Search Results (113)

During the last decade, F-contraction has been a widely investigated problem in the fixed point theory. There are various outcomes regarding the extensions and generalizations of F-contraction in different perspectives, along with the findings concerning the application of those ideas, mostly in the area of differential and difference equations, fractional calculus, etc. The present article concludes some existence and uniqueness outcomes on fixed points for $(\phi ,\mathrm{F})$–contractions in the context of a metric space endowed with a local class of transitive binary relations. Some illustrative examples are furnished to justify that our contraction conditions are more general than many others in this area. The findings presented herein are used to obtain a unique solution to certain fractional boundary value problems. Full article

In this article, we investigate a nonlinear $\psi$-Hilfer fractional order Volterra integro-delay differential equation ($\psi$-Hilfer FRVIDDE) and a nonlinear $\psi$-Hilfer fractional Volterra delay integral equation ($\psi$-Hilfer FRVDIE), both of which incorporate multiple variable time delays. We establish sufficient conditions for the existence of a unique solution and the Ulam–Hyers stability (U-H stability) of both the $\psi$-Hilfer FRVIDDE and $\psi$-the Hilfer FRVDIE through two new main results. The proof technique relies on the Banach contraction mapping principle, properties of the Hilfer operator, and some additional analytical tools. The considered $\psi$-Hilfer FRVIDDE and $\psi$-Hilfer FRVDIE are new fractional mathematical models in the relevant literature. They extend and improve some available related fractional mathematical models from cases without delay to models incorporating multiple variable time delays, and they also provide new contributions to the qualitative theory of fractional delay differential and fractional delay integral equations. We also give two new examples to verify the applicability of main results of the article. Finally, the article presents substantial and novel results with new examples, contributing to the relevant literature. Full article

Bolted connections are critical in deep-sea engineering, yet classical theories (such as VDI 2230) implicitly assume atmospheric pressure conditions, neglecting the volume contraction of components due to hydrostatic pressure. This fundamental flaw hinders accurate prediction of preload retention—especially when bolts and clamped components exhibit differential compressibility (a common scenario in practical applications). To bridge this scientific gap, this paper establishes the first analytical model for bolt preload under pressure-induced volumetric contraction based on deformation coordination relations. The derived closed-form expressions explicitly quantify residual preload as a function of deep-sea ambient pressure, component bulk modulus, and geometric parameters. Model predictions closely match finite element calculations, showing that stainless steel bolts clamping aluminum alloys under 110 MPa pressure can experience up to a 40% preload reduction. This theoretical framework extends classical bolt connection mechanics to high-pressure environments, providing a scientific basis for optimizing deep-sea connection designs through material matching and dimensional control to effectively mitigate pressure-induced preload loss. Full article

As AI systems increasingly structure educational processes, they shape not only what is learned, but also how epistemic authority is distributed and whose knowledge is recognized. This article explores the normative and technopolitical implications of this development by examining two prominent paradigms in AI ethics: Ethical AI and Responsible AI. Although often treated as synonymous, these frameworks reflect distinct tensions between formal universalism and contextual responsiveness, between rule-based evaluation and governance-oriented design. Drawing on deontology, utilitarianism, responsibility ethics, contract theory, and the capability approach, the article analyzes the frictions that emerge when these frameworks are applied to algorithmically mediated education. The argument situates these tensions within broader philosophical debates on technological mediation, normative infrastructures, and the ethics of sociotechnical design. Through empirical examples such as algorithmic grading and AI-mediated admissions, the article shows how predictive systems embed values into optimization routines, thereby reshaping educational space and interpretive agency. In response, it develops the concept of situated ethics, emphasizing epistemic justice, learner autonomy, and democratic judgment as central criteria for evaluating educational AI. To clarify what is at stake, the article distinguishes adaptive learning optimization from education as a process of subject formation and democratic teaching objectives. Rather than viewing AI as an external tool, the article conceptualizes it as a co-constitutive actor within pedagogical practice. Ethical reflection must therefore be integrated into design, implementation, and institutional contexts from the outset. Accordingly, the article offers (1) a conceptual map of ethical paradigms, (2) a criteria-based evaluative lens, and (3) a practice-oriented diagnostic framework to guide situated ethics in educational AI. The paper ultimately argues for an approach that attends to the relational, political, and epistemic dimensions of AI systems in education. Full article

