Search Results (2,092)

Background/Objectives: Managing bone diseases demands novel, natural compounds to bypass the heavy side effects of current therapies. Honey is well-known for its therapeutic traits, yet we know very little about how specific floral varieties impact bone tissue. This study confronts this gap by comparing how acacia, chestnut, and coriander honeys drive human osteoblast behavior in vitro. Methods: After mapping the phenolic/flavonoid profiles and antioxidant capacities of these honeys, we tested them on hFOB 1.19 human osteoblasts. We tracked cell migration via scratch assays and validated osteogenic maturation through Alkaline Phosphatase (ALP) activity and Alizarin Red (AR) mineralization over 7 days. Confocal time-lapse imaging with pharmacological inhibitors monitored intracellular calcium dynamics, while gene shifts were analyzed via qRT-PCR. Results: Coriander honey (CH) packed the highest polyphenol levels and antioxidant power. Biologically, while all honeys accelerated scratch closure, CH drove cell motility most potently. Remarkably, a 7-day treatment with these honeys sparked a significant and robust increase in ALP activity and mineralization, surpassing the osteogenic induction observed with standard osteoinductive media. Mechanistically, CH triggered a sharp [Ca²⁺] spike, relying on external calcium entry and IP3-dependent internal release via PLC activation. qRT-PCR confirmed this anabolic shift via OPG and OPN upregulation. Conclusions: Honey exerts pronounced multi-level osteopromotive effects at both the functional and transcriptional levels, tightly linked to its botanical source. Among the variants, coriander honey stands out for its exceptional ability to fast-track osteoblast migration, differentiation, and early mineral deposition. Therefore coriander honey represents a promising in vitro candidate that warrants further preclinical evaluation for bone repair applications. Full article

The Probability Hypothesis Density (PHD) filter based on the Track-Before-Detect (TBD) approach is a key technique for detecting weak targets whose numbers are unknown and time-varying. To overcome the limitations of existing algorithms, such as high computational cost, poor real-time performance, and low tracking efficiency in dense clutter, this paper proposes a fast non-sequential particle PHD filter for TBD. Specifically, an adaptive particle generation method based on differential localization is introduced in the prediction stage, allowing newly generated particles to quickly concentrate around potential target locations. In the update stage, particles are divided into three groups to simplify weight calculation and improve efficiency. Furthermore, a parallel resampling strategy is adopted to further enhance real-time performance. Numerical experiments demonstrate that the proposed method maintains tracking accuracy with only a small number of particles, thereby significantly reducing computational complexity and improving real-time capability. This work offers a practical reference for the engineering deployment of TBD algorithms. Full article

The electrification of public transportation requires reliable and efficient technologies for energy transfer. Trolleybus systems represent a promising solution, as they combine high energy efficiency with reduced battery requirements. However, a central technical challenge is the precise and automatic positioning of the flexible current collector poles that connect to the overhead line. During positioning through motor actuation, the current collector shoe is caused to oscillate by external disturbances and the movement itself. To reduce oscillations, the current collectors need to be damped actively by respective actuation. This task critically depends on accurate and fast motor speed estimation for real-time control of the actuating motors. Since motor speed is not measured directly in the system, it has to be estimated from the encoder-based motor position, which introduces sensitivity to measurement noise and requires filtering. This work investigates four practical estimation approaches in the context of trolleybus applications. These include discrete-time numerical differentiation combined with FIR and IIR filtering and a modern algebraic differentiation approach. These estimation methods are evaluated under identical experimental conditions and predefined filter specifications focusing on noise suppression and time delay characteristics. The most promising approaches are further validated in closed-loop operation with respect to measurement noise-induced variations in the control input and motor speed tracking accuracy. The results demonstrate that algebraic differentiation achieves a favorable balance between noise suppression, latency, and filter order for the considered current collector system. It therefore provides a suitable basis for real-time deployment in the investigated current collector positioning control and for future active oscillation damping strategies. Full article

