Search Results (2,251)

Background: Evidence-based practice (EBP) is a cornerstone of high-quality and safe nursing care. However, undergraduate nursing students often experience cognitive, methodological, and contextual barriers to learning and applying EBP. Active teaching strategies, such as the flipped classroom, may support the development of EBP competencies, yet qualitative evidence exploring students’ learning experiences remains limited. Objectives: To explore nursing students’ perceptions and experiences of learning evidence-based practice through a flipped classroom model. Methods: A qualitative descriptive study was conducted at the Faculty of Nursing of the University of Murcia (Spain). Purposeful maximum variation sampling was used to recruit undergraduate nursing students from the second and fourth academic years who had completed an EBP course delivered using a flipped classroom approach supported by an online learning platform. Twenty semi-structured interviews were conducted via videoconference. Data were transcribed verbatim and analyzed using reflexive thematic analysis with independent coding by two researchers and consensus procedures. Ethical approval and confidentiality were ensured. Results: Three main themes were identified: (1) transformation of the meaning of EBP learning and professional role, (2) cognitive and metacognitive processes in EBP learning, and (3) the learning experience as a catalyst for deep learning. Students described a shift from initial fear and perceived difficulty toward recognizing the practical value of EBP, accompanied by increased critical thinking, autonomous learning, and a growing evidence-informed professional identity. The flipped classroom model facilitated engagement and understanding, while the transfer of learning to clinical practice was influenced by contextual facilitators and barriers. Conclusions: Learning EBP through a flipped classroom was experienced as a transformative process that fostered critical thinking, self-regulated learning, and the construction of an evidence-oriented professional identity among nursing students. Strengthening information literacy skills and improving alignment between academic and clinical environments may enhance the sustainable application of EBP in clinical practice. Full article

Adaptive graph learning has become a widely adopted paradigm for multivariate time series forecasting when explicit physical topology is unavailable. In these approaches, node embeddings are typically used to construct dense adjacency matrices based on pairwise similarity, implicitly coupling representation learning with relational modeling. However, we observe that under identical training settings but different random initializations, the learned adjacency matrices can vary substantially while predictive performance remains nearly unchanged, indicating that the relational structure is often underdetermined by the forecasting objective. This observation suggests a mismatch between similarity-based structural learning and the forecasting objective. In this work, we revisit node embeddings from a sequence approximation perspective and propose a Diagonal Adaptive Graph (DiAG) module that restricts adaptive learning to diagonal elements. The diagonal coefficients are derived from channel-independent predictions, while off-diagonal interactions are constructed from the similarity of input sequences. This design decouples representation learning from relational modeling, allowing variables to adaptively switch between channel-independent and channel-dependent regimes. Experiments on multiple datasets show that DiAG improves forecasting performance without modifying the channel-independent backbones. These results indicate that channel-dependent forecasting can be achieved as a prediction-driven refinement over channel-independent backbones, without requiring fully learned dense relational structures. Full article

Traditional financial early warning models often rely on the independent and identically distributed (IID) assumption, failing to adequately capture cross-sectional spatial contagion effects and temporal dynamic mutations, and are susceptible to the over-smoothing problem when processing highly imbalanced graph networks. To address these limitations, this study proposes a micro-manifold-based identity-preserving spatiotemporal graph neural network framework (Micro-STAGNN). In the spatial dimension, an identity-preserving graph convolutional operator (IP-GCN) is constructed. By hard-coding a self-preservation coefficient ($\mathsf{\lambda }=0.8$), it quantifies peer risk spillover while mitigating feature dilution, ensuring the transmission of heterogeneous default signals. In the temporal dimension, Long Short-Term Memory networks are cascaded with a temporal attention mechanism to capture the nonlinear temporal inflection points that trigger financial distress. The empirical study utilizes a sample of China’s A-share market from 2015 to 2025, evaluating the model using an Out-of-Time Validation protocol and Focal Loss. Results indicate that under a highly imbalanced distribution with a positive-to-negative sample ratio of approximately 1:50, Micro-STAGNN achieves an OOT ROC-AUC of 0.9095, a minority class default recall of 89%, and reduces the missed detection rate to 11%, outperforming traditional nonlinear cross-sectional models such as XGBoost. Furthermore, temporal attention weights provide explainable support for the early warning results. Full article

