Search Results (85)

The rheological behaviour of complex fluids such as blood and polymer melts is governed by viscosities that are inherently subject to epistemic uncertainty arising from incomplete knowledge of evolving small scales rather than intrinsic randomness. Classical continuum models assume precisely known viscosity functions, an assumption that is physically unjustifiable in such systems, while existing fuzzy approaches have failed to integrate rigorously with the full conservation laws of continuum mechanics. To address this gap, we introduce Fuzzy–Viscous Fluid Dynamics (FVFD), a novel framework in which dynamic viscosity is governed by Dynamic Interval-Valued Intuitionistic Fuzzy Sets (DIVIFS), with membership functions grounded in Coleman–Gurtin internal-variable thermodynamics and evolution equations derived from a Lyapunov dissipation postulate. Employing the parabolic comparison principle together with Galerkin–Leray–Hopf theory, we establish that intuitionistic ordering constraints are preserved over time and prove the existence of global weak solutions to the coupled fuzzy Navier–Stokes equations (FNSEs). An exact analytical solution for fuzzy Couette flow is derived, recovering the classical Newtonian limit and shown, via a structural argument, to be non-linear precisely because and only because FVFD departs from the purely local generalised-Newtonian closure shared by the Power-law, Carreau–Yasuda, and Cross models. Three governing dimensionless parameters, the Reynolds number ($Re$), Damköhler number ($Da$), and fuzzy number ($Fz$), are identified and justified to characterise distinct flow regimes. This framework provides a rigorous, physically grounded alternative to stochastic and data-driven methods for explicitly tracking epistemic uncertainty through interval-valued hesitancy parameters, enabling more accurate modelling of complex fluids whose internal aggregation states remain inaccessible to direct observation. Full article

The study examined the relationship among climate finance (CF), Environmental Risk Accounting (ERA), and firm value for publicly listed non-financial firms in Nigeria and South Africa between 2010 and 2022. Using a carefully balanced panel sample consisting of 520 observations, we construct our independent variables as follows: Climate Finance (CF); Climate Financial Exposure (CFEI), using an AI-powered textual analysis approach; and Greenwashing Gap (GWG). Through fixed-effects panel regression, our results indicate that while climate finance does not directly influence firm value, CF and the quality of ERA practices interact positively, showing that CF only creates value conditional on high-quality ERA. Greenwashing risk is negatively associated with firm value, while environmental-risk-accounting quality is separately associated with higher firm value. Institutional differences across countries have consequences for the role of ERA. These results are examined using a double-theoretic approach that integrates institutional theory to justify how the regulation pressure leads to differences in accounting disclosures in different countries, and the resource-based theory, to justify how these differences influence firm value. The application of difference-in-difference analysis through the adoption of the King IV code by South African firms provides evidence consistent with an appreciable valuation premium by firms in South Africa after the intervention. The findings are broadly consistent across methods such as IV-2SLS, System GMM, Propensity Score Matching, and the Heckman Selection Model. There are important ramifications of the findings for accounting practice and environmental policy within sub-Saharan Africa. Full article

Assessment feedback on complex written reports remains one of the most persistent and resource-intensive challenges in higher education. However, no principled framework exists for deciding which feedback tasks might appropriately involve artificial intelligence and which must remain human responsibilities. This paper addresses that gap by proposing a tripartite feedback framework that distinguishes three analytically distinct levels: low-level structural and presentational feedback, intermediate-level factual content validation, and high-level critical evaluation and synthesis. Grounded in established feedback theory, including Hattie and Timperley’s feedback model and Boud and Molloy’s sustainable feedback design principles, the framework provides pedagogically justified criteria for allocating tasks between AI systems and human assessors, rather than automating whatever technology can technically perform. Five non-negotiable boundary principles govern any AI involvement at the intermediate level, preserving human oversight, academic accountability, and assessment integrity. This paper examines current technological capabilities and limitations at each level, proposes a phased implementation pathway with explicit human-in-the-loop requirements, and addresses implications for feedback literacy, student agency, equity, and security. A comprehensive mixed-methods evaluation design specifying the evidence required for empirical validation is also presented. The framework’s contribution lies not in prescriptive solutions but in providing structured categories, explicit boundary conditions, and validation criteria to guide context-sensitive institutional decision-making about AI integration in assessment. Full article

