Search Results (5,876)

The modal behaviour of the wooden bridge over the Rhine described by Julius Cæsar in the De Bello Gallico is analysed by a simple analytical model, i.e., a Kirchhoff–Love (KL) plate. The overall structure is indeed modelled as a thin plate, representing the walking surface, resting on elastic supports that approximate the compliance of the underlying structure. Firstly, these elastic constraints are represented by linear springs; in a refined step, beam elements with equivalent stiffness and mass are adopted. The system complexity arises from the consequent non-trivial boundary conditions and is tackled by selecting suitable auxiliary functions to operate with discretised equations of motion, in a Galërkin-like approach. MATLAB helped to develop in-house scripts capable of reconstructing the flexural behaviour as the governing parameters vary, without repeated experimental tests. The analytical results are compared with theoretical predictions and between the two assumed elastic supports, allowing verification of model consistency and explanation of differences in the bridge behaviour. The ease of implementation of these codes also enables the evaluation of the structural potential of historical constructions, the investigation of modular characteristics and connections between subcomponents, and the assessment of the effects of external loads. The study of historical structure dynamics is thus relevant not only for reconstruction, but also for modern mechanical design, with potential applications in civil, mechanical, materials, and naval engineering. Full article

Accurate short-term traction load forecasting is crucial for optimizing railway operations. However, the strong fluctuations in high-speed railway loads and the general neglect of the physical relationships between adjacent substations in existing studies pose significant challenges to reliable short-term forecasting. To address these issues, this paper proposes a Lag-Adaptive Gradient Aware Network (LAGA-Net). Unlike isolated forecasting methods, LAGA-Net explicitly combines the physical information of train motion with deep learning methods to achieve collaborative load forecasting between adjacent traction substations (TSs). Specifically, it first calculates the cross-correlation coefficients of the load curves of adjacent TSs to quantify the train lag process and achieve load time-series alignment, effectively utilizing the historical load of upstream substations as prior information for load forecasting at this station. Based on this, a dual-stream gradient sensing encoder is proposed to capture the load amplitude and high-frequency pulses of the two TSs, improving the prediction accuracy of the model in highly volatile scenarios. Finally, an adaptive cross-attention mechanism based on Gaussian masks is designed to achieve spatiotemporal coupling and collaborative forecasting of the loads of two adjacent TSs using the aligned load representation information. Extensive experiments on real adjacent traction substation datasets demonstrate that LAGA-Net significantly outperforms existing state-of-the-art benchmark methods in terms of multi-step prediction and peak prediction accuracy, and exhibits strong robustness to operational uncertainties. Full article

A comprehensive and systematic study was conducted to address a series of key technical challenges encountered in the grinding process at a copper mine. These issues included the complex mechanical properties of the feed ore, which led to low grinding efficiency, difficulty in achieving the required grinding fineness for flotation, uneven particle size distribution in the grinding products, and severe occurrences of overgrinding and undergrinding. Based on the semi-theoretical ball diameter formula, the optimal initial ball size distribution for the ball mill was precisely calculated as Φ70:Φ50:Φ40:Φ30 = 15:25:35:25. Through laboratory-scale grinding tests and Discrete Element Method (DEM) simulations, a systematic analysis of multiple indicators under three different ball loading schemes was performed, including the motion state of particles inside the mill, the collision behavior of the grinding media, and the energy distribution. This analysis confirmed the rationality and effectiveness of the literature scheme. Industrial trial results showed the following: the yield of the +0.20 mm fraction decreased by 4.15 percentage points, and the yield of the −0.010 mm fraction and its proportion relative to the −0.074 mm fraction decreased by 10.17 and 19.10 percentage points, respectively. Conversely, the yields of the intermediate separated fraction (−0.20 + 0.010 mm), the easily separated fraction (−0.074 + 0.018 mm) and the −0.074 mm qualified fraction increased by 14.32, 14.13, and 7.29 percentage points, respectively. The grinding technical efficiency improved by 19.55 percentage points. Furthermore, the specific steel ball consumption decreased by 46 g/t, a reduction of 5.07%. The copper concentrate recovery increased by 0.65 percentage points, resulting in an annual increase of 40.51 tons of copper metal, additional revenue of CNY 3.2483 million, and steel ball cost savings of CNY 603,500. Collectively, this optimization generated a total economic benefit of CNY 3.8518 million. By optimizing the ball size distribution, the particle size composition of the grinding products was significantly improved, the flotation indicators were enhanced, and the grinding media consumption cost was reduced, achieving quality improvement and efficiency increase in the mineral processing. This study provides a valuable reference for solving similar grinding problems. Full article

