Search Results (2,518)

In this paper, the concept of symmetry is utilized to design active fault tolerant trajectory-tracking control of autonomous distributed-drive electric vehicles—that is, the construction and the solution of active fault tolerant trajectory-tracking controllers are symmetrical. This paper presents a hierarchical fault tolerant control strategy for improving the trajectory-tracking performance of autonomous distributed-drive electric vehicles (ADDEVs) under steer-by-wire (SBW) system failures. Since ADDEV trajectory dynamics are inherently affected by complex traffic conditions, various driving maneuvers, and other road environments, the main control objective is to deal with the ADDEV trajectory-tracking control challenges of system uncertainties, SBW failures, and external disturbance. First, the differential steering dynamics model incorporating a 3-DOF coupled system and stability criteria based on the phase–plane method is established to characterize autonomous vehicle motion during SBW failures. Then, by integrating cascade active disturbance rejection control (ADRC) with Karush–Kuhn–Tucker (KKT)-based torque allocation, the active fault tolerant control framework of trajectory tracking and lateral stability challenges caused by SBW actuator malfunctions and steering lockup is addressed. The upper-layer ADRC employs an extended state observer (ESO) to estimate and compensate against uncertainties and disturbances, while the lower-layer utilizes KKT conditions to optimize four-wheel torque distribution to compensate for SBW failures. Simulations validate the effectiveness of the controller with serpentine and double-lane-change maneuvers in the co-simulation platform MATLAB/Simulink-Carsim^® (2019). Full article

Stirling engines are the engines that convert heat energy into mechanical work. This study models a 50 W linear generator designed for integration with a Stirling engine. To develop a model, the base design of the already developed 1 kW model was used, and its size was proportionally reduced to match the stroke of the Stirling engine. By reducing the length of the 1 kW model to a length scale factor (LSF) of 0.5, the stroke level of the engine was determined. However, the radius of the LSF 0.5 linear generator model was adjusted to match the engine. After finalizing the 50 W linear generator dimensions, the model was simulated using MAXWELL v14. software to compute output power and other electrical parameters. This study also analyzed the losses of the 50 W linear generator and its phasor diagram. Later, the output values generated using MAXWELL software were compared with the results obtained using SAGE v11. software for verification. The outcome of this study was a model that achieved an output power of 50 W with an efficiency of 90% and a generator size of 96 mm. Because of its versatility, low weight, and high efficiency, it can be used in a wide range of applications. Due to its small size, it can be utilized for empowering humanoid robots, radioisotope power, space exploration, etc. Full article

Objectives: Sprinting and jumping abilities are key determinants of basketball performance. This study aims to analyze changes in sprinting and jumping performance among professional basketball players during the preseason and to determine whether these adaptations are influenced by specific playing positions (Guards vs. Bigs). Methods: A total of 106 professional basketball players from European leagues were evaluated twice over a 6-week preseason. Neuromuscular assessments included linear sprints (5, 10, and 20 m), a change of direction test, curved sprints, and multiple jump tests: Squat Jump (SJ), Countermovement Jump (CMJ), Single-Leg CMJ (SL-CMJ) and Arm-Swing CMJ (CMJA), Single Leg Hop for Distance (SHDJ), Lateral Bound Jump (LBJ), and Single-Leg Repeated Jumps (SLRJ). The training program integrated 6–8 weekly basketball-specific technical–tactical sessions with two to three strength and conditioning sessions targeting maximal strength, power, and hypertrophy. Results: Players significantly improved linear and curved sprint performance, and jumping ability, particularly CMJ, CMJA, and right-leg SHDJ. Minimal changes were observed in SJ, LBJ, and SLRJ. Positional differences were small, with Guards showing greater gains in CMJA than Bigs (6.85% vs. 1.87%). Conclusions: A 6-week preseason training program may be associated with improvements in sprinting (linear 5, 10, 20 m, and curved sprint) and vertical jump performance (CMJ, CMJA, SHDJ) in professional basketball players, with limited influence of playing position. Guards appear to benefit more from arm-swing vertical jump development. Full article

Super high-rise buildings of 400 m and above are currently rare globally, making their design and construction data invaluable. Due to their enormous size, the structural safety, architectural effect, and construction cost are key concerns of all parties. This study employs parametric analysis to research the lateral force-resisting system and key local structural issues of a 400 m under-construction super-high-rise structure. The overall analysis results show that the 8-mega-column scheme can relatively well balance architectural effect and structural performance; the 5-belt truss design minimizes the steel consumption. The local research results indicate that the inward inclination of bottom columns leads to increased axial forces in floor beams significantly, necessitating reinforcement; horizontal braces directly connected to the core tube enhance folded belt truss integrity under rare earthquakes; failure of bottom gravity columns in the folded secondary frame increases beam bending moments and axial forces substantially. Steel consumption sensitivity analysis shows that when the structural first-order period is reduced by 0.1 s, adjusting the section sizes of the members in the belt truss minimizes the increase in steel consumption, while adjusting steel beams maximizes it. These findings provide essential design insights for similar super-high-rise projects. Full article

