Search Results (14,762)

Multi-energy systems could utilize the complementary characteristics of heterogeneous energy to improve operational flexibility and energy efficiency. However, seasonal fluctuations and uncertainty of load would have a great influence on the effectiveness of the system planning scheme. Regarding this issue, this paper proposes a photovoltaic power (PV) station and thermal energy storage (TES) capacity planning model with considering the electrical load uncertainty based on a stochastic optimization method. And four-season load demand scenarios are built by Generative Adversarial Networks (GANs). At last, the proposed capacity configuration model is tested in a case study, and the results show the influence of seasonal fluctuations in load, scenario number, and TES capacity. Full article

This study focuses on the numerical analysis of heat transfer in biological tissue. The proposed model is formulated using the Pennes equation for a two-dimensional cylindrical domain. The tissue undergoes laser irradiation, where internal heat sources are determined based on the Beer–Lambert law. Moreover, key parameters—such as the perfusion rate and effective scattering coefficient—are modeled as functions dependent on tissue damage. In addition, a fuzzy heat source associated with magnetic nanoparticles is also incorporated into the model to account for magnetothermal effects. A novel aspect of this work is the introduction of uncertainty in selected model parameters by representing them as triangular fuzzy numbers. Consequently, the entire Finite Pointset Method (FPM) framework is extended to operate with fuzzy-valued quantities, which—to the best of our knowledge—has not been previously applied in two-dimensional thermal modeling of biological tissues. The numerical computations are carried out using the fuzzy-adapted FPM approach. All calculations are performed due to the fuzzy arithmetic rules with the application of α-cuts. This fuzzy formulation inherently captures the variability of uncertain parameters, effectively replacing the need for a traditional sensitivity analysis. As a result, the need for multiple simulations over a wide range of input values is eliminated. The findings, discussed in the final Section, demonstrate that this extended FPM formulation is a viable and effective tool for analyzing heat transfer processes under uncertainty, with an evaluation of α-cut widths and the influence of the degree of fuzziness on the results also carried out. Full article

Sub-cloud evaporation changes the isotopic composition of precipitation, which greatly reduces the reliability of precipitation isotopic data as precipitation simulation data. This study employed the precipitation isotope datasets of Haikou in northern Hainan Island from June 2020 to February 2024 to quantitatively study the influence of sub-cloud evaporation on precipitation isotopes in tropical islands. Due to the sub-cloud evaporation, the slope of the local meteoric water line (LMWL: δ²H = 8.33δ¹⁸O + 14.33) is lower than the average slope of the theoretical LMWL (8.48). The average value of the residual ratios of raindrop after evaporation (f) is 86%. The complex and unstable sources of water vapor result in no obvious seasonal variations in the atmospheric humidity, which in turn leads to no obvious seasonal variations in Δd and f. The humid and hot environmental conditions reduced the impact of sub-cloud evaporation on precipitation isotopes. The two main uncertainties in the simulation of below-cloud evaporation are the influence of recycled water vapor on precipitation isotopes and the Stewart model’s assumption that raindrops at the cloud base achieve isotopic equilibrium with the surrounding water vapor, as it is difficult to realize. The results of this study are of great significance for improving the accuracy of precipitation simulation in tropical monsoon islands. Full article

Human-powered electricity generation (HPEG) systems offer promising sustainable energy solutions, yet existing deterministic models fail to capture the inherent variability in human biomechanical performance. This study develops a comprehensive stochastic framework integrating advanced Monte Carlo uncertainty quantification with multi-component fatigue modeling and Pareto optimization. The framework incorporates physiological parameter vectors, kinematic variables, and environmental factors through multivariate distributions, addressing the complex stochastic nature of human power generation. A novel multi-component efficiency function integrates biomechanical, coordination, fatigue, thermal, and adaptation effects, while advanced fatigue dynamics distinguish between peripheral muscular, central neural, and substrate depletion mechanisms. Experimental validation (623 trials, 7 participants) demonstrates RMSE of 3.52 W and CCC of 0.996. Monte Carlo analysis reveals mean power output of 97.6 ± 37.4 W (95% CI: 48.4–174.9 W) with substantial inter-participant variability (CV = 37.6%). Pareto optimization identifies 19 non-dominated solutions across force-cadence space, with maximum power configuration achieving 175.5 W at 332.7 N and 110.4 rpm. This paradigm shift provides essential foundations for next-generation HPEG implementations across emergency response, off-grid communities, and sustainable infrastructure applications. The framework thus delivers dual contributions: advancing stochastic uncertainty quantification methodologies for complex biomechanical systems while enabling resilient decentralized energy solutions critical for sustainable development and climate adaptation strategies. Full article

