Search Results (149)

Background: Physical literacy is a multidimensional construct encompassing physical competence, confidence, motivation, knowledge, and lifelong engagement in physical activity. The Perceived Physical Literacy Instrument (PPLI) has been widely used internationally; however, previous adolescent validations have been based on a reduced 9-item version originally developed for teachers. This study aims to evaluate the validity and test–retest reliability of a Spanish adaptation of the original 18-item PPLI in Spanish adolescents aged 11–18 years. Methods: A multi-phase validation study was conducted with 869 Spanish adolescents (421 females). The procedure included: (1) translation and cultural adaptation, (2) Exploratory Factor Analysis (EFA; n = 290), Confirmatory Factor Analysis (CFA; n = 579) and invariance analyses, and (3) test–retest reliability assessment. Results: EFA supported a three-factor solution comprising 15 items. CFA showed standardized factor loadings ranging from 0.62 to 0.89, indicating that the latent constructs were adequately represented. Although the 15-item model showed acceptable fit, a 5-item unidimensional short form was developed due to limitations in the three-dimensional models. This short form demonstrated good model fit (scaled RMSEA = 0.073; scaled CFI = 0.992; SRMR = 0.026), adequate convergent validity (AVE = 0.558), high reliability (ω = 0.821), moderate test–retest stability (ICC = 0.69), and full configural, metric, and scalar longitudinal invariance. Conclusions: The 15-, 9-, and 5-item versions of the PPLI are valid and reliable options. The 15-item version allows comprehensive assessment and domain-level interpretation. The 9-item version facilitates comparability with previous international research. The 5-item version may be useful in contexts with time constraints but may not be the preferred choice for comprehensive assessment of physical literacy in clinical or detailed pedagogical diagnostic settings. Full article

Background: Although the concepts of meteoropathy and meteosensitivity are not included in official classifications, such as the ICD-11 or DSM-5, they are increasingly being studied as potential symptom complexes linking weather variability to health status. The METEO-Q questionnaire, originally developed in Italy, has been adapted in Japan and Turkey, where it has demonstrated satisfactory reliability parameters, although the authors emphasized the need for further verification of the tool’s temporal stability. The present study aimed to adapt METEO-Q to the Polish language and conduct a critical assessment of its factor structure, measurement invariance, and validity in clinical groups. Methods: This cross-sectional study involved 1128 adults: healthy individuals (n = 711), cardiac outpatients (n = 194), and subclinical group with diagnosed mental disorders (n = 223). Data from healthy participants were divided into a training sample (n = 426) for exploratory factor analysis (EFA) and a test sample (n = 285) for confirmatory factor analysis (CFA). Measurement invariance was assessed in the clinical groups. Validity was verified through correlations with a list of 21 symptoms and measures of anxiety and worry about climate change. Results: A two-factor model (meteoropathy and meteosensitivity) was better fitted to the data than a one-factor model, which is consistent with findings from Italian, Japanese, and Turkish studies. However, absolute fit indices in the test sample indicated significant model misfit [CFA: χ² (43) = 210.192, p < 0.001, RMSEA = 0.120, CFI = 0.927], suggesting the presence of local errors in the tool’s structure. The reliability of the subscales was high (α from 0.86 to 0.93). Multi-group analyses suggested metric and scalar invariance. Patients with mental disorders obtained the highest scores, while cardiac outpatients reported a lower level of meteoropathy (M = 6.13) than healthy individuals (M = 7.24). Conclusions: METEO-Q demonstrates a stable two-factor structure and high internal consistency. The obtained RMSEA index (0.12), although indicative of some misfit, is similar to results obtained in other adaptations, such as the Japanese (RMSEA = 0.10) and the Turkish (RMSEA = 0.11), which suggests it is a consistent feature of this tool across different cultural contexts. Accordingly, the instrument is suitable for research purposes; however, its clinical application requires considerable caution and further work to optimize the model. Full article

Spatial qualities are central to architectural reasoning; yet, in studio-based education, they often remain implicit rather than structured as a shared analytical framework. This study examines how a multi-scalar taxonomy of spatial qualities can function as a collaborative analytical language in studio-based architectural education. Situated in Košanćićev venac and Dorćol, two historically layered areas of Belgrade’s old town, this study integrates expert spatial analysis with a student questionnaire administered across bachelor and master study levels. Empirical testing was conducted to evaluate structural coherence, conceptual differentiation and the distribution of spatial qualities across detail, architectural and urban drawing scales. The findings indicate consistent internal stability, clear differentiation among constructs and statistically significant cross-scale articulation. Form- and composition-related qualities showed high usability, while interpretative constructs were more variable. Master-level students demonstrated greater engagement with cognitive and interpretative constructs, indicating a shift toward more conceptually grounded design reasoning without affecting overall structural coherence. These results suggest that spatial qualities can operate as a level-independent analytical language, supporting inclusive participation, shared interpretation and structured dialogue within the design studio. By positioning spatial qualities as a collaborative pedagogical framework, this study contributes to interdisciplinary communication and more equitable engagement in architectural education. Full article

