Search Results (15,028)

This study aims to assess hand function recovery in stroke patients during the mid-to-late Brunnstrom stages and to encourage active participation in rehabilitation exercises. To this end, a deep residual network (ResNet) integrated with Focal Loss is employed for gesture recognition, achieving a Macro F1 score of 91.0% and a validation accuracy of 90.9%. Leveraging the millimetre-level precision of Leap Motion 2 hand tracking, a mapping relationship for hand skeletal joint points was established, and a static assessment gesture data set containing 502,401 frames was collected through analysis of the FMA scale. The system implements an immersive augmented reality interaction through the Unity development platform; C# algorithms were designed for real-time motion range quantification. Finally, the paper designs a rehabilitation system framework tailored for home and community environments, including system module workflows, assessment modules, and game logic. Experimental results demonstrate the technical feasibility and high accuracy of the automated system for assessment and rehabilitation training. The system is designed to support stroke patients in home and community settings, with the potential to enhance rehabilitation motivation, interactivity, and self-efficacy. This work presents an integrated research framework encompassing hand modelling and deep learning-based recognition. It offers the possibility of feasible and economical solutions for stroke survivors, laying the foundation for future clinical applications. Full article

This study presents the development and first manufacturing realization of the ECOTIRE concept, a modular and sustainable tire system featuring a removable tread mechanically interlocked with a reusable casing. The concept aims to reduce rubber waste and improve recyclability by eliminating adhesive bonding and enabling tread replacement. Building on previous experimental and numerical studies that validated the interlocking performance, this work focuses on producing a scaled prototype using a low-cost molding process, which can serve as the basis for accessible and sustainable manufacturing. VMQ silicone rubber was selected as an eco-friendly material due to its durability, thermal stability, and processing versatility. A custom two-part aluminum mold was designed to replicate the optimized interlocking geometry, enabling accurate casting, curing, and assembly. The resulting prototype achieved precise fit, dimensional uniformity, and easy disassembly, confirming the manufacturing feasibility of the ECOTIRE concept and demonstrating its potential to support circular economy strategies through reduced material waste and extended tire component lifetimes. Full article

Renewable energy sources like wind turbines and solar panels are integrated into modern power grids as Distributed Energy Resources (DERs). These DERs can operate independently or as part of microgrids. Interconnecting multiple microgrids creates Networked Microgrids (NMGs) that increase reliability, resilience, and independent power generation. However, the trustworthiness of individual DERs remains a critical challenge in NMGs, particularly when integrating previously deployed or geographically distributed units managed by entities with varying expertise. Assessing DER trustworthiness ensuring reliability and security is essential to prevent system-wide instability. Thisresearch addresses this challenge by proposing a lightweight trust metric generation system capable of processing structured and semi-structured DER data to produce key trust indicators. The system employs a Small Language Model (SLM) with approximately 16 million parameters for textual data understanding and metric extraction, followed by a regression head to output bounded trust scores. Designed for deployment in computationally constrained environments, the SLM requires only 64.6 MB of disk space and 200–250 MB of memory that is significantly lesser than larger models such as DeepSeek R1, Gemma-2, and Phi-3, which demand 3–12 GB. Experimental results demonstrate that the SLM achieves high correlation and low mean error across all trust metrics while outperforming larger models in efficiency. When integrated into a full neural network-based trust framework, the generated metrics enable accurate prediction of DER trustworthiness. These findings highlight the potential of lightweight SLMs for reliable and resource-efficient trust assessment in NMGs, supporting resilient and sustainable energy systems in smart cities. Full article