In an era marked by volatility, uncertainty, complexity, and ambiguity, constructing resilient regional innovation ecosystems is identified as a critical strategic imperative for achieving high-quality development and advancing sustainable development goals. Drawing on the Technology–Organization–Environment (TOE) integrative framework, this study examines six antecedent conditions of ecosystem resilience from the perspective of digital transformation: digital infrastructure, digital innovation capacity, digital human capital, digital government governance, digital attention, and digital finance. A sample of 48 prefecture-level cities from the Beijing–Tianjin–Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations in China between 2018 and 2022 is selected. Through the application of dynamic Qualitative Comparative Analysis (QCA), the study explores the multiple configurations across temporal and spatial dimensions through which technological, organizational, and environmental factors contribute to enhancing regional innovation ecosystem resilience. The results indicate that ecosystem resilience is jointly driven by multiple interacting factors, and no single condition is found to be necessary. Four distinct causal pathways are identified as sufficient to enhance resilience: (1) a triadic synergy of technology, organization, and environment; (2) a technology-driven, talent-supported configuration; (3) a technology-driven, government-supported configuration; and (4) a dual technology–environment-driven model. While none of the configurations exhibit consistent temporal effects, some are influenced by unobserved factors in specific years. Moreover, cities do not converge on a single dominant configuration when achieving high levels of ecosystem resilience. Full article

Seismic hazards in Thailand are frequently overlooked in disaster management planning, leading to insufficient research and significant economic losses during earthquake events. The 2014 Chiang Rai earthquake exposed critical vulnerabilities in Thailand’s building practices due to widespread non-compliance with building codes and limited preparedness. This exposure prompted the development of empirical vulnerability functions using loss data from 15,031 damaged residences. The study analyzed government compensation records, which were standardized using replacement cost metrics. Three distinct models were developed through probabilistic and possibilistic modeling approaches. Residual analysis demonstrated the superior performance of the possibilistic approach, with the Possibilistic-based Vulnerability Function achieving a 49.84% reduction in residuals for small loss predictions compared to probability-based models. The research findings indicate that possibility theory—capable of addressing multiple uncertainties—provided a more accurate representation of the observed losses. These results offer valuable guidance for enhancing seismic risk assessment and disaster preparedness strategies in local applications. Full article

This paper proposes a learning-driven passivity-based control (PBC) strategy for sensorless induction motors, combining a nonlinear adaptive observer with recurrent neural networks (RNNs) to improve robustness and estimation accuracy under dynamic conditions. The main novelty is the integration of neural learning into the passivity framework, enabling real-time compensation for un-modeled dynamics and parameter uncertainties with only one gain adjustment across a broad speed range. Lyapunov-based analysis guarantees the global stability of the closed-loop system. Experiments on a 1.1 kW induction motor confirm the approach’s effectiveness over conventional observer-based and fuzzy-enhanced methods. Under torque reversal and flux variation, the proposed controller achieves a torque mean absolute error (MAE) of 0.18 Nm and flux MAE of 0.21 Wb, compared to 1.58 Nm and 0.85 Wb with classical PBC. When peak torque deviation drops from 42.52% to 30.85% of the nominal, torque symmetric mean absolute percentage error (SMAPE) improves by 7.6%, and settling time is reduced to 985 ms versus 1120 ms. These results validate the controller’s precision, adaptability, and robustness in real-world sensorless motor control. Full article

Modern distribution networks are increasingly affected by the widespread adoption of photovoltaic (PV) generation and electric vehicles (EVs). The variability of PV output and the fluctuating demand of EVs may cause voltage violations that threaten the safe operation of active distribution networks (ADNs). This paper proposes a voltage control strategy for ADNs to address the voltage violation problem by utilizing the control flexibility of EV charging stations (EVCSs). In the proposed strategy, a state-driven margin algorithm is first employed to generate training data comprising response capability (RC) of EVs and state parameters, which are used to train a multi-layer perceptron (MLP) model for real-time estimation of EVCS response capability. To account for uncertainties in EV departure times, a relevance vector machine (RVM) model is applied to refine the estimated RC of EVCSs. Then, based on the estimated RC of EVCSs, a second-order cone programming (SOCP)-based voltage regulation problem is formulated to obtain the optimal dispatch signal of EVCSs. Finally, a broadcast control scheme is adopted to distribute the dispatch signal across individual charging piles and the energy storage system (ESS) within each EVCS to realize the voltage regulation. Simulation results on the IEEE 34-bus feeder validate the effectiveness and advantages of the proposed approach. Full article

