Search Results (4,994)

Renewable wind power output is characterized by strong randomness, volatility, and spatial dependence, which makes accurate two-adjacent wind farm scenario generation essential for stochastic dispatch and secure operation of power systems. To describe the joint uncertainty of wind farm outputs without directly constructing a complicated joint probability distribution, this paper proposes a Copula-based scenario generation method for two correlated wind farm outputs. Historically measured output data of two wind farms are first transformed into pseudo-observations through non-parametric marginal distribution estimation. Six bivariate Copula tools, including Gaussian Copula, t-Copula, Empirical Copula, Frank Copula, Clayton Copula, and Gumbel Copula, are then compared in terms of correlation coefficient, Spearman coefficient, Kendall coefficient, degrees of freedom, and distance from the Empirical Copula. The results show that the Bivariate t-Copula achieves the smallest fitting distance among all parametric Copula models and can effectively characterize the strong correlation and tail dependence between the two wind farms. Based on the fitted t-Copula, random samples are generated in the Copula space, transformed into wind power output samples through inverse marginal functions, and reduced into six representative scenarios using K-means clustering. Finally, the generated scenarios are introduced into an optimal power flow model with wind scenarios on the IEEE 30-bus system. The calculation results show that the proposed method can provide representative wind power inputs and effectively reflect the influence of wind power uncertainty on conventional unit dispatch. The proposed approach provides a practical reference for scenario generation and stochastic operation analysis of power systems with two correlated wind farms. Full article

Background: Oral diseases remain a major public health challenge globally and in Hungary, where untreated caries, periodontal disease, and edentulism are highly prevalent. Diet is a key modifiable determinant of oral health, yet the cumulative impact of multiple unfavorable nutritional behaviors on self-perceived oral health has not been assessed at the national level in Hungary, nor have population-attributable fraction (PAF) estimates been reported. Methods: This cross-sectional study analyzed data from 5146 adults in the 2019 Hungarian European Health Interview Survey. A six-component nutritional-risk score was constructed from self-reported dietary behaviors (fruit intake, vegetable intake, sugary soft drink consumption, sweets consumption, processed meat consumption, and water intake). Survey-weighted logistic regression estimated associations between nutritional-risk categories and poor self-perceived oral health, adjusting for sociodemographic, lifestyle, and health-related covariates. Counterfactual PAFs with 95% Monte Carlo uncertainty intervals (1000 bootstrap replications) were estimated for three nutritional-risk scenarios. Results: The weighted prevalence of poor self-perceived oral health was 19.2%. Compared with participants with 0–1 unfavorable nutritional factors, those with 3 factors (OR = 1.37; 95% CI: 1.09–1.73) and 4–6 factors (OR = 1.46; 95% CI: 1.15–1.85) had significantly higher odds of poor oral health. Low water intake was independently associated with poor oral health (OR = 1.38; 95% CI: 1.16–1.64). The main counterfactual PAF analysis estimated that 16.1% (Monte Carlo 95% UI: 5.93–26.48%) of poor oral health burden was attributable to unfavorable nutritional profiles. High nutritional risk was also associated with active caries, gum bleeding, and tooth loss. Conclusions: Cumulative nutritional risk is independently associated with poor self-perceived oral health in Hungarian adults. Integrating dietary improvements into oral health prevention strategies may substantially reduce the population-level burden of oral disease. Full article

Background: By 2030, nearly one-quarter of the U.S. population will be over age 65, emphasizing the need to understand factors that promote healthy aging. Vitality is often reduced to physical indicators in clinical frameworks, overlooking its multidimensional nature. Methods: This qualitative descriptive study explored how independently living older adults define and experience vitality, focusing on physical, emotional, cognitive, and social dimensions. Guided by Self-Determination Theory, semi-structured interviews were conducted with 10 participants aged 61–83 years purposively recruited from community and faith-based settings in Northeast Florida. Results: Thematic analysis revealed four interconnected themes: (1) Make Adjustments, Keep Going; (2) Being Mentally Jubilant; (3) Bonds that Keep Me Going; and (4) Thriving Amid Uncertainty. Conclusions: Findings suggest that participants perceived vitality as a dynamic, multidimensional process shaped by adaptation, purpose, and connection rather than physical health alone. These insights underscore the need for holistic approaches to vitality assessment and interventions that support autonomy, competence, and relatedness in later life. Full article

