Search Results (238)

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

This paper develops a boundary-sensitive asymptotic theory for nonparametric conditional U-statistics smoothed by support-adapted asymmetric kernels when the response variable is subject to Missing-at-Random observation. The problem lies at the intersection of three well-established but traditionally separate lines of research: conditional U-statistics, asymmetric smoothing on constrained supports, and incomplete-data inference under MAR sampling. The contribution of the paper is not a novelty claim concerning any of these components in isolation. Rather, it consists in deriving a kernel-specific and MAR-aware limit theory for their simultaneous occurrence, where the estimators are nonlinear complete-case ratios of localized U-statistics and the localization devices are point-dependent approximate identities adapted to the geometry of the covariate support. The analysis covers three principal classes of support-respecting smoothers: Dirichlet kernels on the simplex, Bernstein polynomial smoothers, and multivariate beta kernels on hypercubes, with an additional extension to mixed continuous–categorical regressors. These smoothing schemes are not translation-invariant, and their local moments, effective support, normalizing constants and $L^2$-masses vary with the evaluation point, especially near the boundary. Consequently, their incorporation into conditional U-statistics requires more than a direct transfer of ordinary asymmetric-kernel regression theory. The numerator and denominator of the estimators are localized U-statistics whose stochastic expansions are governed by Hoeffding projections, including canonical components that must be controlled uniformly over the conditioning domain. Under regularity, smoothness and positivity assumptions adapted to the MAR setting, we establish uniform consistency, weak and strong uniform convergence rates, stochastic expansions and asymptotic normality. The results are obtained both on fixed compact subsets and on interior regions approaching the boundary, thereby identifying how support geometry enters the bias and stochastic normalizations. A central feature of the theory is the separation between the deterministic effect of complete-case sampling and its stochastic effect. For the complete-case estimator, the natural deterministic equivalent is obtained by replacing the design density f with the effective complete-case density $pf$, where p is the propensity score. Thus, the MAR mechanism may enter higher-order deterministic bias constants through the local design tilt, whereas the leading stochastic dispersion reflects the loss of effective information through propensity score factors. The precise variance constants and normalizing rates remain kernel-specific, depending on the local $L^2$-structure of the Dirichlet, Bernstein or beta smoothing device. The paper should therefore be viewed as a MAR extension and refinement of the complete-data asymmetric-kernel conditional U-statistic theory. It provides a common probabilistic architecture for several boundary-adapted smoothing schemes while retaining the kernel-dependent bias operators, variance constants, boundary regimes and Hoeffding-projection structures required for sharp asymptotic interpretation. Numerical experiments illustrate the finite-sample behavior predicted by the theory and highlight the interaction between support-adapted smoothing, boundary effects and incomplete response observation. Full article

In electric vehicles, power batteries consist of numerous individual cells connected in series or parallel. Variations in manufacturing, operating conditions, and aging can lead to differences among these cells. Such inconsistencies can compromise the battery pack’s performance, safety, and overall service life. Therefore, accurately diagnosing inconsistencies among battery cells is of great significance for enhancing the reliability of the battery system and ensuring the operational safety of the vehicle. To address the limited computational resources available in vehicles, this paper proposes an end-cloud collaborative fault diagnosis framework and validates its effectiveness using real-world vehicle driving data. On the cloud side, a deep learning-based reconstruction network is developed to enable high-precision reconstruction of cell voltages. On the vehicle side, a second-order equivalent circuit model is used to represent battery dynamics. An adaptive forgetting factor recursive least squares method is introduced for online estimation of the model parameters, enabling accurate local prediction of individual cell voltages. Using the cloud-reconstructed and vehicle-predicted cell voltages, the extreme difference value of voltage for each cell is computed. A comprehensive diagnosis of inconsistency faults is then performed by fusing the extreme difference in voltage results from both the cloud and vehicle sides via the Extended Kalman Filter (EKF); threshold judgment is conducted based on the fused results, and the Cumulative Sum (CUSUM) algorithm is designed to identify cell inconsistency faults. Experimental results show that the proposed method effectively detects battery inconsistency faults and demonstrates strong engineering applicability and practical potential. Full article

