Search Results (1,344)

Collective endorsement of shared values across diverse social groups is essential for the development and sustainability of democratic societies, yet capturing the perspectives of marginalised populations remains a persistent challenge, particularly when examined through ethical, legal, and social (ELS) lenses. This study develops a structured Migration ELS taxonomy to guide a GenAI-assisted semantic classification model designed to identify ELS dimensions in textual data. The model is fine-tuned and evaluated within a human-in-the-loop framework using expert annotations to ensure reliability and interpretive accuracy. As an empirical case, the approach is applied to migration-related official policy documents and narratives of Ukrainian migrants published on the Telegram platform. The resulting framework enables the analysis of alignment between governmental and migrant perspectives, revealing thematic and temporal divergences in ELS dimensions across institutional and user-generated discourse. The findings demonstrate the potential of this scalable framework, which combines taxonomy-driven modelling with generative AI and expert-in-the-loop validation, to reveal patterns of alignment and temporal dynamics in the representation of values across different social groups. Full article

The Judd–Ofelt (J–O) intensity parameters and oscillator strengths are key to understanding the optical transition properties of rare-earth-doped glasses. However, the scarcity of experimental samples and the complex nonlinear relationship between composition and spectral properties pose significant challenges to accurate predictions. To address this, we propose a generalizable framework that integrates conditional generative adversarial network (cGAN)-based data augmentation with an attention-embedded artificial neural network (ANN)–support vector regression (SVR) hybrid model. The cGAN generates physically plausible virtual samples to enrich data distribution and enhance generalization in sparse compositional regions. The attention mechanism in the ANN identifies critical compositional features, which are then leveraged by SVR for robust regression of parameter trends. The framework demonstrates high predictive accuracy for Er³⁺-doped glasses, achieving R² values above 0.93 for Ω₂, Ω₄, and Ω₆, and exhibits strong generalization performance on independent Dy³⁺- and Nd³⁺-doped datasets without task-specific retraining, confirming its practical applicability across multiple rare-earth ions. The model maintains consistency across diverse glass host systems (tellurite, borate, phosphate, silicate/germanate, heavy-metal oxide), and the attention analysis reveals feature importance aligned with established glass chemistry principles. Demonstrated on Er³⁺, Dy³⁺, and Nd³⁺, with potential for a broader range of rare-earth ions through transfer learning and future dataset extensions, this approach offers a data-driven, physics-informed tool for the targeted design of rare-earth optical materials in next-generation optical sensors. Full article

This paper introduces a dual-sector growth model to investigate the “circularity trap,” a phenomenon where increasing the Circular Material Use Rate (CMUR) leads to a decline in total value added per worker in developing economies. While circular economy policies are designed to promote sustainability, we demonstrate that in small open economies with a significant productivity gap between a high-tech manufacturing sector and a predominantly low-tech, labor-intensive recycling sector—a common feature in many low-income contexts—aggressive circularity targets can trigger a form of “Environmental Dutch Disease.” Using a Cobb–Douglas framework, we model the reallocation of labor driven by the processing of imported waste. We show that as the CMUR ($\varphi$) increases, labor is drawn away from manufacturing—a sector characterized by technological learning-by-doing—into the recycling sector, which lacks similar growth externalities. Our results indicate that the circularity trap occurs when the marginal gains from waste processing are outweighed by the structural loss of industrial capacity and the slowing of total factor productivity (TFP) growth. The paper concludes that for low-income nations, circularity policies must be coupled with internal technological innovation to avoid long-term economic stagnation and de-industrialization. Full article