In this paper, the collision-avoidance formation control problem for underactuated surface vessels (USVs) subject to input constraints is investigated. The input constraints include both input amplitude saturation and input rate saturation. A controller based on barrier Lyapunov functions (BLFs) is developed for the considered system. First, a disturbance observer is designed to compensate for environmental disturbances and model uncertainties that degrade system performance. Second, an auxiliary dynamic system is introduced to handle input amplitude saturation and input rate saturation. To guarantee connectivity preservation and collision avoidance within the formation, the distance errors and angle errors are transformed using BLFs. Based on the transformed errors and the disturbance observer, a BLF-based anti-saturation controller is then constructed. Lyapunov stability analysis proves that all signals in the closed-loop system are bounded. Finally, simulation results demonstrate that the proposed method can achieve collision-free formation control under both input amplitude saturation and input rate saturation, and verify that the control system achieves fast convergence, small tracking errors, and collision avoidance while satisfying the input magnitude and rate constraints. Full article

This study aims to accurately predict students’ academic performance trajectories for university entrance examinations by proposing a machine learning framework that explicitly accounts for missing data and uncertainty. Mock examination data are characterized by substantial missing values due to heterogeneous participation in exams, as well as inherent randomness caused by variations in test content and examinee conditions. Conventional single-value imputation methods cannot adequately reconstruct the missing values arising from such heterogeneous participation without introducing strong bias, and existing educational prediction models based on deterministic formulations do not account for the inherent randomness and uncertainty in examination scores, thereby limiting the reliability of their forecasts. To address these challenges, we employ GP-VAE and SAITS, state-of-the-art methods for time-series imputation, to reconstruct incomplete mock examination data. Furthermore, we develop a Bayesian Neural Network (BayesNN) to predict future academic performance while explicitly modeling uncertainty. By integrating temporally aware imputation with probabilistic prediction, the proposed framework aims to provide more accurate and reliable performance forecasts than existing approaches. We evaluate the effectiveness of the proposed method through comparative experiments involving various combinations of imputation techniques and prediction models. Experimental results demonstrate that the proposed framework achieves competitive predictive accuracy: the combination of deep imputation methods and BayesNN yields the lowest average estimation error of 15.98 points, compared with 16.75 points for the conventional combination of mean imputation and linear regression. The contribution of this study does not lie in proposing a new deep learning model itself, but rather in systematically comparing combinations of time-series imputation methods and uncertainty-aware prediction models using real-world mock examination sequence data with missing values, thereby providing effective design guidelines for educational data analysis. Full article

Modern energy internets and large-scale industrial systems are becoming increasingly complex. Consequently, the rapid response capability of energy infrastructure during sudden failures has become a core element to ensure the stable operation of the social economy. Emergency repair path planning (ERPP) is a complex nonlinear combinatorial optimization problem. It is characterized by dynamic uncertainties, such as fluctuating task durations and variable traffic accessibility. This paper proposes a cross-regional emergency repair path planning (CR-ERPP) optimization model considering dynamic path conditions. The model takes into account jurisdiction ownership, cross-regional dispatch costs, path weights (congestion coefficient, grade coefficient, quality coefficient) and accident risk levels. The primary objective of this model is to minimize the total repair cost. Furthermore, an Adaptive Bitterling Fish Optimization with Evolutionary Game Theory (ABFO-EGT) is developed. It introduces adaptive mechanisms, evolutionary game theory, and a symmetric mutation strategy. These enhancements are designed to overcome the inherent limitations of traditional swarm intelligence algorithms, namely unbalanced search behavior and premature convergence to local optima. Performance analysis demonstrates that the ABFO-EGT algorithm exhibits superior convergence stability and global search capability. Case study results show that the proposed method significantly reduces the total repair cost. Specifically, the cost is reduced by 33.2% compared to manual decision-making, 27.6% compared to the GWO algorithm, and 9.1% compared to both the ACO and PSO algorithms. This study provides an efficient and reliable decision support tool for emergency management of large-scale energy systems. Full article