Focused Cardiac Ultrasound (FoCUS) is a targeted bedside imaging modality with an established role in the management of critically ill patients. Acute Coronary Syndrome (ACS) is a common cause of presentation to the Emergency Department (ED), and although electrocardiography (ECG) and cardiac biomarkers are the cornerstones for its diagnosis, FoCUS may facilitate diagnostic evaluation and disposition of patients in different levels of care. Initially, FoCUS plays a crucial diagnostic role through the identification of Regional Wall Motion Abnormalities (RWMAs), enabling direct visualization of the ischemic region and corroboration of ECG findings. Moreover, in patients with ACS complicated by cardiogenic shock, FoCUS is indispensable for determining the extent of ischemia and detecting mechanical complications, including ventricular septal or free wall rupture, or papillary muscle rupture. Likewise, FoCUS aids in the differential diagnosis of patients with ECG abnormalities mimicking ACS. This comprehensive review synthesizes the most recent evidence on the role of FoCUS in accelerating the management of patients with ACS presenting to the ED. Full article

In biomass pellet combustion, the formation of ash layers on particle surfaces severely hinders combustion reactions and heat transfer, while the key parameters governing particle motion behavior and ash pre-separation in rotating cone burners remain insufficiently understood. To address these challenges and to optimize particle mixing and ash separation performance, this study adopts a combined numerical approach. The discrete element method (DEM) coupled with the Hertz–Mindlin (no-slip) contact model is employed to simulate particle motion and mixing dynamics, while a separate cold-state computational fluid dynamics (CFD) model based on the Realizable k-ε turbulence model and the discrete phase model (DPM) with Rosin–Rammler particle size distribution is established to investigate ash separation mechanisms. The Lacey mixing index is used to quantify mixing uniformity, and grid independence verification is performed to ensure numerical reliability. Key findings reveal that the rolling regime (rotational speed: 1.7–11 r/min), a uniform particle size of 25 mm, and a cone inclination angle of 45° collectively optimize particle mixing. Rotational speed is identified as the dominant factor affecting mixing effectiveness. Furthermore, an optimal secondary-to-primary air ratio of approximately 7:3 (within the tested range) balances enhanced centrifugal separation with flow field stability by mitigating backflow and excessive turbulence. This work not only fills the knowledge gap regarding the coupled effects of operational and structural parameters on particle behavior in rotating cone burners but also provides novel, quantitative guidance for the rational design and parameter tuning of such burners to improve combustion efficiency and operational stability. Full article

A comprehensive numerical method was developed to ensure energy-efficient operating modes of a linear motion module powered by an induction motor. The proposed approach is based on minimizing inertial torque, accounting for the inertial properties of the drive components and the load carriage, followed by structural-parametric optimization and dynamic modeling. For the optimization of the drive system, comprising an intermediate gear stage and a primary ball screw mechanism, a normalization-based method combined with numerical parameter sweep was employed. The optimization process yielded optimal values of the screw lead and the number of gear teeth, which were further validated in terms of Pareto optimality. The carriage design was optimized with respect to mass, strength constraints, and dynamic stiffness using the finite element method. For the developed linear motion module, dynamic behavior was simulated by means of a system of nonlinear differential equations, taking into account the electromagnetic characteristics of the induction motor and the nonlinearities of the gear mesh. As a result of the comprehensive approach, the kinematic, force, and energy characteristics of the linear motion module, which was optimized at the design stage, were determined. Full article