Background/Objectives. This study applied the Demands-Resources-and-Individual-Effects(DRIVE)-Nurses-Model to explore and compare the experiences of nurses working in Intensive Care Units (ICUs) and in Neonatal Intensive Care Units (NICUs), by investigating the effects of the interplay (main/mediating/moderating effects) of perceived stress related to dealing with death/critically ill patients (Death-and-Dying-Stressor)—which unavoidably features in the daily life of nurses working in ICU/NICU—with further potential Stressors in Nursing (Conflicts-with-Physicians, Peers, Supervisors, Patients/their families, Uncertainty-Concerning-Treatment, Inadequate-Emotional-Preparation, Discrimination, Workload), Work-Resources (Job-Control, Social-Support, Rewards), and Coping-Strategies (Problem-focused, Seek-Advice, Self-Blame, Wishful Thinking, Escape/Avoidance) on nurses’ psychological health conditions according to the working unit (ICU/NICU). Methods. Overall, 62 critical care nurses (ICU = 35; NICU = 27) completed self-report questionnaires. Main/mediating/moderating effects were tested by using Correlational-Analyses and Hayes-PROCESS-tool by working unit. Results. Nurses working in NICU reported higher Psychological Disease than nurses working in ICU. The detrimental psychological impact of Death-and-Dying-Stressor was mediated by Conflicts-with-Supervisors-Stressor among ICU nurses and by Uncertainty-Concerning-Treatment and Conflicts-with-Physicians stressors among NICU nurses. The recourse to Self-Blame and Escape/Avoidance coping strategies exacerbated the psychological risk among ICU nurses, while perceived Work-Resources (Job-Control/Social-Support) played a protective moderating role among NICU nurses. Conclusions. The application of the DRIVE-Nurses-Model to deepen the experience of nurses working in ICU/NICU could advance the understanding of the mechanisms underlying the relationship between Death-and-Dying-Stressor and nurses’ psychological health, suggesting tailored risk profiles and accounting for key protective factors, to provide nurses with the necessary resources for adjusting to their challenging and emotionally demanding work-related duties and experiences. Full article

Joint degeneration is a major global health issue requiring improved diagnostic and prognostic tools. This review examines whether integrating Bayesian graphical models with multiscale medical imaging can enhance detection, analysis, and prediction of joint degeneration compared to traditional single-scale methods. Recent advances in quantitative MRI, such as T2 mapping, enable early detection of subtle cartilage changes, supporting earlier intervention. Bayesian graphical models provide a flexible framework for representing complex relationships and updating predictions as new evidence emerges. Unlike prior reviews that address Bayesian methods or musculoskeletal imaging separately, this work synthesizes these domains into a unified framework that spans molecular, cellular, tissue, and organ-level analyses, providing methodological guidance and clinical translation pathways. Key topics within Bayesian inference include multiscale analysis, probabilistic graphical models, spatial-temporal modeling, network connectivity analysis, advanced imaging biomarkers, quantitative analysis, quantitative MRI techniques, radiomics and texture analysis, multimodal integration strategies, uncertainty quantification, variational inference approaches, Monte Carlo methods, and model selection and validation, as well as diffusion models for medical imaging and Bayesian joint diffusion models. Additional attention is given to diffusion models for advanced medical image generation, addressing challenges such as limited datasets and patient privacy. Clinical translation and validation requirements are emphasized, highlighting the need for rigorous evaluation to ensure that synthesized or processed images maintain diagnostic accuracy. Finally, this review discusses implementation challenges and outlines future research directions, emphasizing the potential for earlier diagnosis, improved risk assessment, and personalized treatment strategies to reduce the growing global burden of musculoskeletal disorders. Full article

Monitoring and accurately forecasting ultraviolet (UV) radiation is of great importance especially due to its adverse effects on human health. In this study, we develop a numerical model to estimate the UV surface solar radiation with the overarching goal of providing a fully automated UV forecasting tool in the region of Epirus, Greece, and especially at the city of Ioannina. The UV surface solar radiation (SSR) is estimated based on detailed radiative transfer (RT) calculations. To ensure their accuracy, we employ the well-established UVSPEC model included in the libRadtran RT routines. LibRadtran provides a variety of options to set up and modify an atmosphere with molecules, aerosol particles, water and ice clouds and a surface as the lower boundary. As a first step, we performed a sensitivity study of the surface solar UV radiation with respect to ozone, precipitable water, aerosol optical properties and surface albedo. Our calculations are performed initially under clear-sky conditions to eliminate the uncertainties induced by clouds. All our calculations are performed spectrally within the UV spectral range, for a specific date and time at Ioannina, Epirus. Full article