Ammonia is increasingly regarded as a carbon-free energy carrier for hard-to-electrify power sectors, including marine propulsion, heavy-duty transport, and distributed generation. Its direct use in internal combustion engines, however, is constrained by high ignition energy, low laminar flame speed, narrow flammability limits, slow low-temperature chemistry, and strong trade-offs among efficiency, nitrogen-containing emissions, and unburned ammonia slip. Hydrogen enrichment is one of the most effective routes for improving ammonia combustion reactivity, but it also introduces a multivariable control problem: hydrogen fraction, ammonia injection timing, injection mode, air-path dilution, ignition strategy, and aftertreatment operation are tightly coupled and strongly condition-dependent. This review synthesizes recent progress in ammonia–hydrogen and ammonia-based dual-fuel engine control from a control-oriented perspective. The discussion first summarizes application scenarios, nonlinear combustion-mode transitions, emission-formation pathways, and control-relevant metrics. It then compares actuator-level strategies, including ammonia injection timing and staging, port and direct injection, hydrogen energy-fraction scheduling, excess-air-ratio and EGR control, high-energy ignition, and turbulent jet ignition. Advanced optimization methods are further reviewed, with emphasis on model predictive control, control-oriented combustion and emission models, artificial-intelligence-based virtual sensors, and reinforcement-learning control. The analysis shows that the central challenge is no longer whether ammonia can burn in an engine, but how a controller can keep the system inside a narrow moving window bounded by misfire, knock, NO_x, N₂O, and NH₃ slip. Finally, future research priorities are proposed, including engine–aftertreatment co-optimization, physics-informed virtual sensing, digital-twin-assisted calibration, lightweight deployment on electronic control units, and robust control under fuel and aging uncertainty. Full article

High-speed Unmanned Aerial Vehicles (UAVs), characterized by high velocity and maneuverability, represent critical strategic threats within the aerospace security domain. Accurate trajectory prediction is a fundamental prerequisite for effective early warning and decision-making in defense. Despite increasing research in the domain, model-, data-, and hybrid-driven methods have not yet been comprehensively examined under a unified framework, limiting the understanding of their relative strengths and applicability. To address this gap, this paper systematically reviews the evolution and current technical status of these paradigms, categorized by the core logic underlying prediction methods. The principles and applicability limits of physical process modeling and state estimation algorithms are analyzed, along with emerging applications of machine learning and deep learning for trajectory feature extraction and pattern recognition. State-of-the-art architectures involving the integration of physical constraints and data-driven learning are discussed. Standard evaluation metrics are introduced to facilitate performance benchmarking of existing methods. Comparative analysis reveals that no single technical route can fully address the coupled challenges of uncertainty, accuracy, and real-time performance, underscoring that hybrid frameworks are essential for balancing these competing requirements. Lastly, key challenges are summarized, and future research directions are outlined to advance trajectory prediction methodologies. The provided insights can inform method selection and promote the development of high-accuracy prediction systems. Full article

Pedestrian safety at urban intersections is influenced by complex interactions among environmental conditions, social cues, and boundedly rational behavioral responses, making intervention planning under uncertainty particularly challenging. Existing studies have largely emphasized descriptive analysis or predictive modeling, with limited attention to prescriptive decision support under data scarcity. This study proposes a Bayesian-network-based robust optimization framework for pedestrian safety planning, complemented by a large language model (LLM)-assisted parameter-elicitation process. We first construct a three-layer Bayesian network based on the stimulus–organism–response paradigm to represent the propagation of risk from environmental cues through latent psychological mechanisms to behavioral violations and accident risk. To support model initialization when site-specific behavioral data are limited, we introduce an LLM-assisted elicitation protocol that maps literature-based qualitative evidence to intervention mechanisms, effect directions, and qualitative strength classes. Numerical parameter ranges are subsequently assigned through explicit mapping rules rather than generated directly by the LLM. We then formulate a bi-objective robust optimization model that distinguishes between physical interventions, which alter root-node distributions, and cognitive interventions, which modify conditional probability tables. Using the $ϵ$-constraint method, the framework generates Pareto-optimal intervention portfolios and evaluates cost–risk trade-offs under behavioral uncertainty and adverse operating scenarios. Full article

