Search Results (11,317)

This study provides a computational and experimental assessment of the parametric reliability of friction pairs in brake devices for lifting and transport technology. The computational and experimental method for assessing the parametric reliability of various friction units in lifting and transport equipment, based on operational and experimental research data with their permissible dynamic and thermal load of disc-drum and band-shoe brake devices, was developed to evaluate the influence of coupled dynamic and thermal loads on brake friction pair reliability. The operational parameters of the band-shoe brake, considered as a multi-pair friction system, were classified into four functional groups. The external operational parameters of their materials are determined, and the relationship between dynamic and thermal processes, phenomena and effects is established. A new quantitative indicator, the relative coefficient of parameter (RCP), was introduced to characterize the stochastic variability in friction pair performance and to assess parametric reliability. The meaning of parametric reliability for friction pairs is revealed with the subsequent classification of parameters that fit into the structural diagram of its computational and experimental assessment. Based on reliability conditions, the optimal thickness of the transverse or longitudinal section of the metal friction element has been determined, ensuring it does not exceed the permissible level of thermal stress. Experimental investigations of drilling rig winch brakes demonstrated that the RCP values ranged from 0.008 to 0.051 depending on operating conditions. The proposed probabilistic model enables the optimization of metal friction element dimensions while maintaining thermal stresses below critical levels. Full article

Regenerative chatter severely limits productivity in machining operations, and tuned mass dampers (TMDs) are widely used for passive chatter suppression. However, most existing TMD designs neglect workpiece dynamics and rely on two-degree-of-freedom assumptions, leading to suboptimal performance when tool and workpiece dynamics are comparable. This paper presents a three-degree-of-freedom analytical stability model incorporating the coupled dynamics of the cutting tool, workpiece, and TMD. Stability lobes are derived in the frequency domain, and a max–min optimization strategy is proposed to determine optimal TMD parameters across varying workpiece dynamic conditions. The results indicate that variation in workpiece dynamics significantly hinders the improvement in machining stability achieved by the TMD, and its effectiveness is drastically affected when the cutting tool and workpiece have similar dynamic characteristics. To enhance TMD effectiveness in changing workpiece dynamic conditions, tuning parameters should be optimized to reflect these variations. The proposed analytical and optimization framework may provide a basis for future adaptive chatter-control systems that identify changes in tool–workpiece dynamics online and use them to determine appropriate absorber tuning parameters. However, the present study is limited to offline optimization of a passive TMD and does not implement real-time parameter adaptation. Experimental validation using an additively manufactured TMD demonstrates a clear modification of the fundamental dynamic behaviour, wherein the original single resonance is split into two distinct natural frequencies. Though the extent of this frequency separation is marginal, a significant reduction in peak amplitude is observed: 91% in the low-stiffness workpiece and 69.8% in the high-stiffness workpiece, indicating stronger interaction between the absorber and the machining system under compliant conditions. Full article

This study focuses on optimizing the quenching process for insoluble sulfur (IS) production, addressing the issues of high energy consumption and poor safety associated with the existing vaporization method. A process simulation model for a pilot-scale reactor was established using Aspen Plus V11. The research specifically investigated the influence patterns of three key parameters—the inlet temperature of the circulating liquid, the carbon-disulfide-to-sulfur (CS₂/S) mass ratio, and the mass ratio of circulating liquid to sulfur vapor—on the quenching effectiveness. Unlike previous Aspen Plus studies mainly directed toward overall IS production and recovery processes, the present work focuses specifically on the pilot-scale quenching stage and integrates single-factor thermodynamic analysis with a simulation-based orthogonal design to quantitatively rank the effects of the circulating liquid operating parameters. The orthogonal analysis showed that the mass feed ratio of circulating liquid to sulfur vapor was the dominant factor affecting the mixed temperature, followed by the circulating liquid inlet temperature, whereas the CS₂/S mass ratio had a comparatively smaller influence. Based on the simulated quenching behavior and thermodynamic constraints, the proposed process-level operating conditions were a CS₂:S mass ratio of 86:14–88:12, a circulating liquid inlet temperature of 50–56 °C, and a circulating liquid-to-sulfur vapor mass ratio of no less than 260:1. These results provide quantitative guidance for the steady-state optimization of the pilot-scale quenching process. Full article