This study systematically analyzes the microstructure and radial variation of Cercis glabra wood, revealing its adaptive strategies for arid environments. The results show that the wood consists of thick-walled fibers (63%) and vessels (17.7%), with a semi-ring-porous structure and 48.4% average cell wall percentage. Fiber proportion peaks early (4 years), ensuring mechanical support, while vessel adjustment occurs later (19 years), balancing water transport. Rays decline sharply in the first 9 years, stabilizing thereafter, reflecting a shift from growth to structural stability. The high fiber proportion and occasional tyloses enhance durability, making it suitable for high-quality pulp, furniture, and humid environments such as shipbuilding. A rotation period ≥ 20 years ensures stable properties. Genetic breeding could shorten the juvenile stage and optimize vessel distribution. Future research should integrate multi-omics and environmental data to deepen our understanding of its adaptation mechanisms. This study provides a basis for the utilization of C. glabra resources. Full article

Gear transmission error (GTE) is a critical factor influencing the performance and service life of gear systems, as it directly contributes to vibration, noise generation, and premature wear. The present study introduces a combined theoretical and experimental approach to characterizing GTE in a single-stage spur gear system. A six-degree-of-freedom nonlinear dynamic model was formulated to capture coupled lateral–torsional vibrations, accounting for gear mesh stiffness, bearing and coupling characteristics, and a harmonic transmission error component representing manufacturing and assembly imperfections. Simulations and experiments were conducted under healthy and eccentricity-faulted conditions, where a controlled 890 g eccentric mass induced misalignment. Frequency domain inspection of faulty gear data showed pronounced sidebands flanking the gear mesh frequency near 200 Hz, as well as harmonics extending from 500 Hz up to 1200 Hz, in contrast with the healthy case dominated by peaks confined to 50–100 Hz. STFT analysis revealed dispersed spectral energy and localized high-intensity regions, reinforcing its role as an effective fault diagnostic tool. Experimental findings aligned with theoretical predictions, demonstrating that the integrated modelling and time–frequency framework is effective for early fault detection and performance evaluation of spur gear systems. Full article

Nano- and microplastics (NMPs), with nanoplastics posing higher risks due to their smaller size and greater capacity for cellular and subcellular penetration, are being referred to as ubiquitous environmental neurotoxicants, due to their ability to pass through biological barriers, including the blood–brain barrier (BBB) and nasal olfactory epithelium, and to remain lodged in neural tissue. Upon uptake, such particles disturb neuronal homeostasis by multiple converging pathways, including oxidative stress, mitochondrial dysfunction, pathological protein aggregation, and chronic neuroinflammation, all closely involved with the molecular signatures of neurodegenerative disorders (Alzheimer’s, Parkinson’s, Amyotrophic Lateral Sclerosis—ALS). In addition to their neurotoxicity, recent findings suggest that NMPs could disturb synaptic communication and neuroplasticity, thereby compromising the brain’s capacity to recover from an injury, a trauma, or neurodegeneration, thus impacting the progression of the disease, our ability to treat it and eventually the efficacy of rehabilitation approaches. Despite these findings, our understanding remains hampered by analytical issues, the scarcity of standard detection methods, and a total lack of longitudinal studies in humans. This review combines multidisciplinary evidence on brain–plastic interactions and calls for accelerated advances in our ability to monitor bioaccumulation in humans, and to integrate neurotoxicology paradigms in the assessment of this underappreciated but growing threat to brain health. Full article

Osteoarthritis (OA) is a multifactorial chronic musculoskeletal disorder characterised by cartilage degradation, synovial inflammation, and subchondral bone remodelling. Conventional diagnostic modalities, including radiographic imaging and symptom-based assessments, primarily detect disease in its later stages, limiting the potential for timely intervention. Inflammatory biomarkers, particularly Interleukin-6 (IL-6), Tumour Necrosis Factor-alpha (TNF-α), and Myeloperoxidase (MPO), have emerged as biologically relevant indicators of disease activity, with potential applications as companion diagnostics in precision medicine. This review examines the diagnostic and prognostic relevance of IL-6, TNF-α, and MPO in OA, focusing on their mechanistic roles in inflammation and joint degeneration, particularly through the activity of fibroblast-like synoviocytes (FLSs). The influence of sample type (serum, plasma, synovial fluid) and analytical performance, including enzyme-linked immunosorbent assay (ELISA), is discussed in the context of biomarker detectability. Advanced statistical and computational methodologies, including rank-based analysis of covariance (ANCOVA), discriminant function analysis (DFA), and Cox proportional hazards modelling, are explored for their capacity to validate biomarker associations, adjust for demographic variability, and stratify patient risk. Further, the utility of synthetic data generation, hierarchical clustering, and dimensionality reduction techniques (e.g., t-distributed stochastic neighbour embedding) in addressing inter-individual variability and enhancing model generalisability is also examined. Collectively, this synthesis supports the integration of biomarker profiling with advanced analytical modelling to improve early OA detection, enable patient-specific classification, and inform the development of targeted therapeutic strategies. Full article

