Search Results (250)

Facial emotion recognition (FER) in real-world settings is limited by the semantic mismatch between discrete emotion categories and continuous Valence–Arousal–Dominance (V-A-D) dimensions and the lack of reliable uncertainty estimates under incomplete facial evidence. Existing uncertainty-aware FER studies mainly address annotation ambiguity or training-time reliability, leaving the behavior of predictive uncertainty under progressive input degradation insufficiently examined. This paper proposes BGDC (Bayesian Gaussian-mixture Distributional Consistency), a multi-task FER framework that integrates a GMM-based soft consistency module with a context-conditioned Bayesian regression head and explicitly models aleatoric and epistemic uncertainty. To evaluate predictive reliability, a controlled masking protocol is introduced to remove facial information under different spatial configurations. On FER2013-VAD, BGDC attains the highest classification accuracy of 0.6943 and the highest mean V-A-D CCC of 0.6079 among the compared configurations, and it yields a stronger epistemic uncertainty-error correspondence than MC Dropout in a single-model setting. Controlled masking further shows that the epistemic uncertainty of BGDC tracks task-relevant facial information loss rather than masking ratio alone: it rises with regression error when diagnostically important regions are removed, and it contracts when the masked region is largely task-irrelevant. Combining Bayesian uncertainty with the GMM-based distributional prior thus enables reliability-aware multi-task FER, in which controlled masking serves as a diagnostic intervention rather than as a benchmark of accuracy degradation alone. Full article

Quantitative structure–activity relationship and quantitative structure–property relationship (QSAR/QSPR)-based molecular toxicity prediction provides an in silico strategy for prioritizing environmental contaminants when longer-duration bioassay data are sparse. However, many Simplified Molecular-Input Line-Entry System (SMILES)-based machine learning models treat exposure duration as an unconstrained numerical covariate and provide limited information on whether predictions are supported by the observed temporal domain. Here, we evaluated an applicability-domain-aware chemoinformatics framework that combines transformer-derived molecular representations with toxicokinetic-informed temporal encoding and evidential uncertainty estimation. The approach replaces conventional ${log}_{10}$-transformed time encoding with a bounded first-order toxicokinetic saturation feature and combines this representation with Deep Evidential Regression to support a joint chemical–temporal view of the QSAR/QSPR applicability domain. Using experimentally derived U.S. EPA Ecotoxicology Knowledgebase (ECOTOX) fish EC50 mortality records, models were trained on 48,728 acute-duration observations and evaluated retrospectively on 2090 temporally separated longer-duration observations. The combined toxicokinetic and evidential model reduced temporal extrapolation error relative to conventional time encoding while maintaining comparable within-domain validation performance. The learned population-level timescale converged to 221 ± 3 h, consistent with accumulation timescales extending beyond standard acute fish test durations. Epistemic uncertainty was positively associated with absolute prediction error across all 10 folds, suggesting that the uncertainty estimates retained sample-level information relevant to applicability-domain-aware molecular toxicity screening. Cross-species analyses further showed that model behavior depended on training time coverage, with greater convergence when available assays covered a larger fraction of the learned timescale. These results suggest that toxicokinetic-informed temporal encoding can improve uncertainty-aware QSAR/QSPR modeling of environmental contaminant toxicity and support prioritization of compounds for further testing, while complementing rather than replacing chronic bioassays. Full article