Illegitimate tasks are assignments that threaten professional identity by not being related to the intrinsic quality or morality of the main profession. This concept has gained attention within the Stress as Offense to Self (SOS) theory, which emphasizes the impact of self-esteem in stressful situations, particularly in the workplace. The SOS theory suggests that self-esteem plays a critical role in how individuals respond to stress: when self-esteem is threatened, it triggers adverse reactions affecting mental, physical, and behavioral dimensions; conversely, strengthening self-esteem promotes well-being. Illegitimate tasks are perceived as unnecessary or unreasonable, varying by profession and non-voluntary in nature, leading to a lack of purpose and meaning for the employee. The Bern Illegitimate Tasks Scale (BITS) was created to assess and quantify these tasks, demonstrating robust psychometric properties across different languages and cultural contexts, including Spanish, Swedish, and Portuguese adaptations. This study aims to translate and adapt the BITS for a public university context characterized by bureaucratic culture. The sample comprises 601 participants from a Portuguese public higher education institution. The translation process followed rigorous procedures to ensure equivalence between the original and Portuguese versions. Data analysis included descriptive statistics, confirmatory factor analysis (CFA), and internal consistency analysis, revealing satisfactory fit indices and high reliability. Despite contextual limitations, the findings affirm the reliability of the adapted scale for application in similar contexts. Future research should aim for more representative samples to enhance generalizability. Full article

As plant-based diets catalyze a global shift toward sustainable consumption, Chinese plant-based food firms are experiencing rapid growth and seeking to expand their international footprint. This study investigates the mechanisms underlying the internationalization performance of these firms by integrating the Technology–Organization–Environment (TOE) framework with a configurational perspective. We operationalize nine antecedents across three dimensions: the technological dimension (technological maturity, supply chain resilience, and digital transformation), the organizational dimension (food safety certification intensity, strategic partnership intensity, and talent acquisition intensity), and the environmental dimension (market adaptability, compliance and risk management, and product line breadth). Utilizing fuzzy-set qualitative comparative analysis (fsQCA) on a sample of N = 29 publicly listed Chinese plant-based firms, this research identifies three distinct equifinal pathways to superior internationalization performance. The first is the Collaboration-Compliance configuration (Organization–Environment-driven), which is primarily characterized by the synergy between strategic partnerships and regulatory risk management. The second is the Supply Chain-Compliance-Product Diversification configuration (Technology-Environment-driven), where international success is predicated on the interplay among supply chain resilience, institutional compliance, and product variety. The third is the Full-Factor Synergy configuration (Technology-Organization-Environment jointly driven), which emphasizes a holistic coupling of technological innovation, organizational coordination, and external institutional adaptation. By uncovering these complex causal mechanisms, this study moves beyond traditional linear analysis to reveal how diverse capability configurations can lead to equivalent internationalization outcomes. The findings provide actionable strategic guidance for firms navigating the global plant-based market and offer theoretical insights for policy frameworks supporting sustainable dietary transitions. Full article

In recent years, multimodal multi-objective optimization problems (MMOPs) have become a hot research topic in the field of evolutionary computation in recent years, whose main goal is to locate all equivalent Pareto-optimal solution sets. Although existing evolutionary multimodal multi-objective algorithms (MMOAs) perform well when there is no obvious difference in the search difficulty of different Pareto-optimal solution sets, they face great challenges when such difficulty differences are prominent, as most current MMOAs fail to effectively address the imbalance of fitness landscapes, leading to an inability to stably find all Pareto-optimal modes and poor robustness in complex MMOPs. To fill this gap, the main objective of this study is to propose a novel MMOA that can adapt to imbalanced fitness landscapes, thereby improving the ability to locate all Pareto-optimal solution sets and enhancing the algorithm’s robustness. To achieve this objective, a novel multimodal multi-objective evolutionary algorithm based on a two-stage fitness learning model is proposed. First, a multi-subpopulation cooperative search strategy is designed. Based on the principle of speciation, this strategy divides the population into several subpopulations, with the formation of each subpopulation guided by individual similarity in the decision space, thereby guiding the population to perform decentralized search across different modes. Second, a two-stage fitness learning model is developed. In the early and middle stages of evolution, individual fitness is evaluated by integrating Pareto dominance strength and density estimates based on the local outlier factor; in the late stage of evolution, individual fitness is evaluated using fast non-dominated sorting and twin-mirror crowding distance. The former is used to balance the convergence and diversity of the population in the decision space, while the latter is used to improve the convergence and diversity of the population in both the decision space and the objective space. Finally, simulation experiments are conducted on 12 imbalanced multimodal multi-objective optimization problems, and the results are compared to those of seven popular evolutionary multimodal multi-objective optimization algorithms. The results demonstrate that the proposed algorithm can find all modes for different problems and exhibits better robustness. Full article