Structured illumination microscopy (SIM) with π/2 phase-shift modulation traditionally relies on frequency-domain computation, which greatly limits processing efficiency. In addition, the illumination regime inherent in structured illumination techniques often results in poor visual quality of reconstructed images. To address these dual challenges, this study introduces DM-SIM-LLIE (Differential Low-Light Image Enhancement SIM), a novel framework that integrates two synergistic innovations. First, the study pioneers a spatial-domain computational paradigm for π/2 phase-shift SIM reconstruction. Through system differentiation, mathematical derivation, and algorithm simplification, an optimized spatial-domain model is established. Second, an adaptive local overexposure correction strategy is developed, combined with a zero-shot learning deep learning algorithm, RUAS, to enhance the image quality of structured light reconstructed images. Experimental validation using specimens such as fluorescent microspheres and bovine pulmonary artery endothelial cells demonstrates the advantages of this approach: compared with traditional frequency-domain methods, the reconstruction speed is accelerated by five times while maintaining equivalent lateral resolution and excellent axial resolution. The image quality of the low-light enhancement algorithm after local overexposure correction is superior to existing methods. These advances significantly increase the application potential of SIM technology in time-sensitive biomedical imaging scenarios that require high spatiotemporal resolution. Full article

Controlling autonomous vehicles to follow a desired lateral trajectory presents a significant challenge. Developers of lateral control systems often find it difficult to simultaneously bring both lateral error and heading angle error close to zero while smoothly following the curvature of the road. This paper introduces the design and development of a control strategy for lateral trajectory following using the backstepping control method, which successfully achieves the goal of stabilization and tracking. The controller comprises a backstepping feedback control law to regulate the errors and stabilize the vehicle by controlling the yaw rate, along with a dynamic feedforward component to compensate for road curvature and further eliminate steady-state errors on curved roads. The controller is built upon the dynamic bicycle model, enhanced by integrating the error dynamics into the state space equation, which allows for the inclusion of errors as state variables. The global uniform stability of the feedback control law is proven using Lyapunov stability theory and the LaSalle–Yoshizawa theorem. The stability and tracking performance of the controller are validated through simulation and experimental results obtained from a test vehicle on a public highway. Full article

To address the insufficient accuracy of traditional single-sensor navigation methods in dense planting environments of pomegranate orchards, this paper proposes a vision and LiDAR fusion-based navigation line extraction method for orchard environments. The proposed method integrates a YOLOv8-ResCBAM trunk detection model, a reverse ray projection fusion algorithm, and geometric constraint-based navigation line fitting techniques. The object detection model enables high-precision real-time detection of pomegranate tree trunks. A reverse ray projection algorithm is proposed to convert pixel coordinates from visual detection into three-dimensional rays and compute their intersections with LiDAR scanning planes, achieving effective association between visual and LiDAR data. Finally, geometric constraints are introduced to improve the RANSAC algorithm for navigation line fitting, combined with Kalman filtering techniques to reduce navigation line fluctuations. Field experiments demonstrate that the proposed fusion-based navigation method improves navigation accuracy over single-sensor methods and semantic-segmentation methods, reducing the average lateral error to 5.2 cm, yielding an average lateral error RMS of 6.6 cm, and achieving a navigation success rate of 95.4%. These results validate the effectiveness of the vision and 2D LiDAR fusion-based approach in complex orchard environments and provide a viable route toward autonomous navigation for orchard robots. Full article