This paper presents an adaptive third-order fixed-time integral sliding-mode control (A3-FTISMC) scheme for a piezoelectric-driven microinjector. High-order sliding-mode and integral control techniques are adopted to suppress the hysteresis nonlinearity of piezoelectric actuators and eliminate chattering simultaneously. The adaptive laws are designed to remove the reliance on prior knowledge of disturbance upper bounds. The global fixed-time stability of the closed-loop system is rigorously proven, ensuring that the upper bound of the settling time is independent of initial system states and enabling fast stabilization even under large initial deviations from the reference. Both simulations and experiments validate the effectiveness of the proposed method. When tracking a sinusoidal reference signal with 50 µm amplitude, 0.5 Hz frequency and 100 µm bias, the settling time and steady-state error are 0.276 s and 1.12 µm in simulations, and 0.4 s and 2.7 µm in experiments, respectively. Comparative results reveal that the proposed algorithm outperforms existing methods in convergence speed and tracking accuracy. Moreover, the controller achieves fast stabilization under diverse initial conditions and exhibits strong robustness in tracking reference trajectories with varying frequencies and amplitudes. This work lays a theoretical basis for high-performance control of piezoelectric microinjectors and offers practical value for industrial applications of piezoelectric actuation systems. Full article

Background: Venous thromboembolism (VTE) remains a clinically relevant complication following major orthopaedic procedures, particularly total hip arthroplasty (THA), total knee arthroplasty (TKA), and fracture surgery. Although low-molecular-weight heparin (LMWH) and direct oral anticoagulants (DOACs) are widely regarded as standard pharmacological options, aspirin (acetylsalicylic acid, ASA) has gained renewed attention because of its low cost, oral administration, and favourable bleeding profile. However, the available evidence is heterogeneous, and its interpretation is complicated by differences in patient selection, timing and duration of prophylaxis, diagnostic methodology, aspirin dosing regimens, and the increasing adoption of modern fast-track arthroplasty pathways. Methods: A structured evidence-based review was conducted in accordance with PRISMA 2020 principles. PubMed, Embase, Web of Science, and the Cochrane Library were searched through September 2025 for randomised controlled trials (RCTs), major international clinical practice guidelines, and selected high-level studies relevant to the interpretation of aspirin-based orthopaedic thromboprophylaxis. Nine RCTs, four major guideline documents, and sixteen additional Level I–II studies were included. Outcomes of interest were symptomatic deep vein thrombosis (DVT), pulmonary embolism (PE), major bleeding, and mortality. Risk of bias was assessed using the Cochrane ROB 2 framework. Owing to marked methodological heterogeneity, no formal pooled meta-analysis was undertaken. Results: The available RCT evidence suggests that aspirin may perform adequately within structured sequential or risk-stratified prophylaxis strategies, but not in all clinical settings. In arthroplasty, EPCAT II demonstrated non-inferiority of aspirin when introduced after an initial five-day course of rivaroxaban, whereas CRISTAL showed higher early symptomatic VTE rates when aspirin was used as sole primary prophylaxis from postoperative day 0. Importantly, thromboembolic events in CRISTAL occurred earlier in the aspirin cohort, supporting the concept that anticoagulant therapy remains important during the immediate postoperative hypercoagulable phase. In trauma surgery, PREVENT CLOT established non-inferiority of aspirin compared with LMWH for 90-day mortality; however, the predominantly young study population and the inclusion of upper-extremity fractures limit extrapolation to elderly hip fracture patients. Several smaller RCTs reported no major differences between aspirin and anticoagulants, but these studies were frequently underpowered and relied on less sensitive diagnostic strategies. Historical and contemporary guidelines remain heterogeneous, and evidence from modern fast-track arthroplasty pathways suggests that current trial-based conclusions may not be directly generalisable to short-duration prophylaxis settings. Conclusions: Aspirin may have a role in orthopaedic thromboprophylaxis when used within structured, risk-adapted or sequential protocols, particularly in standard-risk arthroplasty patients and selected trauma populations. However, current evidence does not support its universal use as sole primary prophylaxis in major orthopaedic surgery, especially during the early postoperative hypercoagulable phase or in high-risk patients. Furthermore, the available literature does not permit definitive recommendations regarding the optimal aspirin dose or duration of prophylaxis. The generalisability of the existing literature is further limited by methodological heterogeneity and by the absence of RCTs directly evaluating ultra-short anticoagulant regimens versus prolonged aspirin prophylaxis in modern fast-track arthroplasty. Further high-quality, standardised trials are required. Full article

