Search Results (7,156)

This study proposes a canopy-adaptive TAD-IRRT* (target-biased sampling, artificial potential field, and dynamic step-size informed rapidly-exploring random tree star) algorithm to solve the collision-free 3D path-planning problem for a 6-DOF apple-harvesting robotic arm. To improve computational speed and search directionality, the method integrates target-biased sampling and a distance-regulated artificial potential field (APF) into the Informed-RRT* framework. Furthermore, an obstacle-distance-based dynamic step-size mechanism is introduced to optimize spatial exploration. The generated routes undergo greedy path pruning and cubic B-spline smoothing to ensure kinematic executability. The simulation results in complicated ROS-based scenarios demonstrate that the TAD-IRRT* algorithm achieves a 100% planning success rate, reducing the average computational time and joint-space path length by approximately 60.1% and 15.6%, respectively, compared to the standard Informed-RRT*. Kinematic analysis via Fourier curve fitting ($R^2=0.9849$) confirms continuous angular velocity and acceleration without high-frequency chattering. Physical prototype experiments in the dense-obstacle scenarios show that the proposed method increases the path execution success rate by 36.7% and reduces the average execution time by 41% compared to the standard Informed-RRT* algorithm. The proposed approach effectively balances high-quality path generation with low computational overhead, providing a reliable and safe solution that significantly reduces mechanical wear. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

Background/Objectives: Ursodeoxycholic acid (UDCA) is a bile acid widely used for the treatment of cholestatic liver diseases; however, its poor aqueous solubility represents a major limitation for the development of oral liquid formulations, particularly in pediatric patients requiring accurate and flexible dosing. This study aimed to develop and characterize a fully solubilized extemporaneous UDCA oral formulation using the ready-to-use vehicle Wagner, with particular emphasis on the role of hydroxypropyl-β-cyclodextrin (HP-β-CD) as a solubilizing excipient. Methods: Phase-solubility studies, Job’s plot analysis, and ¹H NMR spectroscopy were performed to investigate the host–guest interaction between UDCA and HP-β-CD, confirming the formation of a stable 1:1 inclusion complex responsible for a marked increase in drug solubility. The aqueous solubility of UDCA increased from approximately 0.02 mg/mL in water to 31 ± 1 mg/mL in the Wagner base containing HP-β-CD, compared to ~10 mg/mL in the corresponding cyclodextrin-free vehicle. Chemical stability was evaluated using an HPLC method adapted from the European Pharmacopoeia, employing dual detection (refractive index and photodiode array detector) to ensure specificity and stability-indicating capability. Results: The UDCA solution (20 mg/mL) remained chemically stable for at least 4 months under refrigerated (4–8 °C) and room temperature (25 °C) conditions, with only moderate degradation observed at 40 °C. Physical stability studies confirmed the absence of precipitation, phase separation, or significant pH variations under all storage conditions. Conclusions: Wagner-based formulation enabled the development of a stable and homogeneous UDCA oral solution, providing a complementary formulation strategy to conventional suspension-based preparations. This approach represents a robust and patient-oriented strategy for extemporaneous compounding, particularly suitable for pediatric use. Full article