Background: Epilepsy and cognitive impairment frequently coexist, yet their relationship remains complex and insufficiently understood. This study aims to explore the clinical and electrophysiological features of patients presenting with both conditions in order to identify patterns that may inform more accurate diagnosis and effective management within a personalized medicine framework. Methods: We retrospectively analyzed 14 patients with late-onset epilepsy and coexisting cognitive impairment, including mild cognitive impairment and Alzheimer’s disease. Clinical history, cognitive assessments, neuroimaging, and electroencephalographic recordings were reviewed. EEG abnormalities, seizure types, and treatment responses were systematically documented. Results: Patients were categorized into two groups: (1) those with established Alzheimer’s disease who later developed epilepsy and (2) those in whom epilepsy preceded cognitive impairment. Temporal lobe involvement was a key feature, with EEG abnormalities frequently localizing to the frontal–temporal electrodes and manifesting as background slowing, focal multiform slow waves, and epileptiform discharges. Levetiracetam was the most commonly used antiseizure medication, and it was effective across both groups. Conclusions: This case series highlights the value of EEG in characterizing patients with subclinical and overt epileptic activity and cognitive impairment comorbidity. The inclusion of a substantial number of cases with documented EEG abnormalities provides valuable insight into the interplay between epilepsy and neurodegenerative diseases. By integrating neurophysiological data with clinical and cognitive trajectories, this work aligns with the principles of precision medicine, facilitating a more comprehensive evaluation and tailored management approach. Further longitudinal studies are required to validate prognostic markers and guide optimal therapeutic strategies. Full article

Dengue virus infection frequently involves the eye, manifesting with hemorrhages, uveal inflammation, retinal vascular changes and maculopathy. These ocular manifestations may arise during the acute febrile phase or emerge weeks later. Studies from endemic regions report that up to one-quarter of hospitalized patients develop eye-related symptoms. Furthermore, studies confirm a higher risk of new uveitis cases following dengue infection. Breakdown of the blood–ocular barrier—driven by antibody-mediated enhancement, complement activation and release of inflammatory mediators—leads to vascular leakage, tissue injury and ischemia. Diagnosis relies on clinical examination supplemented by imaging (OCT, angiography) and laboratory confirmation of dengue. Mild anterior inflammation often responds to topical steroids, while sight-threatening posterior disease requires systemic corticosteroids and, in refractory cases, immunomodulatory agents. Visual outcomes depend on the initial severity; anterior uveitis typically resolves without sequelae, whereas vasculitis or foveal involvement may leave lasting deficits. This review integrates the current understanding of dengue-related eye disease, emphasizing its varied presentations and the importance of early recognition. Further research into targeted, mechanism-based therapies is needed to optimize visual outcomes. Full article

Over a decade ago, WHO introduced the ASSURED (Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable to end-users) criteria to guide diagnostic assay development. Today, lateral flow assays (LFAs) best meet these standards, evolving from simple rapid tests to advanced diagnostics integrating AI and nanotechnology for precise, quantitative results. Notably, nanoparticle-enhanced LFAs have achieved limits of detection (LOD) as low as 0.01 pg/mL (a 100-fold improvement over conventional methods), while AI algorithms have reduced interpretation errors by 40% in low-contrast conditions. The COVID-19 pandemic underscored the societal impact of LFAs, with over 3 billion antigen tests deployed globally, demonstrating 98% specificity in real-world surveillance. Beyond infectious diseases, LFAs are revolutionizing cancer screening through liquid biopsy, achieving a 92% concordance rate with gold-standard assays, food safety and environmental monitoring. Despite these advancements, challenges remain in scalability, reproducibility, sustainable manufacturing, and how to enhance the sensitivities and lower the LOD. However, innovations in biodegradable materials, roll-to-roll printing, CRISPR-integrated multiplexing, and efficient functionalization methods like photochemical immobilization technique offer promising solutions, with projected further cost reductions and scalability. This review highlights the technological evolution, diverse applications, and future trajectories of LFAs, highlighting their critical role in democratizing diagnostics. Full article

Precise segmentation of cell nuclei in pathological images is the foundation of cancer diagnosis and quantitative analysis, but blurred boundaries, scale variability, and staining differences have long constrained its reliability. To address this, this paper proposes AL-Net—an adaptive learning network that breaks through these bottlenecks through three innovative mechanisms: First, it integrates dilated convolutions with attention-guided skip connections to dynamically integrate multi-scale contextual information, adapting to variations in cell nucleus morphology and size. Second, it employs self-scheduling loss optimization: during the initial training phase, it focuses on region segmentation (Dice loss) and later switches to a boundary refinement stage, introducing gradient manifold constraints to sharpen edge localization. Finally, it designs an adaptive optimizer strategy, leveraging symbolic exploration (Lion) to accelerate convergence, and switches to gradient fine-tuning after reaching a dynamic threshold to stabilize parameters. On the 2018 Data Science Bowl dataset, AL-Net achieved state-of-the-art performance (Dice coefficient 92.96%, IoU 86.86%), reducing boundary error by 15% compared to U-Net/DeepLab; in cross-domain testing (ETIS/ColonDB polyp segmentation), it demonstrated over 80% improvement in generalization performance. AL-Net establishes a new adaptive learning paradigm for computational pathology, significantly enhancing diagnostic reliability. Full article