Fragmentation processes arise in many physical systems, including droplet breakup, aerosols, sprays, comminution, and granular media. A central difficulty in fragmentation modeling is the identification of the breakup law from observed particle-size distributions. In this work, we propose a physics-constrained neural framework for the inverse identification of fragmentation laws. Starting from a size-structured binary fragmentation equation, the kernel is decomposed into a breakup rate $B\left(s\right)$, depending on the parent size s, and a daughter-size distribution $\kappa \left(z\right)$, depending on the normalized daughter-size fraction z. The unknown functions B and $\kappa$ are represented by neural networks designed to satisfy physical constraints, including non-negativity of the breakup rate, non-negativity and normalization of the daughter distribution, and first-moment conservation in the symmetric binary setting. The direct fragmentation equation is solved using a first-moment-conserving quadrature discretization, and the inverse problem is formulated as a regularized optimization problem constrained by the fragmentation dynamics. Synthetic experiments, generated independently of the inverse solver, show that the proposed method can recover B and $\kappa$ from simulated particle-size distributions, with accurate recovery in the unimodal case and reasonable performance in the more challenging bimodal case. Robustness tests indicate stability with respect to moderate observational noise and highlight the importance of multiple observation times. For experimental droplet-breakup measurements without time-resolved particle-size distributions, the same constrained neural daughter-density representation is used to estimate effective marginal daughter-volume-fraction distributions from normalized daughter-to-parent volume fractions. The resulting distributions distinguish rim and node fragments and different breakup modes, while stratified cluster-bootstrap confidence bands quantify their sampling uncertainty. The experimental analysis is therefore interpreted as constrained density estimation from final fragment measurements rather than as full validation of the PDE-constrained inverse recovery. Full article

The growing demand for electric traction batteries, together with their high content of Critical Raw Materials (CRMs), necessitates comprehensive life cycle environmental assessments. Limited attention in the literature has been devoted to explicitly disentangling the role of individual CRMs in shaping both environmental burdens and potential end-of-life benefits. This study presents a “cradle-to-grave” life cycle assessment (LCA) of a Lithium Iron Phosphate battery for electric vehicle applications, aiming to identify the contributions of individual life cycle phases and quantify the role of CRMs. The life cycle inventory was developed using data from the literature and the Ecoinvent database, with impacts assessed using the ReCiPe 2016 midpoint hierarchist (H) method. LCA results indicate that the material manufacturing phase dominates most impact categories. A gravity analysis demonstrates that a limited set of CRMs (aluminium, copper, and lithium) structurally governs both environmental burdens and recycling benefits. Monte Carlo analysis indicates low uncertainty for most impact categories. A first sensitivity analysis reveals that improvements in round-trip efficiency exert the strongest influence on environmental performance. A second sensitivity analysis assesses the effect of different energy mixes used to model electricity consumption during the use phase. Full article

Accurate and fast fault detection is essential to ensure the stability and reliability of High Voltage AC (HVAC) transmission systems. Conventional protection methods, including impedance-based and traveling wave techniques, may exhibit reduced performance under noisy operating conditions, system uncertainties, and complex fault scenarios while often requiring separate approaches for fault classification, location detection, and stability assessment. This paper proposes a unified Artificial Neural Network (ANN) based framework for simultaneous fault classification, location detection, and stability assessment using Critical Clearing Time (CCT) within a single HVAC transmission line model. A detailed MATLAB Simulink model is developed to generate a structured dataset comprising twelve fault scenarios, including single-line, double-line, three-phase, and ground faults at different locations along the transmission line. Three-phase voltages and currents, along with zero-sequence components, are used as input features. The ANN model is trained using the Levenberg–Marquardt (LM) optimization algorithm, which was comparatively evaluated against Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) and demonstrated faster convergence, lower prediction error, and higher regression accuracy. To further evaluate the robustness of the proposed framework under high-impedance fault conditions, supplementary simulations were performed using fault resistance values of 10 Ω and 50 Ω in addition to the baseline 0.01 Ω case. The resulting datasets were combined to form an expanded training and evaluation dataset, enabling comprehensive validation of the proposed LM-trained ANN under varying fault resistance conditions. Using the baseline dataset, the proposed framework achieved a high regression coefficient (R = 0.9882) and low mean squared error (MSE = 0.1386), demonstrating accurate fault classification and precise per-kilometer fault location estimation. Furthermore, the integration of fault inception time and duration enables direct computation of CCT, allowing the model to distinguish between stability-critical and non-critical fault conditions. The results confirm that the proposed framework provides a comprehensive and efficient solution for real-time fault analysis by combining classification, localization, temporal analysis, and stability-aware decision support within a single model. Full article