This study explores the case of the Wuhan East Lake National Independent Innovation Demonstration Zone (East Lake High-Tech Zone), investigating an advanced-scale stormwater and sewage co-treatment system alongside a “low-position, differentiated, vacuum” sewage collection approach. These systems operate within the framework of the “five-builds-one-management” model, which covers sewage collection, treatment, sludge disposal, reclaimed water utilization, tailwater discharge, and operation and maintenance management. The proposed system was associated with measurable before–after improvements: the sewage collection rate increased by 17%, the influent BOD₅ concentration at the sewage treatment plant rose from approximately 92 mg/L to 112 mg/L (~+22%), and water level fluctuations in the tailwater receiving area were reduced by 75%. This planning framework offers a valuable reference for similar urban areas, though calibration based on local hydrological conditions, industrial structure, and population size is essential. Full article

Multi-object tracking and segmentation (MOTS) aims to jointly perform pixel-level instance segmentation and temporal identity association for multiple objects in video sequences. Existing online decoupled MOTS methods face several challenges in complex scenarios, including limited front-end mask quality, corruption of memory representations under prolonged occlusion, and unstable data association and trajectory recovery. To address these limitations, we propose TrackRefine, a plug-and-play decoupled enhancement framework. TrackRefine enhances overall performance through back-end refinement without modifying the architecture of the front-end instance segmenter or relying on additional end-to-end joint training. Specifically, we introduce a lightweight Fast GrabCut-based mask refinement module to optimize mask boundaries, a multimodal long-short-term memory bank that integrates appearance, semantic, and shape cues for identity modeling, and a progressive three-stage association strategy for stable matching and long-term trajectory recovery. Experimental results on MOTS20 show that TrackRefine achieves 69.4 sMOTSA, 82.7 MOTSA, and 478 Frag. Experimental results on KITTI MOTS show that it achieves 62.4/73.7 sMOTSA and 78.0/85.4 MOTSA for pedestrians and cars, respectively. Extensive experiments with different front-end instance segmenters verify its plug-and-play flexibility and decoupled design, while ablation studies confirm the effectiveness of each core module. These results show that TrackRefine provides an efficient and practical solution for online MOTS in complex scenarios. Full article

Slip ring–brush assemblies are widely used in satellite mechanisms to transmit power and signals across rotating interfaces. Under authentic space environments—vacuum, radiation-dominated thermal exchange, and long-duration operation—the coupled effects of mechanical contact dynamics, electrical conduction, intermittent separation, and arcing can accelerate wear and degrade reliability. This paper presents a surface-resolved multiphysics model for multi-track slip rings with staggered brushes. The ring surface is discretized on a circumferential–axial grid and endowed with correlated 3D roughness, enabling interference-based asperity contact. Brush normal dynamics (mass–spring–damper) convert runout and micro-vibration into normal-force ripple and separation events. Electrical conduction is modeled by a parallel admittance network combining pressure-dependent micro-contact conduction and an event-based arc channel activated by separation, opening velocity, and current density with stochastic ignition. A 2D thermal model with ADI integration accounts for Joule/friction heating, radiative cooling, and optional hub conduction. Wear evolves via an Archard-type mechanical term and an arc-energy-driven erosive term. A FAST–MACRO multiscale scheme (20 s FAST, 100 h MACRO with periodic recalibration) enables tractable long-horizon wear prediction while preserving arc statistics. Baseline simulations for a 28 V bus demonstrate rare but nonzero arc activity and predict spatially non-uniform wear at the micrometer scale after 100 h. Full article

Maximum power point tracking (MPPT) is a critical function for maximizing energy extraction in photovoltaic (PV) systems. Due to the inherently dynamic nature of the maximum power point under varying irradiance conditions, achieving fast convergence, low steady-state oscillations, and high tracking efficiency remains a challenging research problem. This paper proposes a hybrid ANN-based MPPT strategy for photovoltaic systems operating under rapidly changing environmental conditions. The proposed approach integrates a rule-based operating-condition estimation stage with a recurrent ANN-based control stage, enabling adaptive duty-cycle generation using measured PV voltage and current signals. Unlike conventional MPPT techniques, the proposed method utilizes operating-region estimation together with an extended ANN input feature vector and a recurrent backpropagation neural network to improve dynamic tracking performance under abrupt irradiance variations. In addition, a composite loss function is adopted to enhance tracking accuracy, guidance consistency, and control smoothness. The ANN is initially trained offline and subsequently refined online using lightweight incremental adaptation to maintain effective operation with a low computational burden. The proposed MPPT strategy is evaluated against P&O, FLC, and SMC. Simulation results demonstrate improved tracking performance, faster dynamic response, and reduced steady-state oscillations under abrupt irradiance variations. Full article