Background/Objectives: Contemporary healthcare systems face compound challenges (including technological acceleration, demographic aging, rising chronic disease burden, and growing patient expectations) that demand models that are simultaneously efficient, high-quality, and person-centered. Despite a substantial body of research addressing organizational design, human capital management, and clinical competencies, these dimensions have largely been theorized in isolation. This study aims to construct and justify an integrated theoretical framework explaining how organizational models, human capital, and transversal competencies may jointly shape care quality, patient safety, and institutional sustainability in healthcare organizations. Methods: A narrative literature review was conducted, integrating contributions from business economics, healthcare management, organizational psychology, and nursing sciences. This design was selected for its suitability in synthesizing heterogeneous, multidisciplinary knowledge into a coherent conceptual framework, a purpose for which systematic meta-analytic approaches are not appropriate. Sources encompassed 79 references: peer-reviewed journals (PubMed, JSTOR, Google Scholar), institutional reports (WHO, OECD, European Commission, Joint Commission), normative standards (ISO 30414:2018), and Italian regulatory frameworks, spanning foundational twentieth-century contributions through the most recent literature (2025). Results: Four principal findings emerged: (1) healthcare organizations are evolving from rigid hierarchical structures toward flexible, value-based configurations, with the Value-Based Healthcare (VBHC) paradigm redirecting institutional attention from service volume to patient-meaningful outcomes per unit of cost; (2) transversal competencies (communication, empathy, emotional intelligence, teamwork, and transformational leadership) are closely associated with care quality and patient safety, with 70–80% of sentinel events associated with communication failures; (3) human capital, encompassing technical expertise and relational capacity, constitutes the primary lever of competitive advantage in healthcare institutions; and (4) the trajectory from pyramidal toward participatory and self-managed models is supported by international evidence, including the Buurtzorg experience in the Netherlands. Conclusions: The integrated three-pillar framework (combining resource-based theory and dynamic capabilities, Value-Based Healthcare, and evolutionary organizational theory) provides a theoretically grounded basis for understanding how organizational structure, human capital, and transversal competencies are jointly associated with clinical performance. Healthcare institutions should systematically integrate soft-skills training into professional education and invest in participatory organizational structures. Health policy should revise financing mechanisms to incentivize patient-meaningful outcomes over service volumes and support the broader transition toward Value-Based Healthcare models. The Italian SSN is discussed as an illustrative national context rather than as the primary empirical focus of the review. Full article

The subject of this paper is the problem of knowledge revealed by the emergence of advanced natural language processing technology, called large language models (LLMs). It proposes an interpretation of the appearance of this phenomenon as an emergent effect of a complex system, such as LLMs. The meaning of this interpretation is based on the observation of the computational trajectory of computational units (vectors) which, on the other hand, represent discrete semantic units, tokens. To justify the autonomy and relevance of this interpretation of knowledge, the paper invokes the concept of discursive space, which allows, in particular, for the reliance on language as a container of knowledge and for the generalization of the idea of knowledge to other semantic environments. To describe more general units of knowledge, the strong version of the theory proposes the introduction of the institution of gnosemes, of which discourse is a special case. Gnosemes find application in the case of LLMs. Due to the different ontical contexts of the entities described, the paper proposes a transdisciplinary approach, the axis of which is the field of social sciences. Full article

Bandstructure methods occupy a central place in the physics of nanostructures, and the multiband $\mathit{k}·\mathit{p}$ theory of Luttinger, Kohn, and Kane has served as one of the most widely used computational frameworks for modelling electronic states and energies in low-dimensional semiconductor systems for several decades. Despite its broad success, the theory harbours a fundamental mathematical difficulty that has been largely overlooked: the multiband Luttinger–Kohn Hamiltonians are non-elliptic partial differential operators for the overwhelming majority of common III–V and III-nitride crystalline materials, a fact that violates the axiomatic requirements of quantum mechanics and is the root cause of the long-standing problem of spurious solutions. In this paper, we derive ellipticity conditions rigorously for the $6\times 6$, $8\times 8$, and $14\times 14$ zinc-blende Hamiltonians, demonstrating that non-ellipticity affects a substantially larger class of materials than previously reported. We develop and justify a systematic parameter rescaling procedure for the $8\times 8$ Kane Hamiltonian and obtain admissible parameter sets for GaAs, AlAs, InAs, GaP, AlP, InP, GaSb, AlSb, InSb, GaN, AlN, and InN. The inversion-asymmetry parameter B is shown to play an essential and previously unrecognized role in maintaining ellipticity, and it is used to optimize the bandstructure fit of the rescaled parameter sets. Analysis of several known $14\times 14$ models reveals structural sources of non-ellipticity, pointing to the need for a revision of perturbative assumptions regarding out-of-basis band contributions. The consistent parametrization framework developed here provides the rigorous mathematical foundation required by inverse design methodologies, AI-enhanced electronic structure calculations, and data-driven multifidelity approaches in nanoscience and nanotechnology. Full article

