Search Results (4,024)

The actomyosin complex—nature’s dynamic engine composed of actin filaments and myosin motors—is emerging as a versatile tool for bio-integrated nanotechnology. This review explores the growing potential of actomyosin-powered systems in biosensing and actuation applications, highlighting their compatibility with physiological conditions, responsiveness to biochemical and physical cues and modular adaptability. We begin with a comparative overview of natural and synthetic nanomachines, positioning actomyosin as a uniquely scalable and biocompatible platform. We then discuss experimental advances in controlling actomyosin activity through ATP, calcium, heat, light and electric fields, as well as their integration into in vitro motility assays, soft robotics and neural interface systems. Emphasis is placed on longstanding efforts to harness actomyosin as a biosensing element—capable of converting chemical or environmental signals into measurable mechanical or electrical outputs that can be used to provide valuable clinical and basic science information such as functional consequences of disease-associated genetic variants in cardiovascular genes. We also highlight engineering challenges such as stability, spatial control and upscaling, and examine speculative future directions, including emotion-responsive nanodevices. By bridging cell biology and bioengineering, actomyosin-based systems offer promising avenues for real-time sensing, diagnostics and therapeutic feedback in next-generation biosensors. Full article

The immune system is crucial in protecting against disease, but it can also contribute to chronic illnesses when it malfunctions, with different conditions involving either inflammation or immune suppression. Current treatments often fall short due to limited effectiveness and side effects. Nanomedicine, particularly cerium oxide nanoparticles (nanoceria), offers promising potential due to its unique therapeutic properties and role in modulating macrophages. Nanoceria (<5 nm) possess the catalytic ability to mimic natural enzymes such as superoxide dismutase, peroxidase, and catalase, enabling effective scavenging of reactive oxygen species (ROS), which play a central role in the pathogenesis of chronic inflammation and cancer. This review comprehensively summarizes the current advances in the application of nanoceria for inflammatory and anti-inflammatory therapy, including their modulatory effects on immune cell activation, cytokine production, and resolution of inflammatory responses. We discuss the mechanisms underlying their immunomodulatory actions in various disease contexts, such as rheumatoid arthritis, women’s health conditions (e.g., endometriosis), wound healing, and cancer. Additionally, the review highlights biocompatibility, therapeutic efficacy, adaptability in imaging (theranostics), and challenges in translating nanoceria-based therapies into clinical practice. The multifunctionality of nanoceria positions them as innovative candidates for next-generation immunotherapy aimed at efficiently controlling inflammation and promoting tissue repair. Full article

Identifying the activation status of advanced driver assistance systems (ADAS) in real-world driving environments is crucial for safety, responsibility attribution, and accident forensics. Unlike prior studies that primarily rely on simulation-based settings or unsynchronized data, we collected a multimodal dataset comprising synchronized controller area network (CAN)-bus and smartphone-based inertial measurement unit (IMU) signals from drivers on consistent highway sections under both ADAS-enabled and manual modes. Using these data, we developed lightweight classification pipelines based on statistical and deep learning approaches to explore the feasibility of distinguishing ADAS operation. Our analyses revealed systematic behavioral differences between modes, particularly in speed regulation and steering stability, highlighting how ADAS reduces steering variability and stabilizes speed control. Although classification accuracy was moderate, this study provides one of the first data-driven demonstrations of ADAS status detection under naturalistic conditions. Beyond classification, the released dataset enables systematic behavioral analysis and offers a valuable resource for advancing research on driver monitoring, adaptive ADAS algorithms, and accident forensics. Full article

Effective production control requires aligning strategic planning with real-time execution under dynamic and stochastic conditions. This study proposes a scalable dual-agent Deep Reinforcement Learning (DRL) framework for the joint optimisation of Work-In-Process (WIP) control and job sequencing in flow-shop environments. A strategic DQN agent regulates global WIP to meet throughput targets, while a tactical DQN agent adaptively selects dispatching rules at the machine level on an event-driven basis. Parameter sharing in the tactical agent ensures inherent scalability, overcoming the combinatorial complexity of multi-machine scheduling. The agents coordinate indirectly via a shared simulation environment, learning to balance global stability with local responsiveness. The framework is validated through a discrete-event simulation integrating agent-based modelling, demonstrating consistent performance across multiple production scales (5–15 machines) and process time variabilities. Results show that the approach matches or surpasses analytical benchmarks and outperforms static rule-based strategies, highlighting its robustness, adaptability, and potential as a foundation for future Hierarchical Reinforcement Learning applications in manufacturing. Full article

