Search Results (4,151)

This study develops a mathematical optimization framework to determine optimal inspection policies for imperfect production systems subject to stochastic deterioration. System degradation is modeled using a Weibull power law process, which captures the increasing likelihood of transitions from in-control to out-of-control states over time. When deterioration occurs, a reverse-order inspection strategy based on negative binomial sampling is employed, wherein an inspection continues until a predefined number of conforming items is obtained. The proposed model integrates inspection decisions with production learning effects and Bayesian demand updating. Learning-by-doing is incorporated through an experience-dependent production cost function, while demand uncertainty is addressed using Bayesian posterior estimation. A comprehensive expected total cost function is formulated, including production, inspection, inventory holding, warranty, and rework costs. The analytical properties of the model are examined, demonstrating that the expected total cost function is strictly convex with respect to the inspection decision variable. This convexity guarantees the existence and uniqueness of the optimal solution. Numerical experiments and sensitivity analyses illustrate the effects of defect rates, learning parameters, warranty periods, and demand uncertainty on the optimal inspection policy. The results show that jointly optimizing inspection intensity, learning effects, and demand information leads to significant cost reductions and robust decision-making in deteriorating production systems. Full article

Recently, different techniques have been proposed for the scheduling and loading problems in cooling plants with chillers in a parallel configuration. Two broad groups can be considered: the online control-based group and the offline optimization-based group. The first group is exemplified by Model Predictive Control, where the selection of control moves provides a solution to both scheduling and loading. The second group includes Optimal Chiller Loading and Optimal Chiller Sequencing algorithms. They usually derive operating plans with some lead time in a batch-like fashion for long horizons. Both groups use forecasts of important factors such as the cooling demand and ambient conditions; hence, they have to deal with inaccuracies in the forecasts. In this paper, a comparison among these two groups is made considering demand uncertainties. The severity of the uncertainty is shown to play a role in the results as well as the controller tuning in the case of the predictive approach. The results are favorable to OCS with respect to overall consumption (up to 15%) but uses more on/off changes in the chiller’s operation (double in some cases). Full article

This study develops a comprehensive workflow for the analytical seismic vulnerability and structural performance assessment of a special-importance steel building located in a region of elevated seismic hazard in southern Spain. The work addresses the need for reliable analytical methodologies for facilities that must remain operational during earthquakes. The proposed framework integrates a probabilistic seismic hazard assessment, including uniform hazard spectra and hazard disaggregation to identify control earthquakes. Additionally, an analytical vulnerability assessment under the Spanish seismic design code, NCSE-02, is performed. Operational modal analysis and nonlinear analysis are combined to retrofit the numerical model of the building and capture the building’s realistic seismic response. The resulting demand spectra are derived from site-specific ground-motion scenarios for Los Barrios (Cádiz, Spain). Retrofitting strategies are designed and assessed to ensure compliance with the code-defined performance requirements. Results indicate that the retrofitted model reproduces the building’s dynamic behaviour with improved reliability, and that the strengthening interventions enhance seismic performance while still allowing moderate damage in specific components. These findings highlight the importance of analytical vulnerability approaches and code-oriented retrofitting when evaluating the seismic performance and vulnerability of essential facilities. The study demonstrates that rigorous analytical methods provide a robust basis for defining seismic vulnerability in special-importance buildings and support improved decision-making for structural safety and resilience. Full article

Centella asiatica has emerged as a strategic biomass for the sustainable production of high-value biochemicals at the interface of traditional medicine and modern biotechnology. This review consolidates the current knowledge on its phytochemical diversity, emphasizing triterpenoid saponins—asiaticoside, madecassoside, asiatic acid, and madecassic acid—as core bioactive molecules relevant to pharmaceutical, dermatological, nutraceutical, and functional-ingredient applications. Advances in green extraction technologies, including ultrasound-assisted, microwave-assisted, ohmic-heating, and supercritical CO₂ systems, have demonstrated superior efficiency in recovering high-purity biochemicals while significantly reducing solvent use, energy demand, and environmental impact compared with conventional methods. Complementary analytical and standardization platforms, such as HPLC, UPLC, and GC–MS, enable rigorous quality control across the entire value chain, supporting the development of reproducible and regulatory-compliant biochemical extracts. From a biomass valorization and biorefinery perspective, C. asiatica offers multiple metabolite streams that align with circular economy and field-to-market sustainability principles. Key challenges remain, including agronomic variability, scaling up green extraction, and supply chain resilience. However, emerging solutions, such as Good Agricultural and Collection Practices (GACP) guided cultivation, plant tissue culture, metabolic engineering, and integrated biorefinery frameworks, show strong potential for establishing a reliable and environmentally responsible production system. Collectively, C. asiatica represents a model species for sustainable biochemical production, combining scientific efficacy with industrial, economic, and ecological relevance. Full article