Computer-based engineering design tools can quicken the cadence for machine design, which enables companies to compete better in the global marketplace. The application of nonlinear optimization and tradespace analysis methods allows the exploration of design variables within dynamic mechanisms. In this paper, the design of a classical machine, the Seth Thomas pendulum street clock, which offered precision timekeeping and time display at the turn of the 20th century, will be investigated from a modern perspective. A mathematical model serves as the basis for the genetic algorithm optimization method to assess the system design in terms of accuracy, mass, quality factor, and bending stress. To validate the model, experimental data was collected on a 1906 Seth Thomas Model 04 movement. The engineering study findings indicate that the target accuracy, quality factor, and bending stress can be achieved with pendulum mass and gear thickness reductions of 1.4% and 50.3%, respectively. The tradespace exploration offers a visualization of the machine’s performance per design variable adjustments for greater insight into the original solution and subsequent recommended changes. Overall, this mechanical machine review enables an assessment of original design choices made over a century ago and provides an awareness of engineering’s progress during this period. Full article

Structural failure of the lead-carbon battery casing under external loads poses a serious threat to the safety of its energy storage function. To overcome the limitations of traditional protective casings regarding specific energy absorption (SEA) and crush force efficiency (CFE), this study proposes a novel thin-walled protective structure utilizing graded aperture honeycomb sandwich panels fabricated via additive manufacturing (AM). Finite element (FE) models were established using HyperMesh and validated against experimental data. Subsequently, the impact resistance and energy absorption characteristics of four distinct cellular topologies were systematically investigated under varying pore-size gradients, impact directions, and velocities. Experimental and numerical simulation results indicate that, among the investigated configurations, the triangular honeycomb structure exhibits superior impact resistance and energy absorption capability under both axial and lateral loading conditions. Furthermore, the synergistic enhancement mechanism based on topological configuration and gradient design effectively optimizes the progressive crushing mode, thereby reducing the initial peak crushing force transmitted to the battery and resulting in a pronounced advantage in impact performance. This research provides a novel design approach for optimizing next-generation high-performance, lightweight protection systems for energy storage devices. Full article

Ceramsite foam concrete (CFC), recognized for its lightweight, thermal insulation, and eco-friendly properties, is a promising material for composite structures. The interfacial shear bond behavior between CFC and normal concrete (NC) critically governs the structural integrity of CFC-NC systems. This study investigates the interfacial shear bond strength through direct double shear tests on twelve cubic specimens with controlled interface roughness and casting intervals. Quantitative analysis reveals that increased roughness enhances shear strength by up to 28.6~59.5%, while prolonged casting intervals reduce strength by 22.3~34.6%. Notably, excessive roughness shifts failure modes from interfacial debonding to material failure within CFC, where shear bond strength becomes governed by CFC’s compressive strength. A rigid–plastic model is developed to characterize the shear bond behavior of CFC-NC interface and demonstrates 96% accuracy in predicting experimental results. The findings provide useful insights for improving CFC-NC composite design in engineering applications. Full article

High-precision non-contact online voltage monitoring has attracted considerable attention due to its improved safety. Based upon existing research works and validation of non-contact voltage measurement techniques, an enhanced approach for online voltage monitoring is proposed in this paper. By analyzing the influence of the relationship between coupling capacitance and input capacitance on monitoring results, an RC-type signal input circuit with enhanced adaptability has been designed for practical engineering scenarios that may involve large input capacitance. Furthermore, a mixed-signal measurement method based on phase dithering is proposed to eliminate detection errors caused by relative phase drift during synchronous sampling in existing signal injection approaches. This improvement enhances measurement accuracy and offers a more robust theoretical basis for selecting injection signal frequencies. The hardware circuit architecture and data processing scheme presented in this work are straightforward and have been validated using an experimental prototype tested at 50 Hz/500 V and 2000 Hz/300 V. Long-term energized testing demonstrates that the system operates stably at room temperature with a relative measurement error below 0.5%. This study provides a high-precision, easily implementable non-contact measurement solution for online monitoring of low-frequency, low-voltage signals in complex electromagnetic environments such as industrial control signals, low-voltage power signals, and rail transit signals. Full article