Mistreatment is a pervasive and normalized feature of medical culture. In medical residencies, it functions as a structural rite of passage that shapes professional socialization. While the prevalence of mistreatment is documented, there is a lack of qualitative research exploring its role as a mechanism of identity construction. The aim of this study was to understand the experiences of mistreatment among internal medicine residents in Medellín, Colombia, through the lens of ritual theory and symbolic violence. A particularistic ethnographic study was conducted with 12 residents selected via theoretical sampling. Data were collected through semi-structured interviews and a reflexive field journal. Rigor was ensured using investigator triangulation and analytical bracketing to manage researchers’ biases. The training process follows a three-stage rite. (1) Separation: Symbolic violence and social pressure to specialize frame general medicine as “mediocre,” turning admission into a “battlefield” where self-worth is tied to success. (2) Marginalization (Liminality): Residents endure systemic mistreatment, including sleep deprivation (3.5 h rest cycles), public ridicule (“pimping”), and physical/verbal abuse (e.g., being hit with stethoscopes or called “testicles/jerks”). This stage is governed by a “purificatory logic” where suffering is internalized as a meritocratic requirement. This leads to high morbidity, with clinical diagnoses of anxiety and depression. (3) Integration (Postliminality): Professional autonomy and financial stability act as a “redemption” that justifies past suffering. Mistreatment is not an isolated interpersonal issue but a structurally embedded ritual and a core element of the hidden curriculum. It reinforces toxic hierarchies and a “tyranny of merit” that obscures structural barriers. These findings offer analytically transferable insights for global medical education, calling for a deconstruction of ritualized violence to foster more humanistic training environments. Full article

In China’s increasingly stratified education system, a growing number of high-scoring junior secondary students are choosing the Vocational Secondary–Undergraduate Articulation Program over key academic senior high schools, challenging conventional assumptions about merit, school choice, and vocational education. Existing studies have mainly examined this pathway from institutional and policy perspectives, with limited attention to the micro-level decision-making logic of students and families. Drawing on rational choice theory and a theoretical thematic analysis of online public discourse, this study explores how high-scoring students and their families interpret, evaluate, and justify this educational choice. The findings show that participation in the articulation program is organized around four interrelated mechanisms: action foundations based on academic strengths and family evaluation; action purposes aimed at securing a relatively stable route to a bachelor’s degree while gaining vocational advantages; action consequences involving the weighing of risks and expected returns; and institutional and cultural impacts produced by the interaction between policy incentives and persistent academic hierarchies. The study argues that choosing this pathway is not a deviant decision, but a rational response to educational competition and structural constraints, and it sheds light on the changing legitimacy of vocational pathways in contemporary China. Full article

This article presents methods and hardware for the multi-criteria non-destructive determination of chicken egg quality parameters, implemented using a multifunctional rotary system. Unlike traditional single-criteria sorting, which relies primarily on weight, the proposed approach utilizes a combination of physical and geometric parameters, including weight, linear dimensions, cross-sectional area and perimeter, volume, density, and shape. The experimental framework for the study was formed by measuring the parameters of 750 chicken eggs, covering the entire range of product categories and morphological variations. Geometric parameters were determined using machine vision methods, weight was determined using a strain gauge, and derived parameters were calculated using formalized models. A multi-criteria evaluation algorithm based on fuzzy set theory was used to make the classification decision, accounting for overlapping feature ranges and regulatory differences between EU and EAEU standards. The results of statistical and correlation analysis showed that egg density is identified as a relatively independent diagnostic parameter, weakly correlated with weight and geometric characteristics, justifying its inclusion in the quality model. A comparison of manual and automatic classification revealed differences in boundary categories during single-criteria sorting and indicated the potential of a multi-criteria approach. The obtained results support the feasibility of the developed methods and hardware under the conditions of the present study. Full article