Coordinated control across multiple signalized intersections is essential for mitigating congestion propagation in urban road networks. However, existing DQN-based approaches often suffer from unstable action switching, limited interpretability, and insufficient capability to model spatial spillback between adjacent intersections. To address these limitations, this study proposes a Policy-Regulated and Aligned Deep Q-Network (PRA-DQN) for cooperative multi-intersection signal control. A differentiable policy function is introduced and explicitly trained to align with the optimal Q-value-derived target distribution, yielding more stable and interpretable policy behavior. In addition, a cooperative reward structure integrating local delay, movement pressure, and upstream–downstream interactions enables agents to simultaneously optimize local efficiency and regional coordination. A parameter-sharing multi-agent framework further enhances scalability and learning consistency across intersections. Simulation experiments conducted on a 2 × 2 SUMO grid show that PRA-DQN consistently outperforms fixed-time, classical DQN, distributed DQN, and pressure/wave-based baselines. Compared with fixed-time control, PRA-DQN reduces maximum queue length by 21.17%, average queue length by 18.75%, and average waiting time by 17.71%. Moreover, relative to classical DQN coordination, PRA-DQN achieves an additional 7.53% reduction in average waiting time. These results confirm the effectiveness and superiority of the proposed method in suppressing congestion propagation and improving network-level traffic performance. The proposed PRA-DQN provides a practical and scalable basis for real-time deployment of coordinated signal control and can be readily extended to larger networks and time-varying demand conditions. Full article

Rice seed variety classification is crucial for seed quality control and breeding, yet practical deployment is often limited by the computational and memory demands of modern deep models. We propose SimpleEfficientCNN (SimpleEfficient: simple & efficient; CNN: convolutional neural network), an ultra-lightweight convolutional network built on depthwise separable convolutions for efficient fine-grained seed classification. Experiments were conducted on three datasets with distinct imaging characteristics: a self-constructed Guangdong dataset (7 varieties; 10,500 seeds imaged once and expanded to 112 K images via post-split augmentation), the public M600 rice subset (7 varieties; 9100 original images expanded to 112 K images using the same post-split augmentation pipeline for scale-matched comparison), and the International dataset (75 K images; official train/validation/test split provided by the original release and used as-is without any preprocessing or augmentation, 5 varieties). SimpleEfficientCNN achieved 98.52%, 88.07%, and 99.37% accuracy on the Guangdong, M600, and International test sets, respectively. With only 0.231 M parameters (≈92× fewer than ResNet34), it required 20.5 MB peak GPU memory and delivered 2.0 ms GPU latency (RTX 4090D, batch = 1, FP32) and 1.8 ms single-thread CPU median latency (Ryzen 9 7950X3D, batch = 1, FP32). These results indicate that competitive accuracy can be achieved with substantially reduced model size and inference cost, supporting deployment in resource-constrained agricultural settings. Full article

Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals are driving the development of precision agriculture solutions. In this context, early disease detection is crucial; however, current visual inspection methods are hindered by subjectivity, cost, and delayed symptom recognition. This study presents a fully autonomous robotic platform developed within the Agrimet project, enabling continuous, high-frequency monitoring in vineyard environments. The system integrates a tracked mobility base, multimodal sensing using RGB-D and thermal cameras, an AI-based perception framework for leaf localisation, and a compliant six-axis manipulator for biological sampling. A custom control architecture bridges standard autopilot PWM signals with industrial CANopen motor drivers, achieving seamless coordination among all subsystems. Field validation in a Sicilian vineyard demonstrated the platform’s capability to navigate autonomously, acquire multimodal data, and perform precise georeferenced sampling under unstructured conditions. The results confirm the feasibility of holistic robotic systems as a key enabler for sustainable, data-driven viticulture and early disease management. The YOLOv10s detection model achieved good precision and F1-score for leaf detection, while the integrated Kalman filtering visual servoing system demonstrated low spatial tolerance under field conditions despite foliage sway and vibrations. Full article