This study presents a reproducible evaluation framework for hybrid quantum-classical neural networks (HQCNNs) in healthcare classification, rather than a new architecture. We assess a four-qubit HQCNN combining a compact classical encoder, a two-layer parameterized quantum circuit (PQC), and a classical readout (441 trainable parameters) against carefully tuned classical baselines on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset under identical five-fold cross-validation. The work is framed as a single-dataset proof-of-concept: the contribution is a documented, shared-fold evaluation protocol with a parameter-matched classical control and a quantified epistemic-informativeness analysis, not a demonstration of general quantum advantage. The HQCNN reached $96.49\pm 1.96\%$ accuracy and $99.44\pm 0.60\%$ ROC-AUC. A parameter-matched classical multilayer perceptron (441 parameters) reached $95.08\pm 1.81\%$ accuracy; the HQCNN’s $+1.41$ percentage-point edge at equal capacity was not statistically significant (paired t, $p=0.056$). Across five shared folds, no HQCNN-versus-classical accuracy difference survived Holm–Bonferroni correction (all adjusted $p\ge 0.625$), so we report the HQCNN as competitive with, not superior to, strong tuned classical baselines. A multi-split depth ablation showed that circuit depth $L\in \{1,2,3\}$ had no statistically detectable effect on accuracy ($L=2$ vs. $L=3$: Wilcoxon $p=1.00$); we therefore adopt two variational layers as a practical default rather than an optimum. Under a low-noise simulator (depolarising and amplitude-damping channels, $p=0.01$), accuracy was $96.49\%$, indicating robustness only at modest uniform error rates; realistic hardware noise is higher. We additionally apply Bayesian surprise as an epistemic-informativeness heuristic—not a formal generative model—to rank which findings are most worth building on. The framework offers a reproducible, documented evaluation procedure that can support cumulative comparison of hybrid quantum-classical models in healthcare. Full article

Venture novelty enables startups to overcome entry barriers and establish differentiated competitive advantages. However, research examining its antecedents from an epistemic control perspective remains limited. Drawing on survey data from 230 entrepreneurs and employing structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), this study investigates how entrepreneurs’ critical thinking influences venture novelty. The findings reveal a dual effect. On the one hand, critical thinking promotes venture novelty by fostering interactive learning, which facilitates the integration of heterogeneous information and the refinement of entrepreneurial opportunity insights. On the other hand, critical thinking increases cognitive depletion, thereby constraining the cognitive resources available for innovative activities. Furthermore, imagination moderates these relationships by strengthening the positive effect of interactive learning while attenuating the negative impact of cognitive depletion. FsQCA results further identify four configurational pathways to high venture novelty. This study contributes to the literature by stating both the enabling and constraining mechanisms of entrepreneurs’ critical thinking, clarifying its dual role in epistemic control, and providing configurational evidence regarding the role of imagination in fostering entrepreneurial innovation. Full article

This article critically examines the use of generative AI (specifically ChatGPT-4) as a tool for designing teaching materials in university courses on quantitative social research methods. It is conceived as a concept paper grounded in an illustrative, AI-assisted co-design session. The purpose is not to evaluate learning outcomes or produce generalizable empirical findings, but to develop a theory-informed analytical framework for examining AI-generated materials as epistemic artifacts. The analysis illustrates how seemingly neutral AI outputs embed specific assumptions and can actively shape the way social research is approached, intensifying constitutive methodological conventions. By critically unpacking the simulated outputs, the article proposes a framework for integrating AI-generated content into quantitative methods education as an object of critical inquiry. Full article

Background/Objectives: Large language models (LLMs) and generative AI are rapidly being integrated into healthy ageing initiatives for tasks ranging from companionship and cognitive support to personalised health advice and reduction in social isolation among older adults. Current ethical discussions predominantly address bias, privacy, and accuracy, leaving unresolved three critical governance questions: How do LLM sentiments towards transformative technologies diverge from human values in ageing contexts? What epistemic status do LLM outputs hold when applied to geriatric care? When is trust in those outputs justified for older adults? And who bears responsibility when AI-informed decisions affect functional ability or well-being? Methods: The framework was developed through normative conceptual analysis, synthesizing philosophical principles of medical knowledge and trust, ethical theories of responsibility, empirical evidence on LLM sentiment divergence, digital ageism, and applications of AI in geriatric care (structured searches in PubMed, PhilPapers, and relevant databases, January 2020–March 2026). Results: The integrated framework produces (i) adaptation of SAIA for multidimensional evaluation of human–machine value convergence specific to healthy ageing values (functional ability, autonomy, dignity, equity); (ii) a four-tier classification of LLM outputs tailored to geriatric scenarios; (iii) conditions for warranted trust calibrated to age-related vulnerabilities such as cognitive decline and digital divide; and (iv) responsibility allocation via RACI models with testable hypotheses linking governance design to trust calibration and patient safety outcomes. Conclusions: Without explicit societal alignment and epistemic governance, generative AI risks reinforcing benevolent ageism, automation bias, and responsibility gaps in healthy ageing. The 2025–2027 period offers a decisive window to shape institutional norms that place functional capacity, human dignity, and value convergence at the centre of AI deployment in geriatric care. Full article