Satellite remote sensing has rapidly evolved from an experimental support tool into a structural component of preventive archaeology and cultural heritage governance. Drawing on scientific publications and policy-oriented grey literature from 2010–2025, this study provides an integrated review of how optical, SAR, and multi-sensor satellite data are used to detect archaeological sites, monitor landscape and structural change, and support risk-informed planning across diverse legal and institutional contexts. A multi-platform workflow combines AI-assisted semantic querying (Consensus), bibliometric searches (Scopus), and the collaborative management and geospatial visualisation of references through Zotero, VOSviewer (1.6.19), and QGIS (3.44)-based literature mapping, thereby linking thematic trends, co-authorship networks, and geographical patterns of research and regulation. The results show non-linear but marked publication growth, a strongly interdisciplinary profile, and the consolidation of international hubs that drive advances in Sentinel-2-based prospection, Landsat and night-time lights urbanisation metrics, and SAR time series for deformation, looting, and conflict-damage mapping. Parallel analysis of grey literature and institutional initiatives (Copernicus Cultural Heritage Task Force, national “extraordinary plans”, regional declarations, and UNESCO guidelines) reveals the codification of satellite Earth observation within rescue archaeology protocols, emergency archaeology, and long-term conservation strategies. Overall, the evidence indicates a transition towards data-driven, multi-sensor, and multi-scalar research, underpinned by open satellite data, reproducible workflows, and AI-supported evidence synthesis. Full article

Knowledge Tracing (KT) is a fundamental task in intelligent education systems, designed to track students’ evolving knowledge states and predict their future performance. While Deep Learning-based Knowledge Tracing (DLKT) models have advanced the field, they often face significant limitations in jointly capturing short-term performance fluctuations and long-term knowledge retention, which restricts their predictive precision in complex learning trajectories. This paper proposes the Extended Deep Knowledge Tracing (xDKT) model, which integrates the Extended Long Short-Term Memory (xLSTM) architecture to enhance multi-scale temporal learning representations. Specifically, through rigorous ablation studies over extended learning sequences (up to 1000 steps), our analysis indicates that the exponential gating and advanced scalar memory of sLSTM units are the primary drivers of performance. This architecture effectively captures both short-term performance shifts and long-term knowledge retention without the vanishing gradient degradation inherent to standard LSTMs. We evaluate xDKT across six diverse benchmark datasets, including Synthetic, Algebra2005–2006, Statics2011, and the ASSISTments series, covering over 22,000 learners. Experimental results show that xDKT yields improved Area Under the ROC Curve (AUC) scores on Statics2011 (0.8562) and ASSISTments2009 (0.8318) compared to baseline models such as DKT, DKVMN, and AKT. Finally, through extensive validation, these findings suggest that xDKT architecture provides a robust and promising framework for accurate and adaptive learning environments. Full article

Heterogeneous multi-core systems (HMCSs) typically face a dilemma: heuristics (e.g., Linux CFS) are fast but blind to global constraints, while meta-heuristics (e.g., GAs) are globally optimal but too slow for real-time OS interaction. To bridge this gap without relying on “black-box” neural networks, we introduce the Tissue P System-Inspired Task Allocator (TPSTA). By mapping HMCS and parallel task scheduling to Tissue P System models and vectorized linear algebra problems, TPSTA achieves a computational complexity of $\mathrm{O}\left(\mathrm{M}/\mathrm{W}\right)$, effectively compressing the decision space. Our rigorous evaluation across four dimensions reveals a system strictly bound by physical constraints rather than algorithmic heuristics. (1) Under sufficient resource provisioning (four chips), TPSTA achieves a 0.00% Deadline Miss Ratio (DMR). Crucially, stress tests on constrained hardware (two chips) show graceful degradation to a 12.88% DMR, matching the optimal theoretical bound of EDF, whereas standard heuristics collapse to failure rates > 68%. On a massive 4096-core cluster, TPSTA outperforms the Linux GTS scalar baseline by 14.4×, maintaining low latency where traditional algorithms fail (>8 s). (3) Adaptability: The system demonstrates adaptive routing in handling hardware heterogeneity; without explicit rule-coding, it autonomously prioritizes data locality during NUMA transfers and migrates compute-bound tasks during thermal throttling events. (4) Physical Limits: Finally, our roofline analysis confirms that while the algorithmic speedup is theoretically linear, practical performance saturates at ~375× due to the Memory Wall, validating the isomorphism between synaptic bandwidth and hardware memory channels. Full article