Voltage spikes are particularly severe in low-voltage high-current inverters due to the influence of parasitic inductance during the turn-off process. A resistor-capacitor (RC) snubber circuit is commonly connected in parallel with the switching device to suppress excessive voltage overshoot. However, the snubber circuit inevitably introduces additional power losses, making the selection of snubber parameters critical for achieving low-loss operation. In this paper, the voltage spike suppression mechanism of the RC snubber circuit is first analyzed. Then, a comprehensive turn-off loss model is established by considering the MOSFET turn-off loss, the RC snubber loss, and the loss associated with the DC-bus parasitic inductance. Based on the proposed model, the influence of the snubber capacitance on the total turn-off loss is investigated. The results show that an optimal capacitance value exists, with which the total switching loss can be minimized. Finally, both simulation and experimental results are presented to validate the proposed loss model and demonstrate the effectiveness of the optimized snubber capacitance in minimizing inverter turn-off losses. Full article

Axial blood pumps serve as vital auxiliary therapeutic devices for patients with end-stage heart failure, and parameter optimization of the controller is critical to improve system performance. Existing optimization methods cannot satisfy the parameter optimization requirements of variable-speed axial blood pump controllers in terms of optimization accuracy. Therefore, this paper investigates a many-objective optimization method adapted to the operating characteristics of blood pumps. Firstly, a many-objective optimization model is established for the controller. To efficiently solve the proposed model, an optimization algorithm integrating deep reinforcement learning, named DQN-NSGA-CT, is developed. On the basis of the population evolution state, the optimal strategy learned by DQN dynamically adjusts the crossover probability and mutation probability, which adaptively balances population diversity in the early iteration stage and convergence speed in the later iteration stage. Meanwhile, a novel environmental selection strategy is used to reconcile population convergence and diversity. To select the best compromise solution, an entropy weight–Copula–TOPSIS comprehensive evaluation method is proposed, which realizes objective weight assignment, objective correlation correction and multi-attribute ranking. Experimental results verify the efficiency of the DQN-NSGA-CT algorithm in solving the many-objective optimization model of the controller. The research addresses the many-objective optimization problem of variable-speed axial blood pump control. Full article

To address the inefficiencies of two-stage sweet potato harvesting in southern China, an integrated machine for synchronous haulm shredding and tuber excavation was developed. The equipment features a front-mounted, reverse-rotating crushing knife roller and a rear-mounted, adjustable grate-type digging shovel. The performance of the prototype was systematically evaluated through two-stage field trials in clay loam soil. First, an orthogonal test was employed to assess the haulm shredding quality. The results indicated that the knife roller speed significantly increased the qualified rate of crushed stems and leaves, whereas the forward speed exerted a negative effect. Additionally, the blade-to-ridge clearance primarily dictated the ridge-top stubble length. Second, a quadratic orthogonal rotational composite design was utilized to optimize the integrated harvesting parameters. The analysis demonstrated that shovel inclination significantly enhanced the tuber exposure rate, while both clearance and inclination exhibited quadratic nonlinear effects on the tuber damage rate. Multi-objective optimization established the optimal operational parameters as a blade-to-ridge clearance of 66.6 mm and a shovel inclination of 34.0°. Field validations under these settings achieved a tuber exposure rate of 83.7% and a damage rate of 4.3%, confirming the high reliability of the predictive models. The integrated equipment effectively shortens the harvesting cycle and demonstrates robust adaptability to clayey moist soils, thereby advancing the mechanization of sweet potato production. Full article

Aquatic antibiotic pollution represented by ofloxacin (OFX) causes serious ecological hazards and endangers public health due to the high persistence and bioaccumulation of antibiotic residues. Conventional water treatment techniques are insufficient for OFX elimination, limited by low removal efficiency, high energy consumption, and poor operational stability. Herein, a novel MoS₂/CuS heterojunction composite was fabricated via a hydrothermal method and applied to an ultrasound-driven piezocatalysis-coupled peroxymonosulfate (PMS) advanced oxidation system for OFX wastewater remediation. The introduction of CuS effectively remedies the inherent shortcomings of pristine MoS₂, including insufficient active sites and rapid photogenerated carrier recombination. The constructed heterojunction induces a strong interfacial built-in electric field, which significantly accelerates the migration of piezoelectric charges. The synergistic photo-piezoelectric effect further promotes continuous PMS activation and facilitates the massive generation of reactive oxygen species (ROS). The influences of key operating parameters and common water inorganic anions on OFX degradation performance were systematically investigated. Radical trapping experiments confirmed the synergistic mechanism between piezocatalysis and PMS activation during the catalytic reaction. The optimized MoS₂/CuS heterojunction exhibits remarkable OFX degradation efficiency and excellent cyclic stability. This work provides a feasible strategy for the rational design and fabrication of high-efficiency piezocatalysts and offers a promising technical route for the remediation of refractory antibiotic wastewater via piezocatalysis-coupled PMS advanced oxidation. Full article

