Search Results (13,208)

This paper studies spacecraft control under communication congestion, multi-source uncertainties, and input constraints. To reduce communication load, a static event-triggered mechanism is used so that transmissions occur only when necessary. Unknown nonlinearities are estimated online by a radial basis function neural network (RBFNN). To address sensor outliers and external disturbances, an event-triggered stubborn composite disturbance observer (ESCDO) is proposed, and sufficient conditions are derived to ensure its globally uniformly bounded stability. Based on this, an MPC-based composite anti-disturbance controller is designed to satisfy input constraints, and conditions are provided to guarantee the uniform bounded stability of the closed loop. Numerical simulations are conducted to demonstrate the effectiveness of the proposed approach. Full article

Background/Objective: Transformational leadership (TL) by nurse managers is a modifiable organizational factor consistently linked to improved staff outcomes. However, evidence from the Arab Gulf region, particularly the Kingdom of Saudi Arabia (KSA), is limited. This study aimed to assess the relationship between nurse managers’ TL and staff nurses’ intent to stay and determine which TL dimensions predict intent to stay. Methods: A cross-sectional online survey was conducted among staff nurses at a three-hospital academic medical city in Riyadh, KSA. A total of 523 eligible respondents successfully completed the survey, utilizing probabilistic cluster sampling to guarantee representation from various nursing units within the medical city. Nurse managers’ TL was assessed across five dimensions by using the multifactor leadership questionnaire, and staff nurses’ intention to stay was measured using intent to stay scale. Descriptive statistics summarized the respondents’ demographic profile, nurse managers’ TL and staff nurses’ intent to stay. Normality was evaluated using Shapiro–Wilk and Kolmogorov–Smirnov tests. Relationships were examined using Spearman’s rank correlation, and multivariable ridge regression modeled the predictive contributions of the overall TL and its five dimensions to intent to stay. Results were considered significant if p < 0.05. Results: Nurse managers’ TL exhibited a positive association with staff nurses’ intention to stay in their current positions (r = 0.22, p < 0.001). Moreover, every dimension of TL demonstrated a strong positive relationship with the intent to stay (all p-values < 0.001). Multivariable ridge regression analysis revealed that the overall TL was a significant predictor of the intent to stay (β = 0.13, p < 0.001). Conclusions: The findings corroborate prior evidence linking TL to retention intentions. This underscores the practical salience of leadership competencies and attributes of nursing leaders, particularly TL, which recognizes the individual needs of staff nurses. This recognition subsequently fosters retention intentions, cultivates supportive nursing work environment, and enhances overall organizational success. Full article

With the continuous expansion in China’s vehicle fleet, emissions of CO₂ and air pollutants from the on-road transportation sector are widely projected to be rising, posing a challenge to realizing China’s targets of carbon peaking in 2030 and carbon neutrality in 2060, as well as the national target for air quality improvement. Therefore, vehicle electrification in the on-road transportation sector is urgently needed to reduce emissions of CO₂ and air pollutants, as it serves as a key pathway to align transportation development with sustainability goals. While vehicle electrification is supposed to be the primary solution, there is a research gap in quantifying the provincial, environmental, and economic impacts of implementing such a policy in China. To bridge this gap, we projected the provincial-level ownership of different types of vehicles based on historical trends, assessed the emission reduction potential for CO₂ and air pollutants using the LEAP model from 2021 to 2060, and predicted the provincial marginal abatement costs at different mitigation stages under various scenarios with different strategies of vehicle electrification and development patterns of electricity structure. Our results show that the implementation of vehicle electrification lowers the national carbon peak by 0.2–0.6 Gt yr⁻¹ and advances its achievement by 1–3 years ahead of 2030. The marginal abatement cost ranges from $532 to $3466 per ton CO₂ (tCO₂⁻¹) in 2025 and from -$180 to -$113 tCO₂⁻¹ in 2060 across scenarios. The provincial marginal abatement cost curves further indicate that China’s vehicle electrification should be prioritized in cost-effective regions (e.g., Shanghai and Guangdong), while concurrently advancing nationwide grid decarbonization to guarantee the supply of low-carbon electricity across the country. This optimized pathway ensures that transportation decarbonization aligns with both environmental and economic requirements, providing actionable support for China’s sustainable development strategy. Full article

