Search Results (6,434)

We provide sufficient conditions for the existence of $(\omega ,c)$-periodic solutions of a general class of nonlinear functional integral equations. This study extends and generalizes previous contributions in the literature. As an application of the developed theory, we establish the existence of $(\omega ,c)$-periodic solutions for recurrent neural networks with time-varying coefficients and mixed delays, as well as for a class of nonlinear Volterra–Stieltjes integral equations with infinite delay. Full article

Background/Objectives: Japan’s mental healthcare system is characterized by the world’s highest number of psychiatric beds, widespread “social hospitalization,” and a structurally entrenched managerial support model that frequently undermines patient autonomy. Joint Crisis Plans (JCPs)—collaboratively developed crisis management documents—have been increasingly adopted as a care coordination tool; however, their role in transforming professional practice towards recovery-oriented support remains underexplored. This study aimed to elucidate the experiences of professionals utilizing JCPs across diverse facility types and to develop a theoretical understanding of the process by which they redefine their role from ‘manager’ to ‘recovery companion’. Methods: A qualitative design using the Modified Grounded Theory Approach (M-GTA), grounded in symbolic interactionism, was employed. Semi-structured interviews were conducted with 13 professionals (7 nurses, 6 mental health and welfare workers) across nine facilities (psychiatric hospitals, 24-h residential facilities, outpatient facilities) in the Kanto region of Japan. Theoretical sampling continued until saturation. Data were analyzed using the constant comparative method, with validity ensured through team checking. Results: Nine categories and 23 subcategories were extracted. A three-stage support transformation process emerged: (1) Stage of Motivation and Initial Support, in which professionals confronted the limitations of managerial practice; (2) Stage of Collaborative Role Redefinition and Practice, involving joint crisis management, strength-based support, and network building; and (3) Stage of Integration of Support Perspectives and Recovery-Oriented Practice, in which professionals witnessed individual recovery and integrated new support values into their practice. Negative cases revealed that JCP effectiveness is contingent on the co-construction of shared meaning rather than procedural compliance. Conclusions: JCP was suggested to function as a potential tool to facilitate navigating and reframing structural managerial barriers in Japanese mental healthcare. The creation of a shared language through JCP was associated with supporting conditions for individual self-determination, alleviating professional conflicts, and contributing to shifts in organizational culture. Full article

While general network dynamics have been extensively modeled using stochastic methods, the emergence of dense Internet of Things (IoT) ecosystems demands a more specialized analytical framework. IoT environments are characterized by extreme non-linearity and sensitivity to initial conditions, where traditional models often fail to account for chaotic latency and packet loss. This paper introduces a specialized approach that integrates Chaos Theory with the innovative paradigm of Vibe Coding—an AI-assisted development and analysis methodology that allows for the `encoding’ and interpretation of the dynamic `vibe’ or signature of network fluctuations in real-time. By categorizing network behavior into four distinct scenarios (quiescent, perturbed, attacked, and perturbed–Attacked), the proposed framework utilizes deep learning to transform chaotic signals into actionable intelligence. Our findings demonstrate that this specialized synergy between chaos analysis and Vibe Coding provides superior classification of adversarial threats, such as DoS and injection attacks, fostering intelligent native security for next-generation IoT infrastructures. Full article

To address the issues that carbon trading fails to cover the full life cycle and that traditional demand response achieves poor emission reduction due to a lack of accurate carbon-intensity feedback in park integrated energy systems (PIESs) during low-carbon transition, this study proposes a two-layer optimal scheduling method synergizing life-cycle stepwise carbon trading and low-carbon demand response (LCDR) to balance low-carbon performance and economic efficiency. Firstly, based on life cycle theory, embodied carbon from new energy equipment manufacturing and transportation is incorporated into accounting, with a stepwise carbon trading mechanism designed. Secondly, corrected dynamic carbon emission factors for power and heating networks are constructed to quantify real-time carbon intensity. A dual-driven LCDR model (electricity price and carbon factor) is established to coordinate shiftable and sheddable electric-thermal loads and is combined with a two-layer scheduling model (pre-scheduling and re-scheduling) targeting the minimal total operation cost. Simulation results of a South China park show that life-cycle stepwise carbon trading reduces emissions by 16.7%, and LCDR further cuts 4.05%. Their synergy achieves significant carbon reduction with a slight cost increase, while supplementary sensitivity analyses further confirm the scalability and robustness of the proposed framework under varying load levels and demand response capabilities. Full article