Background: The relationship between physical exercise and human longevity constitutes one of the most consequential intersections in contemporary preventive medicine. Although international guidelines recommend 150 min of moderate-intensity exercise weekly, growing evidence suggests that the architecture of optimal exercise is far more complex, encompassing dose, modality, timing across the lifespan, and the paradox risks imposed by extreme endurance. Methods: We included in this narrative review landmark cohort studies, randomized controlled trials, meta-analyses, and expert physiological frameworks published in high-impact cardiovascular, sports medicine, and longevity journals from 1966 to 2024. Results: Cardiorespiratory fitness (CRF), indexed by maximal oxygen uptake (VO₂ max), demonstrates the strongest and most linear dose–response relationship with all-cause mortality identified in preventive medicine, with every 1 metabolic equivalent of task (MET) increment associated with a 12–15% reduction in mortality risk. The optimal dose of vigorous-intensity exercise follows a J-shaped dose–response curve: 3–5 sessions per week generating 1–2.4 h of vigorous activity is associated with the lowest all-cause mortality risk in large prospective cohorts, whereas chronic extreme endurance exercise incurs measurable atrial remodeling, patchy myocardial fibrosis, and a 5.3-fold increase in the risk of atrial fibrillation. The importance of exercise types shifts profoundly across the lifespan, transitioning from aerobic capacity effort in the third decade to resistance training in the seventh decade and neuromuscular stability in the eighth. Based on our interpretation of the available evidence, we propose a structured, personalized four-step exercise pathway integrating CRF assessment, lifespan-adapted prescription, lifestyle co-interventions, and periodic reassessment. Conclusions: Among currently available lifestyle interventions, regular exercise is consistently associated with some of the largest and most reproducible reductions in all-cause and cardiovascular mortality observed in prospective cohort data, while remaining accessible and cost-effective. Full article

Fourier Neural Operators (FNOs) exhibit mode saturation on high-contrast inhomogeneous media, and recent multi-scale extensions (MscaleFNO) further worsen out-of-distribution (OOD) generalization. We introduce the Helmholtz Neural Operator (HNO), a physics-informed, FFT-free branch–trunk operator in the DeepONet family, with a hybrid SIREN+learnable-Fourier trunk and a dual-path rank-32 hypernetwork branch, with bounded multiplicative gating on per-mode coefficients. At a matched parameter count (∼1.05 M, five seeds), HNO achieves a 2.6× lower OOD generalization gap than FNO (19.6% vs. 50.6%, $p=1.7\times {10}^{-3}$, Cohen’s $d=5.1$), 5.1× lower than vanilla DeepONet (19.6% vs. 99.9%, $p=8.2\times {10}^{-3}$), and 6.0× lower than MscaleFNO (19.6% vs. 117.4%, $p=2.4\times {10}^{-6}$); MscaleFNO’s deficit grows at 4.2× more parameters, ruling out capacity starvation. HNO is 4.6×/16.4× faster than FNO/MscaleFNO and 64×–245× faster than multi-threaded FD-PML (MKL PARDISO, 12 cores; 183×–698× vs. single-thread scipy.spsolve), making it suitable as a forward surrogate inside many-query workflows. Absolute accuracy on extreme-contrast (15:1) OOD samples is limited (relative $L^2\approx 1$), so HNO is positioned as a many-query surrogate or warm start for refinement loops, not a stand-alone replacement for direct solvers. A scope limitation is that HNO underperforms FNO on elliptic Darcy Flow, confirming specialization for hyperbolic/wave equations rather than universal operator learning. Full article

Elevated concentrations of fine particulate matter ($P{M}_{2.5}$) are a critical threat to respiratory health worldwide. Therefore, there is an urgent need for precise urban forecasting systems for public health management. Technological advancements in the domains of continuous environmental monitoring and deep learning have enabled large-scale data acquisition, processing, and modeling. Existing predictive models typically depend on auxiliary meteorological inputs, which are frequently inaccessible within standard ground-level monitoring networks. Furthermore, conventional approaches often fail to adequately capture the complex spatio-temporal interactions of pollutants. To address these limitations, this study presents the Spatio-Temporal Endogenous Attention Network (STEA-Net), a forecasting framework designed to operate exclusively without weather variables. Validated on a comprehensive multi-year historical dataset (Jan 2015–Feb 2020) from diverse monitoring stations in India, STEA-Net employs a hybrid adjacency matrix that integrates physical geographical distances with functional clustering to accurately map pollutant transport pathways. Utilizing this structural map, a Graph Attention Network dynamically evaluates the spatial influence of neighboring nodes, while a Bidirectional LSTM processes the underlying temporal sequences. Experimental results demonstrate that STEA-Net substantially surpasses traditional machine learning algorithms and provides competitive performance against advanced deep learning baselines. The proposed model achieves a peak Coefficient of Determination ($R^2$) of 0.9294 (5-seed average: $0.9273\pm 0.0023$) and a peak RMSE of 14.38 µg/m³ (5-seed average: $14.59\pm 0.23$ µg/m³), effectively adapting to the dynamic volatility of urban pollution levels. The model exhibits architectural stability with a Monte Carlo dropout verified deviation of $\pm 2.22$ µg/m³. This research provides a forecasting architecture that retains competitive predictive performance under the strict operational constraint of meteorology-free deployment in resource-constrained urban monitoring environments. Full article

