Search Results (1,285)

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation. Full article

Background/Objectives: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid (CSF) leakage and is typically diagnosed by clinical presentation and characteristic MRI signs; however, objective tools for monitoring physiological changes and treatment response remain limited. Cine phase-contrast MRI (PC-MRI) enables noninvasive quantification of aqueductal CSF dynamics, yet reliable analysis is challenging since the cerebral aqueduct is extremely small and susceptible to low contrast, partial volume effects, and ROI-dependent measurement variability—particularly in SIH where CSF pulsatility is often reduced. Methods: We propose an end-to-end automated framework that integrates (1) a cascade localization–segmentation strategy, consisting of Tiny YOLOv4 detection followed by MultiResUNet segmentation on a YOLOv4-derived cropped ROI; (2) physiology-informed pulsatility-based segmentation (PUBS) to refine anatomical masks into functional flow ROIs; and (3) one-dimensional convolutional neural networks (1D-CNNs) to extract exploratory waveform morphology features from 32-phase cardiac-cycle velocity waveforms. The study includes 39 participants, yielding 59 cine PC-MRI examinations: 11 controls, 28 Pre-treatment SIH scans and 20 Post-treatment Recovery scans. Results: The cascade model significantly improves segmentation robustness compared with a full-image baseline, achieving higher Dice scores and markedly lower boundary errors across cohorts (e.g., Pre-treatment SIH HD95: 1.66 ± 0.74 px vs. 15.37 ± 44.98 px). PUBS refinement reduces quantification deviation from expert manual references in SIH (mean relative error: 7.4% to 5.6%) and improves diagnostic performance for multiple hemodynamic parameters (e.g., downward mean flow AUC: 0.747 to 0.792). For waveform morphology analysis, the end-to-end 1D-CNN classifier was evaluated using repeated-seed participant-level grouped LOOCV. The repeated-seed ensemble prediction showed modest out-of-sample discrimination between Normal controls and Pre-treatment SIH scans, with an AUC of 0.646, a bootstrap 95% confidence interval of 0.455–0.826, and a permutation-test p-value of 0.072. Separately, exploratory analysis of the final baseline-trained 1D-CNN latent space showed marked, apparent Normal-versus-SIH separability and an intermediate recovery distribution in PCA space, suggesting that aqueductal waveform morphology may encode SIH-related physiological information. Conclusions: These findings suggest that SIH-related information may be reflected not only in flow magnitude but also in aqueductal CSF waveform morphology. However, the modest and statistically non-significant out-of-sample performance of the end-to-end 1D-CNN classifier indicates that morphology-based AI features should currently be regarded as exploratory biomarker candidates rather than validated stand-alone diagnostic tools. Larger independent cohorts are required to confirm their reproducibility, physiological meaning, and clinical utility. Full article

This contribution presents a simulation-based approach for optimizing injection molding processes using digital twins. It combines surrogate modeling via response surface methodology (RSM) with the evolutionary algorithm NSGA-II to efficiently capture complex relationships between process parameters and objectives. A key element is the adaptive enhancement of the training dataset within the decision-relevant region of interest (ADEROI) by a modified greedy max–min algorithm. This strategy closes data gaps, improves model accuracy in the potentially optimal region, and directs additional simulations to informative areas. Leave-one-out (LOO) and hold-out (HO) cross-validations show strong root mean square error (RMSE) and $R^2$ values for deformation, shrinkage, cycle time, and mass. NSGA-II converges after 403 generations and results in 191 Pareto-optimal solutions, which are consolidated into preference-consistent operating points. These points make trade-offs between analyzed objectives’ deformation, shrinkage, and cycle time explicit for process pre-design. Preferred solutions are identified through weighted sums of normalized objectives and inversely mapped process parameters. Their agreement with the physics-based digital twin at the hundredths level supports the plausibility of the selected operating points within the investigated simulation-based workflow. A retrospective benchmark against a scaled single-stage LHS baseline shows that ADEROI achieves ROI-equivalent point density with fewer simulation runs for the investigated case, reducing the estimated runtime by $39.1\%$ and resulting in a $1.64\times$ speed-up. The quantitative validation is limited to one thin-walled PP keyholder component; further geometries, mold layouts, and polymer materials are required to empirically assess generalizability. Full article