This article examines how algorithmic classification systems participate in the production of meta-identities, understood as operational classificatory constructs that mediate the visibility, circulation, and interpretation of digital content and its authors. The study employs a mixed-methods design combining controlled analytical simulation with qualitative interpretive analysis, systematic thematic coding, and comparative statistical procedures. Empirical data are derived from the analysis of 150 audiovisual works produced in formative workshops and interpreted by four types of agents: authors, peers, specialized human analysts, and two Large Language Model-based AI systems (ChatGPT and Gemini). Interpretations were analyzed across micro, meso, and macro levels, using a consolidated system of thematic categories with hierarchical weighting and normalization procedures to ensure inter-agent comparability. The results demonstrate a systematic and structural divergence between human and algorithmic classifications. While human agents preserve semantic plurality and contextual anchoring, AI systems tend to reorganize thematic hierarchies through semantic aggregation and stabilization, thereby privileging broad, reusable categories. This process produces recurring, opaque classificatory patterns that serve as infrastructural references for subsequent algorithmic decisions. The article contributes methodologically by offering a replicable framework for comparing human and algorithmic regimes of meaning production in digital environments. Full article

The weak-field, quasi-static regime of gravity is commonly described by the Newton–Poisson equation as an effective response law. We construct this response within a cost-first discrete variational framework. The Recognition Composition Law (RCL) uniquely selects a reciprocal closure cost within the restricted quadratic symmetric composition class; together with the discrete ledger axioms AX1–AX5 (including conservation) and standard DEC refinement, the Newton–Poisson baseline is then recovered in the instantaneous-closure limit. Conditional on Assumption AS1 (scale-free latency) and Assumption AS2 (causal frequency–wavenumber ansatz), allowing finite equilibration introduces fractional memory into the response, yielding a scale-free modification of the source–potential relation characterized by a power-law kernel $w_{\ker }\left(k\right)=1+C{(k_0/k)}^{\alpha }$ in Fourier space. The kernel exponent $\alpha =\frac{1}{2}(1-{\phi }^{-1})\approx 0.191$, where $\phi =(1+\sqrt{5})/2$, is derived from self-similarity of the discrete ledger closure; the amplitude $C={\phi }^{-2}\approx 0.382$ is identified as a hypothesis from a three-channel factorization argument. We evaluate this quasi-static kernel-motivated response against SPARC galaxy rotation curves under a strict global-only protocol (fixed $M/L=1$, no per-galaxy tuning, conservative ${\sigma }_{\mathrm{tot}}$), using a controlled multiplicative surrogate for the full nonlocal disk operator implied by the kernel. In this deliberately over-constrained setting, the surrogate interface achieves $\mathrm{median}({\chi }^2/N)=3.06$ over 147 galaxies (2933 points), outperforming a strict global-only NFW benchmark and remaining less efficient than MOND under identical constraints. The analysis is restricted to the non-relativistic, quasi-static sector and should be read as a falsifier-oriented galactic-regime consistency check of the scaling window, not as a relativistic completion or a claim of Solar System viability without additional UV regularization/screening. Full article

Background/Objectives: This qualitative study investigated the perspectives and lived experiences of individuals with obesity, with a specific focus on psychological needs, beliefs, attitudes, and experiences related to psychological support. The study aimed to identify perceived barriers and facilitators to adherence in weight management and to examine participants’ views on digital psychological interventions designed to promote mental health and well-being. These findings represent the preliminary phase of a broader research project aimed at developing and implementing personalized digital psychological interventions to enhance engagement, treatment effectiveness, and equity of care in obesity management. Methods: Five focus groups were conducted with a purposive sample of 35 patients (48.6% female) diagnosed with obesity and enrolled in a four-week multidisciplinary weight-reduction program at the IRCCS Istituto Auxologico Italiano, San Giuseppe Hospital, Piancavallo (VB), Italy. Sessions were audio-recorded, supplemented with field notes, transcribed verbatim, and analyzed using reflexive thematic analysis to identify recurrent patterns of meaning across participants’ narratives. Results: Six overarching themes were identified: (1) obesity as an embodied and pervasive experience; (2) the interplay between emotions, weight stigma, and identity construction; (3) family and social relationships as both supportive and ambivalent; (4) personal agency and self-regulation processes in weight management; (5) access to healthcare services and experiences with healthcare professionals; and (6) the perceived role of psychological support within multidisciplinary care. Participants described obesity as a complex, multidimensional condition encompassing physical, emotional, relational, and contextual challenges that directly influence treatment engagement and adherence. Conclusions: Psychological support emerged as a central component of comprehensive obesity care. Findings underscore the need for personalized, flexible, and digitally supported psychological interventions to enhance long-term adherence, acceptability, and overall well-being. Full article