Federated learning enables distributed model training across edge nodes without direct raw data sharing, but model parameter transmission still poses significant privacy risks. To address this vulnerability, a Distributed Logistic Regression Gaussian Perturbation (DLGP) algorithm is proposed, which integrates the Alternating Direction Method of Multipliers (ADMM) with a calibrated differential privacy mechanism. The centralized logistic regression problem is decomposed into local subproblems that are solved independently on edge nodes, where only perturbed model parameters are shared with a central server. The Gaussian noise injection mechanism is designed to optimize the privacy–utility trade-off by introducing calibrated uncertainty into parameter updates, effectively obscuring sensitive information while preserving essential model characteristics. The ${\ell }_2$-sensitivity of local updates is derived, and a rigorous $(ϵ,\delta )$-differential privacy guarantee is provided. Evaluations are conducted on a real-world dataset, and it is demonstrated that DLGP maintains favorable performance across varying privacy budgets, numbers of nodes, and penalty parameters. Full article

Stock price modeling under privacy constraints presents a unique challenge at the intersection of computational economics and machine learning. Financial institutions and brokerage firms hold valuable yet sensitive data that cannot be centrally aggregated due to privacy laws and competitive concerns. To address this issue, we propose a novel Fast-Converging Federated Learning (FCFL) framework that enables decentralized and privacy-preserving stock price modeling. FCFL employs a dual-stage adaptive optimization strategy that dynamically tunes local learning rates and aggregation weights based on inter-client gradient divergence, accelerating convergence in heterogeneous financial environments. The framework integrates secure aggregation and differential privacy mechanisms to prevent information leakage during communication while maintaining model fidelity. Experimental results on multi-institutional stock datasets demonstrate that FCFL achieves up to 30% faster convergence and 2.5% lower prediction error compared to conventional federated averaging approaches, while guaranteeing strong $\epsilon$-differential privacy. Theoretical analysis further proves that the framework attains sublinear convergence in $\mathcal{O}\left(\log T\right)$ communication rounds under non-IID data distributions. This study provides a new direction for collaborative financial modeling, balancing efficiency, accuracy, and privacy in real-world economic systems. Full article

Linear programming and polyhedral representation conversion methods have been widely applied to game theory to compute equilibria. Here, we introduce new applications of these methods to two game-theoretic scenarios in which players aim to secure sufficiently large payoffs rather than maximum payoffs. The first scenario concerns truncation selection, a variant of the replicator equation in evolutionary game theory where players with fitnesses above a threshold survive and reproduce while the remainder are culled. We use linear programming to find the sets of equilibria of this dynamical system and show how they change as the threshold varies. The second scenario considers opponents who are not fully rational but display partial malice: they require a minimum guaranteed payoff before acting to minimize their opponent’s payoff. For such cases, we show how generalized maximin procedures can be computed with linear programming to yield improved defensive strategies against such players beyond the classical maximin approach. For both scenarios, we provide detailed computational procedures and illustrate the results with numerical examples. Full article

This paper investigates the existence of square-mean S-asymptotically $(\omega ,c)$-periodic solutions for a class of neutral impulsive stochastic differential equations driven by fractional Brownian motion, addressing the challenge of modeling long-range dependencies, delayed feedback, and abrupt changes in systems like biological networks or mechanical oscillators. By employing semigroup theory to derive mild solution representations and the Banach contraction principle, we establish sufficient conditions–such as Lipschitz continuity of nonlinear terms and growth bounds on the resolvent operator—that guarantee the uniqueness and existence of such solutions in the space ${\mathcal{S}AP}_{\omega ,c}([0,\infty ),L^2(\Omega ,\mathbb{H}))$. The important results demonstrate that under these assumptions, the mild solution exhibits square-mean S-asymptotic $(\omega ,c)$-periodicity, enabling robust asymptotic analysis beyond classical periodicity. We illustrate these findings with examples, such as a neutral stochastic heat equation with impulses, revealing stability thresholds and decay rates and highlighting the framework’s utility in predicting long-term dynamics. These outcomes advance stochastic analysis by unifying neutral, impulsive, and fractional noise effects, with potential applications in control theory and engineering. Full article