Background/Objectives: Large language models (LLMs) such as ChatGPT are rapidly being integrated into healthcare for tasks ranging from clinical documentation to diagnostic support. Current ethical discussions focus predominantly on bias, privacy, and accuracy, leaving three critical governance questions unresolved: What kind of knowledge does an LLM output represent in clinical reasoning? When is a clinician’s or patient’s trust in that output justified? Who bears responsibility when an AI-informed decision leads to patient harm? This study proposes the Epistemic Authority–Trust–Responsibility (ETR) Architecture, a normative conceptual framework that addresses these three questions as an integrated governance challenge. Methods: The framework was developed through normative conceptual analysis—a method that constructs governance proposals by synthesising philosophical principles, ethical theories, and empirical evidence. The literature was identified through structured searches of PubMed, PhilPapers, and EUR-Lex (January 2020–March 2026), drawing on the philosophy of medical knowledge, the ethics of trust and testimony, and the moral philosophy of responsibility. Results: The ETR Architecture produces four outputs: (i) a four-tier classification system that distinguishes LLM outputs—from administrative drafts to clinical evidence claims—and matches each tier to appropriate verification requirements; (ii) the concept of the ‘epistemic placebo’, formally defined as a governance measure that creates a documented appearance of compliance while lacking at least one operative element of genuine oversight; (iii) a model specifying four conditions under which trust in healthcare AI is justified; (iv) four testable hypotheses with associated research designs connecting governance design to trust calibration and patient safety. Conclusions: The 2025–2027 regulatory transition period offers a critical window for shaping how healthcare institutions govern AI. We argue that deploying LLMs without explicitly classifying their outputs and building appropriate oversight risks allows governance norms to be set by technology vendors rather than by evidence-informed, patient-centred policy. Full article

The primary objective of water supply systems is to ensure a reliable delivery of water in appropriate quantity, quality, and pressure. Designing water supply networks involves determining their geometric layout and capacity by selecting suitable pipe routes and sizes. Since the network layout influences pipe diameters, routing and sizing should be conducted simultaneously. This paper presents an application of the fractal-based method for designing water distribution networks (WDNs) in which the pipe routes and diameters are mathematically justified. The proposed approach takes into account the total pipe length, the total angular change in pipeline routing, construction costs, and water delivery priorities. Additionally, the method was tested under both quasi-real conditions (in the virtual city of Micropolis) and in real-world complex settlement. The results of the sizing process were also compared with those obtained using the genetic algorithm approach. Verification of the proposed method in both quasi-real and real-world scenarios showed a smaller total pipe length (by 9.53% and 12.17%), a lower maximum water age (11 and 87 h), and a comparable energy demand. The SRS method enables simultaneous determination of pipe diameters and layout routing, while ensuring proper hydraulic performance of the network due to the application of evolution theory rules which results in quasi-optimal solutions for WDN designing. Full article

In the context of rural sustainability and poverty alleviation within the developing world, a key dilemma facing the international community is to identify suitable strategies and mechanisms to bring multiple stakeholders together to work in efficient and sustainable ways. This paper focuses on the Public–Private–Producer Partnership (4P), a model that involves cooperation between government agencies, business firms, and small-scale producers to foster mutual trust and enhance collaboration through infrastructure development and capacity building in the agricultural value chain. Drawing on evidence from China, Indonesia, Rwanda, Ghana, and Nigeria, this study examines the impact of 4P on crop productivity, agricultural infrastructure, market access, stakeholder empowerment, employment, the land tenure system, and household income. This paper combines value chain analysis, Theory of Change mapping, and both qualitative and quantitative evaluation techniques to assess how the 4P model functions in different institutional and ecological contexts. While the model promotes inclusive growth, it also faces challenges such as price volatility, insufficient long-term sustainability, and limited integration of smallholder farmers into formal value chains. The paper discusses policy implications for improving the 4P model’s effectiveness in poverty alleviation and local economic development, highlighting the importance of better governance structures, financial mechanisms, and market stability. This paper sheds new light on inclusive, justified, and sustainable collaboration mechanisms for participatory agencies and individuals. Full article