The development of accessible and experimentally validated robotic systems for engineering education is a challenge, especially in academic environments where industrial manipulators are economically inaccessible. This paper presents the design, mechanical validation, and experimental evaluation of a robotic arm-based didactic module developed for the classification of objects according to color and morphology. The proposed system integrates a five-degree-of-freedom articulated configuration, a servomotor drive, motion planning with a trapezoidal speed profile, and a web-based control interface, enabling local and remote operation within an educational environment aligned with Industry 4.0 principles. The mechanical structure was designed using CAD modeling and validated through static structural analysis to ensure mechanical integrity and adequate safety factors. The selection of actuators was made considering the torque, angular velocity, and load requirements. A trapezoidal speed profile was implemented in order to ensure smooth trajectories and minimize positioning errors. Experimental validation was carried out through repetitive tests under controlled laboratory conditions, evaluating the accuracy and repeatability metrics. Statistical indicators such as mean error, standard deviation, and root mean square error (RMSE) were calculated. The results show the stable performance of the system, with low variability in multiple test cycles, confirming the viability of the proposed architecture for its implementation in automation and educational robotics laboratories. The integration of structural validation, motion control strategy, and experimental quantitative evaluation contributes to bridging the gap between theoretical teaching of robotics and its practical application, offering a scalable, low-cost platform for engineering training. Full article

In heavy-haul railway systems, effective empty railcar distribution (ERD) can optimize composition planning and meet empty railcar requirements (ERRs) at all loading ends, thereby improving the efficiency of train operations. To solve practical challenges such as the imbalanced supply–demand of empty trains, redundant loading and unloading cycles, and prolonged waiting times, this study establishes a multi-objective and 0–1 integer programming model for ERD at the loading end of a heavy-haul railway. The model can simultaneously maximize the fulfilment of all ERRs, minimize the ERD delay time, and reduce the waiting time in the heavy-train combination problem under complex constraints, including the passing capacity of sections, combination capacity of stations, and ERR at the loading end. While traditional optimization methods such as mathematical programming or heuristic algorithms partially address these issues, they are ineffective under dynamic constraints and state-space explosion. Furthermore, traditional reinforcement learning-based methods, such as Q-learning, exhibit limitations in railway scheduling due to the state-space explosion problem and inadequate model generalization. To overcome these limitations, this study proposes an innovative framework; the ERD at the loading end of the heavy-haul railway is formalized as a Markov decision process and optimized using deep Q-network (DQN) reinforcement learning. In addition, this study proposes an experience data fusion mechanism that integrates the empirical rules of the dispatchers through a modular architecture, achieving real-time constraint compliance while maintaining scalability for practical implementation. The NSGA-II genetic algorithm for multi-objective problems is used in this study to evaluate the performance of the DQN algorithm. The experimental results demonstrate that the DQN algorithm can fully meet ERRs with zero delay and produce optimal schemes for train combinations. Meanwhile, NSGA-II presents superior performance in minimizing the combination waiting time and same-destination train combinations. Meanwhile, the DQN algorithm can identify superior ERD strategies in the expanded-action and state spaces, enabling the effective handling of complex constraint-based ERD. Full article