Koji production is a critical process that determines the flavor and quality of the final soy sauce product. However, the complex mechanisms underlying microbial metabolism and the evolution of the physicochemical environment still require further analysis. This study focuses on three parallel koji rooms in an industrialized koji fermentation process. This work tracked the dynamics of physicochemical indices, volatile flavor compounds, and microbial communities over a full 40 h cycle. Data integration and correlation analysis elucidated the close linkage between the microbial community, the fermentation environment, and flavor formation. Koji moisture declined gradually, with faster losses at later fermentation stages. This physiological dehydration arose from microbial metabolic heat, forced aeration and structural loosening of koji, not simple physical evaporation. System pH displayed a typical U-shaped trend across fermentation. Values dropped early, most likely driven by accumulating organic acids, before rising from mid to late fermentation. This pH rebound was tentatively attributed to ammonia release from proteolytic breakdown, which may neutralize acidic compounds. These observations cast doubt on the conventional assumption that organic acid levels may be reliably estimated solely from pH measurements. Physicochemical analysis showed continuous accumulation of amino acid nitrogen (0.6–0.9 g/100 g) and total acidity throughout fermentation. By contrast, reducing sugar concentrations differed across individual koji rooms, presumably owing to divergent microbial adaptation in early fermentation. A total of 77 common compounds were identified, among which 13 key odor-active compounds with OAV ≥ 1, such as 4-vinylguaiacol and 3-methylbutyraldehyde, constitute the characteristic flavor profile of soy sauce starter culture. High-throughput sequencing uncovered a distinct ecological pattern: eukaryotic communities, dominated by Aspergillus oryzae, converged under controlled regulation. While prokaryotic communities differentiated dynamically, driven by spatial heterogeneity in the semi-open fermentation environment. Spearman correlation analysis further indicated potential functional partitioning: high-abundance taxa (e.g., Aspergillus oryzae, Weissella) were predominantly associated with macromolecular substrate degradation, whereas rare low-abundance taxa (e.g., Alternaria) displayed significant correlations with the biosynthesis of key characteristic flavor compounds. This study clarifies the synergistic regulatory mechanisms linking physicochemical conditions, microbial metabolism, and flavor precursor formation during industrial koji production. The findings establish a scientific foundation for optimizing process parameters and achieving standardized quality control in soy sauce manufacturing. Full article