With the rapid progress of industrial technology in recent years, servo controllers have the characteristics of precise control and short response time and are widely used in different industrial fields. As for the electro-hydraulic servo valve being an important control element of the entire hydraulic system, the quality of its own characteristics has a significant impact on the normal operation and safety of the mechanical equipment. Therefore, the working stability of the servo valve in actual operation is of great importance to its body and the overall servo system. Similarly, during the vibration test of the electro-hydraulic servo shaking table, servo valve inevitably experiences various vibrations and shocks, which requires the servo system to be able to withstand the test and assessment under the extreme conditions in actual operation to ensure the smooth operation. This paper takes function of the shaker as the research target and studies the servo valve under various vibration conditions by constructing a digital modeling system. On this basis, an adaptive format filter is established, and corresponding vibration suppression methods are adopted for the vibration conditions inside the system. Finally, simulation examples are used to prove that this method can more effectively control the vibration in the servo valve and suppress the interference with shaking table function. Full article

Oxidative stress and chronic low-grade inflammation are recognized key drivers of diabetic complications. Lysosomal dysfunction, cellular senescence, and inter-organ stress signaling further aggravate the Redox–Inflammation–Organ Stress Axis in type 2 diabetes mellitus (T2DM). Recent studies suggest that reactive oxygen species (ROS) are not always harmful. Through mitohormesis, mild and transient increases in ROS levels can trigger antioxidant defenses, strengthen mitochondrial function, and limit chronic inflammation. Evidence from caloric restriction, exercise, and ketone body studies supports this adaptive redox balance, underscoring the importance of maintaining a “hormetic window” rather than indiscriminate antioxidant supplementation. In our prospective study, sodium-glucose cotransporter 2 inhibitor treatment significantly reduced albuminuria and serum levels of inflammatory markers, e.g., tumor necrosis factor receptors 1 and 2, while paradoxically increasing urinary 8-hydroxy-2′-deoxyguanosine levels and biological antioxidant potential (BAP), suggestive of adaptive ROS responses consistent with mitohormesis. Concomitant glucagon-like peptide-1 receptor agonist use emerged as an independent explanatory factor for increased urinary levels of oxidative stress markers, suggesting that multiple metabolic therapies converge on shared hormetic pathways. Emerging evidence that stressed adipocytes can communicate mild ROS signals via extracellular vesicles expands this paradigm to inter-organ mitohormesis. Collectively, these insights caution against indiscriminate antioxidant use and underscore the therapeutic potential of controlled redox modulation to disrupt the vicious cycle of senescence, inflammation, and organ stress. Incorporating redox biomarkers like urinary 8-hydroxy-2′-deoxyguanosine, reactive oxygen metabolite derivatives, and BAP into clinical monitoring, alongside pharmacological and lifestyle interventions, may facilitate the realization of precision metabolic medicine for multi-organ protection in T2DM. Full article

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

This study aims to reduce reverse power and improve frequency regulation performance in hydropower systems. To achieve this objective, a refined hydropower plant (HPP) simulation model is developed and coupled with a battery energy storage system (BESS), implementing an Integrated Adaptive Virtual Droop Control (IAVDC) strategy. The refined HPP model achieves a simulation accuracy of 98.5%, representing a 26.2% improvement over conventional simplified models. With the BESS integrated under the IAVDC strategy, reverse power is completely eliminated, and frequency regulation time is substantially shortened. The results demonstrate that the joint HPP-BESS frequency regulation effectively mitigates the adverse impact of water hammer, while the proposed IAVDC strategy enhances system responsiveness and reduces frequency recovery time, thereby improving the quality of primary frequency control. Full article