This study presents a comprehensive review of more than 100 research papers on sign language recognition (SLR) published between 2020 and 2026. The analysis focuses on deep learning approaches applied to video-based SLR, including spatiotemporal feature extraction, temporal modeling, attention mechanisms, motion-based representations, hybrid frameworks, transfer learning methods and other methods. Particular attention is given to how these methods model spatiotemporal dynamics and capture subtle gesture characteristics in sign language communication. The review highlights several recent developments, such as the introduction of specialized datasets, the emergence of real-time recognition systems, and the integration of multimodal fusion strategies. At the same time, persistent challenges remain, including data scarcity in low-resource sign languages, limited linguistic standardization of datasets, and insufficient model interpretability. The findings underline the importance of developing scalable and generalizable models capable of handling diverse datasets and user variability. The distinct contributions of this review are fourfold: (1) a comprehensive synthesis of over 100 studies published between 2020 and 2026, covering the full spectrum of deep learning architectures for video-based SLR; (2) a structured six-category taxonomy enabling systematic cross-architectural comparison; (3) a comprehensive focus on low-resource sign languages, which remain underrepresented in the existing literature; and (4) a critical analysis of the current benchmark landscape for low-resource sign languages, identifying key gaps and outlining strategic directions for future dataset development. These contributions are intended to guide further research toward more robust, inclusive, and universally applicable SLR systems. Full article

This study introduces a novel physics-inspired framework to quantify defensive pressure in football from tracking data. We model defender–attacker interactions as a variable-mass dynamical system, translating Newtonian mechanics into operational metrics that combine spatial configuration and motion. From this formulation we derive interpretable quantities at dyad, player, and team level, including a Center of Pressure (CP), Defensive Momentum, Defensive Force, and Defensive Work. We illustrate the framework in a single-match proof-of-concept using professional optical tracking data, analysing both full-match behaviour and football-specific phases such as counter-pressing, set-pieces, and throw-ins. Results show how the proposed metrics separate persistent spatial constraint (pressure) from energetically demanding defensive actions (work), enable identification of high-cost match-ups and workload concentration, and support time-resolved descriptions of coordinated pressing sequences. The framework provides a transferable, mechanically grounded toolkit for applied defensive performance analysis and motivates future validation on larger datasets. Full article

Elevators exemplify mechatronics by integrating mechanical, electrical, and software systems. This paper discusses a four-story tabletop elevator model developed to demonstrate mechatronics and automation concepts in engineering education. The system utilized an Arduino MEGA microcontroller, 3D-printed components, an integrated servo motor, and standard electronics to replicate commercial elevator logic. The physical design features a ball screw linear actuator for vertical motion. It replicates dual-door systems with one door on the moving car and fixed doors at each floor that open simultaneously upon arrival. Development included designing the physical model, prototyping control algorithms, and integrating hardware and software. The model successfully demonstrated key functions: automatic dual-door operation, safety interlocks, smooth inter-floor motion, responsive floor-selection buttons with LED feedback, and efficient routing algorithms prioritizing requests based on current direction and location. Performance testing confirmed that the model accurately replicates modern elevator behavior and serves as an effective educational tool. Full article

With the development of shipping to harsh marine environment, it is very important to understand the transient behavior of a marine diesel engine in high sea conditions. Wave-induced hull motion will lead to severe load fluctuations and air-fuel ratio imbalance. In this study, an integrated simulation platform coupled with environmental loads, hull dynamics, propeller characteristics and a high-fidelity thermodynamic engine model was constructed to explore the response characteristics of the propulsion system. The model integrates a zero-dimensional multi-zone combustion method, turbocharger dynamic characteristics and an incremental PID governor, and has been verified based on the bench test data of TBD234V12 diesel engine and the 20 m Wigley standard ship. The simulation results under the sea conditions from level 7 to 9 show that the transient load has a nonlinear amplification effect. Specifically, from sea state 7 to sea state 9, the engine load fluctuation range expands by 2.0 times, while the main peak amplitude of speed fluctuation increases by 3.7 times. Furthermore, the peak exhaust pressure rises by 1.8 times, and the exhaust temperature fluctuation amplitude broadens by 35%. Frequency domain analysis further identified the low-frequency energy concentration phenomenon in the exhaust pressure spectrum and the precursor characteristics of compressor surge. The research results quantify the deterioration law of thermodynamic stability and mechanical stress under wave disturbance, and provide an important reference for the formulation of an engine robust control strategy and fatigue life assessment under high sea conditions. Full article