The increasing integration of photovoltaic (PV) systems and nonlinear loads intensifies harmonic distortion and reactive power imbalance in modern power networks. Conventional shunt active power filters (SAPFs) often employ control strategies that perform poorly under uncertain and dynamic grid conditions. This paper develops a hybrid sliding mode control with disturbance observer (SMC+DOB) technique for a PV-integrated SAPF to achieve effective harmonic mitigation, reactive power compensation, and enhanced system robustness. The study models the PV-SAPF system in MATLAB/Simulink (R2025b), where the SMC ensures robust current tracking, while the DOB estimates and suppresses unknown disturbances in real-time. The controller’s performance is evaluated under varying nonlinear and reactive load conditions, as per IEEE 519-2014 standards. Simulation results show that the proposed SMC+DOB scheme reduces total harmonic distortion (THD) by 96.7%—from 31.45% to 1.05%—while maintaining DC-link voltage stability and unity power factor. The integrated control architecture enhances the dynamic performance of SAPF, providing superior harmonic suppression, fast transient recovery, and improved grid stability for PV-connected systems. Full article

Truck-to-excavator assignment is a time-critical control problem in open-pit earthmoving systems (mines, quarries, and large cut-and-fill construction sites) where stochastic travel and service times, changing queues, and equipment outages continually alter the best dispatch decision. A deep reinforcement learning (DRL) dispatch policy is developed and trained using a discrete-event simulation (DES) digital twin of the Sungun copper mine haulage system. The dispatch task is formulated as a Markov decision process using state features that represent fleet locations, excavator and dump queues, and short-term congestion conditions. The resulting deep artificial neural network (DANN) policy is tuned via systematic hyperparameter optimisation and evaluated against a priority-based rule-of-thumb dispatch baseline under long-horizon operating tracks. Results show that the final trained policy improves the average production rate per truck cycle by approximately 17% while reducing avoidable waiting and maintaining stable performance over extended operation, with inference fast enough for real-time dispatch use. Model fidelity is supported by close agreement between simulated and observed daily completed-cycle counts. Robustness is assessed through controlled truck load-capacity perturbations, and scalability is examined through fleet-size sensitivity, which reveals diminishing returns as additional trucks are added under a fixed excavation–haulage configuration. Practical deployment considerations and implications for construction earthmoving logistics are discussed. Full article

To address the performance degradation caused by coupling interference between control and identification filters in the active control of ship hydraulic systems, a multi-strategy collaborative optimization algorithm based on “Signal–Amplitude–Time” is proposed. The method constructs a variable-power white-noise module based on power factors to reduce auxiliary noise interference. It employs an improved variable-step-size LMS algorithm to achieve fast and high-precision online identification of the secondary path. Furthermore, an adaptive prediction error filter is introduced to decouple the control and identification processes, effectively resolving the conflict between convergence speed and steady-state precision. Simulation and experimental results demonstrate that the proposed optimization algorithm exhibits superior robustness and adaptive capability under various operating conditions. It can track complex load fluctuations in real time and achieve a line-spectrum pulsation attenuation of more than 90%. This multi-strategy collaborative scheme significantly enhances the pulsation suppression accuracy and dynamic response capability of ship hydraulic systems, providing an efficient and reliable technical approach for the acoustic stealth control of naval ship hydraulic systems. Full article

This paper proposes a robust barrier-function-based fuzzy adaptive super-twisting integral terminal sliding-mode control (BF-FAST-ITSMC) for robotic manipulators subject to external disturbances. Initially, an integral terminal sliding-mode manifold is designed to ensure finite-time error convergence and eliminate steady-state offsets. To reduce model dependence, the unknown nonlinear function is approximated and compensated using a fuzzy approximator. By combining the super-twisting algorithm (STA) and the barrier-function-based adaptive gains, the designed BF-FAST-ITSMC can suppress actuator chattering effectively, which allows control gains to increase automatically as the error approaches the prescribed boundary. This mechanism ensures that tracking errors are strictly confined within a predefined bound. Comparative simulations on an inverted pendulum and robotic manipulators with one to three degrees of freedom demonstrate that the proposed method provides superior tracking precision, smooth control torque, and enhanced robustness compared to conventional and fuzzy ITSMC schemes. Full article