After the introduction of the relation-theoretic contraction principle, the branch of metric fixed-point theory has attracted much attention in this direction, and various fixed-point results have been proven in the framework of relational metric space via different approaches. The aim of this article is to establish some fixed-point outcomes in the framework of relational metric space verifying a generalized nonlinear contraction utilizing three test functions $\mathrm{\Phi }$, $\mathrm{\Psi }$ and $\mathrm{\Theta }$ satisfying the appropriate characteristics. The findings obtained herein expand, sharpen, improve, modify and unify a few well-known findings. To demonstrate the utility of our outcomes, several examples are furnished. We utilized our outcomes to investigate a unique solution of second-order ordinary differential equations prescribed with specific boundary conditions. Full article

Employee turnover remains a major concern for businesses globally. In Western contexts, the concept of psychological contract breach (PCB) is often employed to understand this phenomenon. This paper takes Bangladesh’s readymade garment (RMG) sector as a case study to explore the factors that support employee retention in their jobs, despite reported poor working conditions and associated issues in garment factories. Data were gathered among 400 RMG workers and linear regression analysis was used to answer this question. We demonstrated that while PCB was positively related to turnover intention, its impact on the retention of Bangladeshi garment workers was relatively minimal. Qualitative data showed how cultural and social factors distinct from known Western retention causes shaped our findings. The evidence presented in this paper sheds new light on employee retention in a Bangladeshi context, where socio-cultural issues challenge the PCB theory, which was developed largely based on Western economies. Full article

Environmental monitoring is the basis of environmental protection. China’s existing environmental monitoring system has been relatively perfect, but there are still data fraud and other illegal issues. Blockchain technology can well meet the requirements of environmental monitoring, but there are many obstacles in its adoption process, so this paper combines the characteristics of blockchain technology to integrate the two stakeholders of government and polluting enterprises into a unified model and introduces parameters related to smart contracts and corruption. The dynamic evolutionary game theory, combined with numerical simulation, is used to explore the behavioral decision-making characteristics and change rules of relevant stakeholders. The results show that there are stable conditions for the three strategies. Compared with the development cost of blockchain, the management cost of blockchain has a greater impact on the strategy choice of polluting enterprises because the income of polluting enterprises adopting blockchain technology can greatly affect the strategy choice of polluting enterprises, and there is a positive correlation between the income and the willingness of polluting enterprises to choose blockchain technology; only the construction cost of blockchain will cause fluctuations in the government’s strategy choice, and other factors will not have a greater impact on the government’s choice. This study provides a useful reference for promoting the adoption of blockchain technology in the field of environmental protection. Full article

This study examines how the organizational work climate shapes the effectiveness of supervision on employee performance. While traditional management theory assumes supervision universally enhances productivity, we observe a puzzling paradox: facing identical tasks and wage systems, some firms rely heavily on hierarchical supervision while others thrive with minimal oversight. Through a four-month field experiment across two Chinese agricultural enterprises (5851 observations), we test whether the supervision’s effectiveness depends on the alignment between leadership practices and organizational climate. In formal management firms (FMFs) characterized by hierarchical governance and arm’s-length employment relationships, directive supervision significantly reduces task completion times by 0.126 standard deviations, equivalent to approximately 4.3 s or 2.8% of the average completion time, with this effect remaining stable throughout the workday. Conversely, in network-embedded firms (NEFs) operating through trust-based relational contracts and social norms, identical supervisory practices yield no performance gains, as informal social control mechanisms already ensure high effort levels, rendering formal supervision redundant. These findings challenge the “best practices” paradigm in strategic HRM, demonstrating that HR success requires a careful alignment between leadership approaches and the organizational climate—an effective HR strategy is not about implementing standardized practices but about achieving a strategic fit between supervisory leadership styles and existing work climates. This climate–leadership partnership is essential for optimizing both employee performance and organizational success. Full article