With the rapid growth of data, feature selection has become essential for improving machine learning performance. However, most existing unsupervised feature selection methods rely on greedy strategies, which often lead to suboptimal solutions. Moreover, traditional information–theoretic approaches are primarily designed for discrete data and require discretization when applied to continuous data, potentially causing information loss. To address these issues, this paper proposes a global unsupervised feature selection method based on fuzzy mutual information (UFS-FMI). The proposed method integrates fuzzy set theory with information measures to quantify feature relevance and redundancy, and formulates a fractional optimization model. A combination of projection neural networks and kWTA neural networks is employed to achieve global optimization. Experimental results on nine UCI benchmark datasets demonstrate that UFS-FMI consistently outperforms several representative methods in terms of classification accuracy, clustering accuracy, and normalized mutual information (NMI). In particular, on datasets such as Movement_libras, Ionosphere, and Control, the proposed method achieves significantly improved classification performance, confirming its effectiveness and robustness. Full article

The Historic Urban Landscape (HUL) approach provides a vital and extensive framework for heritage conservation. However, local practices often struggle to spatially translate qualitative assessments into quantitative controls at the urban block level, the most effective basic scale for administrative implementation, thereby limiting effective responses to the Management of Change. By integrating HUL with the theory of Dynamic Integrity, this study constructs a multi-dimensional evaluation index system and proposes a HUL evaluation method based on Character-Based Zoning. Taking the 125 urban block units of the historic urban area of Jingdezhen as a case study, this research integrates historical mapping, GIS spatial analysis, and Co-occurrence Network Analysis to reveal the internal structural logic of the heritage system. The study finds that the HUL of Jingdezhen is a multi-nodal dynamic system driven by four core elements: ritual beliefs, administrative management, production activities, and commercial guilds. Critically, modern visual intrusions severely impact the core heritage components within this system, specifically the Dubang and ritual culture. Based on the three dimensions of Heritage Richness, Landscape Sensitivity and Value Centrality, the study systematically identifies a total of 11 types of urban block units within the plots that characterize distinct historic landscape features and transformation patterns. This research not only deepens the localized application of HUL theory but also provides a scientific basis and methodological support for the Management of Change and periodic assessment in dynamic heritage environments. Full article

Accurate social bot detection relies on simulated data to alleviate the scarcity of labeled real-world datasets. Synthetic graph data serves as the core training resource for detection models within simulated data; nevertheless, edge deficiency in real social networks (induced by privacy constraints and data collection limitations) gives rise to “pseudo-isolated nodes” and distorts the quality of synthetic graph data. Furthermore, mainstream data-driven synthetic graph generation methods lack systematic and credible comparative analyses. To tackle these problems, this study optimizes two representative synthetic graph generation approaches (the Chung-Lu model and the Random Classifier-based Multi-Hop (RCMH) sampling + diffusion model) and puts forward an edge completion strategy grounded in sociological theories. Multiple groups of comparative experiments are conducted to assess the performance of the improved methods and the edge completion strategy. Experimental results demonstrate that the “interest + social association” edge completion strategy achieves an F1-score (F1) of 0.7051, and the improved sampling + diffusion model integrated with edge completion reaches an F1-score of 0.7071, which performs better than traditional and unmodified methods to a certain extent. This work preliminarily enhances the reliability of synthetic graph generation methods and provides relatively high-quality synthetic social graph data for social bot detection. It should be noted that the proposed methods are validated solely on Twitter-derived datasets, and their effectiveness remains to be verified in cross-platform adaptation and dynamic social network scenarios. Full article

Wireless sensor networks (WSNs) are fundamental building blocks of modern ubiquitous sensing systems. In many practical WSN deployments, sensing devices are tightly constrained in buffer capacity, while device mobility leads to topology decentralization. These characteristics pose significant challenges for reliable and timely data delivery across WSNs. In this paper, we propose a general multi-probing opportunistic routing strategy tailored for buffer-constrained WSNs, aiming to enhance transmission opportunity utilization under realistic sensing device limitations. With the help of Queueing Theory and Markov Chain Theory, we capture the sophisticated queueing processes for the buffer space of sensors, which enables the limiting distribution of the buffer occupation state to be determined. On this basis, we develop a theoretical performance modeling framework to evaluate the fundamental performance metrics of the WSN with the multi-probing opportunistic routing, including the per-flow throughput and the expected end-to-end delay. The validity of the performance modeling framework is verified by network simulations. Moreover, extensive numerical results demonstrate the network performance behaviors comprehensively and reveal some insightful findings that can serve as important guidelines for the configuration and operation of WSNs. Full article