The assessment of reliability in non-repairable subsystems of mining electronic equipment represents a computationally challenging problem, particularly for complex and highly connected structures. This study presents a systematic comparative analysis of several deterministic approaches for reliability estimation, focusing on their computational efficiency, accuracy, and applicability. The investigated methods include classical boundary techniques (minimal paths and cuts), analytical decomposition based on the Bayes theorem, the logic–probabilistic method (LPM) employing triangle–star transformations, and the algorithmic Structure Convolution Method (SCM), which is based on matrix reduction of the system’s connectivity graph. The reliability problem is formally represented using graph theory, where each element is modeled as a binary variable with independent failures, which is a standard and practically justified assumption for power electronic subsystems operating without common-cause coupling. Numerical experiments were carried out on canonical benchmark topologies—bridge, tree, grid, and random connected graphs—representing different levels of structural complexity. The results demonstrate that the SCM achieves exact reliability values with up to six orders of magnitude acceleration compared to the LPM for systems containing more than 20 elements, while maintaining polynomial computational complexity. Qualitatively, the compared approaches differ in the nature of the output and practical applicability: boundary methods provide fast interval estimates suitable for preliminary screening, whereas decomposition may exhibit a systematic bias for highly connected (non-series–parallel) topologies. In contrast, the SCM consistently preserves exactness while remaining computationally tractable for medium and large sparse-to-moderately dense graphs, making it preferable for repeated recalculations in design and optimization workflows. The methods were implemented in Python 3.7 using NumPy and NetworkX, ensuring transparency and reproducibility. The findings confirm that the SCM is an efficient, scalable, and mathematically rigorous tool for reliability assessment and structural optimization of large-scale non-repairable systems. The presented methodology provides practical guidelines for selecting appropriate reliability evaluation techniques based on system complexity and computational resource constraints. Full article

Functional analysis plays a critical role in early-stage aircraft system design by defining system functions that guide downstream architectural development and verification. In practice, many design deficiencies originate not from incorrect physical realization but from incomplete or ambiguous functional definitions established at conceptual stages. This challenge is particularly pronounced in aircraft systems, where interaction- and physical-effect-induced functions tend to remain implicit and weakly justified. To address this issue, in this study, we conduct a case-study-based comparative evaluation of three functional analysis paradigms: design-theory-oriented functional decomposition, systems-engineering-based functional allocation, and scenario-driven functional analysis. Using an aircraft ground deceleration scenario as a controlled context, this comparison examines how different function-derivation mechanisms influence the identification and justification of interaction- and effect-induced functions. Through structured cross-paradigm comparison, three distinct and, in principle, reproducible derivation mechanisms, namely decomposition-driven, responsibility-driven, and physical-effect-driven, are identified. In this study, the physical-effect-driven mechanism is examined through an effect-strengthened implementation of the scenario-driven paradigm. While all paradigms consistently identify mission-oriented functions, the examined scenario-driven implementation enhances transparency in functional justification and improves sensitivity to interaction- and effect-induced functions, thereby reducing the risk of omission during conceptual design. By formalizing these derivation mechanisms and clarifying their complementary roles, this study contributes to a clearer methodological understanding of functional identification in early-stage complex system design, while providing practical guidance for methodological selection and integration in aircraft system design. Full article

Experimental results are compiled to show apparent hysteresis seen in hydride thermal precipitation–dissolution cycling in zirconium alloys using X-ray diffraction, dynamic elastic modulus techniques, and differential scanning calorimetry (DSC). Gibbs’ phase rule is used to justify a description of a stable hydride in the H-Zr system in terms of a control volume with a hydride at its core, surrounded by a stress gradient that produces a stabilizing gradient of hydrogen in the solution. The conditions for a stable hydride are derived when the flux of hydrogen in solid solution is zero. DSC heat flow curves are analyzed with a thermodynamic model that predicts concentrations of hydrogen in a solution during temperature cycling and a description of experimental results that show how concentrations evolve at a constant temperature to the same final state when cycling is paused, from which hysteresis is deemed an illusion. The control volume is supported by previous energy calculations, performed with density functional theory. Implications of replacing the order parameter for phase field methods with the gradient of the yield stress are discussed. A practical method for forming a stable hydride is presented. Full article