Background: Demoralization, anxiety, irritability, and depression are common among hospital patients and are associated with poorer outcomes and greater healthcare burden. Early identification is essential, but simultaneous screening across multiple domains is often impractical with questionnaires. Computerized Adaptive Testing (CAT) offers a solution by tailoring item administration, reducing test length while preserving measurement precision. The aim of this study was to develop and validate FRIDA (Four-factor Rapid Interactive Diagnostic Assessment), a freely accessible, web-based CAT for rapid multidimensional screening of psychopathology in hospital patients. Methods: We analysed data from 472 medically ill in-patients at a University Hospital. Item calibration was performed using a four-factor graded response model (demoralization, anxiety, irritability, depression) in the mirt package. CAT simulations were run with 1000 virtual respondents to optimize item selection, exposure control, and stopping rules. The best configuration was applied to the real dataset. Criterion validity for demoralization was evaluated against the Diagnostic Criteria for Psychosomatic Research (DCPR). Results: The four-factor model showed good fit (CFI = 0.947, RMSEA = 0.080). Factor correlations were moderate to high, with the strongest overlap between demoralization and depression (r = 0.93). In simulations, the CAT required, on average, 7.8 items and recovered trait estimates with high accuracy (r = 0.94–0.97). In real patients, mean test length was 11 items, with accuracy of r = 0.95 across domains. FRIDA demonstrated good criterion validity for demoralization (AUC = 0.816; sensitivity 80%, specificity 67.5%). Conclusions: FRIDA is the first freely available, multidimensional CAT for rapid screening of psychopathology in hospital patients. It offers a scalable, efficient, and precise tool for integrating mental health assessment into routine hospital care. Full article

Low-Dimensional Nanohybrids (LDNHs) have emerged as potent multifunctional platforms for neurosensing and neuromodulation, providing elevated spatial-temporal precision, versatility, and biocompatibility. This review examines the intersection of LDNHs with artificial intelligence, brain–computer interfaces (BCIs), and closed-loop neurotechnologies, highlighting their transformative potential in personalized neuro-nano-medicine. Utilizing stimuli-responsive characteristics, optical, thermal, magnetic, and electrochemical LDNHs provide real-time feedback-controlled manipulation of brain circuits. Their pliable and adaptable structures surpass the constraints of inflexible bioelectronics, improving the neuronal interface and reducing tissue damage. We also examined their use in less invasive neurological diagnostics, targeted therapy, and adaptive intervention systems. This review delineates recent breakthroughs, integration methodologies, and fundamental mechanisms, while addressing significant challenges such as long-term biocompatibility, deep-tissue accessibility, and scalable manufacturing. A strategic plan is provided to direct future research toward clinical use. Ultimately, LDNHs signify a transformative advancement in intelligent, tailored, and closed-loop neurotechnologies, integrating materials science, neurology, and artificial intelligence to facilitate the next era of precision medicine. Full article

Smartphone addiction has emerged as a significant public health concern, particularly among adolescents and young adults. This narrative review, conducted in line with the ANDJ checklist, synthesizes evidence from 25 systematic reviews and meta-analyses, complemented by randomized controlled trials and clinical studies, to provide a structured overview of the field. The study selection flow and publication trends reveal a rapidly expanding research landscape, with most evidence produced in the last decade, reflecting both the ubiquity of smartphones and increasing awareness of their health impacts. The synthesis highlights converging findings across reviews: excessive smartphone use is consistently associated with psychosocial, behavioral, and academic challenges, alongside sleep disturbances and mental health symptoms. Common messages include the recognition of smartphone addiction as a multidimensional phenomenon, while emerging themes point to heterogeneity in definitions, tools, and methodological approaches. Comparative analysis of reviews underscores both shared risk factors—such as emotional dysregulation and social isolation—and differences in study designs and target populations. Importantly, this review identifies critical gaps, including the lack of standardized definitions, limited longitudinal evidence, and scarce cross-cultural validation. At the same time, promising opportunities are noted, from lifestyle-based interventions (e.g., physical activity) to educational and policy-level strategies fostering digital literacy and self-regulation. The post-pandemic context further emphasizes the need for sustained monitoring and adaptive responses. Overall, this review calls for youth-centered, multi-sector interventions aligned with WHO recommendations, supporting coordinated, evidence-based action across health, education, and policy domains. Full article