Large-scale cultural complexes serve significant numbers of child users, yet existing spatial assessment approaches are predominantly developed from adult perspectives and rarely consider child-height environmental exposure conditions at children’s own eye level. To address this gap, this study introdus a novel dual-height proxy assessment framework that integrates semantic segmentation with explainable machine learning, enabling scalable proxy-based spatial diagnosis without requiring direct child participation. This study proposes a proxy-based assessment framework combining dual-height street-view imagery (adult: 1.6 m; child: 1.2 m), semantic segmentation (DeepLabV3+ and PSPNet), GIS analysis, literature-informed proxy perceptual indices, and explainable machine learning (XGBoost with SHAP) applied across 480 sampling locations at the Longgang Cultural Centre, Shenzhen. The results reveal substantial differences in environmental exposure characteristics between adult-height and child-height viewpoints, with child-height imagery exhibiting 34% lower signage visibility and 30% higher spatial enclosure. Exploratory associations between environmental features and proxy perceptual indices yielded R²values ranging from 0.14 to 0.39, with walking distance, openness, and visual complexity emerging as the most influential variables within the proxy models. SHAP analysis identified non-linear relationships between environmental characteristics and proxy perception-related outcomes, and spatial mismatch mapping identified 120 locations warranting design attention. The study proposes a scalable and data-driven spatial proxy assessment framework to support child-friendly environmental screening and spatial diagnosis. The proposed proxy indices are grounded in developmental psychology literature and are not intended to substitute for children’s direct perceptual responses; rather, they are intended to characterise comparative child-height environmental exposure patterns within large-scale cultural environments. Validation using child-reported perception data, behavioural observation, participatory methods, and experimental wayfinding studies remains an important direction for future research. Full article

Computational thinking (CT) is increasingly recognized as essential in education, yet teacher preparation programs struggle to develop both computational proficiency and pedagogical readiness in pre-service teachers (PSTs). This study examines an AI-mediated, game-making course grounded in the emerging “vibe coding” paradigm, where 24 novice PSTs iteratively constructed programs through natural language prompting. Adopting a mixed-methods design, the study drew on pre- and post-course attitude questionnaires, reflective accounts of prompting strategies, and open-ended responses. Results indicate that participants substantively engaged with core CT practices, particularly debugging, iterative refinement, and problem decomposition. Nonetheless, this downward recalibration in self-reported coding and teaching confidence represents a productive adjustment rather than a failure. Conversely, attitudes toward game-making improved significantly, with a statistically significant medium effect size for perceived instructional value (d = 0.51), the largest practical effect observed across dimensions. Most participants intended to integrate CT into future teaching. These findings suggest that prompt-driven learning environments support meaningful engagement with computational processes when carefully scaffolded, but do not inherently ensure pedagogical readiness, particularly for higher-order CT practices such as abstraction and pattern recognition. Unlike prior research that has examined game-making processes or PST attitudes toward CT in isolation, this study empirically integrates all three within a single scaffolded instructional design using vibe coding. This integration enables a process-level account of how CT is enacted—and how it develops—when code generation is partially delegated to AI systems. Beyond documenting attitude shifts, the study introduces an analytical rubric for identifying CT engagement in AI-mediated prompting and derives evidence-based design principles that specify the pedagogical conditions under which vibe coding supports, rather than bypasses, computational reasoning. Full article

Team handball is an intermittent sport characterized by variable kinematic and neuromuscular demands, which require precise monitoring to optimize training design. The aim of the present study was to establish data-driven intensity thresholds for external load variables and to examine their distribution across training tasks in elite women’s handball. External load was monitored in 17 professional female handball players during five training sessions within a competitive microcycle using inertial measurement units. Kinematic variables (speed, acceleration, and deceleration) and a neuromuscular variable (jump impulse) were analyzed. A k-means clustering approach was applied to classify each variable into five intensity zones. Subsequently, the distribution of these zones across different training tasks was evaluated. The results showed a predominance of low-to-moderate intensity actions across all the variables, with a progressive reduction in the frequency of higher-intensity efforts. Acceleration values were consistently higher than deceleration across all the zones. Jump impulse also followed a similar distribution, reflecting the neuromuscular demands of training. A task-based analysis revealed clear differences in intensity profiles, with tasks involving opposition eliciting higher proportions of moderate-to-high-intensity actions, while tasks without opposition showed an absence of high-intensity zones. These findings provide objective reference values for external load in elite women’s handball and highlight the importance of task design in modulating physical and neuromuscular demands. The use of data-driven thresholds and ecologically valid training tasks may contribute to more effective and individualized load prescription. Full article