Background/Objectives: Age-related functional decline is increasingly linked to chronic low-grade inflammation (inflammaging) and sarcopenia, two interconnected processes contributing to frailty, metabolic dysregulation, and impaired physical function. These conditions share several underlying mechanisms, including immune dysregulation, mitochondrial dysfunction, oxidative stress, and impaired anabolic signaling. This narrative review critically evaluated the mechanistic and translational interactions between natural bioactive compounds and lifestyle interventions in modulating inflammaging and sarcopenia. Methods: Evidence from molecular, experimental, epidemiological, and clinical studies was synthesized to examine the effects of bioactive compounds—including polyphenols, flavonoids, carotenoids, and omega-3 fatty acids—as well as physical activity and dietary patterns. Particular emphasis was placed on inflammatory regulation, redox homeostasis, mitochondrial adaptation, and muscle metabolism, including NF-κB, AMPK–mTOR, and Nrf2 signaling pathways. Results: Observational studies and randomized controlled trials generally indicate that anti-inflammatory dietary patterns and regular physical activity are associated with improved muscle strength, physical performance, and inflammatory status in older adults. Mechanistically, nutritional bioactives and exercise appear to converge on several pathways involved in mitochondrial function, oxidative stress, anabolic signaling, and immune activation. Emerging evidence suggests potential convergence and interaction of biological pathways affected by nutritional and lifestyle interventions; however, formal evidence demonstrating true synergistic effects in humans remains limited. Nevertheless, substantial heterogeneity persists regarding intervention protocols, dosage strategies, bioavailability, and long-term clinical outcomes. Conclusions: Natural bioactive compounds and lifestyle-based interventions represent promising approaches for targeting biological processes implicated in inflammaging and sarcopenia. By integrating current evidence within a hormesis-oriented geroscience framework, this review highlights the importance of adaptive redox regulation, metabolic resilience, and evidence-based lifestyle strategies in healthy aging. Future well-designed longitudinal and intervention studies are needed to clarify the clinical relevance of these interactions and optimize translational implementation. Full article

This study focuses on the design and development of the Double Purpose Bench Vise to address safety, efficiency, and adaptability challenges in welding and fabrication environments. The project responds to limitations of conventional vises that restrict precision and increase the risk of strain-related injuries when handling heavy, irregular, or vertically oriented workpieces. Through an engineering-based development approach involving analysis, design, fabrication, and performance evaluation, the study introduces a Double Jaw Vertical Bench Vise equipped with a dual-clamping system and an integrated hydraulic jack mechanism for precise vertical adjustment with minimal physical effort. The device is designed to securely hold various materials, including metal bars, pipes, and wooden components, during cutting, grinding, shaping, welding, and assembly operations. Evaluation results from functional testing and user feedback indicate improved clamping stability, alignment accuracy, and ergonomic performance compared to traditional models, although refinements in structural optimization, weight distribution, and user interface components are recommended. The study suggests further prototype enhancement, extended field testing, and integration of advanced ergonomic and safety features to maximize durability, usability, and overall productivity in professional workshops and technical training laboratories. Full article

Automatic Modulation Classification (AMC) is essential for waveform-level signal characterization. It supports spectrum sensing, signal identification, and adaptive resource allocation in cognitive radio and next-generation wireless systems. However, channel impairments such as multipath propagation, frequency offset, fast fading, and noise degrade modulation signatures, making reliable AMC challenging. Existing deep learning-based approaches often rely on purely data-driven learning, leading to insufficient modeling of modulation-relevant features, loss of transient characteristics, and limited exploitation of hierarchical relationships among modulation types. To address these issues, this paper proposes PHM-Net, a physics-informed hierarchical multi-scale network for robust AMC. The model employs a hierarchical backbone with residual encoder blocks. A Transient Feature Gating (TFG) module enhances modulation-relevant representations, a Cross-Resolution Signal Aggregation (CRSA) module fuses multi-stage features, and a Physics-Informed Hierarchical Loss (PI-HL) enforces consistency between coarse- and fine-grained predictions. Experimental results on three benchmark datasets (RML2016.10a, RML2016.10b, and RML2018.01a) show that PHM-Net consistently achieves the highest average accuracy among all compared models. On RML2018.01a, which contains 1024-sample sequences and 24 classes, PHM-Net achieves an average accuracy of 64.59% and a best-case accuracy of 98.42%, surpassing AMC_Net by 11.14 and 17.09 percentage points and CNN-Transformer by 9.43 and 11.15 percentage points, respectively. PHM-Net provides a robust and interpretable solution for AMC under complex channel conditions. Full article