To address the issues of increased energy consumption and reduced engine efficiency in plug-in hybrid electric vehicles (PHEVs) under low-temperature conditions due to cabin heating demands, this paper investigates the coupling characteristics between the powertrain system and the cabin thermal management system and proposes an adaptive energy management strategy tailored for low-temperature environments. First, a comprehensive model incorporating vehicle dynamics, the engine, and the passenger compartment thermal management system was established. The impact of different ambient temperatures and equivalent factors on the system’s energy consumption characteristics was then quantitatively analyzed under WLTC conditions. Based on this, an adaptive strategy for minimizing equivalent fuel consumption that accounts for cabin heating demand was designed. By using real-time cabin heating demand and engine waste heat power as state feedback, the equivalent factor is dynamically adjusted to coordinate the allocation of power between propulsion and heating. Simulation and hardware-in-the-loop test results indicate that the optimized strategy, by promoting early engine engagement and improving waste heat recovery efficiency, reduces PTC energy consumption by 0.47 kWh under −20 °C WLTC conditions, decreases additional fuel consumption caused by low temperatures by approximately 59%, and improves the vehicle’s equivalent fuel economy by 4.6%, while effectively maintaining passenger compartment thermal comfort. This study contributes to sustainable transportation by reducing low-temperature-induced energy waste, lowering equivalent fuel consumption, and promoting efficient use of engine waste heat, thereby supporting carbon emission reduction goals in hybrid electric vehicle operations. Full article

Vortex-induced vibration (VIV) is the main cause of fatigue failure in steel catenary risers (SCRs). This study developed a fluid–structure interaction (FSI) model, combining Reynolds-Averaged Navier–Stokes (RANS)-based computational fluid dynamics (CFD) with the Newmark-β algorithm, to simulate VIV responses under ocean currents and platform heave motion. First, the FSI model analyzed SCR behaviors under steady currents, then was adapted to oscillatory flow mimicking heave motion. A finite element model (FEM) was built, using the simulated VIV response as displacement boundary conditions to compute the equivalent stress time history along the riser. Finally, Miner’s rule was applied to quantify fatigue damage in three scenarios: current-only, heave-only, and the combined action of both factors. The results indicate that, in the South China Sea’s 10-year return period sea state, the SCR experiences a broad vortex-induced resonance interval under ocean current loads, with a maximum vibration amplitude of 0.7D. At the associated resonant height, platform heave motion triggers near-complete lock-in of the SCR’s VIV. The peak fatigue damage induced by ocean currents alone, platform heave motion alone, and their combined action all concentrates at the riser touchdown point (TDP). Over the 600 s VIV response duration, fatigue damage from platform heave motion alone constitutes 8.48% of that caused by ocean currents alone, while the combined action results in fatigue damage 1.847 times that of ocean currents alone. Thus, the combined action significantly amplifies both the magnitude and spatial non-uniformity of VIV-induced fatigue damage in SCRs. Full article

Accurate state of charge (SOC) estimation is essential for reliable battery management systems operating over a wide temperature range. This study proposes a joint estimation framework that combines a temperature-matched dual open-circuit-voltage (OCV)-SOC model, online forgetting-factor recursive least squares (FFRLS), and an adaptive improved multi-innovation unscented Kalman filter (AIMIUKF). The dual OCV-SOC model separately calibrates charging and discharging branches at 0 °C, 25 °C, and 45 °C, reducing the voltage bias caused by thermal dependence and charge–discharge hysteresis. On this corrected voltage baseline, FFRLS identifies the time-varying parameters of the second-order RC equivalent circuit model. The updated parameters are then used by AIMIUKF, where a finite multi-innovation window improves convergence under initial SOC deviation, and covariance matching adjusts process and measurement noise online. Validation on the CALCE 18650 dataset under the Dynamic Stress Test (DST) profile shows sub-1% SOC errors at all tested temperatures. Full article