Most existing fractional models of COVID-19 describe only the infection process without explicitly accounting for the role of vaccination. In this study, a refined Caputo fractional model is proposed that incorporates a vaccinated class to better understand how immunization influences disease progression. The mathematical formulation guarantees the existence, uniqueness, and positivity of solutions, ensuring that all system trajectories remain biologically valid. The equilibrium points are obtained, and the reproduction number is derived to identify the conditions for disease control. The stability investigation covers local behavior alongside Ulam–Hyers and its extended variants, ensuring the system remains stable under small perturbations. Numerical experiments performed with the Adams–Bashforth–Moulton algorithm illustrate that vaccination reduces infection peaks and shortens the epidemic duration. Overall, the proposed framework enriches fractional epidemiological modeling by providing deeper insight into the combined effects of memory and vaccination in controlling infectious diseases. Full article

In light of urbanization, climate change, and the escalation of extreme weather events, flood management is becoming more and more important. Improving community resilience and reducing flood risks require prompt decision-making and effective communication. This study investigates how flood management systems can incorporate Large Language Models (LLMs), especially those that use Retrieval-Augmented Generation (RAG) architectures. We suggest a multimodal framework that uses a Flood Knowledge Graph to aggregate data from various sources, such as social media, hydrological, and meteorological inputs. Although LLMs have the potential to be transformative, we also address important drawbacks like governance issues, hallucination risks, and a lack of physical modeling capabilities. When compared to text-only LLMs, the RAG system significantly improves the reliability of flood-related decision support by reducing factual inconsistency rates by more than 75%. Our suggested architecture includes expert validation and security layers to guarantee dependable, useful results, like flood-constrained evacuation route planning. In areas that are vulnerable to flooding, this strategy seeks to strengthen warning systems, enhance information sharing, and build resilient communities. Full article

In today’s rapidly advancing healthcare landscape, integrating Artificial Intelligence (AI) and Machine Learning (ML) has the potential to significantly improve patient care and streamline medical processes. The utilization of confidential patient data to train and develop these technologies, however, raises significant concerns regarding authenticity, security, and privacy. In this study, we introduce MediChainAI, a safe and practical framework that allows patients full ownership over their own health data by integrating Self-Sovereign Identity (SSI), Blockchain, and sophisticated cryptography techniques. By clearly outlining the goals and parameters of this access, MediChainAI allows patients to safely and selectively share data with healthcare providers and researchers. While SSI guarantees that patients have ownership of their data, the framework uses Blockchain technology to keep things transparent and secure. Further, MediChainAI makes use of Merkle trees, which provide verified access to subsets of data without jeopardizing the privacy of the whole dataset. The encryption mechanism, which is based on smart contracts, is a distinctive feature of the framework that allows researchers and medical practitioners controlled and secure access to patient data. In order to improve the accuracy and reliability of medical diagnoses and treatment, this strategy makes sure that only confirmed, legitimate data is utilized to train medical models. A significant step toward safer and more personalized healthcare, MediChainAI encourages ethical and patient-focused innovation by effectively resolving essential issues regarding data security and patient privacy. Full article

Floating offshore wind turbines (FOWTs) face complex aero-hydro-servo-elastic interactions that challenge conventional modeling, monitoring, and control. This review critically examines how artificial intelligence (AI) is being applied across four domains—design and surrogate modeling, structural health monitoring, control and operations, and digital twins—with explicit attention to uncertainty and reliability. Using PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a Scopus search identified 412 records; after filtering for articles, conference papers, and open access, 115 studies were analyzed. We organize the literature into a taxonomy covering classical supervised learning, deep neural surrogates, physics-informed and hybrid models, reinforcement learning, digital twins with online learning, and uncertainty-aware approaches. Neural surrogates accelerate coupled simulations; probabilistic encoders improve structural health monitoring; model predictive control and trust-region reinforcement learning enhance adaptive control; and digital twins integrate reduced-order physics with data-driven calibration for lifecycle management. The corpus reveals progress but also recurring limitations: simulation-heavy validation, inconsistent metrics, and insufficient field-scale evidence. We conclude with a bias-aware synthesis and propose priorities for future work, including shared benchmarks, safe RL with stability guarantees, twin-in-the-loop testing, and uncertainty-to-decision standards that connect model outputs to certification and operational risk. Full article