This study evaluates the On-Demand All-Red Interval (ODAR) at signalized intersections to address red-light running (RLR) issues. Traditional fixed all-red intervals fail to adapt to dynamic traffic conditions, leading to potential safety risks and unnecessary delays. This study introduces a novel approach for dynamically extending the all-red interval on demand to enhance intersection efficiency while maintaining safety by eliminating unnecessary clearance intervals when no risk exists. Utilizing software-in-the-loop simulation, the study assesses the effectiveness of the ODAR method compared to conventional fixed-duration and Dynamic All-Red Extension (DARE) methods, allowing realistic controller testing without field deployment. The ODAR method adapts to real-time traffic conditions by incorporating vehicle speed and signal timing, ensuring vehicles with high collision risk clear the intersection safely. The study is conducted using a microsimulation model based on the Washington Street arterial network in Lake County, Illinois, validated against real traffic conditions. The results demonstrate that ODAR increases throughput and, in specific scenarios, reduces delays and stop occurrences compared to FAR and DARE strategies, based on a field-calibrated microsimulation dataset of a real-world arterial corridor. Importantly, these efficiency improvements are achieved while maintaining comparable intersection safety outcomes, as measured by red-light-running events, conflict frequency, and conflict severity. Full article

Growing pressure on electricity grids and the increasing availability of smart meter data have intensified the need for accurate, interpretable, and scalable methods to analyze and forecast household electricity consumption. In this context, this study presents a general, data-agnostic methodology for predicting and classifying household energy consumption. The proposed workflow unifies data preparation, feature engineering, and machine learning techniques (including clustering, classification, regression, and time series forecasting) within a single interpretable pipeline that supports actionable insights. Rather than proposing new prediction algorithms, this work contributes a fully reproducible, end-to-end methodological pipeline that enables the controlled evaluation of the impact of contextual variables, customer segmentation, and cold-start conditions on household energy forecasting. A distinctive aspect of the pipeline is the explicit use of household- and dwelling-level contextual variables to derive customer typologies via clustering and to enrich forecasting models. The models are evaluated for predictive accuracy, reliability under varying conditions, and suitability for operational use. The results show that incorporating contextual variables and clustering significantly improves forecasting accuracy, particularly in cold-start scenarios where no historical consumption data are available. Although numerous public datasets of residential electricity consumption exist, they rarely provide, in an openly accessible form, both detailed load histories and rich contextual attributes, while many are subject to privacy or licensing restrictions. To ensure full reproducibility and to enable controlled experiments where contextual variables can be switched on and off, the experiments are conducted on a synthetically generated dataset that reproduces realistic behavior and seasonal usage patterns. However, the proposed methodology is independent of the specific data source and can be directly applied to any real or synthetic dataset with similar structure. The approach enables applications such as short- and long-term demand forecasting, estimation of household energy costs, and forecasting demand for new customers. These findings demonstrate that the proposed pipeline provides a transparent and effective framework for end-to-end analysis of household electricity consumption. Full article

In the face of escalating energy demand, this research proposes a demand-side management (DSM) strategy that focuses on appliance-level load shifting in residential environments. The proposed approach utilizes detailed energy consumption forecasts that are generated by ensemble machine learning models, which predict usage at both whole-household and individual appliance levels. This granular forecasting enables the development of customized load-shifting schedules for controllable devices. These schedules are optimized using a metaheuristic genetic algorithm (GA) with the objectives of minimizing consumer energy costs and reducing peak demand. The iterative nature of GA allows for continuous fine-tuning, thereby adapting to dynamic energy market conditions. The implemented DSM technique yields significant results, successfully reducing the daily energy consumption cost for shiftable appliances. Overall, the proposed system decreases the per-day consumer electricity cost from 237 cents (without DSM) to 208 cents (with DSM), achieving a 12.23% cost saving. Furthermore, it effectively mitigates peak demand, reducing it from 3.4 kW to 1.2 kW, which represents a substantial 64.7% reduction. These promising outcomes demonstrate the potential for substantial consumer savings while concurrently enhancing the overall efficiency and reliability of the power grid. Full article