The electric propulsion system is a critical determinant of unmanned aerial vehicles’ (UAVs’) operational capabilities, particularly endurance performance. This paper proposes a high-precision modeling framework for UAV electric propulsion systems to improve endurance estimation. By integrating dimensional analysis based on the Buckingham $\pi$ theorem with data-driven parameter fitting, the method accurately predicts propeller thrust, power, and motor current under varying inflow conditions using limited experimental data. The proposed models and complete implementation are publicly available, facilitating reproducibility and further research. The key novelty of this work lies in the tight integration of dimensional analysis (via Buckingham’s $\pi$ theorem) with a data-driven torque-based motor current model, enabling accurate cross-configuration predictions for both propeller aerodynamics and motor electrical characteristics using limited experimental data. The model is rigorously validated against the UIUC propeller database, a custom-built inflow test rig, and actual flight tests. The results demonstrate that the proposed approach achieves superior prediction accuracy across multiple propeller-motor configurations while significantly reducing computational costs. This work provides a reliable foundation for improving UAV endurance estimation and propulsion system design. Full article

In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with low power consumption. The present work demonstrates the process of design, implementation and experimental evaluation of a single-cell lithium-ion battery monitoring prototype, intended for standalone operation or integration into other systems. The architecture is compact and energy efficient, with a reduction in complexity and memory usage: modular architecture with clearly distinguished responsibilities, avoidance of unnecessary dynamic memory allocations, centralized error handling, and a low-power policy through the usage of deep sleep mode. The data is stored in a cloud platform, while minimal storage is used locally. The developed system combines the functional requirements for an embedded external battery monitoring system: local voltage and current measurement, approximate estimation of the State of Charge (SoC) using a look-up table (LUT) based on the discharge characteristic, and visualization on a monochrome OLED display. The conducted experiments demonstrate the typical U(t) curve and the triggering of the indicator at low charge levels (LOW − SoC ≤ 20% and CRITICAL − SoC ≤ 5%) in real-world conditions and the absence of unwanted switching of the state near the voltage thresholds. Full article

Accurate gas analysis plays a critical role in aerospace missions, including spacecraft safety assurance, crew health monitoring, and deep-space scientific exploration. Although conventional gas chromatography (GC) techniques are well established, their large size, high power consumption, and long analysis time limit their applicability in modern aerospace missions that require miniaturized, low-power, and highly integrated analytical systems. The development of microelectromechanical systems (MEMS) technology provides an effective pathway for the miniaturization of gas chromatography. MEMS-based micro gas chromatography columns enable the integration of meter-scale separation channels onto centimeter-scale chips through micro- and nanofabrication techniques, significantly reducing system volume and power consumption while improving analysis speed and integration capability. Compared with conventional GC systems, MEMS µGC exhibits clear advantages in size, weight, energy efficiency, and response time. This review systematically summarizes the fundamentals, structural designs, fabrication processes, and stationary phase preparation of MEMS micro gas chromatography columns. Representative aerospace application cases along with related experimental and engineering validation studies are highlighted; we re-evaluate these systems using Technology Readiness Levels (TRL) to distinguish flight heritage from concept demonstrations and propose a standardized validation roadmap for environmental reliability. In addition, key technical challenges for aerospace deployment are discussed. This work aims to provide a useful reference for the development of aerospace gas analysis systems and the engineering application of MEMS-based technologies. Full article

The predominant approach in the realm of industrial process monitoring and control involves the utilization of HMI (Human–Machine Interface) interfaces and conventional SCADA (Supervisory Control and Data Acquisition) systems. This limitation restricts user mobility, interaction with industrial equipment, and process status assessment. In the context of Industry 4.0, the ability to monitor and control industrial processes in real time is paramount. The present paper designs and implements a system for monitoring and controlling an industrial assembly line based on mixed reality. The technology employed to facilitate communication between the system and the industrial line is S7.Net. These elements facilitate direct communication with the industrial process equipment. The system facilitates the visualization of operating parameters and the status of the equipment utilized in the industrial process and its control. All data is superimposed on the physical environment through virtual operational panels. The system functions independently, negating the necessity for intermediate servers or other complex structures. The system’s operation is predicted on a series of algorithms. These instruments facilitate the automated analysis of industrial process parameters. These devices are utilized to ascertain the operational dynamics of the industrial line. The experimental results were obtained using a real industrial line. These models are employed to demonstrate the performance of data transmission, the identification of the system’s operating states, and the system’s ability to shut down in the event of operating errors. The proposed system is designed to function in a variety of industrial environments within the paradigm of Industry 4.0, facilitating the utilization of multiple virtual interfaces that enable user interaction with various elements through which the assembly process is monitored and controlled. Full article