Mature technical business-to-business (B2B) markets challenge the canonical commitment–trust hierarchy because product parity, informational asymmetry, long-cycle repeat purchasing and enduring dyadic relations may decouple the antecedents of trust from those of commitment. This study re-specifies the commitment–trust model by proposing a dual-pathway architecture in which communication is associated with commitment via trust, whereas product quality and salesperson behaviour are associated with commitment through perceived value. The model was tested through a cross-sectional survey of Portuguese business customers in the paint sector (n = 101), analysed using partial least squares structural equation modelling. The results corroborate the five positive hypotheses: communication strongly predicts trust; trust is the strongest predictor of commitment; product quality and salesperson behaviour both predict perceived value; and perceived value retained a smaller but significant direct effect on commitment. The theoretically motivated non-effect of perceived value on trust is also supported, indicating that, under the specified scope conditions, trust is formed primarily through epistemic and communicational mechanisms rather than through utilitarian value aggregation. The study contributes by refining the commitment–trust tradition, distinguishing epistemic–relational and utilitarian routes to commitment, and offering managers a pathway-specific basis for allocating relational investments in mature technical B2B markets. Full article

Proactive defense planning in Industrial Control Systems (ICS) is critically constrained by two intertwined types of uncertainty: epistemic uncertainty, arising from the defender’s limited observability of the system state and incomplete knowledge of attacker strategies, and aleatoric uncertainty, stemming from the stochastic nature of state transitions and the propagation of disturbances through inter-device dependencies. These factors significantly complicate the quantitative assessment of defense strategies before deployment. To address this challenge, this study proposes a Dynamic Bayesian Network (DBN)-based framework that explicitly models four sources of uncertainty. Within this framework, the expectation of the effectiveness differential is coupled with its information entropy to jointly quantify expected performance and prediction uncertainty. A casestudy on a typical substation automation system, complemented by systematic ablation experiments, demonstrates that the framework can effectively distinguish the relative effectiveness of defense strategies. The framework maintains robust assessment results under up to 15% noise in Conditional Probability Tables (CPTs). The ablation experiments further quantify the individual contributions of observability, dependency propagation, and attacker strategy to prediction uncertainty, and reveal a non-trivial coupling between epistemic and aleatoric uncertainty. This research provides theoretical and methodological support for resilience-oriented cyber defense planning in ICS. Full article

Imprecise probability models generated from data represent epistemic uncertainty by replacing the precise empirical distribution with a set of compatible probability distributions. When this set is described by reachable probability intervals, the induced bounds are tight, so the represented imprecision is not inflated by unattainable interval limits. This paper studies the informational effect of this replacement through the epistemic entropy gap, defined as the difference between the maximum entropy over the induced credal set and the Shannon entropy of the empirical distribution. The gap is a differential quantity: it measures the additional uncertainty introduced by the imprecise model beyond the observed frequencies. We analyze it for three reachable interval models generated from multinomial data: the Imprecise Dirichlet Model, the $ϵ$-contamination model and the approximated Non-Parametric Predictive Inference model. The analysis covers its main properties, its asymptotic behavior and its role in entropy equivalent calibration of model parameters. The results show that the entropy gap offers a common informational scale for comparing how different imprecise models represent the same empirical evidence, and helps interpret the degree of caution associated with limited data reliability and with empirical distributions that may otherwise lead to overconfident uncertainty assessments. Full article

This article presents a structured literature review of remittances, migration and economic policymaking in countries of origin, with a focus on Kenya, Kerala (India), and Sri Lanka. It examines three linked bodies of scholarship: migration as a driver of economic growth, the political economy of migration policymaking, and evidence-informed policymaking (EIPM). Conducted with a scoping orientation, the review focuses on contemporary academic and policy literature published since 2000 and shows that the evidence base on the economic value of international remittances in the context of labour migration is extensive, including findings on poverty reduction, macroeconomic stability, financial inclusion and diaspora engagement. However, this evidence is unevenly integrated into policymaking. The review finds that under-utilisation is not simply a problem of insufficient data or weak analytical capacity. Rather, it reflects structural, political and epistemic dynamics that shape how evidence is produced, legitimised, filtered and used in origin-country settings. It further shows that destination-centred perspectives continue to dominate migration scholarship, while gender and digitalisation are best understood as cross-cutting features of evidence systems rather than peripheral themes. The article concludes that strengthening the developmental contribution of migration and remittances requires greater attention to the institutional and political conditions under which economic evidence becomes policy-relevant and actionable. Full article