A comprehensive thermodynamic and molecular-level investigation of adsorption on MgY and NH₄Y zeolites is presented using inverse gas chromatography at infinite dilution (IGC-ID), combined with a Hamaker-based formalism and an extended five-parameter Lewis acid–base model. The study introduces a unified framework that integrates dispersive, polar, and donor–acceptor interactions while explicitly accounting for temperature-dependent intermolecular geometry. The results demonstrate that the London dispersive free energy exhibits a highly linear temperature dependence (R² > 0.999), while the corresponding surface energy decreases linearly with temperature (e.g., ${\gamma }_s^d\left(T\right)=-0.297T+189.48$ mJ·m⁻² for MgY), reflecting the progressive weakening of dispersion forces. Simultaneously, the intermolecular separation distance follows a linear relation $r\left(T\right)=r_0+{\alpha }_{\mathrm{eff}}T$, with ${\alpha }_{\mathrm{eff}}$ values on the order of (2–3) × 10⁻³ Å·K⁻¹ for MgY, enabling the determination of intrinsic contact distances $r_0$ at 0 K, varying between 4.00 Å and 6.60 Å. A major finding is that the molecular surface area of adsorbed probes is not constant but follows a quadratic temperature dependence with excellent accuracy (R² > 0.999), establishing adsorption cross-section as a thermodynamic variable. The comparison between MgY and NH₄Y reveals two distinct adsorption regimes: MgY exhibits a structured and strongly dispersive interaction field associated with Mg²⁺ cations, whereas NH₄Y displays enhanced polarity, stronger specific interactions, and greater molecular flexibility driven by hydrogen bonding and protonic effects. Thermodynamic analysis of Lewis acid–base interactions shows that classical linear models are insufficient. Statistical evaluation (R² ≈ 0.986, minimum AIC/BIC, lowest RMSE) demonstrates that the five-parameter Hamieh model provides the most accurate and physically meaningful description, capturing nonlinear donor–acceptor interactions and amphoteric coupling effects. Overall, this work establishes a novel thermodynamic methodology that quantitatively links macroscopic surface energetics to microscopic interaction parameters, providing new insight into adsorption mechanisms and a robust framework for the rational design of porous materials in catalysis, separation, and energy applications. Full article

This paper develops an analytical treatment of vibro-acoustic wave propagation in a cylindrical waveguide containing two clamped elastic membranes and a central flexible-shell segment. The acoustic field obeys the time-harmonic Helmholtz equation, the shell motion is described by Donnell–Mushtari thin-shell theory under axisymmetric loading, and the membrane response is governed by classical membrane theory and incorporated through a tailored Galerkin scheme. The resulting coupled fluid–structure boundary-value problem is solved by the Mode-Matching Method: the acoustic potentials are expanded in orthogonal radial eigenfunctions within each subregion, and continuity of pressure, normal velocity, and structural displacement are enforced at every interface. The mirror symmetry of the configuration is exploited by an exact decomposition into symmetric and anti-symmetric sub-problems, each of which reduces to a truncated linear algebraic system of dimension $4N+4$ for the unknown modal amplitudes. Acoustic power-balance identities provide a quantitative consistency check on the numerical implementation and diagnose convergence with respect to the truncation order; structural damping is accommodated through complex-modulus substitutions for the shell and the membrane tension without altering the algebraic structure of the system. The numerical results demonstrate that the dual-membrane configuration delivers transmission-loss values exceeding $25\mathrm{dB}$ across the low-frequency band relevant to HVAC and automotive applications, with a representative plateau near $13\mathrm{dB}$ at the reference geometry, through resonance-driven modal coupling between the acoustic field and the compliant interfaces. Parametric studies identify the excitation frequency, the inner-membrane radius, the shell radius, and the chamber length as effective design parameters for tuning the attenuation. The formulation furnishes a unified and computationally efficient analytical tool for predicting and optimising noise attenuation in flexibly coupled cylindrical duct systems. Full article

Designing ultra-high-performance concrete (UHPC) mixtures requires balancing multiple, often conflicting, performance criteria, particularly mechanical strength and rheological behavior. However, the limited availability of publicly accessible datasets containing synchronized multi-property measurements, together with cross-source heterogeneity, poses a major challenge for robust data-driven modeling under small-sample conditions. To address this issue, this study proposes an integrated framework combining cross-source data harmonization, Variational Autoencoder (VAE)-based latent-space augmentation, multi-output deep learning, interpretability analysis, and Genetic Algorithm (GA)-driven inverse design. A dataset comprising 139 valid UHPC records was curated from 22 peer-reviewed studies and expanded to 2780 samples through VAE-based augmentation. Using the augmented dataset, a multi-output deep neural network was developed to jointly predict compressive strength, flexural strength, yield stress, and plastic viscosity. On the independent test set, the model achieved R² values of 0.8601, 0.9212, 0.8464, and 0.6603, respectively. Comparative benchmarks and augmentation ablation analyses further showed that VAE-based augmentation consistently improved predictive performance and generalization, especially under small-sample conditions. SHAP and partial dependence analyses identified curing age, steel fiber content, water-to-binder ratio, and superplasticizer dosage as the dominant factors governing UHPC performance. Finally, the trained surrogate model was coupled with a GA for multi-objective inverse optimization, and experimental validation of three candidate mixtures confirmed good agreement between predicted and measured values. This study provides a transparent and engineering-oriented methodology for the integrated prediction, interpretation, and optimization of UHPC mixtures. Full article