Lithium-ion batteries serve as critical energy storage devices and are extensively utilized across diverse applications. The accurate estimation of State of Charge (SOC) is critically important for Battery Management Systems. Traditional SOC estimation methods have achieved progress, such as the Extended Kalman Filter (EKF) and particle filter. However, when there exist uncertainties in battery model parameters and the parameters change dynamically with operating conditions, the EKF tends to produce accumulated errors, which leads to a decline in estimation accuracy. This paper proposes a hybrid approach integrating the EKF with a Hidden Markov Factor Graph (HMM-FG). First, this method uses the EKF to achieve a real-time estimation of the SOC. Then, it treats the EKF-estimated value as an observation through the HMM-FG and combines current and voltage measurement data. It also introduces a factor function to describe the temporal correlation of the SOC and the uncertainty of EKF modeling errors, thereby performing Maximum A Posteriori (MAP) estimation correction on the SOC. Different from the traditional EKF, this method can use future observation information to suppress the error accumulation of the EKF under dynamic parameter changes. Experiments were conducted under different temperatures (0 °C, 25 °C, 45 °C), and a variety of different dynamic operating conditions (FUDS, DST), and comparisons were made with the EKF, Extended Kalman Smoother (EKS), and data-driven method based on LSTM. Full article

In today’s fast-paced release cycles, mobile app user reviews offer a valuable source for tracking the evolution of user needs. At the core of these needs lies a structure of interdependencies—some enhancements are only relevant in specific usage contexts, while others may conflict when implemented together. Identifying these relationships is essential for anticipating feature interactions, resolving contradictions, and enabling context-aware, user-driven planning. The present work introduces an ontology-enhanced AI framework for predicting whether the requirements mentioned in reviews are interdependent. The core component is a Bidirectional Encoder Representations from Transformers (BERT) classifier retrained within a large-language-model-driven active learning loop that focuses on instances with uncertainty. The framework integrates contextual and structural reasoning; contextual analysis captures the semantic intent and functional role of each requirement, enriching the understanding of user expectations. Structural reasoning relies on a domain-specific ontology that serves as both a knowledge base and an inference layer, guiding the grouping of requirements. The model achieved strong performance on annotated banking app reviews, with a validation F1-score of 0.9565 and an area under the ROC curve (AUC) exceeding 0.97. The study results contribute to supporting developers in prioritizing features based on dependencies and delivering more coherent, conflict-free releases. Full article

The increasing deployment of photovoltaic-storage systems in distribution-level microgrids introduces a critical control conflict: traditional maximum power point tracking algorithms aim to maximize energy harvest, while grid-forming inverter control demands real-time power flexibility to deliver frequency and inertia support. This paper presents a novel two-layer co-optimization framework that resolves this tension by integrating adaptive traditional maximum power point tracking modulation and virtual synchronous control into a unified, grid-aware inverter strategy. The proposed approach consists of a distributionally robust predictive scheduling layer, formulated using Wasserstein ambiguity sets, and a real-time control layer that dynamically reallocates photovoltaic output and synthetic inertia response based on local frequency conditions. Unlike existing methods that treat traditional maximum power point tracking and grid-forming control in isolation, our architecture redefines traditional maximum power point tracking as a tunable component of system-level stability control, enabling intentional photovoltaic curtailment to create headroom for disturbance mitigation. The mathematical model includes multi-timescale inverter dynamics, frequency-coupled battery dispatch, state-of-charge-constrained response planning, and robust power flow feasibility. The framework is validated on a modified IEEE 33-bus low-voltage feeder with high photovoltaic penetration and battery energy storage system-equipped inverters operating under realistic solar and load variability. Results demonstrate that the proposed method reduces the frequency of lowest frequency point violations by over 30%, maintains battery state-of-charge within safe margins across all nodes, and achieves higher energy utilization than fixed-frequency-power adjustment or decoupled Model Predictive Control schemes. Additional analysis quantifies the trade-off between photovoltaic curtailment and rate of change of frequency resilience, revealing that modest dynamic curtailment yields disproportionately large stability benefits. This study provides a scalable and implementable paradigm for inverter-dominated grids, where resilience, efficiency, and uncertainty-aware decision making must be co-optimized in real time. Full article

This paper addresses the challenge of stabilizing an unmanned aerial vehicle with an arm (UAM) on a pipeline with disturbance, where the disturbance factors include white noise, mass uncertainty, and wind disturbance. An anti-disturbance control method is proposed utilizing nonlinear model predictive control (NMPC). Initially, the natural wind field model is developed. Considering wind disturbance, the UAM dynamics are analyzed utilizing Newton–Euler theory. Subsequently, the no-slip constraints and the terminal constraints are defined to prevent UAM from destabilizing and falling. The NMPC-based algorithm is developed to ensure the stable control of UAM, transforming the optimization problem into a nonlinear programming problem. The terminal cost function and the inequality constraints for establishing the state variables using linear quadratic regulator (LQR) are meticulously studied. Finally, numerical simulations are carried out to further verify the proposed method, considering internal disturbance about physical parameters and external disturbance about wind. Simulation results show that the disturbance is well compensated, and the UAM tilt angle is less than 0.3 deg. Therefore, the proposed control method can comprehensively consider the input energy consumption and the realization of stability, and has a certain degree of anti-interference. Full article