Driven by China’s “dual carbon” (carbon peak and carbon neutrality) goals and the national strategy of new-type urbanization, prefabricated construction has emerged as a pivotal pathway toward industrialized and sustainable development in the construction sector—leveraging its distinctive advantages in construction efficiency, cost optimization, environmental performance, and design adaptability. Nevertheless, the inherently sequential and interdependent nature of the full construction process—encompassing off-site component manufacturing, logistics transportation, and on-site assembly—introduces pronounced cross-stage risk transmission mechanisms, with prefabricated components serving as critical risk carriers. Such transmission dynamics significantly impede the scalable and safe deployment of prefabricated construction. To date, scholarly efforts on construction safety in prefabricated buildings have predominantly addressed isolated, stage-specific risks, falling short in quantitatively modeling the coupled propagation of risks across stages, accommodating epistemic uncertainties and latent (i.e., unknown or unobserved) risks, and informing targeted, evidence-based mitigation strategies. To bridge this gap, this study develops a rigorous quantitative framework for assessing cross-stage risk transmission in prefabricated construction safety. Specifically, it aims to (i) uncover the structural patterns and driving mechanisms underlying inter-stage risk propagation; (ii) reduce the likelihood of safety incidents throughout the construction life cycle; and (iii) deliver actionable theoretical insights and methodological guidance for practitioners and policymakers. Methodologically, we first conduct a systematic identification of safety-critical risk factors and establish a hierarchical risk indicator system comprising three first-level dimensions and twenty second-level indicators. Second, using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, causal relationships among risk factors are clarified, while incorporating the Leaky Noisy-or Gate (LNOG) extended model to account for unknown risks. Risk data are processed using triangular fuzzy functions, and a Bayesian network (BN) topology diagram is constructed via the GeNIe 5.0 platform, forming a DEMATEL-LNOG-BN-based model for assessing cross-phase risk transmission. Finally, applying the model to an actual project—”a prefabricated construction project in Shanghai”—the study conducts a cross-phase risk transmission analysis. Through forward probability inference, backward causality tracing, sensitivity analysis, and pathway decomposition, sensitivity comparisons are performed under different LNOG unknown risk parameters. Results are compared with those from the traditional DEMATEL-BN model to validate the stability and consistency of high-sensitivity risk factor identification, comprehensively verifying the applicability and predictive reliability of the proposed DEMATEL-LNOG-BN model. The study quantitatively reveals the progressive diffusion and amplification mechanisms of risks across the production–transportation–assembly process, providing scientific support and practical reference for precise safety risk prevention, critical node control, and the optimization of management systems in prefabricated construction sites. Full article

The rapid expansion of space activities requires manufacturing strategies that align environmental performance with engineering functionality, yet sustainability assessments of additive manufacturing (AM) remain affected by significant data uncertainty. This study presents an uncertainty-informed Life-Cycle Assessment (LCA) framework to evaluate the environmental and performance trade-offs between Laser Powder Bed Fusion (LPBF) and conventional CNC machining for a satellite mounting bracket. The assessment adopts a process-based cradle-to-gate approach and integrates a hybrid uncertainty propagation methodology combining Dempster–Shafer theory for epistemic uncertainty with Monte Carlo simulation for aleatory variability. Environmental impacts are represented as interval-valued outcomes with associated belief–plausibility measures, enabling explicit quantification of epistemic uncertainty. In parallel, a performance-based benefit metric based on stiffness-to-mass ratio is introduced and propagated under uncertainty using a consistent framework. Environmental and performance indicators are normalised and combined into a composite trade-off metric, allowing the evaluation of manufacturing alternatives across a range of environmental weighting scenarios. Decision outcomes are expressed in terms of belief and plausibility, capturing both support and indeterminacy under uncertainty. Results indicate that CNC machining exhibits lower midpoint environmental impacts and narrower uncertainty intervals across key categories, while LPBF shows higher potential impacts and substantially wider epistemic uncertainty, primarily driven by powder production and limited inventory data. However, when performance benefits are considered, LPBF may become preferable under specific trade-off conditions. These findings highlight the importance of explicitly accounting for epistemic uncertainty and performance considerations when evaluating sustainability trade-offs in aerospace manufacturing. The proposed framework supports early-stage eco-design by enabling robust decision-making under incomplete knowledge. Full article