Traditional villages embody regional culture and local knowledge, yet culture–tourism integration often suffers from a mismatch between resource value and effective transformation. To address this problem, this study proposes a two-dimensional “benefit–obstacle” diagnostic and strategy-matching framework and tests its case-based applicability in Dayuwan Village. First, a cultural landscape gene (CLG) atlas was constructed for the village based on a geo-information coding scheme, covering both tangible and intangible CLGs. Second, a four-dimensional evaluation system was operationalized through five expert judgments and 106 valid on-site questionnaires collected from tourists (n = 67) and residents (n = 39). Criterion weights were determined using an AHP–entropy combination approach, and the comprehensive benefit closeness coefficient was calculated via TOPSIS. Third, an obstacle degree identification model was employed to pinpoint key constraints and derive composite obstacle degrees. Results within the Dayuwan case show that the TOPSIS closeness coefficients of the 17 genes ranged from 0.653 to 0.782 (mean = 0.714), with 4, 6, and 7 genes classified as excellent, good, and medium, respectively; composite obstacle degrees ranged from 0.0228 to 0.1975. In Dayuwan Village, higher obstacle degrees clustered mainly in intangible CLGs, whereas Ming–Qing architecture and frequently practiced folk-cultural genes showed comparatively lower obstacle degrees. The transformation process is constrained by four mechanisms—landscape character protection, economic transformation, social identity, and market demand—with economic transformation constraints being the most prominent. Based on the benefit–obstacle matrix, 17 CLGs were classified into five activation scenarios and matched with corresponding revitalization strategies. This framework links benefit ranking, obstacle diagnosis, and strategy matching, and provides a case-based diagnostic reference for the conservation and culture–tourism integration of villages with comparable heritage conditions, subject to local recalibration of indicators, weights, and thresholds. Full article

Imageability is a cognitive measure of environmental differentiation and place memory. However, the existing literature focuses mainly on static morphological descriptions or subjective perception, without systematic quantitative studies of how physical environment and behavioral activity jointly generate the imageability of characteristic districts. This limits active responses to the rise of “placelessness” in numerous cities. Based on the S-O-R theory, this study proposes a “visual–activity” two-channel mediation model. Based on 65 typical characteristic districts in Wuhan, and using multi-source data in the research, PLS-SEM was employed to systematically study the process that influences imageability in urban environments. It was found that (1) behavioral activity serves as the core mediating link between the physical environment and imageability; (2) scenic beauty exerts a partial mediating effect between visual sensitivity and imageability; (3) vitality exerts a full mediating effect between activity support and imageability. This study is expected to provide a scientific foundation for design refinements, quality enhancement, and place identity construction in urban characteristic districts oriented toward perceptual experience in the post-industrial era. Full article

Background: Autobiographical memory undergoes qualitative changes across the lifespan, influencing self-understanding, emotional regulation, and psychological adaptation. Research shows memory is a dynamic process, reconstructed through retrieval, narration, and social interaction. How narrative construction and empathic engagement shape memory reconsolidation and self-continuity remains insufficiently integrated. Objectives: This narrative review synthesizes theoretical, empirical, and applied findings on autobiographical memory, narrative processes, and empathy, proposing an integrative model linking memory reconsolidation, identity reconstruction, and adaptive functioning. Methods: A theory-oriented narrative review was conducted across psychology, neuroscience, gerontology, and narrative research, drawing on literature from PubMed, PsycINFO, Web of Science, Scopus, J-STAGE, and CiNii. Peer-reviewed empirical studies, systematic reviews, and theoretical papers were organized around three interrelated conceptual domains: (1) autobiographical memory and self-related processes, (2) neurobiological and emotional mechanisms relevant to memory updating and reconsolidation, and (3) narrative construction within empathically mediated social interaction contexts, with additional consideration of evidence from narrative-based and creative interventions. Results: The reviewed literature suggests that autobiographical memory functions as a plastic, socially embedded system supporting self-continuity, although the strength and consistency of evidence vary across studies and contexts. Narrativization within empathically responsive and psychologically safe contexts enhances narrative coherence, emotional integration, and perspective-taking, promoting psychological stability, although these effects are not uniformly observed across all populations and study designs. Creative narrative activities further facilitate retrieval and meaning reconstruction, extending memory updating beyond recall, while the underlying mechanisms and causal pathways remain to be fully established. Conclusions: We propose an empathy-mediated narrative reconstruction model in which creative activity, narration, empathic response, and retelling interact cyclically to support memory reconsolidation and self-narrative updating. By integrating cognitive, social, and creative dimensions, this model provides a theoretically grounded framework with implications for clinical, educational, gerontological, and creative applications. Full article