To address the increasing demands for faster response and higher control accuracy in the braking systems of electric and intelligent vehicles, a novel brake-by-wire actuation unit and its braking force control methods are proposed. The braking unit employs a permanent-magnet linear motor as the driving actuator and utilizes the lever-based force-amplification mechanism to directly generate the caliper force. Compared with the “rotary motor and motion conversion mechanism” configuration in other electromechanical braking systems, the proposed scheme significantly simplifies the force-transmission path, reduces friction and structural complexity, thereby enhancing the overall dynamic response and control accuracy. Due to the strong nonlinearity, time-varying parameters, and significant thermal effects of the linear motor, the braking force is prone to drift. As a result, achieving accurate force control becomes challenging. This paper proposes a model-free adaptive control method based on compact-form dynamic linearization. This method does not require an accurate mathematical model. It achieves dynamic linearization and direct control of complex nonlinear systems by online estimation of pseudo partial derivatives. Finally, the proposed control method is validated through comparative simulations and experiments against the fuzzy PID controller. The results show that the model-free adaptive control method exhibits significantly faster braking force response, smaller steady-state error, and stronger robustness against external disturbances. It enables faster dynamic response and higher braking force tracking accuracy. The study demonstrates that the proposed brake-by-wire scheme and its control method provide a potentially new approach for next-generation high-performance brake-by-wire systems. Full article

Natural ventilation, as a key passive strategy in building energy-efficient design, holds potential for reducing energy consumption and improving indoor air quality in high-rise office buildings and contributes directly to the advancement of sustainable urban development. However, its application in cold regions during winter is constrained by the conflict between low outdoor temperatures and indoor heating demands. Perforated panel external windows, as a novel ventilation form, can maintain the integrity and safety of the building curtain wall while ensuring ventilation rates through reasonable perforation design. Nevertheless, their ventilation performance and winter applicability lack systematic research. This paper combines wind tunnel tests and Computational Fluid Dynamics (CFD) simulations to validate the effectiveness of the porous medium model in simulating ventilation through perforated panels and systematically analyzes the impact of window opening size and perforation rate on ventilation effectiveness. Furthermore, taking Beijing as an example, the study explores ventilation effectiveness and the indoor thermal environment under different window opening forms and proportions during winter in cold regions. Results indicate that ventilation effectiveness primarily depends on the effective ventilation area and has little correlation with the window opening size. Under winter conditions, rationally controlling the window opening proportion and perforation rate can achieve effective ventilation while maintaining the indoor minimum temperature (≥18 °C). The ventilation strategies proposed in this paper provide a theoretical basis and practical guidance for the natural ventilation design of high-rise office buildings that balances energy savings and comfort during the cold season. The proposed ventilation strategies provide practical guidance for sustainable design in high-rise office buildings, offering a viable pathway toward energy-saving, healthy, and climate-responsive built environments during the heating season. Full article

Multi-axle vehicles are commonly used for heavy-duty special operations, which easily leads to high driving torque demands when adopting distributed electric drive configurations. This study achieves the objective of reducing the driving torque of each in-wheel motor while controlling the stability of multi-axle vehicles. Taking a five-axle distributed drive test vehicle as the research object, a hierarchical control strategy integrating active all-wheel steering and direct yaw moment control is proposed. The upper layer is implemented based on model predictive control, with fuzzy control introduced to dynamically adjust control weights; the lower layer accomplishes the allocation of targets calculated by the upper layer through minimizing the objective function of tire load ratio. A linear parameter varying (LPV) tire model is introduced into the vehicle model to improve the calculation accuracy of tire lateral forces, and a neural network method is employed to solve the real-time performance issue of the model predictive control (MPC) controller. The proposed strategy is verified through a combination of simulation and real vehicle tests. High-speed condition simulations demonstrate that the AWS/DYC strategy significantly outperforms the ARS/DYC approach: compared to the active rear-wheel steering strategy, while the sideslip angle is reduced by 90.98%, the peak driving torque is reduced by 30.78%. Notably, tire slip angle analysis reveals that AWS/DYC maintains relatively uniform slip angle distribution across axles with a maximum of 4.7°, entirely within the linear working region, optimally balancing tire performance utilization with lateral stability while preserving safety margin, whereas ARS/DYC causes slip angles to exceed 11.9° at the rear axle, entering saturation. Low-speed real vehicle tests further confirm the engineering applicability of the strategy. The proposed method is of significant importance for the application of distributed drive configurations in the field of special vehicles. Full article