Forging represents a very important process in the metal processing industry, for which risk analysis and continuous improvement activities are required in order to satisfy customer requirements regarding product quality and mechanical properties. In practice, Process Failure Mode and Effects Analysis (PFMEA) is used for risk identification and assessment. However, since this analysis provides only recommended process improvement measures as an output, without prioritizing them according to technical, economic, and operational aspects, this study develops a Multi-Criteria Decision-Making (MCDM) approach based on the integration of the Analytic Hierarchy Process (AHP) and the Square-Root-Based Evaluation Method (SREM), extended through the application of Interval-Valued Pythagorean Fuzzy Numbers (IVPFNs) to model uncertainty in the assessments of the expert team. In this way, a decision-making framework was developed that enables the prioritization of process improvement measures identified through PFMEA in an exact and mathematically based manner. The proposed approach was tested through a case study conducted in a company primarily engaged in the production of forgings as a supplier to various industrial sectors. A total of six process improvement measures were considered and evaluated with respect to seven technical, economic, and operational criteria. The results of the study clearly demonstrated that the additional die-leading measure ranked first and represented the most stable solution, maintaining its leading position regardless of the changes introduced through the sensitivity analysis. Full article

The output of photovoltaic power generation exhibits significant randomness, volatility, and intermittency, and its uncertainty will have an impact on the planning assessment, dispatch decision-making, and real-time operation of the power system. To systematically understand the development status of modeling methods for the uncertainty of photovoltaic power generation, this paper conducts a review around the generation of annual scenarios and multi-timescale power prediction of photovoltaic power, and analyzes the correlations between photovoltaic output, influencing factors, and system applications from the perspective of statistical relationships and artificial intelligence. For the annual scale, the focus is on the generation methods of meteorological-driven scenarios for long-term sequences, including probability statistical methods, deep generation methods, constraint relations and engineering application evaluation issues; for the day-ahead scale, the historical power, meteorological variables and numerical weather forecasts are used to explore feature extraction, probability prediction and robust modeling methods; for the intraday scale, the signal decomposition, deep learning, regional collaborative modeling and multi-source perception methods for short-term power fluctuations perception are summarized. On this basis, further analysis is conducted on subsequent research directions such as multi-timescale collaborative modeling, multi-source heterogeneous information fusion, controllable generative modeling, extreme scenario characterization, and engineering closed-loop verification. This paper can serve as a reference for scenario generation, power prediction, and power system operation analysis under the condition of a high proportion of photovoltaic power integration. Full article

Background/Objective: Research has indicated that providing care during the COVID-19 pandemic negatively impacted healthcare providers’ wellbeing and burnout. Much of the literature has focused on emergency department (ED) physicians and nurses, including the distinct challenges they faced caring for persons experiencing homelessness (PEH), including addressing unmet basic needs, limited discharge options, and disruptions to community-based health and social services during the pandemic. Comparatively little research has examined the experiences of other multidisciplinary team members caring for PEH. This study explored the shared experiences of multidisciplinary ED staff caring for PEH during the COVID-19 pandemic to inform workforce planning and future pandemic preparedness. Methods: Using inductive qualitative analysis, we examined self-reported experience, mental health impacts and solutions for workforce planning and pandemic preparedness of multidisciplinary ED staff caring for PEH. Three virtual focus group discussions involving 12 multidisciplinary ED staff (registered nurses, clerical staff, security staff, community support workers, and a clinical assistant) were conducted during the first two years of the COVID-19 pandemic, a period characterized by evolving public health restrictions, uncertainty, and disruptions to health and social services. Results: Inductive qualitative analysis identified four key themes: (1) emotional experiences of care provision (including gratitude, compassion fatigue, and moral distress), (2) hospital- and community-led adaptations to support care delivery, (3) strategies for wellness and resilience building among staff, and (4) recommendations for future workforce planning and pandemic preparedness. Conclusions: Caring for PEH during the intense pandemic period intensified staff distress by exposing them to unmet social needs and system-level resource gaps, while also fostering interprofessional collaboration and resilience. Staff collaborated to facilitate organizational adaptations that enabled continuity-of-care. Future pandemic preparedness should include targeted mental health supports for staff, flexible institutional contingency planning, and formalized hospital–community partnerships to sustain care for PEH and other socially vulnerable populations. Full article