Non-convex stochastic optimization presents fundamental mathematical challenges across machine learning, wireless networks, data center resource allocation, and optical wireless communication systems, where complex loss landscapes with multiple local minima and saddle points impede classical variational inference methods. This paper introduces the Quantum-Inspired Variational Inference (QIVI) framework, which systematically integrates quantum mechanical principles (superposition, entanglement, and measurement operators) into classical variational inference through rigorous mathematical formulations grounded in Hilbert space theory and operator algebras. We develop a unified optimization framework that encodes classical parameters as quantum-inspired states within finite-dimensional complex Hilbert spaces, employing unitary evolution operators and adaptive basis selection governed by gradient covariance eigendecomposition. The core mathematical contribution establishes that QIVI achieves a convergence rate of $\mathit{O}({log}^{2}T/T^{1/2})$ for $\sigma$-strongly non-convex functions, provably improving upon the classical $\mathit{O}\left(T^{-1/4}\right)$ rate, yielding a theoretical speedup factor of $1.85$–$1.96\times$. Comprehensive experiments across synthetic benchmarks, Bayesian neural networks, and real-world applications in network optimization and financial portfolio management demonstrate 23–$47\%$ faster convergence, 15–$35\%$ superior objective values, and 28–$46\%$ improved uncertainty calibration. The principal contributions include: (i) a rigorous Hilbert space-based mathematical framework for quantum-inspired variational inference grounded in operator algebras, (ii) a novel hybrid quantum–classical algorithm (QIVI) with adaptive basis selection via gradient covariance eigendecomposition, (iii) formal convergence proofs establishing provable improvement over classical methods, (iv) comprehensive empirical validation across diverse problem domains relevant to machine learning and network optimization, and (v) demonstration of the framework’s applicability to optimization problems arising in wireless networks, data center resource allocation, and network system design. Statistical validation using the Friedman test (${\chi }^2=847.3$, $p<0.001$) and post hoc Wilcoxon signed-rank tests with Holm–Bonferroni correction confirm that QIVI’s improvements over all baseline methods are statistically significant at the $\alpha =0.05$ level across all benchmark categories. The framework discovers $18.1$ out of 20 true modes in multimodal distributions versus $9.1$ for classical methods, demonstrating the potential of quantum-inspired optimization approaches for challenging stochastic problems arising in machine learning, wireless communication, and network optimization. Full article

Existing studies on greenwashing have primarily focused on post-incident supervision, with limited attention given to proactive mechanisms. This study aims to develop an early warning evaluation model for greenwashing behavior in building materials enterprises exposed to negative public opinion. The main findings are as follows: (1) Drawing on actor network theory, gray system theory, the analytic network process, and gray fuzzy comprehensive evaluation, this study constructs an early warning evaluation model for greenwashing behavior in building materials enterprises. This model comprises 5 first-level dimensions and 20 s-level indicators, integrating key stakeholders (i.e., government, negative public opinion, media, the public, and enterprise) and is validated through case analysis. (2) Government dimension: Environmental regulation intensity emerges as the most critical indicator. (3) Negative public opinion dimension: Attention is the most critical indicator. (4) Media dimension: Media visibility ranks as the most critical indicator. (5) Public dimension: Public sentiment is the most influential indicator. (6) Enterprise dimension: The environmental performance level is the most critical indicator. This study offers both theoretical and practical foundations for the early warning, monitoring, and governance of enterprise greenwashing, contributing to the advancement of sustainable development and transparent environmental communication in the building materials industry. Full article

The safety and reliability of intelligent transportation systems (ITSs) can be greatly enhanced through adding redundancy in the information-sharing network of the vehicles. In this paper, we first model the mixed traffic of human-driven and autonomous vehicles as a distributed system observability problem using a network of communicating vehicles. We clearly show that a strongly connected network with a minimum of $n$ links (with $n$ as the network size) is sufficient for the observability of a mixed-traffic network. Then, we present graph-theoretic results on adding redundancy to the changing network of vehicles to make it resilient to the failure of a certain number of vehicles/sensors or their data-sharing links. Finally, we employ a distributed observer design to validate our results using a simple mixed-traffic example. Full article

Constructing resilient Ecological Security Patterns (ESPs) in polycentric urban agglomerations is computationally challenging due to persistent scale mismatches between local planning and regional strategies. To address this, we developed a novel Proactive Integration Mechanism (PIM), a computational framework that dynamically optimizes ESPs by algorithmically fusing multi-source geospatial data. The PIM integrates three innovative components: (1) a Function–Structure–Policy data fusion approach that couples Self-Organizing Map clustering of ecosystem services with Morphological Spatial Pattern Analysis and policy data to identify ecological sources; (2) a Dual-Feedback Mechanism that hybridizes circuit theory with an Improved Ant Colony Optimization algorithm for dynamic corridor delineation; and (3) complex network analysis to derive targeted interventions from topological properties. Applied to a node city of the Chengdu-Chongqing Economic Circle, the PIM identified 22 integrated ecological sources and 37 corridors. The optimized network showed enhanced resilience: a deterministic 20.5% increase in circuit redundancy (α-index) and an 8.6% improvement in overall connectivity (γ-index), achieved through minimal topological modifications. Temporal validation (2000–2020) confirmed the high stability of the identified patterns. This study provides a potentially replicable and computationally robust framework that bridges spatial ecology with optimization algorithms, offering a promising paradigm for constructing ESPs in node cities within subtropical urban agglomerations. Full article