Chronic back pain (CBP) associated with intervertebral disc degeneration (IVDD) is a leading cause of medical consultations, decreased quality of life, and temporary and permanent disability. The mechanisms of CBP development and persistence in patients with IVDD have been studied for many years, but this issue remains far from resolved. The search for predictive biomarkers that could help identify patients with IVDD at high risk for CBP continues. In recent decades, research has shown increasing interest in identifying epigenetic biomarkers for this disorder. to summarize the results of preclinical and clinical studies on the role of microRNAs (miRs) as epigenetic biomarkers of the development and progression of CBP in patients with IVDD. English-language articles; original experimental (preclinical) studies; original clinical study; assessment of changes in systemic (in blood) and/or local (in intervertebral disk (IVD)) levels of miR expression in IVDD, either independently or in comparison with healthy controls; and studies that were completed and the results of which were published. PubMed, Springer, Google Scholar, Scopus, Oxford Press, Cochrane, and e-Library databases. Charting for this scoping review involved developing a data extraction form to summarize extracts and organize data from included studies. This was an iterative process where the charting tables and figures may be refined as the review progresses. 126 studies were analyzed in detail, focusing on their study designs and comparing changes in miR expression in animal models of IVDD and in patients with IVDD compared to healthy controls. During the preparation of this scoping review and upon subsequent detailed review of the original publications, it turned out that the results of one study were not justified by the authors due to identified technological problems (the article was withdrawn by the editorial board of the journal). Therefore, we excluded the results of this study from the subsequent analysis. As a result, this section summarizes the results of 60 preclinical and 65 clinical studies. Some miRs (e.g., miR-21 and miR-132) are associated with the regulation of inflammatory pathways that contribute to increased degradation of IVD extracellular matrix and enhanced nociceptive signaling through various mechanisms, contributing to the progression of CBP. Other miRs (e.g., miR-145 and miR-223) exert protective effects, enhance regenerative potential, and alleviate CBP. Despite the promising results of these studies, there are limitations in the use of miRs as perspective epigenetic biomarkers of CBP in patients with IVDD because the pattern of potentially predictive and protective miRs in relation to the mechanisms of CBP formation and progression in IVDD has not yet been sufficiently studied. The results of some preclinical and clinical studies are contradictory. Further research is needed to clarify the role of miR signatures in animal models and clinical trials on IVDD-specific CBP. Full article

To address the challenge of imprecise detection and control of fertilizer application rates caused by high granular flow during fertilization operations, a parallel diversion detection method with real-time application rate regulation is proposed. The mechanism of uniform distribution of discrete particles formed by high-throughput aggregated granular fertilizer was elucidated. Key components including the uniform fertilizer tube, sensor detection structure, six-channel diversion cone disc, and fertilizer convergence tube underwent parametric design, culminating in the innovative development of a six-channel parallel diversion detection device. A multi-channel parallel signal detection method was studied, and a synchronous multi-channel signal acquisition system was designed. Through calibration tests, relationship models were established between the measured flow rate of granular fertilizer and voltage, as well as between the actual flow rate and the rotational speed of the fertilizer discharge shaft. A fuzzy PID control model was constructed in MATLAB2023/Simulink. Using overshoot, response time, and stability as evaluation metrics, the control performance of traditional PID and fuzzy PID was compared and analyzed. To validate the control system’s precision, device performance tests were conducted. Results demonstrated that fuzzy PID control reduced the time required to reach steady state by 66.87% compared to traditional PID, while overshoot decreased from 7.38 g·s⁻¹ to 1.49 g·s⁻¹. Divergence uniformity tests revealed that at particle generation rates of 10, 20, 30, and 40 g·s⁻¹, the coefficient of variation for channel divergence consistency gradually increased with rising tilt angles. During field operations at 0–5.0° tilt, the coefficient of variation for channel divergence consistency remained below 7.72%. Bench tests revealed that the fuzzy PID control system achieved an average accuracy improvement of 3.64% compared to traditional PID control, with a maximum response time of 0.9 s. Field trials demonstrated detection accuracy no less than 92.64% at normal field operation speeds of 3.0–6.0 km·h⁻¹. This system enables real-time, precise detection of fertilizer application rates and closed-loop regulation. Full article