Bioeconomy policy requires timely, economy-wide evidence; however, two persistent measurement constraints remain: official input–output (IO) tables are published with time lags, novel start-up and novel prospective or hybrid bio-based activities are rarely identified as separate sectors in national accounts. This study develops an applied framework that combines IO nowcasting with an accounting-consistent sector-embedding procedure under limited data availability. Using Ireland’s national IO system and an existing bioeconomy IO framework as the accounting backbone, we update the 2015 table to 2022 through calibration to macroeconomic control totals, providing a timely structural baseline. We then introduce a transparent method for constructing new bioeconomy sectors based on dominant input shares, import intensity, and output allocation, while preserving national accounting identities. The approach is demonstrated for aquaculture systems, anaerobic digestion scenarios, and plant-based protein value chains. Demand-driven Leontief multipliers reveal heterogeneity in domestic propagation effects across activities and development stages. The framework offers a resource-efficient and replicable tool for evaluating bioeconomy strategies under real-world data constraints. The paper finds that the bioeconomy is structurally heterogeneous rather than a single uniform sector. Aquaculture is strongly transport- and service-linked, anaerobic digestion is more manufacturing-oriented, and plant-based protein production combines agricultural and industrial inputs while showing relatively high import dependence. Full article

Mobile crane repositioning and on-site logistics coordination constitute a highly coupled, nonlinear decision problem in constrained construction environments. Existing approaches largely decouple these tasks, limiting achievable system-level efficiency. This study introduces EVCrane, a kinematics-informed evolutionary optimization framework that simultaneously optimizes crane stopping positions, stockpile deployment, and task allocation within a unified mixed continuous–binary formulation. Unlike distance-based approximations, the proposed model propagates geometric decisions through coordinated crane motion components—including radial boom adjustment, slewing rotation, and vertical hoisting—ensuring physically consistent cycle-time estimation. A real industrial case study was used to benchmark five optimization algorithms under identical MATLAB R2026a implementations. The Genetic Algorithm (GA) achieved the lowest total crane engaged time (34.516 h), reducing operational duration by 6.45% and utilization cost by 6.32% compared with a deterministic nonlinear programming baseline. Comparative analysis reveals that recombination-based evolutionary search exhibits superior compatibility with assignment-driven non-convex landscapes, outperforming swarm-based and trajectory-based alternatives. Sensitivity analysis confirms structural robustness of optimal spatial configurations under parametric perturbations. The proposed framework advances crane planning from decoupled geometric heuristics toward integrated, physics-consistent, and computationally robust optimization, supporting intelligent and sustainable construction site management. Full article

Social media platforms have become critical communication environments during disasters, where individuals express emotions, share information, and engage in public discourse. These platforms also reflect heterogeneous communication patterns shaped by different actor groups. However, existing studies predominantly focus on emotion classification and often overlook the combined role of actor identity and conflict dynamics. To address this gap, this study proposes an integrated AI-based analytical framework for actor-aware emotion and conflict analysis in post-disaster social media. An expert-annotated Turkish tweet dataset was constructed based on Ekman’s emotion model, including anger, fear, sadness, happiness, and surprise, along with an additional irrelevant/off-topic category and conflict-level labels. A Transformer-based model (BERTurk) was fine-tuned for multi-class emotion classification. Experimental results show that the proposed model achieves strong classification performance, with an accuracy of 0.931 and an F1-score of 0.912, outperforming conventional machine learning and deep learning baselines. Actor-based analysis reveals systematic differences in emotional and conflict patterns across groups. Scientists, journalists, and individual users exhibit higher levels of conflict and more pronounced negative emotional expressions, whereas institutionally oriented actors display comparatively balanced and supportive communication patterns. In addition, a web-based decision support system was developed to enable interactive visualization and actor-level exploration of emotional and conflict dynamics. Overall, the proposed framework provides a scalable, analytically robust approach to understanding social media discourse in disaster contexts and offers practical implications for AI-driven crisis communication and decision-support systems. Full article