Building fires involve numerous interacting hazard factors, making it difficult to identify which combinations are most likely to cause an incident and to design targeted interventions. Existing methods address only part of this problem: structural approaches map causal pathways but cannot quantify the probability of specific factor combinations, while probabilistic models compute risk values but offer little guidance on where to intervene. To bridge this gap, we develop the Causal Hierarchy Model (CHM), a data-driven framework that integrates causal structure analysis with probability calculation. Factor influence is derived from empirical co-occurrence data to distinguish driving factors from dependent ones. A hierarchical structure is then constructed using two layering rules, revealing causal transmission gradients and critical hub nodes. Finally, coupling probabilities are computed within the hierarchical constraints and weighted by the influence of hubs. Applying CHM to building fire records from California reveals clear functional differentiation among hazard factors. Coupling strength attenuates asymmetrically across hierarchy levels but amplifies sharply along pathways that pass through high-prominence hubs. By uniting structure and probability, CHM provides a quantitative basis for shifting fire safety management from uniform inspection toward risk-differentiated strategies. Full article

Artificial intelligence (AI) is transforming how organizations develop human potential, offering scalable and data-driven support for coaching and capability building. This study proposes and validates a conceptual framework for integrating AI into organizational coaching processes to enhance competence development and strategic alignment. AI-supported coaching in this research is treated as an emerging organizational technology whose potential organizational value depends less on model capability and more on governance design, decision rights, and auditable evaluation outputs. Following a mixed-methods, multi-phase design, the research combined a Systematic Literature Review (SLR) with the construction of a layered design architecture in which OSCAR serves as the primary coaching-process scaffold, complemented by KSA for competency specification, Situational Leadership for adaptive guidance, and KPIs for monitoring and governance. The framework structures AI-supported coaching across 10 interrelated phases, from contextual anchoring to review and measurement, while preserving iterative re-entry to earlier phases whenever review evidence, contextual change, or insufficient progress makes adjustment necessary. Prototyping demonstrated feasibility and coherence across models, while the focus group provided qualitative expert feedback on the framework’s clarity, governance needs, and perceived usefulness for competence development. At this stage, however, the KPI structures generated by the framework and the descriptive comparison across AI tools should be interpreted as prototype-level outputs rather than as empirically validated performance measures or evidence of added value over baseline approaches. Because the evaluation relied on two fictional prototyping scenarios and a small expert-oriented focus group (n = 6), the findings should be interpreted as evidence of prototype demonstration and qualitative refinement rather than of real-world effectiveness or organizational impact. The study also does not include a control group or comparison with traditional human coaching, so the added value of the AI-supported framework over alternative coaching arrangements remains a question for future empirical testing. Findings suggest that AI can usefully support organizational coaching by personalizing dialogue, structuring reflection, and generating auditable development artefacts, provided ethical safeguards and human oversight remain integral. The research contributes a preliminarily validated, ethics-informed, and governance-aware framework for AI adoption in organizational coaching and offers practical insights for embedding AI-enabled development in learning organizations. Full article