Background/Objectives: Older adults with hearing loss frequently report increased listening effort and fatigue, particularly in complex auditory environments. These subjective experiences may reflect increased cognitive resource allocation during both auditory and visual tasks, yet the impact of hearing aids on task-related effort and fatigue remains unclear. This study examined subjective effort and fatigue in experienced older adult hearing aid users while completing cognitively demanding auditory and visual tasks in quiet and background noise, with and without hearing aids. Methods: Thirty-one adults aged 60–87 years completed a cognitive battery assessing inhibition, attention, executive function, and auditory and visual working memory across four listening conditions: aided-quiet, unaided-quiet, aided-noise, and unaided-noise. Subjective effort was measured using the NASA Task Load Index, and task-related fatigue was assessed using a situational fatigue scale. Linear mixed-effects models controlled for age and pure-tone average hearing thresholds. Results: Participants reported significantly lower effort and fatigue in quiet compared to background noise, regardless of hearing aid use. The aided-quiet condition was rated as the least effortful and fatiguing, whereas the unaided-noise condition was rated as the most demanding. Subjective effort and fatigue were moderately to strongly correlated across conditions, particularly in noise. Auditory working memory performance was significantly associated with subjective fatigue across listening conditions, while visual working memory was not associated with effort or fatigue. Hearing aid use did not produce significant reductions in effort or fatigue across conditions. Conclusions: Background noise substantially increases perceived task-related effort and fatigue during cognitively demanding auditory and visual tasks in older adults with hearing loss. While hearing aids did not significantly reduce effort or fatigue across conditions, optimal listening environments were associated with the lowest subjective reports. Auditory working memory emerged as a key factor related to fatigue, highlighting the interplay between hearing, cognition, and subjective listening experiences in older adulthood. Full article

Background: Primary school teachers are experiencing unprecedented occupational stress due to technological demands, varied student needs, and the enduring psychological effects of the COVID-19 pandemic. Although burnout research is extensive globally, evidence regarding Greek primary educators remains scarce, particularly in post-pandemic contexts where Mediterranean cultural values, economic constraints, and centralized governance may yield unique patterns. Methods: This cross-sectional study examined professional burnout among 102 primary school teachers in Achaia prefecture, Greece, during autumn 2022. The Greek-validated Maslach Burnout Inventory-Educators Survey assessed emotional exhaustion, depersonalization, and personal accomplishment. The psychological impact of COVID-19 was evaluated alongside demographic and occupational factors. Analyses included descriptive statistics, nonparametric tests, correlation analyses, hierarchical clustering, and multiple regression models. Results: The sample exhibited mixed burnout profiles, with 42.2% indicating low emotional exhaustion (while 35.3% showed high levels) and 67.6% showing minimal depersonalization. Bivariate analysis revealed that the psychological burden of COVID-19 was significantly correlated with depersonalization (r = 0.339, p < 0.001) but not with emotional exhaustion (r = 0.078, ns) or personal achievement. However, multivariate regression controlling for demographic factors revealed a suppression effect: pandemic burden emerged as the strongest predictor of emotional exhaustion (β = 0.52, p < 0.001), while its association with depersonalization became non-significant. Cluster analysis identified four distinct profiles: Emotionally Strained (49.0%), Resilient (32.4%), Detached (15.7%), and At-Risk (2.9%). Gender significantly predicted emotional exhaustion (model R² = 0.136), while rural location and years of service predicted depersonalization (model R² = 0.225). Conclusions: Greek primary school teachers demonstrated remarkable resilience after the pandemic, maintaining professional effectiveness despite emotional challenges. The suppression effect observed for COVID-19 burden—significantly associated with depersonalization bivariately but with emotional exhaustion multivariately—highlights the importance of examining both direct and demographically mediated stress pathways. The dimensional independence observed, particularly personal achievement's resilience to external stressors, contests unified burnout models and indicates that targeted interventions addressing specific burnout dimensions may be more effective than holistic approaches. Full article