Academic stress is one of the main challenges affecting the psychological well-being of university students due to its impact on mental health, academic performance, and quality of life. The aim of this study was to analyze and model the factors associated with academic stress by integrating unsupervised and supervised machine learning techniques. The study was conducted with a sample of 605 students from the Universidad Nacional de San Martín (Peru), who completed validated psychometric instruments, including the PSS-10, LASSI, MBI-SS, PSQI, and A-CEA. In the first stage, dimensionality reduction and clustering techniques were applied to identify latent profiles, resulting in four distinct groups reflecting different levels of adaptation and psychological vulnerability. In the second stage, eight supervised regression models were evaluated: Linear Regression, Ridge, Lasso, Elastic Net, Random Forest, Gradient Boosting, XGBoost, and CatBoost. Lasso and Elastic Net showed virtually equivalent performance, achieving coefficients of determination (R²) close to 0.61 on the independent test set. Variable importance analysis revealed that academic burnout, sleep quality, and coping strategies were the main factors associated with perceived stress, together with contextual variables with lower relative importance. Overall, the results confirm the multidimensional nature of academic stress and show that integrating unsupervised and supervised approaches provides a more comprehensive understanding of the phenomenon in university settings. Full article

Intensive agricultural production contributes significantly to greenhouse gas (GHG) emissions, accounting for between 10 and 12% of global anthropogenic emissions, at a time when the agricultural sector is facing increasing pressure to adapt to ever-stricter environmental regulations. This study develops and applies a multi-objective Goal Programming model to identify the optimal mix of crops and management practices that simultaneously minimize the carbon footprint and maximize productivity, at the level of a 300-hectare (ha) model agricultural system in Romania. The life cycle assessment (LCA) methodology, in accordance with ISO 14040/14044 standards and Ecoinvent 3.8 emission factors, was applied to nine crops distributed across three soil types, within four management scenarios, over an annual planning horizon. The unit of measurement used is a ton of CO₂ equivalent per agricultural system. The results show that the optimized configuration achieves near-zero total carbon emissions (0.33 t CO₂eq for the entire farm), reduces synthetic nitrogen inputs to 35.7% of the limit set by the EU Nitrates Directive, and generates water savings of 48%. However, these environmental gains entail a 52.9% production trade-off relative to the maximum target of 3000 tons, highlighting a Pareto-optimal structural conflict between climate and food security objectives. The sensitivity analysis identifies the nitrogen emission factor and crop yield as the most influential parameters. The results confirm the technical feasibility of the European Green Deal targets through systematic mathematical optimization, while also demonstrating that achieving economic parity requires policy support of 110–165 EUR/ha/year. Full article

This paper investigates harmonic compensation for an LCL-filtered cascaded H-bridge (CHB) STATCOM operating in medium-voltage distribution networks under grid-frequency deviations and nonlinear loads. A hybrid current control strategy is proposed by combining a deadbeat (DB) inner-current loop with a Thiran all-pass filter-based frequency-adaptive repetitive controller (FARC). Weighted average inductor current (WAIC) feedback is adopted to reduce the third-order LCL filter to an equivalent first-order plant, thereby simplifying the current loop design while retaining the dominant low-frequency dynamics. The Thiran all-pass fractional delay filter is then embedded in the repetitive controller to realize a noninteger-period internal model at a fixed sampling frequency. This enables the controller to maintain harmonic compensation accuracy when the grid frequency deviates from its nominal value. A 10 kV LCL-filtered CHB STATCOM model is developed in MATLAB/Simulink, and the proposed method is compared with a conventional repetitive controller (CRC) under nominal frequency, frequency drift, nonlinear loading, harmonic load-switching conditions and grid impedance variation. Simulation results show that the proposed controller reduces the grid-current THD from 4.35% to 3.88% at 50 Hz, from 5.20% to 2.37% at 49.6 Hz, and from 6.51% to 3.56% at 50.4 Hz. In the tested frequency range of 49.5–50.5 Hz, the proposed method also maintains the power factor close to unity. These quantitative results demonstrate improved frequency robustness, harmonic suppression, and current-tracking performance compared with the CRC scheme, indicating that the proposed method can enhance STATCOM-based power quality compensation and support more reliable and efficient operation of medium-voltage distribution networks. Full article