Alzheimer’s disease (AD) remains an incurable neurodegenerative disorder. The concept of theranostics—combining diagnostic and therapeutic functions within a single nanoplatform—has been explored for over a decade. Despite a growing number of publications, no theranostic system has yet reached clinical application for AD. This critical review analyzes the fundamental conceptual contradictions that hinder the clinical translation of theranostic nanoplatforms for AD and identifies alternative strategies where nanotechnology may still be beneficial. The review presents key aspects essential for understanding theranostics challenges: AD molecular targets, analysis of existing nanoplatforms, identification of three inherent conceptual conflicts, and viable alternative approaches. Our analysis reveals three core conceptual conflicts: the pharmacokinetic conflict, where diagnostics demand rapid accumulation and clearance while therapy requires prolonged retention—exacerbated by minimal brain delivery (1–2% ID/g) and peripheral toxicity risks; the dose conflict, characterized by orders-of-magnitude disparities between diagnostic and therapeutic dosing, rarely quantified for identical particles; and the temporal conflict, pitting one-time diagnostics against chronic therapy needs, as long-persisting particles generate irremovable brain background signals. We further identify a pervasive methodological trap: predominant focus on mature β-amyloid (Aβ) fibrils overlooks soluble oligomers as the primary toxic species. We conclude by proposing viable alternatives: preclinical intervention for time-limited “hit-and-clear” applications; coordinated theranostic monitoring with separate diagnostics/therapy; theranostic pairs using ligand-matched, function-optimized particles; and external stimuli for temporal function separation. A practical roadmap guides the transition from conceptual demonstrations to clinical translation. Addressing these contradictions can transform theranostics from elegant chemical constructs into clinically meaningful AD tools. Full article

This study quantifies how confinement changes the orientational phase space of proteins by comparing hanging-drop (HD) with Langmuir–Blodgett (LB) conditions within a unified probabilistic framework grounded in structural data from the Protein Data Bank (PDB). For each protein, principal moments of inertia are computed from atomic coordinates, trace-normalized, and used to define a geometry-based benchmark for the probability of occupying a predefined productive-orientation set. In parallel, a Hamiltonian-weighted probability is obtained within a classical statistical–mechanical treatment by reconstructing the orientational distribution over the polar–azimuthal domain under a fixed global confinement protocol. The analysis is carried out on a ten-protein panel spanning diverse sizes and anisotropies, and the HD→LB contrast is characterized through probability gains, distributional distances, and an energy-basin decomposition that distinguishes basin depth from basin measure. Under identical parameterization, LB globally produces higher productive-orientation probabilities than HD across all proteins, establishing a uniform direction of the confinement effect while preserving protein-dependent magnitudes. The inertia-based benchmark exhibits broader dispersion in LB/HD amplification, whereas the Hamiltonian construction yields a more regular cross-protein gain, consistent with LB acting as a global reweighting of orientational phase space rather than a protein-specific re-tuning. By integrating PDB-derived structural descriptors with a statistical–mechanical operator, the framework provides a transparent bridge between molecular geometry and confinement-driven ordering and offers a compact basis for comparing crystallization-relevant confinement protocols across structurally heterogeneous proteins. Full article

By comparing Buber and Levinas, this article aims to clarify the constitution of the self as a place of identity. Both authors recognise, at first, that the self is only defined in relation to others and, ultimately, in relation to the You. However, the definition of that relationship is opposite in both authors. Levinas’ interpretation echoes Luther and Kierkegaard, as it establishes an insurmountable difference between you and me. Buber, although he borrows many concepts from the Danish philosopher, fundamentally, the definition of the individual as a relationship, contradicts Kierkegaard by defining the relationship not as a simple ‘facing’, but as a third party between me and you. Finally, it is concluded that although the individual must be preserved in the I–You relationship, this cannot be understood as something in itself, as a third part, and, on the other hand, there must be a certain knowledge about the Thou for the relationship to be real. Full article