Background/Objectives: Neonatal sepsis remains a major cause of morbidity and mortality worldwide, underscoring the critical need for early and accurate diagnostic biomarkers. This study aimed to evaluate the predictive value of the red cell distribution width-to-albumin ratio (RAR) for the diagnosis of neonatal sepsis and to compare its diagnostic performance with the C-reactive protein-to-albumin ratio (CAR) and other hematological parameters. Methods: This retrospective cohort study was conducted in the Neonatology Department of Karadeniz Technical University Faculty of Medicine between January 2015 and June 2025. A total of 874 neonates with a gestational age > 36 weeks were included: 411 with blood culture-confirmed sepsis (108 Gram-negative, 303 Gram-positive) and 463 healthy controls. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis, with optimal cut-off values determined by the Youden index. Results: RAR demonstrated modest diagnostic accuracy for neonatal sepsis, with an area under the curve (AUC) of 0.647 (95% CI: 0.61–0.69), a cut-off value of 5.108, sensitivity of 50.7%, and specificity of 76.7%. RAR was significantly elevated in the Gram-negative group compared with Gram-positive cases and controls (p < 0.001). However, the clinical significance of this subgroup difference requires further investigation. In multivariable logistic regression analysis, RAR remained independently associated with culture-confirmed neonatal sepsis after adjustment for gestational age, birth weight, sex, and mode of delivery (aOR = 2.190, 95% CI: 1.838–2.608, p < 0.001). Among all evaluated markers, CAR achieved the highest diagnostic performance (AUC: 0.981, sensitivity: 88.5%, specificity: 98.7%, PPV: 98.4%, NPV: 90.7%, LR+: 68.47), followed by C-reactive protein (AUC: 0.978), while neutrophil-to-lymphocyte ratio showed the lowest discriminatory ability (AUC: 0.592). Conclusions: RAR showed limited standalone discriminatory performance for culture-confirmed neonatal sepsis, although its association with sepsis remained significant after adjustment for relevant demographic and perinatal factors. CAR demonstrated significantly greater discriminatory performance than RAR. Further studies are needed to determine whether RAR provides incremental value when incorporated into multivariable diagnostic models. Full article

High-moisture rice straw processed by whole-feed combine harvesters often exhibits high cutting resistance and uneven particle size distribution after comminution. To address these issues, a straw comminution device integrated with the straw outlet of the threshing and cleaning system was developed, and the shear fracture mechanism and operating parameters were investigated. The geometric characteristics, density, contact properties, and bending properties of rice straw cultivars Yongyou 7301 and Kenuigeng 1 were measured. A hollow flexible straw discrete element model was established using the Hertz–Mindlin with Bonding contact model, and its parameters were calibrated and validated through quasi-static shear cutting tests. The effects of shear cutting angle on maximum cutting force, bond failure evolution, and load transfer behavior were analyzed at shear angles of 30°, 45°, and 60°. Device-scale DEM simulations combined with field experiments were further conducted to optimize the guide plate angle and rotor speed. The results showed that the maximum cutting force under quasi-static single-stalk cutting conditions initially decreased and then increased with increasing shear angle. At a shear angle of 45°, the maximum cutting force was 78 N, representing a 44.8% reduction compared with that at 30°. Meanwhile, the fracture zone expanded along the blade sliding direction and stress concentration was alleviated. The DEM model effectively characterized the fracture behavior of rice straw, with an average relative error of 11.07% between simulated and experimental cutting forces. The optimized operating parameters under the tested conditions were a shear angle of 45°, guide plate angle of 55°, and rotor speed of 2500 r/min, resulting in average chopped lengths of 17.3 mm in simulation and 20.5 mm in field experiments, with a comminution qualification rate of 95.26%. These findings provide theoretical support for improving the fracture characteristics and chopping performance of straw comminution systems. Full article