A real-time optimized model predictive control–equivalent consumption minimization strategy (MPC-ECMS) is proposed for the energy management of shipboard gas turbine–photovoltaic hybrid energy storage (GT-PV-HESS) power systems. Different from conventional MPC-ECMS methods that only adopt single-level SOC-based feedback regulation, the strategy aims to overcome the limitations of conventional methods, including the poor adaptability of rule-based strategies and the lack of foresight in traditional ECMS, which cannot achieve simultaneous improvements in fuel economy, generation efficiency, and battery lifespan while maintaining system stability under dynamic operating conditions. The proposed strategy integrates the forward-looking optimization ability of MPC and the real-time decision-making advantage of ECMS. MPC is used to predict short-term load and photovoltaic power and identify operating modes, and a two-level equivalent factor adjustment mechanism is designed based on predicted conditions and battery state of charge (SOC). The optimized factor is applied in ECMS to achieve optimal power allocation between the gas turbine and battery under system constraints, while the supercapacitor implements power secondary correction to suppress bus voltage fluctuations caused by gas turbine operation. The architectural novelty lies in the two-level coordination mechanism and the marine-oriented hybrid energy storage cooperation. Simulation studies are conducted on the MATLAB/Simulink R2021b platform, and the results validate that it yields superior performance to the rule-based control and traditional ECMS under typical ship operating conditions. It increases gas turbine efficiency to 15.62% (0.47% and 6.24% higher than the two conventional methods). Over the 120 s simulation period, the proposed strategy reduces total fuel consumption to 1.049 kg, which is lower than 1.054 kg for the rule-based strategy and 1.192 kg for conventional ECMS. The battery SOC fluctuation is restricted to only 3.89%. The maximum DC bus voltage fluctuation rate is controlled within 3.28%, which meets the stability requirements of shipboard DC microgrids. The proposed strategy achieves a comprehensive and superior balance among fuel economy, power generation efficiency, and battery life while ensuring stable system operation under all working conditions. This two-level MPC-ECMS framework provides a high-performance and practically feasible energy management solution for shipboard hybrid power systems. Full article

Music plays a significant role in mood regulation, influencing emotional states, reducing stress, and enhancing overall well-being. However, validated instruments to assess the use of music as a mood-regulation strategy are limited. This study aimed to adapt and validate the Brief Music in Mood Regulation Scale (B-MMR), a 21-item self-report measure, for use in Portuguese. Data were collected from 493 Portuguese students in an online survey. Participants completed the B-MMR alongside the Basic Emotions Questionnaire. Confirmatory factor analyses, reliability assessments, measurement invariance testing, and external validity analyses were conducted to evaluate the scale’s psychometric properties. Confirmatory factor analysis supported the original seven-factor structure, with three items per factor, with acceptable global fit indices. The Portuguese version showed satisfactory reliability. Invariance analyses revealed equivalence of the factor structure, loadings, and intercepts across sex groups, supporting meaningful comparisons of scores between men and women. Correlations between B-MMR dimensions and basic emotions were small to moderate and are interpreted as preliminary and indirect indicators of external validity, as they index emotional experiences rather than regulatory processes. Findings suggest that the Portuguese B-MMR is a promising tool for research on music-based mood regulation in student populations. A comprehensive validation across more diverse and clinical samples, using established emotion regulation measures, is needed before clinical or applied use. Full article

Urban canyons, integral components of the built environment, significantly influence microclimatic conditions and thermal comfort. This review investigates their combined effects with green infrastructure on thermal comfort, offering a comprehensive framework for supporting urban design and greening strategies. The review is based on a structured literature analysis of peer-reviewed studies retrieved from major scientific databases (Scopus and Web of Science), following defined selection and screening criteria. Urban canyon orientation determines solar exposure and its interaction with prevailing wind patterns, affecting ventilation and heat dissipation. The urban canyon aspect ratio influences shading and airflow regulation, while their sky view factor moderates radiative cooling and daylight availability. Urban greening—encompassing street trees, green roofs, and vertical green walls—complements urban geometry by reducing air temperatures, enhancing evapotranspiration, and modifying local wind dynamics. Tree shading can reduce the physiological equivalent temperature in urban canyons, mitigating extreme heat stress. Key vegetative parameters, such as leaf area index and canopy density, are critical for quantifying cooling contributions. Key findings underscore the role of higher aspect ratios in enhancing shading and ventilation while they emphasize the critical influence of street orientation and sky view factor on microclimatic regulation. Vegetation emerges as a vital component, with tree shading contributing substantially to cooling effects and reducing physiological equivalent temperature. The beneficial synergistic interaction between urban geometry and vegetation optimizes thermal comfort. Tailored strategies based on urban canyon typologies balance urban development with environmental sustainability. The proposed framework provides actionable strategies for designing resilient and thermally optimized urban spaces, promoting climate-adaptive urban planning by addressing the dual challenges of the urban heat island and thermal discomfort in cities. Full article