Introduction: Schizophrenia is characterised by distributed abnormalities in electrophysiological dynamics and large-scale brain networks, yet unimodal EEG or MRI alone cannot fully explain how fast neural computations relate to spatially organised circuit dysfunction. Multimodal EEG–MRI approaches offer a bridge across temporal and anatomical scales by explicitly modelling cross-modal coupling. Methods: Following PRISMA 2020 guidance, we conducted a systematic, mechanistic review of human studies (adults ≥ 18 years) comparing schizophrenia-spectrum groups with healthy controls using EEG combined with at least one MRI modality (fMRI, structural MRI, and/or diffusion MRI) and explicit EEG–MRI integration (e.g., EEG-informed fMRI, joint ICA, mCCA/MCCA, coupled matrix–tensor factorisation, DCM-based fusion). Searches were performed in PubMed/MEDLINE, Embase, Web of Science, Scopus, PsycINFO, IEEE Xplore, ResearchGate, and Google Scholar for January 2000–December 2025, supplemented by citation tracking. Risk of bias was assessed with ROBINS-I, and due to heterogeneity, results were synthesised narratively by integration of families. Results: From 148 records, 23 studies met the inclusion criteria. Studies used mainly simultaneous EEG–fMRI at 3T and spanned resting-state designs and task paradigms dominated by auditory processing (oddball, MMN/N100–P200, ASSR/aeGBR), with additional work in affective context, working memory, semantic processing (N400), sensory gating, and pharmacologic challenge. Across tasks, the most reproducible multimodal signature was disrupted coupling between electrophysiological markers and the recruitment of large-scale networks, rather than isolated changes in EEG or fMRI metrics. Target detection/oddball paradigms converged on reduced late ERP responses (especially P300, sometimes N2) alongside reduced expression or loss of coupling to salience/ventral attention and control circuitry (including ACC/anterior insula/TPJ). Resting-state studies most consistently indicated altered “coupling rules” (frequency specificity, timing/lag structure, and directionality), including abnormalities detectable even when unimodal summaries were weak. Extended multimodal studies (adding sMRI/DTI and/or classification) suggested that combining modalities can improve discrimination, though performance was sensitive to sample size, demographic imbalance, and feature-selection/validation choices. Conclusions: Multimodal EEG–MRI studies support schizophrenia as a disorder involving persistent structural and circuit-level abnormalities whose functional expression varies dynamically across cognitive states and task demands. Future progress will depend on harmonised acquisition/artefact-control practices for simultaneous EEG–fMRI, larger and more diverse samples (including early/CHR and longitudinal designs), and cross-site replication of mechanistically interpretable coupling biomarkers. Full article

In air-to-air counter-UAV scenarios, vision–language tracking for tiny drones still lacks a dedicated benchmark. Unlike traditional UAV tracking or ground-based Anti-UAV settings, air-to-air counter-UAV tracking involves simultaneous motion of both the tracking platform and the target platform. In addition, the target typically appears as a tiny object and is subject to rapid viewpoint changes, fast background transitions, and interference from similar drones, making it difficult to systematically assess the capability boundaries of existing methods. To address this gap, we present the ATA dataset. To the best of our knowledge, ATA is the first vision–language tracking dataset specifically designed for real air-to-air tiny-object UAV countermeasure scenarios. ATA contains 50 real-flight video sequences with 38,094 frames in total, and provides frame-wise bounding box annotations together with video-level English language descriptions. It supports two unified task settings, namely BBox-only and Language-assisted tracking. The dataset covers diverse real-world low-altitude scenarios with complex backgrounds. Notably, the average target area accounts for only $0.03\%$ of the full image, exhibiting pronounced tiny-object characteristics. ATA also captures several key challenges in this setting, including dual-dynamic disturbances, complex background changes, and multi-drone interference. Based on ATA, we establish a benchmark covering both vision-only and vision–language tracking methods, and conduct a systematic evaluation of eight representative recent trackers. Experimental results show that current mainstream methods still perform unsatisfactorily in this scenario, with evident limitations in tiny-object representation, cross-frame association, robustness to complex backgrounds, and interference suppression. Furthermore, we validate a lightweight temporal enhancement module, AFTE, and show that explicitly leveraging adjacent-frame information consistently improves the performance of multiple baseline models. Overall, ATA provides a unified benchmark for vision–language tracking in air-to-air counter-UAV scenarios of tiny drones and highlights temporal modeling as a promising direction for improving tracking performance in this challenging setting. Full article