Wastewater treatment systems are under increasing pressure to improve nitrogen removal while reducing carbon emissions, yet the deployment of anaerobic ammonium oxidation (anammox) remains constrained by uncertainty about technical readiness, operational robustness, nitrous oxide (N₂O) emissions, life-cycle carbon performance, monitoring capacity, and transferability across wastewater contexts. This study uses dynamic topic modelling and trend assessment of 998 publications from 2001 to 2025 to synthesize deployment-relevant evidence for anammox-based wastewater treatment. The results indicate that the field has shifted from reactor start-up and process-parameter optimization toward microbial regulation, mainstream process integration, coupled nitrogen-removal strategies, and intelligent control. Building on these topic-evolution patterns and reported engineering evidence, this study provides an evidence-based qualitative appraisal of deployment-readiness signals and evidence gaps, distinguishing comparatively mature side-stream applications from mainstream systems that still require monitored demonstrations, transparent N₂O accounting, life-cycle assessment, and locally validated operating data. The study argues that anammox should be evaluated as a technology with potential but conditional carbon-saving benefits: its potential carbon-saving benefits depend on operational evidence specific to each application stage, carbon-accounting credibility, and implementation capacity, rather than assuming that research activity alone justifies broad deployment. Full article

Accurate estimation of SOC for lithium-ion batteries is a very important job in battery management systems, but under complex dynamic operating conditions, model misalignment often happens, and filtering algorithms usually do not make enough use of historical data, so the estimation accuracy is lowered. This paper puts forward a lithium-ion battery SOC estimation method that is based on weighted multi-innovation unscented Kalman filtering (WMIUKF); a hybrid parameter identification strategy that combines FFRLS and PSO is introduced to supply initial values for the global optimization of the model and to track dynamic drifts. To deal with the problems that the unscented Kalman Filter (UKF) does not make effective use of historical information and lacks an adaptive correction mechanism, multi-innovation theory and exponentially decaying weighting factors are incorporated into it; then, by fusing current and historical multi-step prediction residuals, a weighted freshness matrix can be constructed, and through this the method, we can improve the utilization efficiency of historical data and the system’s ability to resist interference. The performance of the proposed algorithm was validated through comparative experiments under various typical dynamic operating conditions, as well as at different temperatures (0 °C–45 °C) and discharge rates (0.5 C–2 C). The results indicate that the PSO-FFRLS hybrid parameter identification effectively improves model accuracy; compared to the UKF, MIUKF, and PSO-MIUKF algorithms, the WMIUKF achieved optimal SOC tracking under all types of dynamic operating conditions, with a root mean square error (RMSE) of no more than 0.58%. Even under extreme temperatures and high-rate discharge conditions, the error remained stable at a low level, demonstrating good environmental adaptability and robustness. Full article

Accurate cross-dataset state-of-health prediction for lithium-ion batteries remains challenging due to distribution shifts arising from diverse cathode chemistries, operating temperatures, and charge–discharge protocols across heterogeneous battery fleets. Drawing upon established machine learning paradigms, this study tailors a Domain-Adaptive Mixture-of-Experts (DA-MoE) framework to the battery prognostic context, automatically selecting the optimal domain adaptation strategy for each target domain through a physics-aware, lightweight linear gating network comprising merely 32 learnable parameters. The framework integrates a shared Transformer-based backbone with four adaptation strategies spanning the full spectrum of target-domain information utilization, namely zero-shot transfer, Test-Time Adaptation, Fine-Tuning, and Model-Agnostic Meta-Learning. A comprehensive evaluation on 564 battery cells from seven publicly available datasets under Leave-One-Domain-Out Cross-Validation protocol demonstrates that the proposed framework achieves an average coefficient of determination of 0.864 with perfect oracle strategy alignment under full domain training and maintains competitive generalization at an average R² of 0.795 when each target domain is held out during gating network training. Hard argmax selection consistently outperforms weighted fusion across all seven domains with an average margin of +0.027 in R², confirming that the four adaptation strategies compete rather than cooperate in this application context. A feature ablation analysis identifies sample count as the dominant determinant of strategy selection with performance degradation of ΔR² = −0.182 upon removal, followed by the early-cycle degradation slope and early-cycle nonlinearity index as secondary signals, all of which are computable at deployment time without future ground-truth SOH information. The proposed framework provides a practically deployable solution for battery management systems operating across heterogeneous fleets with minimal computational overhead and strong cross-dataset generalization capability. Full article