Emergency Alert and Warning Systems (EAWSs) are essential components of sustainable disaster risk reduction, providing communities with timely information to prepare for and respond to impending hazards. In the Philippines, one of the world’s most disaster-prone countries, earthquakes, typhoons, and other natural hazards occur frequently. However, national statistics from 2018 indicated that only 40% of Filipinos considered themselves well prepared for disasters, while 31% reported being slightly prepared or not prepared at all. This study investigates the perceived effectiveness of EAWSs in enhancing disaster awareness and preparedness among Filipino residents. Guided by the Theory of Planned Behavior (TPB), the research develops an integrated framework to examine behavioral, technical, and perceptual factors influencing preparedness intentions. Data were collected from 200 respondents through a structured survey. Structural Equation Modeling (SEM) was employed to identify significant linear relationships among the constructs, while an Artificial Neural Network (ANN) analysis was subsequently applied to capture nonlinear patterns and rank the relative importance of key predictors. Unlike previous studies that rely solely on SEM or descriptive approaches, the combined SEM–ANN framework enables a more comprehensive understanding of both causal relationships and complex behavioral dynamics influencing disaster preparedness. The findings reveal that behavioral intention, system reliability, message clarity, and trust in EAWS substantially affect individuals’ preparedness behavior and risk mitigation actions. These results underscore the importance of strengthening EAWS design and communication strategies to support long-term disaster resilience. The study provides practical insights for national agencies, local governments, and policymakers on refining emergency communication systems and developing sustainable, evidence-based disaster preparedness initiatives. Full article

Intelligent-assisted assessment of pilot flight training ability is a method of automating the evaluation of pilots’ flight skills using artificial intelligence. Currently, using AI to assist or replace human instructors in flight skill assessment has become a mainstream research direction in the field of intelligent aviation. Existing flight skill assessment methods suffer from limitations in data types and insufficient assessment accuracy. To address these issues, we evaluate and predict pilot performance in simulated flight missions based on physiological signals. Following the “OODA loop” theory, we established a multimodal dataset including pilot eye movement, electroencephalogram (EEG), electrocardiogram (ECG), electrodermal signaling (EDS), heart rate, respiration, and flight attitude data. This dataset records changes in physiological rhythms and flight behaviors during pilots’ flight training at different difficulty levels. To enhance the signal-to-noise ratio, we propose an enhanced wavelet fuzzy thresholding denoising algorithm utilizing LSTM optimization. We address the problem of isolated features across different time frames in multimodal data modeling by introducing a multi-feature fusion algorithm based on STFT. Furthermore, by combining a high-efficiency sub-attention mechanism with a Transformer network, we construct a multi-classification network for intelligent-assisted assessment of pilot flight training ability, further improving the output accuracy of each category. Experiments show that our designed algorithm can achieve a classification accuracy of up to 85% on the dataset (5-fold cross-validation), which meets the requirements for auxiliary assessment of flight capabilities. Full article

Underground infrastructure systems are typically managed as discrete technical assets rather than as integrated, adaptive systems. This paper develops the Body Underground framework, a structured biological analogy that synthesizes prior clinical and epidemiological metaphors into a multiscale conceptual model linking materials, facilities, networks, and governance. Building on Little’s clinical framing of infrastructure health and Bardet and Little’s epidemiological analysis of network failure clustering, the framework extends biological interpretation to anatomical, physiological, and homeostatic scales. The approach maps structural, hydraulic, sensing, protective, and regulatory functions to functional equivalents in living systems using explicit criteria of feedback, regulation, and measurability. The central objective of the study is to determine whether biological regulatory concepts—particularly homeostasis and hierarchical organization—can provide a coherent interpretive structure for understanding infrastructure health across material, facility, network, and governance scales. The resulting framework reframes resilience as dynamic regulatory balance rather than static robustness alone. It clarifies the methodological basis for constructing biological–infrastructure analogies, identifies measurable “vital signs” for infrastructure health, and outlines pathways toward operational translation through integrated monitoring and governance feedback. While conceptual in nature, the framework provides a structured synthesis linking material science, infrastructure engineering, systems resilience theory, and policy coordination. By organizing resilience concepts through cross-scale regulatory logic, the Body Underground model offers a coherent structure for integrating monitoring, diagnosis, and governance in the proactive management of underground infrastructure systems. Full article