Laser cladding is an essential method for strengthening and restoring component surfaces. To increase its efficacy and provide a reliable surface treatment technique, it is necessary to optimize process parameters, enhance material adhesion, and guarantee high-quality, reliable coatings. These measures help to extend the lifespan of components. In this study, the surfaces of AISI 904L stainless steel samples were cladded to prepare various Co-based composite coatings with single and multiple layers reinforced with WC–CoCr–Ni powder. The phases within the newly developed layers were investigated using X-ray Diffraction (XRD), while the microstructure was examined using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX). Further tests were performed to assess the hardness, wear resistance and corrosion performance of the deposited coatings. Analyzing and comparing the coatings, it was observed that the coating performance increased with increasing thickness and generally due to a lower amount of Fe present within the microstructure. Full article

The study focuses on an advanced numerical framework designed to optimize an electric car’s aerodynamic efficiency through the slanting front bumper. The study begins with a comparative analysis of four angular configurations (−4°, 0°, 4°, and 8°) using computational fluid dynamics (CFD). It concludes that an angle of 4° improves resource productivity and dynastic balance by reducing drag (Cd = 0.26) and guaranteeing controlled lift (Cl = 0.030). In order to further this research, ANSYS DesignXplorer 2019 R3 was used for parametric optimization, which included direct parameterization of the angle in the simulation process. A quadratic response surface was constructed using the CFD findings, and an optimality point with a Cd value of 0.2601 and a Cl value of 0.0302 was found at 3.9998°. Because this solution is part of the Pareto front, its use demonstrates the significance of the chosen geometric configuration. The approach is innovative because it combines a simple geometric transformation with an automated, repeatable simulation method to a degree appropriate for an industrial setting. The results show that modifying the front bumper in a particular way is a successful way to improve the aerodynamic performance of electric vehicles, with the added potential to function at other required locations on the vehicle body. Full article

Stochastic differential equations (SDEs) with multiple conserved quantities are ubiquitous in scientific fields, modeling systems from molecular dynamics to celestial mechanics. While geometric numerical integrators that preserve single invariants are well-established, constructing efficient and high-order numerical schemes for SDEs with multiple conserved quantities remains a challenge. Existing approaches often suffer from high computational costs or lack desirable numerical properties like symmetry. This paper introduces two novel classes of projection-based numerical methods tailored for SDEs with multiple conserved quantities. The first method projects the increments of an underlying numerical scheme onto a discrete tangent space, ensuring all invariants are preserved by construction. The second method leverages a local coordinates approach, transforming the SDE onto the manifold defined by the invariants, solving it numerically, and then projecting back, guaranteeing the solution evolves on the correct manifold. We prove that both methods inherit the mean-square convergence order of their underlying schemes. Furthermore, we propose a simplified strategy that reduces computational expense by redefining the multiple invariants into a single one, offering a practical trade-off between exact preservation and efficiency. Numerical experiments confirm the theoretical findings and demonstrate the superior efficiency and structure-preserving capabilities of our methods. Full article

The fatigue of wood is becoming increasingly important in modern engineering, as the safety of the structure must be guaranteed and the use of materials must be optimized at the same time. Predicting the fatigue behavior of wood remains a challenge for many researchers. Interest and the number of studies in this field have increased, highlighting the need for a comprehensive overview of the current state of knowledge on wood fatigue. In this paper, we focus on the study of the fatigue of wood-based materials to understand the similarities and peculiarities of fatigue behavior compared to other engineering materials and to identify opportunities for new research. We present the influence of physical and mechanical properties on fatigue life and identify similarities in the fatigue behavior of wood, polymeric materials and steel. The basic properties that differentiate the fatigue life of wood from that of other materials are heterogeneity, orthotropy, viscoelasticity, hygroscopicity, mechanosorptivity and the lack of a clear threshold value for fatigue strength. The differences in fatigue life between solid wood and laminated wood are not uniformly defined by researchers. We provide an overview of the measurement methods used to monitor the fatigue state, the models used to predict fatigue life and the simulations of the stress–strain response to cyclic loading. We identify areas where wood is subject to fatigue and determine which areas are most critical under cyclic loading. We make suggestions for further research that would contribute significantly to a better understanding and management of wood fatigue. Due to the wide variety of wood species used in the studies, it is impossible to compare the results. In order to obtain a comprehensive overview of the response of wood to fatigue under different test conditions, the test methods need to be standardized. Full article