Microsatellites operate in highly dynamic thermal environments due to severe physical constraints, making temperature telemetry a critical onboard health indicator. Conventional threshold-based monitoring fails to distinguish normal operational mode transitions from genuine faults, causing excessive false alarms. To address this, we propose a two-stage framework integrating unsupervised thermal mode discovery with mode-specific deep learning prediction. Raw temperature telemetry is downsampled and segmented into orbital cycles. Unsupervised clustering identifies two nominal thermal regimes and four canonical fault-type libraries (step, spike, drift, and noise), each corresponding to distinct in-orbit failure mechanisms. For each nominal mode, a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) is trained on 7-day historical windows to forecast 3-day temperature evolution. Post-downlink, incoming cycle mode is inferred via nearest-neighbor DTW classification; anomalies are flagged when prediction residuals exceed mode-adaptive thresholds. Validation on Macau Science Satellite-1B (MSS-1B, COSPAR 2023-069-B, NORAD 56732) in-orbit telemetry from a 41° inclination low-Earth orbit—where solar illumination dominates external thermal loading and internal heat from the data-communication module and scientific payload constitutes the primary internal thermal source—shows the method reduces anomaly flags by 96.6% and improves prediction mean absolute error by 51.3% compared to a non-classified global baseline under nominal operating conditions, correctly detecting a known operational transient while suppressing spurious alarms. A synthetic fault injection experiment with four anomaly types and five baseline methods further confirms the framework’s detection capability, achieving an overall F1 score of 0.725 vs. 0.258 for the global baseline—a 2.8× improvement driven primarily by a 4× precision gain. Sensitivity analysis reveals that the two-stage advantage is most pronounced for low-magnitude and short-duration faults, where mode-specific context is essential. This work advances microsatellite autonomous health management by providing reliable anomaly detection with quantified fault detection performance. Full article

Background: Research on the influence of the menstrual cycle on female athletic performance remains limited. This study investigated menstrual cycle characteristics, menstrual disorders, and phase-specific variations in perceived performance among female volleyball players. Methods: Eighty-four recreational and competitive athletes (M = 25.62 ± 6.43 years) completed a comprehensive survey between March and April 2025, including a modified Menstrual Distress Questionnaire (MEDI-Q) assessing physical and psychological well-being, perceived sport performance, training quality, and motivation across the menstrual, follicular, and luteal phases. Results: Perceived sport performance differed significantly across phases, with the highest scores in the follicular phase (M = 1.70 ± 1.51), followed by the luteal (M = 0.88 ± 1.13) and menstrual (M = 0.64 ± 1.00) phases (p < 0.001). Perceived performance impairments were greatest during menstruation and lowest in the follicular phase. Motivation exhibited a similar trend, peaking in the follicular (M = 1.74 ± 1.55) and declining during menstruation. Menstrual disorders were reported by 75% of participants, and 59.5% experienced dysmenorrhea. Knowledge scores (M = 11.13/18) indicated a moderate understanding of menstrual physiology. Conclusions: These findings demonstrate significant menstrual phase-related variations in subjective performance and motivation, emphasizing the importance of menstrual cycle awareness, athlete education, and individualized, phase-based training strategies to optimize performance and support female athlete welfare. Full article

Concrete contributes significantly to global CO₂ emissions due to high energy demand for cement production. This research integrates multiple advanced ensemble ML-based prediction models by combining experimental evaluation, explainable framework, and life cycle sustainability analysis for SCM (supplementary cementitious materials)-incorporated concrete mixtures. A comprehensive experimental program was conducted to evaluate the compressive and tensile strength of concrete revealing that the hybrid mix of GF4 with a 40% replacement level of cement with fly ash (FA) and ground granulated blast furnace slag (GGBFS) exhibited optimum synergistic performance due to balanced hydration kinetics and improved microstructure characteristics. For computational model development, a k-fold cross validation technique was deployed to evaluate robustness across multiple data partitions and to control overfitting in models. Model performance was assessed through multiple metrics including R², RMSE, and MAE with particular emphasis on the gap between training and testing performance. The best performing model was optimized using Particle Swarm Optimization (PSO) and Bayesian Optimization (BO) techniques providing an additional safeguard against overfitting. Shapley Additive Explanation (SHAP) interpretation revealed w/b ratio and curing age as key parameters for compressive strength, while fine aggregate content and curing age influenced tensile strength. For compressive strength, XGBoost model performed well with an R² value of 0.879 which was increased to 0.918 with the PSO optimization technique. For tensile strength, the Gradient Boosting model was selected with an R² value of 0.840 which was optimized to 0.879 after the PSO optimization technique. Moreover, life cycle assessment was performed to evaluate the environmental impacts in terms of embodied carbon and energy associated with concrete mixes. The hybrid GF4 mix demonstrated a 36% reduction in embodied carbon compared to the control mix, indicating strong potential for low carbon concrete applications. This integrated research contributes to the advancement of green construction practices and supports global efforts to reduce atmospheric impacts through the circular use of industrial byproducts. Full article

