine with a differential drive, whose continuous control enables safe navigation, and (ii) a hydraulic manipulator equipped with an impact hammer, responsible for the fragmentation and dismantling of rock piles through continuous joint torque actuation. Environmental perception is modeled using processed perceptual variables obtained from point clouds generated by an overhead depth camera, complemented with state variables of the machinery. System performance is evaluated in unstructured and uncertain simulated environments using process-oriented metrics, including operational safety, task effectiveness, control smoothness, and energy consumption. The results show that the proposed framework yields robust, stable policies that achieve superior overall process performance compared to equivalent hierarchical configurations and ablation variants, thereby supporting its potential applicability to DRL-based mining automation systems. Full article

Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article

Atherosclerosis (AS) is a disease characterized by chronic vascular wall inflammation and lipid deposition. Although lipid-lowering drugs such as statins have significantly reduced cardiovascular event rates, “residual inflammatory risk” remains a key factor driving disease progression and plaque rupture. As a central regulator of the inflammatory response, the nuclear factor-κappaB (NF-κB) signaling network comprises both canonical pro-inflammatory pathways and functionally more complex non-canonical pathways. Increasing evidence in recent years indicates that abnormal and sustained activation of the non-canonical NF-κB signaling pathway plays a pivotal role in driving plaque rupture. This review first elaborates on the shift in AS strategies from “lipid-lowering” to “anti-inflammatory” approaches, followed by an in-depth analysis of the molecular activation mechanisms of the NF-κB signaling pathway and its distinctiveness in the AS pathological process, along with its epigenetic regulation. It emphasizes how this pathway drives pathological angiogenesis and regulates vascular smooth muscle cell (VSMC) phenotypic switching and macrophage function, thereby forming a vicious cycle that amplifies inflammation and structural damage, ultimately leading to acute cardiovascular events. Finally, we systematically summarize current progress and challenges in drug development targeting the NF-κB pathway (e.g., targeting key kinases like NIK and IKKα), aiming to provide theoretical foundations and future directions for novel therapeutic strategies to stabilize coronary plaques and prevent acute coronary syndromes. Full article