For fractals on Riemannian manifolds, the theory of iterated function systems often does not apply well directly, as these fractal sets are often defined by relations that are multivalued or non-contractive. To overcome this difficulty, we introduce the novel notion of iterated relation systems. We study the attractor of an iterated relation system and formulate a condition under which such an attractor can be identified with that of an associated graph-directed iterated function system. Using this method, we obtain dimension formulas for the attractor of an iterated relation system on locally Euclidean Riemannian manifolds, under the graph open set condition or the graph finite type condition. This method improves the one by Ngai and Xu, which relies on knowing the specific structure of the attractor. We also study fractals generated by iterated relation systems on Riemannian manifolds that are not locally Euclidean. Full article

Recent developments in fixed-point theory have focused on iterative techniques for approximating solutions, yet there remain important questions about whether different methods are equivalent and how well they resist perturbations. In this study, two recently proposed algorithms, referred to as the DF and AR iteration methods, are shown to be connected by proving that they converge similarly when applied to contraction mappings in Banach spaces, provided that their control sequences meet specific, explicit conditions. This work extends previous research on data dependence by removing restrictive assumptions related to both the perturbed operator and the algorithmic parameters, thereby increasing the range of situations where the results are applicable. Utilizing a non-asymptotic analysis, the authors derive improved error bounds for fixed-point deviations under operator perturbations, achieving a tightening of these estimates by a factor of 3–15 compared to earlier results. A key contribution of this study is the demonstration that small approximation errors lead only to proportionally small deviations from equilibrium, which is formalized in bounds of the form $\parallel s^{*}-{\tilde{s}}^{*}\parallel \le O(\epsilon /(1-\lambda ))$. These theoretical findings are validated through applications involving integral equations and examples from function spaces. Overall, this work unifies the convergence analysis of different iterative methods, enhances guarantees regarding stability, and provides practical tools for robust computational methods in areas such as optimization, differential equations, and machine learning. By relaxing structural constraints and offering a detailed sensitivity analysis, this study significantly advances the design and understanding of iterative algorithms in applied mathematics. Full article

This study investigates how bank mergers and acquisitions (M&As) reshape the monitoring architecture of syndicated loans and, by extension, borrowers’ financing conditions. Using a global panel of 20,299 syndicated loan contracts, originating in 43 countries between 1982 and 2020, we link LPC DealScan data to Securities Data Company M&A records to trace each loan’s lead arrangers before and after consolidation events. Fixed-effects regressions, enriched with borrower- and loan-level controls, reveal three key patterns. First, post-merger loans exhibit significantly more concentrated syndicates: the Herfindahl–Hirschman Index rises by roughly 130 points and lead arrangers retain an additional 0.8–1.1 percentage points of the loan, consistent with heightened monitoring incentives. Second, these effects are amplified when information asymmetry is acute, i.e., for opaque or unrated firms, supporting moral hazard theory predictions that lenders internalize greater risk by holding larger stakes. Third, relational capital tempers the impact of consolidation: borrowers with repeated pre-merger relationships face smaller increases in syndicate concentration, while switchers experience the most significant jumps. Robustness checks using lead arranger market share, alternative spread measures, and lag structures confirm the findings. Overall, the results suggest that bank consolidation strengthens lead arrangers’ incentives to monitor but simultaneously reduces risk-sharing among participant lenders. For borrowers, the net effect is a trade-off between potentially tighter oversight and reduced syndicate diversification, with the balance hinging on transparency and prior ties to the lender. These insights refine our understanding of how structural shifts in the banking sector cascade into corporate credit markets and should inform both antitrust assessments and borrower funding strategies. Full article