The reliability of engineering structures is essential to ensure safety, durability, and sustainability. In reinforced concrete (RC), shear resistance is one of the most uncertain design aspects due to the natural variability of material properties and construction quality. Conventional design methods defined by Eurocode rely on characteristic values and partial safety factors that may not reflect the actual performance of in situ concrete. This study proposes a probabilistic framework for shear assessment that integrates material variability derived from conformity testing. Statistical parameters, including mean value and coefficients of variation (COV) of compressive strength, are incorporated into comparative reliability analysis using the First-Order Reliability Method (FORM) and Latin Hypercube Sampling (LHS). Parametric analyses are performed to quantify the influence of material variability on the reliability index β and failure probability $P_f$. The effect of varying the coefficient of variation (CoV) of the concrete compressive strength is investigated in the range from 0.01 to 0.2, both under the assumption of statistical independence and with consideration of correlation between selected variables. The sensitivity analysis is carried out to provide clear insight into the influence of uncertainty in the input parameters on the reliability of the considered limit state. The proposed framework provides a more realistic representation of structural safety and supports data-driven, performance-based management of concrete infrastructures. Full article

Modeling of the operational process of technical objects, particularly complex and intelligent systems, constitutes one of the key challenges of contemporary technical diagnostics. Such models play an important role both at the design stage of technical systems and in decision-making processes related to maintenance and evaluation of operational performance. In classical approaches, the analysis of possible operating modes of a technical object is typically based on a finite set of discrete states, which often proves insufficient to describe intermediate degradation stages and interaction-driven effects occurring during real operation. The model presented in this paper enables a comprehensive analysis of the operational process of complex technical objects by explicitly accounting for intermediate technical states and their influence on system behavior. By employing state probability functions and technical availability functions within a five-valued diagnostic framework, the proposed approach supports modeling of the operational process at an early design stage, assessment of compliance between actual operation and expected maintenance schedules, and evaluation of potential operational outcomes. As a result, the model provides a more detailed and realistic basis for reliability assessment of complex technical systems, particularly in situations where gradual degradation and subsystem interactions play a significant role. The proposed approach can be directly applied to a wide class of complex engineering systems characterized by interacting subsystems and progressive degradation processes, supporting reliability-oriented design and maintenance optimization under real operating conditions. Full article

The accelerating convergence of geopolitical volatility, technological disruption, environmental stress, and societal transformation has rendered traditional strategic management frameworks insufficient. Organizations now operate in environments defined not only by disruptions with existential implications but by wickedness—conditions in which problems are ambiguous, stakeholders disagree, and solutions reshape the challenge itself. Building on the premise that strategy itself is a wicked problem, this article advances a central claim: organizational resilience is best understood as an architectural capability largely grounded in humanity-based identity. Unlike organizational structure, mission, or even current strategy, each of which may be transient in turbulent environments, organizational identity, which is a construct that derives from individuals and humanity, provides an enduring basis for harmonizing the organization and its environment. Utilizing the lens of “humanity”—in its two dimensions of humankind and humaneness—we synthesize research on wicked problems, organizational identity, dynamic capabilities, modular design, alliances and smart power, and hybrid intelligence. We then propose an integrative model linking humanity-driven identity to resilience through three vectors—Inspirational Transformative Ambition, Innovative Value Networks, and Hybrid Intelligence Ecosystems—operationalized via a recently developed diagnostic tool. Finally, we offer corroborative evidence for the “Business of Humanity” logic, arguing that aligning humankind (opportunity across the full market spectrum) with humaneness (values-based evaluation) strengthens resilience by expanding opportunity sets while enhancing legitimacy, trust, and stakeholder alignment. Full article

Large-scale hydrogen storage is expected to play a critical role in balancing the variability of renewable energy systems, particularly those driven by wind power. However, the combined influence of storage capacity and deliverability on supply reliability remains insufficiently characterized. This study investigates the reliability limits of hydrogen storage systems operating under variable hydrogen production and time-varying demand. A dimensionless modeling framework is developed to map system performance across a wide range of storage capacities and deliverability levels. The results reveal a clear transition between reliable and unreliable operating regimes. Reliable operation requires a minimum deliverability level approximately equal to the mean hydrogen production rate, corresponding to a value of about 1.05–1.10 times the average production across the range of intermittency conditions considered in this study (from moderate to highly variable production). Below this threshold, increasing storage capacity alone cannot prevent supply shortfalls. Once this threshold is exceeded, further increases in deliverability provide diminishing returns and storage capacity becomes the dominant factor governing reliability. In this regime, the required storage capacity approaches a plateau on the order of 10–30 days of average hydrogen throughput, depending on the level of production variability. The proposed regime-based framework provides a practical tool for evaluating storage feasibility and guiding preliminary capacity design in renewable hydrogen systems. Full article