Background: Patellar tendinopathy is a common musculoskeletal condition that may impair functional performance and limit physical activity. While structural and mechanical factors have been widely investigated, the role of proprioceptive function and its interaction with behavioral and central pain-related mechanisms remain unclear. This study aimed to investigate the relationship between knee joint position sense, kinesiophobia, and central sensitization in individuals with chronic patellar tendinopathy. Methods: A total of 42 recreational athletes with clinically diagnosed chronic patellar tendinopathy were included in this cross-sectional study. Knee joint proprioception was assessed using joint position sense testing at multiple knee flexion angles with a continuous passive motion device. Kinesiophobia and central sensitization were evaluated using the Tampa Scale of Kinesiophobia and the Central Sensitization Inventory, respectively. Joint position sense values of the involved and contralateral extremities were compared, and correlation analyses were performed to examine associations between joint position sense and psychosocial variables. Results: The involved extremity demonstrated significantly greater absolute angular error compared to the contralateral side at higher knee flexion angles (≥60°, p < 0.01), whereas no significant differences were observed at lower angles. A moderate positive correlation was found between joint position sense error and kinesiophobia at higher flexion angles (≥30°, p < 0.01). No significant association was identified between joint position sense error and central sensitization across any of the tested angles (p > 0.05). Conclusions: Proprioceptive function is impaired in individuals with chronic patellar tendinopathy, particularly under increased mechanical demand. The association between joint position sense deficits and kinesiophobia, but not central sensitization, suggests a potential relationship between movement-related fear and sensorimotor alterations. These findings highlight the importance of incorporating proprioceptive assessment and addressing kinesiophobia in the clinical management of patellar 36 tendinopathy. Full article

The rapid advancement of flexible electronics technology has endowed flexible sensors with significant application potential in fields such as wearable sensors, bionic skin, and human–machine interaction, owing to their excellent conformability, stretchability, and comfort. However, as application scenarios continue to expand and deepen, higher requirements are imposed on sensor performance in terms of sensitivity, stability, biocompatibility, environmental friendliness, and multifunctional integration. Polyurethane composites, leveraging their intrinsic characteristics, including tunable molecular structure, superior flexibility, and good biocompatibility, can effectively impart properties such as electrical conductivity, self-healing capability, and high sensitivity through compositing with various functional materials, thereby precisely aligning with the diverse demands of next-generation flexible sensors. This article systematically reviews the synthesis strategies of polyurethane composites; provides a detailed analysis of the roles of fillers—including carbon-based materials, polymers, and metal nanoparticles/nanowires—in enhancing the mechanical, electrical, and functional properties of the composites; and further summarizes the research progress of polyurethane composite-based flexible sensors in cutting-edge areas such as eco-friendly sensing, human motion monitoring, health monitoring, and bionic electronic skin. Future development trends are also discussed, aiming to provide insights for the design and development of high-performance flexible sensors. Full article