Background: Menstrual-cycle-associated symptoms and menstrual health conditions are common among female athletes and may influence recovery, perceived readiness, training availability, and athletic performance. However, evidence regarding interventions aimed at managing these symptoms and their functional implications in athletes remains limited and heterogeneous. Objective: The objective of this study is to synthesize the available evidence on pharmacological and non-pharmacological interventions related to menstrual health and menstrual-cycle-associated symptoms in female athletes and to evaluate their impact on performance, recovery, functional capacity, and symptom burden. Materials and Methods: A systematic review with narrative synthesis was conducted following PRISMA 2020 guidelines. Studies evaluating interventions associated with menstrual health or menstrual-cycle-related symptoms in female athletes were included when they reported outcomes related to athletic performance, recovery, functional capacity, or symptom burden. Results: Five studies published between 2023 and 2025 were included. The interventions evaluated included hormonally related strategies involving oral contraceptive timing, recovery interventions such as cryotherapy, mindfulness-based yoga, nutritional supplementation, and pharmacological pain-modulation approaches. However, findings regarding objective athletic performance outcomes were inconsistent, and the included studies showed substantial methodological heterogeneity. Conclusions: The available evidence suggests that certain interventions related to menstrual health may contribute to improvements in symptom burden, perceived recovery, or selected functional outcomes in female athletes. Nevertheless, the current evidence base remains limited, heterogeneous, and insufficient to support strong performance-related recommendations. Further high-quality studies specifically designed in female athlete populations are needed to inform evidence-based sports medicine practice. 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 recycling of acrylonitrile-butadiene-styrene (ABS) is crucial for a circular plastics economy, but repeated extrusion induces degradation that limits its reuse. This study establishes a comprehensive structure-property evolution mechanism for ABS 757K over five extrusion cycles and develops a novel image-recognition model for aging degree prediction. Multi-faceted characterization revealed that chain scission, oxidation of the polybutadiene (PB) phase, and the formation of chromophores led to progressive embrittlement, yellowing, and reduced thermal-oxidative stability. A key finding from Energy Dispersive Spectroscopy (EDS) was the stability and homogeneous distribution of sulfur-based antioxidants, which underpin the material’s superior resistance to degradation by effectively scavenging free radicals, which function as effective free radical scavengers. This mechanism underpins the material’s superior resistance to thermo-oxidative degradation. Consequently, significant molecular weight reduction and property deterioration were delayed until later extrusion cycles. Furthermore, a deep learning model based on the DeepLabV3+ architecture was trained to predict extrusion history directly from scanning electron microscopy (SEM) images of impact-fractured surfaces. The model achieved an average prediction accuracy exceeding 96.5%. Remarkably, it demonstrated excellent generalizability, maintaining high accuracy on two unseen commercial ABS grades. This indicates that the micro-morphological evolution pathway is a universal fingerprint of thermo-mechanical aging in ABS. This work not only elucidates the multi-scale degradation mechanism of recycled ABS but also provides a rapid, non-destructive tool for intelligent quality assessment in plastic recycling streams, bridging advanced machine learning with practical sustainability challenges. Full article

Accurately predicting the capacity degradation of lithium-ion batteries is crucial for ensuring the reliability and safety of electric vehicles and energy storage systems. However, existing methods—including those based on physical principles, deep learning, and traditional machine learning—all face challenges in balancing accuracy, computational efficiency, and adaptability to non-linear aging dynamics. This study proposes a new framework that combines multi-scale data preprocessing and a divide-and-conquer strategy to address these limitations. Firstly, a hybrid Wavelet–SG filter is applied to suppress noise, and a set of specialized XGBoost micro models is trained, with each model predicting capacity for a specific cycle, enabling precise trajectory prediction at different aging stages. The evaluation on the Toyota-MIT-Stanford dataset (118 batteries under different operating protocols) shows that this method achieves an average MAPE of 1.16% and a maximum of no more than 2.5% on the unfamiliar protocol test set. In terms of accuracy, it achieves performance comparable to CNN, LSTM, and CNN-LSTM benchmarks. Importantly, its parallel architecture enables fast inference (400 milliseconds on CPU), making it suitable for edge deployment in battery management systems. The model also has interpretability consistent with physical laws and can autonomously capture stage-dependent degradation mechanisms. This work provides a reliable, efficient, and interpretable solution for real-world battery health monitoring. Full article