Digital twins (DTs) based on agent-based artificial intelligence (Agentic AI) provide a transformative framework for streamlining nondestructive testing (NDT) of 3D-printed rehabilitation equipment. This study applies a conceptual research methodology based on the integration and analysis of recent advances in Agentic AI, digital twin architectures, additive manufacturing, NDT technologies, and intelligent rehabilitation systems to establish a framework for autonomous quality monitoring and lifecycle management of 3D-printed medical devices. By creating intelligent virtual replicas of physical devices, these systems enable continuous monitoring of structural integrity, functional performance, and degradation mechanisms throughout the product lifecycle. Unlike conventional AI-based DTs, Agentic AI-driven DTs can autonomously perceive, reason, plan, and execute corrective actions based on real-time sensor data, NDT results, manufacturing information, and historical knowledge. The main conclusion of this work is that Agentic AI-enhanced DTs have the potential to transform NDT from a passive inspection approach into an intelligent, predictive, and autonomous decision-support system for rehabilitation equipment. Advanced machine learning and autonomous decision-making algorithms enable the identification of early signs of material degradation, manufacturing defects, fatigue accumulation, and performance anomalies, supporting predictive maintenance and proactive quality assurance. Integrating Agentic AI DTs with additive manufacturing processes enables real-time optimization of printing parameters, adaptive process control, and continuous refinement of inspection strategies without production interruption or destructive sampling, thereby supporting Industry 4.0 and smart manufacturing principles. The main innovation of this research lies in proposing an autonomous closed-loop framework that combines Agentic AI, DTs, additive manufacturing, and NDT into a unified system capable of continuous learning, reasoning, and operational optimization. Compared with existing studies that mainly focus on AI-assisted defect detection or static digital twin models, this approach introduces autonomous agents capable of coordinating sensing, simulation, diagnosis, prediction, and corrective actions across the entire lifecycle of 3D-printed rehabilitation devices. The proposed concept extends current digital twin applications by incorporating virtual stress testing, autonomous simulation, patient-specific customization, and adaptive device management, reducing dependence on physical prototypes, minimizing material waste, and accelerating design validation. By combining autonomous reasoning with predictive analytics, Agentic AI-based DTs represent a next-generation solution for intelligent, adaptive, and sustainable nondestructive testing, advancing both additive manufacturing technologies and personalized rehabilitation engineering. Full article

The aquatic products industry has experienced rapid development due to the growing global demand for high-quality proteins. However, conventional production models remain constrained by multifaceted impediments, including disease outbreaks, environmental stressors, microbial contamination, and operational inefficiencies in processing, quality control, and logistics. Artificial intelligence (AI) offers targeted methodological solutions to these challenges. From a functional perspective, this review categorizes artificial intelligence into four major types: perception, prediction, control, and generation, and systematically evaluates its application progress in aquaculture, processing, quality inspection, and traceability fields. Perception AI constitutes the data acquisition and digitization layer, utilizing computer vision, sonar, and multimodal fusion technologies to establish digital mappings from environmental parameters to biological indicators. Prediction AI employs machine learning and deep learning algorithms to transform historical datasets into quantitative forecasts regarding water quality dynamics, disease risks, and production trends. Control AI translates decision-making protocols into precise, autonomous regulatory actions for aquaculture environments and processing workflows through fuzzy logic and model-based predictive control. Generation AI leverages large language models and generative adversarial networks to demonstrate innovative capabilities in data augmentation, solution optimization, and virtual simulation. Collectively, these applications optimize core production processes while significantly enhancing product quality, processing efficiency, safety management, and traceability systems. Future research directions will prioritize the development of robust, interdisciplinary AI technologies with superior integration capabilities. Full article

This paper applies the von Mises family of circular distributions to model seasonal timing of heavy rainfall extremes in Kimberley, South Africa. Unlike conventional modelling approaches that emphasize the modelling of mean rainfall, circular modelling captures phase-specific characteristics, such as the onset, peak clustering, and transitional shoulders of extreme rainfall. We consider three variants of the von Mises family of distributions, namely the standard von Mises, generalized von Mises, and sine-skewed von Mises, and then use the maximum likelihood estimation with box-constrained optimization to estimate parameters. The results show that the standard von Mises distribution consistently outperforms more complex alternatives, thus providing the most parsimonious and reliable representation of Kimberley’s summer rainfall regime. Furthermore, the results reveal a dominant seasonal peak in January–February, a recurrent onset in November, and a dry season in June–July, which confirms the episodic and clustered nature of rainfall in semi-arid regions. Phase-shift analyses further indicate that there is approximately a 19-day delay in the seasonal peaks between baseline and monitoring periods, alongside stronger clustering of extremes. Although formal circular tests did not detect statistically significant differences, the phase analysis highlights emerging tendencies toward a later and more compressed rainfall season. These findings have practical implications for flood preparedness, reservoir operations, and agricultural scheduling, where timing rather than totals drives risk and resilience. This study demonstrates the methodological importance of circular statistics in extreme rainfall phase analysis, thus bridging a critical gap in semi-arid climate studies. Full article