A virtual training environment offers clear advantages for agricultural robotics. It provides a safe setting in which perception, navigation, and control algorithms can be evaluated without risking damage to either the robot or the crop. It also supports efficient data generation: large volumes of training data can be collected under diverse environmental conditions that would be costly, slow, and often season-dependent in real-world deployments. This broader variability improves model adaptability, reduces the risk of overfitting, and leads to more robust operation. In this paper, we argue that digital twin technology should therefore be understood not merely as a passive mirror of a physical robot, but as an active training environment in which multiple sensor-related subprocesses can be developed, tested, validated, and refined jointly. This paper is based on our experiences with digital twin technology used in the development of a vineyard robot, including a self-driving rover, sensor simulation, procedural map generation, and agriculture-specific movement models. Our contribution is threefold: we reinterpret the digital twin as a training space, propose a layered framework for training agricultural robots in virtual environments, and explain why agriculture is a particularly strong use case, given variable field conditions, expensive real-world experimentation, and persistent labor scarcity. To validate this framework, we present the simulation-based evaluation of an autonomous reinforcement learning agent. The agent has been trained entirely in this virtual environment, which successfully navigated to 155 out of 161 target points in a simulated vineyard demonstration environment. Full article

In complex low-altitude environments, unmanned aerial vehicles (UAVs) require reliable positioning, yet Global Navigation Satellite System (GNSS) signals are vulnerable to occlusion and interference. Pseudolite-augmented cooperative localization, which combines ground base-station signals with inter-UAV relative observations, can complement GNSS in such environments. However, two practical issues remain in real-world deployment: UAV-to-base-station (U-B) and UAV-to-UAV (U-U) observations have markedly different error statistics that a unified noise adjustment cannot handle, and the conservative covariance estimates produced by Covariance Intersection (CI) fusion bias the innovation-based adaptive noise estimation in distributed architectures. To address these issues, this paper proposes a Distributed Group Covariance Compensation Adaptive Kalman Filter (DGCC-AKF) for collaborative enhancement of UAV regional localization. DGCC-AKF establishes a group adaptive mechanism that independently adjusts the noise covariance matrices of U-B and U-U observations, enabling observation-type-level adaptive weighting that suppresses anomalous U-B or U-U measurements at the group level. In addition, a bounded covariance compensation factor is incorporated to alleviate the CI-induced conservatism in the adaptive noise estimation. The proposed method is evaluated on a 2800 km² semi-physical testbed based on the Ground-based High-precision Local Positioning System (GH-LPS) pseudolite network using measured U-B observations and high-dynamic (>300 km/h) flight trajectories collected from a fixed-wing platform across three independent flight sessions. Results demonstrate that under observation fault periods, the proposed method improves 3D positioning accuracy by up to about 75% over single-UAV extended Kalman filter (EKF). Compared with two advanced algorithms in this field, variational Bayesian adaptive Kalman filter (VBAKF) and maximum correntropy criterion Kalman filter (MCC-EKF), it is the only scheme that remains accurate and stable across all UAVs and fault types. The framework provides a practical step toward field deployment for resilient multi-UAV cooperative navigation in pseudolite-augmented GNSS-denied regions. Full article