A dew-point evaporative air cooler incorporating a novel segmented heat exchange design, demarcated according to the humidity state of moist air, is proposed. The system employs a porous fibrous material to create a wetted evaporative surface, which is continuously maintained in a moistened condition through a self-wicking water supply mechanism to enhance latent heat transfer. Circular fins are installed on the heat exchanger’s partition surface once the moist air reaches saturation, thereby improving sensible heat exchange between the dry and wet channels. The performance of a prototype was evaluated under controlled conditions in a standard enthalpy chamber. Experimental results indicate that, under typical summer conditions (inlet dry-bulb and wet-bulb temperatures of 33.8 °C and 25.4 °C, respectively), with an air mass flow ratio of 0.7 and an air velocity of 1.5 m/s, the wet-bulb effectiveness reaches 114.4% and the dew-point effectiveness achieves 84.8%. The maximum temperature reduction occurs in the sensible heat exchange section, reaching up to 6.1 °C, demonstrating its substantial sensible heat recovery capability. The device exhibits an energy efficiency ratio (EER) ranging from 9.1 to 31.8. The proposed compact configuration not only enhances energy efficiency but also reduces material costs by approximately 15.4%, providing a valuable reference for the future development of dew-point evaporative cooling systems in residential buildings. Full article

Atomic Layer Deposition (ALD) is a key thin-film fabrication technique that enables the growth of ultra-thin, conformal, and compositionally controlled layers for applications in nanoelectronics, optoelectronics, and energy devices. However, the high cost and operational complexity of commercial ALD systems limit their accessibility in academic and emerging research environments. In this work, a low-cost, automated thermal ALD system is designed, assembled, and experimentally validated for the deposition of zinc oxide (ZnO) thin films. The developed system enables precise control of precursor dosing, purge sequences, and substrate temperature via an integrated LabVIEW–Arduino control architecture, allowing reproducible and stable thin-film growth. The design allows the use of various precursors through high-precision three-way diaphragm valves. In addition, the system allows continuous purge gas flow in the reaction chamber, which enhances the drag velocity of the precursor gas, reducing dosage requirement, accelerating chamber saturation time and lowering the total consumption of precursors per deposition cycle. ZnO thin films were successfully grown on silicon and glass substrates at 200 °C using diethylzinc (DEZ) as the metal precursor and hydrogen peroxide (H₂O₂) as the oxidant. The process exhibited self-limiting growth characteristics typical of ALD, yielding a growth per cycle of approximately 0.8 Å. The deposited ZnO films exhibited optical transparency of 70–80% in the visible region, a refractive index of approximately 1.9, and an optical bandgap close to 3.4 eV, which are consistent with values reported for high-quality ZnO films grown in commercial ALD systems. These results demonstrate that the proposed low-cost platform is capable of producing functional ZnO thin films with properties comparable to those obtained with conventional commercial reactors. Overall, this work presents an accessible and scalable thermal ALD system that significantly reduces equipment costs while maintaining reliable process control and film quality, offering a practical framework for expanding thin-film research capabilities across microelectronics, optoelectronics, and nanotechnology laboratories. Full article