Stroke-related gait impairments are frequently associated with deficits in trunk control, movement coordination, and dynamic stability. Although robotic-assisted gait rehabilitation has shown promising clinical benefits, phase-specific biomechanical adaptations following rehabilitation remain incompletely understood. This study investigated phase-specific biomechanical adaptations following robotic-assisted gait rehabilitation in individuals with stroke using sensor-derived waveform analysis. Rehabilitation was performed three times per week over approximately 5–6 weeks using treadmill-based robotic gait training under dynamic body-weight support conditions. Pre- and post-intervention kinematic data were collected using a sensor-based motion analysis system. Joint kinematics, trunk motion, and center of gravity (COG) displacement were analyzed across the normalized gait cycle using waveform-based effect size analysis, statistical parametric mapping, principal component analysis, and k-means clustering to explore inter-individual adaptation patterns. Thirteen post-stroke hemiplegia patients (10 males; age = 63.9 ± 13.8 years), including six subacute and seven chronic stroke survivors, completed 16 rehabilitation sessions. The most prominent improvements were observed in trunk lateral flexion, particularly during loading response (d = 0.47, p < 0.01), indicating enhanced frontal plane trunk stability. Trunk flexion–extension showed reduced compensatory motion, whereas hip and knee adaptations were smaller and phase-dependent. COG displacement decreased across the gait cycle, reflecting improved dynamic stability. Step length increased significantly on both hemiplegic (Δ = +5.73 cm, p = 0.024) and intact sides (Δ = +8.83 cm, p = 0.007), while cadence and load symmetry remained unchanged. Clustering analysis revealed heterogeneous adaptation profiles rather than distinct responder groups. Chronic participants demonstrated greater variability within the Principal Component Analysis space compared to subacute participants, suggesting more variable and individualized biomechanical reorganization patterns rather than clearly separable recovery categories. Overall, robotic rehabilitation induced inter-individual biomechanical adaptations, predominantly involving proximal trunk control and stabilization strategies. 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

Background/Objectives: Contrast-enhanced (CE) breast MRI is highly sensitive for evaluating breast cancer extent and response to neoadjuvant therapy (NAT) but requires intravenous administration of gadolinium-based contrast agents (GBCA), increasing cost, time, patient discomfort, and health concerns. This study explored the feasibility of reducing GBCA use in treatment monitoring using a deep learning (DL) model to synthesize CE-MRI from non-contrast MRI. Methods: This IRB-approved retrospective pilot study evaluated women with breast cancer enrolled in an ongoing trial using serial MRI to monitor NAT prior to surgery. A pre-trained DL model was used to synthesize CE-MRI from T1-, T2-, and diffusion-weighted MRI. Changes in tumor volume at early (post-1-cycle NAT) and mid-treatment were measured on synthetic and acquired CE-MRI. Performance for predicting residual cancer burden (RCB) class 0/1 was evaluated using AUC and compared with DeLong’s test. Results: 27 women were included in the study (median age, 47 years [range = 28–75]); 14 (52%) achieved RCB class 0 and six (22%) achieved class 1. Synthetic CE-MRI-derived tumor volumes showed strong correlation with those from acquired CE-MRI at pre-treatment ($\rho$ = 0.92, p < 0.001) and early treatment ($\rho$ = 0.83, p < 0.001), but lower agreement at mid-treatment ($\rho$ = 0.57, p = 0.002). Change in tumor volume on synthetic CE-MRI was numerically similar to acquired CE-MRI for predicting RCB class 0/1 vs. 2/3 at both early (AUC = 0.84 vs. 0.86, p = 0.83) and mid-treatment (AUC = 0.73 vs. 0.75, p = 0.80). Conclusions: Synthetic CE-MRI demonstrates preliminary feasibility as a non-contrast surrogate for predicting favorable outcomes (RCB class 0/1) in this pilot study, but inconsistencies in tumor volume measurement vs. acquired CE-MRI warrant further model refinement and validation. Full article