Large language models (LLMs) are increasingly used to support writing, reasoning, translation, and decision-making, often on the assumption that easier access to information improves judgment. This integrative conceptual review argues that this assumption is incomplete because LLMs interact not with neutral information processors, but with users who bring prior beliefs, directional motivations, cognitive-effort constraints, and varying willingness to verify. The article develops the concept of artificial confidence: a relational and systemically reinforced form of unwarranted certainty that emerges when prompt-shaped, fluent, and seemingly authoritative AI outputs are experienced as independent validation. Drawing on research in judgment and decision-making, motivated reasoning, automation bias, processing fluency, epistemic vigilance, LLM sycophancy, and systems thinking, the review distinguishes artificial confidence from related constructs and proposes a socio-technical feedback model linking user motivations, prompt framing, model accommodation, perceived validation, reduced verification, and institutional normalization. The framework also identifies boundary conditions under which LLMs can improve judgment by preserving epistemic friction, source checking, uncertainty awareness, and accountability. The article concludes by offering operational definitions, behavioral indicators, testable hypotheses, and design and governance implications for AI-augmented systems in which human judgment remains revisable, accountable, and evidence-sensitive. Full article

Reliable landslide mapping in active mining regions is constrained by two coupled issues: severe domain shift from public datasets and extremely limited local annotations. In line with Transformer-centric intelligent interpretation of complex remote-sensing scenes, this study proposes a label-efficient transfer segmentation framework from a public source corpus to target open-pit mines built on SegFormer with a lightweight hybrid adapter that couples global context modeling with mining-specific directional cues. The pipeline combines source-domain Transformer pre-training, class-conditional feature alignment, Bayesian uncertainty estimation, and human-guided active learning. First, the backbone is pre-trained on the GDCLD source domain to learn transferable landslide morphology priors. Second, a joint optimization stage with class-conditional alignment reduces source and target embedding discrepancy during adaptation. Third, Monte Carlo dropout is enabled at inference to estimate predictive distributions, and sample acquisition is driven by mutual-information-based querying to prioritize epistemically informative target patches, addressing the small-sample supervision challenge emphasized in remote-sensing deep learning. This design turns uncertainty into an operational annotation policy rather than a passive diagnostic output. Experimental results show that the framework consistently outperforms deterministic counterparts and strong active-learning baselines in spectrally complex mine scenes, while approaching the fully supervised upper bound with only a small fraction of local labels. The approach is especially effective in shadowed benches and fault-adjacent slopes, supporting trustworthy deployment for geohazard monitoring and disaster-relevant slope safety workflows; extension to multi-modal constraints (e.g., SAR or elevation) is discussed as future work. Full article

Questioning is widely recognised as a key dimension of learning in science education, yet learner questioning has often been treated as a secondary aspect of classroom participation rather than as a central pedagogical and epistemic practice. This article offers a conceptual examination of questioning in relation to science education for sustainability, informed by a critical interpretive engagement with literature on questioning, participation, classroom dialogue, engagement, and science education. It argues that science education for sustainability requires more than the transmission of scientific knowledge, calling instead for pedagogical spaces in which learners can engage with complexity, uncertainty, interpretation, and the ethical and social dimensions of socio-scientific issues. The article’s main contribution lies in repositioning learner questioning as a central condition of science education for sustainability and in showing that questioning is shaped not only by knowledge and motivation, but also by participation, hesitation, silence, and broader dynamics of voice, legitimacy, and power. In this perspective, fostering questioning becomes essential to more inclusive, dialogic, reflexive, and transformative approaches to science education for sustainability. The article further argues that fostering questioning in this way contributes directly to the educational ambitions embedded in SDG 4, SDG 13, and SDG 16—making questioning-centred pedagogy not merely a methodological choice, but a condition for more democratic, just, and transformative science education for sustainable development. Full article