In field exploration, disaster rescue, and complex terrain operations, the accuracy of ground type recognition directly affects the walking stability and task execution efficiency of legged robots. To address the problem of terrain recognition in complex ground environments, this paper proposes a high-precision classification method based on single-leg triaxial force signals. The method first employs a one-dimensional convolutional neural network (1D-CNN) module to extract local temporal features, then introduces a long short-term memory (LSTM) network to model long-term and short-term dependencies during ground contact, and incorporates a convolutional block attention module (CBAM) to adaptively enhance the feature responses of critical channels and time steps, thereby improving discriminative capability. In addition, an improved whale optimization algorithm (iBWOA) is adopted to automatically perform global search and optimization of key hyperparameters, including the number of convolution kernels, the number of LSTM units, and the dropout rate, to achieve the optimal training configuration. Experimental results demonstrate that the proposed method achieves excellent classification performance on five typical ground types—grass, cement, gravel, soil, and sand—under varying slope and force conditions, with an overall classification accuracy of 96.94%. Notably, it maintains high recognition accuracy even between ground types with similar contact mechanical properties, such as soil vs. grass and gravel vs. sand. This study provides a reliable perception foundation and technical support for terrain-adaptive control and motion strategy optimization of legged robots in real-world environments. Full article

Online peer support platforms are vital, scalable resources for mental health, yet their effectiveness is frequently undermined by inefficient user matching, severe participation inequality, and subsequent “super-helper” burnout. This study introduces TrailMap, a novel peer-matching algorithm inspired by the decentralised foraging strategies of ant colonies. By treating user interactions as paths that gain or lose “pheromone” based on helpfulness ratings, the system enables the community to collectively and adaptively identify its most effective helpers. A two-phase validation study was conducted. First, an agent-based simulation demonstrated that TrailMap reduced the mean time to a helpful response by over 70% and improved workload equity compared to random routing. Second, a four-week randomised controlled pilot study with human participants confirmed these gains, showing a 76% reduction in median wait time and significantly higher perceived helpfulness ratings. The findings suggest that by balancing the workload, TrailMap enhances not only the efficiency but also the socio-technical sustainability of online support communities. TrailMap provides a practical, nature-inspired method for building more resilient and equitable online support communities, enhancing access to effective mental health support. Full article

Advanced energy storage solutions are required to mitigate grid destabilization caused by high-penetration renewable energy integration. Superconducting Magnetic Energy Storage (SMES) offers ultrafast response (<1 ms), high efficiency (>95%), and almost unlimited cycling life. However, its commercialization is hindered by the complex modeling of critical current with anisotropic behaviors and the computational inefficiency of high-dimensional optimization for megajoule (MJ)-class magnets. This paper proposes an integrated design framework synergizing a two-dimensional axisymmetric magnetic field model based on Conway’s current-sheet theory, a critical current anisotropy characterization model, and an adaptive genetic algorithm (AGA). A superconducting magnet optimization model incorporating co-calculation of electromagnetic parameters is established. A dual-module chromosome encoding strategy (discrete gap index + nonlinear increment) and parallel acceleration techniques were developed. This approach achieved efficient optimization of MJ-class magnets. For a single solenoid, the critical current increased by 22.6% (915 A) and energy storage capacity grew by 41.8% (1.12 MJ). A 20-unit array optimized by coordinated gap adjustment achieved a matched inductance/current of 0.15 H/827 A (20 MJ), which can enhance transient stability control capability in smart grids. The proposed method provides a computationally efficient design paradigm and user-friendly teaching software tool for high-current SMES magnets, supporting the development of large-scale High-Temperature Superconducting (HTS) magnets, promoting the deployment of large-scale HTS magnets in smart grids and high-field applications. Full article

This paper studies joint data collection and wireless power transfer in a UAV-assisted IoT network. A rotary-wing UAV follows a fly–hover–communicate cycle. At each hover, it simultaneously receives uplink data in full-duplex mode while delivering radio-frequency energy to nearby devices. Using a realistic propulsion-power model and a nonlinear energy-harvesting model, we formulate trajectory and hover control as a multi-objective optimization problem that maximizes the aggregate data rate and total harvested energy while minimizing the UAV’s energy consumption over the mission. To enable flexible trade-offs among these objectives under time-varying conditions, we propose a dynamic, state-adaptive weighting mechanism that generates environment-conditioned weights online, which is integrated into an enhanced deep deterministic policy gradient (DDPG) framework. The resulting dynamic-weight MODDPG (DW-MODDPG) policy adaptively adjusts the UAV’s trajectory and hover strategy in response to real-time variations in data demand and energy status. Simulation results demonstrate that DW-MODDPG achieves superior overall performance and a more favorable balance among the three objectives. Compared with the fixed-weight baseline, our algorithm increases total harvested energy by up to 13.8% and the sum data rate by up to 5.4% while maintaining comparable or even lower UAV energy consumption. Full article