A simple Lagrangian travelling slice model has been successfully used to predict the relations between the process parameters and the strand temperatures in the continuous casting of steel. The present paper aims to include a simple macrosegregation, grain structure and mechanical stress and deformation model on top of the thermal slice framework. The basis of all the mentioned models is the slice heat-conduction model that considers the complex heat extraction mechanisms in the mould, with the sprays, rolls, and through radiation. Its main advantage is the fast calculation time, which is suitable for the online control of the caster. The macroscopic thermal and species transfer models are based on the continuum mixture theory. The macrosegregation model is based on the lever rule microsegregation model. The thermal conductivity and species diffusivity of the liquid phase are artificially enhanced to consider the convection of the melt. The grain structure model is based on cellular automata and phase-field concepts. The calculated thermal field is used to estimate the thermal contraction of the solid shell, which, in combination with the metallostatic pressure, drives the elastic-viscoplastic solid-mechanics models. The solution procedure of all the models is based on the meshless radial basis function generated finite difference method on the macroscopic scale and the meshless point automata concept on the grain structure scale. Simulation results point out the areas susceptible to hot tearing. Full article

This study investigates how state-owned enterprises (SOEs) contribute to rural revitalization in China through systemic interventions that enable the transfer of social capital. Addressing the gap between external resource inputs and internal development needs, the study adopts a systems thinking framework to conceptualize social capital as comprising structural, relational, and cognitive components. Drawing on multi-case evidence from assistance projects led by China Southern Power Grid, this study selects 11 assistance projects from a broader pool of 199 cases, to demonstrate how SOEs act as institutional nodes to reshape rural governance systems. They rebuild local organizational networks (structural capital), establish long-term trust through “strong commitment–weak contract” mechanisms (relational capital), and localize technical knowledge to align with rural contexts (cognitive capital). These interlinked processes form an integrated system that enhances rural governance capacity and promotes sustainable development. The findings highlight that SOEs are not merely resource providers but systemic catalysts that support cross-scalar collaboration and social infrastructure building. The study contributes a novel perspective by integrating social capital theory with a systemic governance lens and offer a actionable insights into the institutional design of assistance models for the future interventions by SOEs and similar entities in underdeveloped areas. Full article

Urban green infrastructure (UGI) projects rely on collaboration and involve a diverse team of professionals, including constructors, designers, green builders, and maintenance staff. This socially oriented case study focuses on the relational dynamics among UGI professionals, their roles in landscape construction processes, and how these relationships can influence the project’s success and its capacity to implement carbon-smart solutions. “Carbon-smart solutions” refers here to practices aimed at maximising carbon sequestration and storage while minimising carbon emissions. Data for this study were collected through semi-structured in-depth interviews and analysed using deductive qualitative analysis. A coding framework, investigator triangulation, and a representative sample of various professionals were employed to confirm the data’s validity. This study identified several relational factors that either challenge or drive the project’s success and carbon smartness. At the interpersonal level, the determinant drivers and challengers in UGI professionals’ relations were linked to the definition of working roles, power dynamics, the building of mutual trust through open communication, and the possession of the necessary sustainability skills. At the institutional level, relations concerning the shared principles and rationales of the project, as well as the project design process and diverse working cultures, presented both constraints and advances in project success and carbon-smart solutions. Full article

The growing complexity of distributed energy systems and the rise of peer-to-peer energy markets demand innovative solutions for efficient, resilient, and sustainable energy management. However, existing research often remains fragmented, with limited integration between control strategies, optimization frameworks, and practical implementation. This paper presents a comprehensive systematic review, following the PRISMA methodology, that synthesizes findings from 94 high-quality studies and addresses the lack of consolidated insights across technical, operational, and architectural layers. This review highlights advancements in six key areas: optimization and modeling, multi-agent systems, simulations, blockchain and smart contracts, robust frameworks, and electric machines. Despite progress, several studies reveal challenges related to scalability, data privacy, computational complexity, and system adaptability, particularly in dynamic and decentralized environments. Stochastic–robust optimization and multi-agent systems improve decentralized coordination, while blockchain enhances security and automation in peer-to-peer trading. Simulations validate energy strategies, bridging theory and practice, and electric machines support renewable integration and grid flexibility. The synthesis underscores the need for unified frameworks that combine artificial intelligence, predictive control, and secure communication protocols. This review aims to provide a roadmap for advancing distributed energy systems toward scalable, resilient, and sustainable energy solutions. Full article