In single-phase PWM rectifiers, due to the inherent time-varying characteristics of the source voltage and current as well as the periodic operation of the converter bridge, the instantaneous input power on the AC side inevitably exhibits a twice-fundamental-frequency pulsation. This phenomenon consequently generates a double-line-frequency (100 Hz) voltage ripple on the DC-link capacitor, which causes an inherent contradiction in conventional voltage outer-loop control between steady-state ripple suppression and dynamic response speed. To address this issue, this paper proposes a control strategy based on an Adaptive Time-Delayed Feedforward Neural Network (Adaptive TD-FNN). The proposed method explicitly introduces the delayed voltage error of half a ripple period into the network state input, thereby achieving time-domain decoupling of the 100 Hz low-frequency disturbance. In addition, a physics-driven training framework is constructed by integrating the rectifier’s discrete difference equation, thereby strengthening the network’s capacity to learn the dynamic characteristics of the system. On this basis, a dynamic adaptive smoothness-weight penalty mechanism is designed to adjust the weighting factor of the current command smoothness constraint in the loss function according to the system operating state. Specifically, the penalty weight is increased under steady-state conditions to suppress command oscillations caused by ripple disturbances, while it is rapidly reduced during load or grid-voltage transients to release the network’s transient optimization capability. Simulation and experimental results show that the proposed Adaptive TD-FNN controller can simultaneously achieve smooth steady-state current command output and fast dynamic voltage regulation without introducing additional complex digital notch-filtering algorithms. Compared with conventional dual-loop control, the proposed strategy reduces the total harmonic distortion (THD) of the grid-side input current from 8.45% to 3.42%, satisfying grid-connected power quality requirements. Meanwhile, under large load transients and grid-voltage disturbance conditions, the DC-link voltage recovery time is about 40 ms, verifying the comprehensive advantages of the proposed method in ripple suppression, dynamic response, and operating-condition adaptability. Full article

Proton exchange membrane fuel cell–battery hybrid power systems provide an effective solution to overcome the limited endurance of battery-powered multirotor unmanned aerial vehicles. However, the highly transient power demands of quadcopter platforms, combined with balance-of-plant losses and operational constraints, create significant challenges for reliable energy management. This study proposes a degradation-aware stress-mitigation model predictive control-based energy management framework to maximize mission endurance under realistic conditions. A control-oriented, physics-consistent model is developed using manufacturer polarization data from a 500 W Aerostak proton exchange membrane fuel cell. The model captures polarization behavior, balance-of-plant loads, battery dynamics, and direct current-bus power balance. The model predictive control strategy optimally allocates power by maintaining direct current-bus stability, regulating battery state-of-charge within safe limits, and constraining fuel cell power ramp rates to mitigate degradation. High-fidelity simulations are conducted under stochastic wind disturbances and mission-dependent load profiles, including takeoff, climb, cruise, and maneuvering phases. The results show continuous power delivery without unmet load demand. The hybrid system achieves a flight endurance of 220–224 min, consuming a total of 89.99 g of hydrogen at an average rate of 0.398–0.412 g/min, indicating a notable reduction under the considered operating conditions. Additionally, long-term analysis indicates that over 97% of initial endurance is preserved after 100 cycles, demonstrating robustness against fuel cell aging. An analytical real-time feasibility assessment further indicates that the control-oriented formulation is compatible with the computational resources of typical unmanned aerial vehicle-class onboard processors, while the integration of adaptive and robust predictive control techniques is identified as a direction for future work. Full article

When operating in complex and obstacle-dense environments, micro UAV swarms often face severe cooperative positioning failures due to transient non-line-of-sight (NLOS) interference and cascaded inertial sensor drift. To address this, this work proposes a fault-tolerant positioning framework integrating multi-agent deep reinforcement learning with cooperative extended Kalman filtering (MADRL-CEKF). The system incorporates a link-level dynamic soft isolation mechanism that dynamically adjusts observation covariance to effectively sever paths of cooperative error contagion. An adaptive Markov smoothing constraint is mathematically embedded to mitigate high-frequency control jitter typical of AI-driven policies. Crucially, the framework implements a resource-aware multi-objective reward architecture tailored for micro UAVs. Evaluated through high-fidelity simulations and offline physical datasets, the proposed framework achieves a 96.01% reduction in average tracking error (RMSE) under extreme multi-node cascaded failures, completely preventing system divergence. Furthermore, through autonomous multi-objective trade-offs, the system reduces processing delay by 44% (to 25.1 ms) and computational energy consumption by 41% with only a marginal accuracy compromise of 0.16 m, strictly keeping the execution time within the 50 ms real-time threshold. The MADRL-CEKF framework effectively bridges the gap between sophisticated AI decision-making and strict engineering constraints, providing a highly robust and resource-efficient navigation paradigm for swarm robotics. Full article