Tomato (Solanum lycopersicum) cultivation is highly vulnerable to fungal, bacterial, and viral leaf diseases that can significantly reduce crop yield and fruit quality when not detected at early stages. Although recent deep learning approaches have achieved remarkable performance in plant disease classification, many state-of-the-art architectures remain computationally expensive and therefore difficult to deploy on resource-constrained edge devices commonly used in smart agriculture environments. To address this challenge, this paper introduces EcoTomHybridNet, an adaptive resource-aware CNN–Transformer framework designed for efficient tomato leaf disease classification under edge-computing constraints. The proposed architecture combines a lightweight convolutional backbone with a dual-branch inference mechanism composed of a fast convolutional branch for computationally efficient prediction and a Transformer-enhanced branch with local self-attention for richer contextual feature extraction. Unlike conventional lightweight hybrid models relying on static inference pipelines, EcoTomHybridNet integrates a lightweight policy-guided routing mechanism that dynamically allocates inputs between the fast convolutional branch and the Transformer-enhanced branch according to input complexity. This adaptive inference strategy dynamically reduces unnecessary Transformer computations for simpler samples while preserving strong predictive performance on more challenging inputs through policy-guided branch allocation. To further improve representation capability without significantly increasing computational complexity, the proposed student network is trained using knowledge distillation from a ViT-Tiny teacher model. Experimental results on the PlantVillage tomato dataset demonstrate that EcoTomHybridNet achieves 99.42% test accuracy and 99.0% validation accuracy under the full hybrid inference configuration. Additional validation strategies, including 5-fold cross-validation and robustness evaluation under Gaussian noise and motion blur perturbations, indicate stable performance across different data splits and moderate image degradations, suggesting improved generalization capability beyond simple dataset memorization. Furthermore, adaptive routing experiments using a lightweight threshold-based policy mechanism achieved 99.20% test accuracy while reducing computational complexity from 0.36 GFLOPs to 0.25 GFLOPs per image, corresponding to approximately 30% computational savings. These results demonstrate the effectiveness of policy-guided adaptive inference for balancing predictive performance and computational efficiency in edge-oriented plant disease classification. Overall, EcoTomHybridNet provides an efficient and adaptive framework for intelligent plant disease monitoring in IoT-enabled smart agriculture systems. Full article

Lower-limb amputation remains a significant clinical and socio-economic challenge, while the high cost of microprocessor-controlled prostheses (MPKs) limits their widespread accessibility. This paper presents the design and preliminary laboratory-scale evaluation of a low-cost microprocessor-controlled ankle prosthesis intended as a feasibility-oriented alternative platform for future active prosthetic system development. Building upon the previously developed V1 mechanical architecture, an updated CAD model was created in the SolidWorks 2024 environment, and the kinematic configuration was refined using a ball-screw transmission (SFU1204-300) driven by a NEMA 17 stepper motor. The electronic control system integrates an ESP32 microcontroller, an MPU9250 inertial measurement unit (IMU), a limit switch for initial-position detection, and a WiFi-based REST API interface for communication and control. Laboratory no-load experiments demonstrated controlled positional behavior, repeatable angular response, and successful operation of the homing procedure within a motion range of 0–4200 motor steps. The prototype actively generated dorsiflexion–plantar flexion motion in the sagittal plane, while a passive inversion–eversion mechanism was incorporated and intended to improve structural adaptability. IMU-based measurements enabled preliminary monitoring of angular displacement and positional behavior during the experiments. The presented prototype represents an initial engineering feasibility study of a low-cost active ankle actuation architecture and provides a foundation for future investigations involving load-bearing experiments, biomechanical gait analysis, and closed-loop control implementation. Full article

Siamese tracking is widely used in object tracking due to its efficient dual-branch symmetric structure, deep feature matching mechanism, and flexible template strategy. Existing mainstream Siamese tracking algorithms typically employ static template matching or linear combination-based template updating to localize the target in the next frame. However, these mechanisms often struggle to ensure template accuracy in complex environments involving changes in target appearance, scale, occlusion, and motion blur, thereby compromising robustness and stability. To address these issues, this paper proposes a confidence-guided adaptive template update with a re-detection (CATUR) network. CATUR constructs a tracking confidence assessment module that uses average peak-to-correlation energy (APCE) and a dynamic threshold mechanism to determine the current tracking state, providing a basis for template updates and target re-detection. It also designs an adaptive template update network that effectively combines the initial, historical, and current-frame templates, enhancing adaptation to target appearance variations. By integrating a global search module and a re-detection module, CATUR achieves precise target re-localization, rapid template updating, and tracking recovery. Extensive experiments and ablation studies on LaSOT and TrackingNet demonstrate that CATUR improves AUC, PNorm, and P by 4.0%, 4.0%, and 3.2%, respectively, significantly enhancing tracking accuracy and robustness in complex environments. Full article