Coastal cities are complex spatial systems shaped by intertwined economic, environmental, demographic, and governance pressures. This study develops a multidimensional comparative framework to analyze coastal cities in the Black Sea basin across five dimensions: physical–morphological structure, demographic scale, economic–functional profile, transportation and accessibility, and urban quality–governance. To address cross-country data heterogeneity, an ordinal (0–1–2) indicator system is employed and analyzed through multiple multivariate techniques, including Gower dissimilarity, NMDS, Ward hierarchical clustering, MCA, Spearman rank correlation, network analysis, and rank-transformed PCA. Findings indicate that Black Sea coastal cities do not form a single homogeneous typology but cluster around distinct structural patterns. A major axis of differentiation separates port–industrial production-oriented cities from tourism–service-oriented cities, while a considerable group of multifunctional and transitional cities exhibits moderate values across several dimensions. Results show that city typologies are shaped less by national planning regimes than by structural dynamics such as port scale, economic specialization, accessibility, and spatial pressure. By integrating non-metric and metric approaches, the study proposes a context-sensitive and multi-criteria comparative methodology. The findings highlight the need for multi-scalar and multidimensional planning perspectives to better understand structural differentiation in coastal urban systems within semi-enclosed marine regions such as the Black Sea. Full article

The global shift toward renewable energy systems raises major challenges related to the variability of solar and wind power and their poor alignment with electricity demand. This paper addresses energy adequacy, defined as the ability of an energy system to reliably meet demand by balancing generation, storage, transmission, and reserves for unforeseen events. Within this framework, energy storage systems are identified as strategic components, requiring a diversified and multi-scale set of solutions-from territorial to building scale-to respond to infrastructural constraints and user behaviour. The study adopts a multi-scalar and interdisciplinary methodology combining deductive and inductive approaches. The deductive analysis examines global, European, and Italian electricity systems, highlighting issues such as overcapacity and grid instability caused by the uncoordinated development of renewable generation and network infrastructures. The inductive approach focuses on existing storage technologies, with particular attention to two types of thermal energy storage selected for their simplicity, scalability, and replicability. Hydropower reservoirs are also considered due to their multifunctional role in energy balancing. Two case studies developed by the research group—a public building energy retrofit in Milan and a modular off-grid housing prototype—demonstrate how integrated storage solutions can enhance system flexibility. The results emphasize the necessity of a systemic design approach that combines storage technologies, adaptable energy use, and active user participation to ensure energy adequacy in scenarios with high renewable penetration. Full article

This paper examines public architecture in Baghdad during the late nineteenth and early twentieth centuries, focusing on how public buildings contributed to the formation of urban centrality and how this process interacted with local architectural practices. Rather than approaching public construction solely through administrative or ideological frameworks, the study conceptualizes public buildings as structuring components in the reconfiguration of the urban fabric. Methodologically, the research adopts a two-stage, multi-scalar approach. First, public buildings in Beirut, Damascus, and Baghdad are identified and comparatively analyzed using QGIS-based spatial analysis, employing Kernel Density Estimation and DBSCAN clustering to examine patterns of spatial concentration, distribution, and relationships with major urban axes. This comparative stage establishes a comparative spatial framework for understanding urban centrality in provincial capitals. In the second stage, Baghdad is examined as a focused case study through building-scale architectural analysis, incorporating plan organization, construction techniques, material use, and environmental adaptation based on archival documents, historical maps, and visual sources. The results indicate that public buildings in Baghdad were not isolated institutional entities but integral components in the formation of new urban focal areas structured along river-oriented and infrastructural axes. Architecturally, these buildings exhibit a hybrid character, combining standardized public building programs with locally embedded materials, construction methods, and spatial adaptations. The study concludes that public architecture in late Ottoman Baghdad emerged through a negotiated process between centralized planning principles and local architectural knowledge, producing a distinct yet contextually grounded form of urban centrality. Full article

Forest and landscape restoration (FLR) represents a critical nexus of climate change mitigation, biodiversity conservation, and sustainable development. Despite substantial federal investments and commitments, empirical subnational research quantifying the relationships between governance structures, funding mechanisms, and restoration outcomes remains scarce, and integrated implementation frameworks bridging institutional, technical, and socio-economic dimensions are largely absent from the literature. This study presents a mixed-methods analysis of FLR implementation gaps across Maryland, Virginia, and West Virginia. Three Mid-Atlantic Appalachian states selected for their contrasting ecological conditions, governance structures, and restoration trajectories that collectively represent the heterogeneity of subnational restoration challenges. We examined 147 restoration projects (2019–2024), conducted 25 stakeholder interviews, and analyzed federal funding allocations ($428 million) through spatial and temporal frameworks. Our findings reveal five critical implementation barriers: (1) policy incoherence across federal–state–local jurisdictions creating 34% project delays; (2) chronic underfunding with 63% of projects receiving less than 60% of planned budgets; (3) technical capacity deficits affecting 71% of rural communities; (4) inadequate stakeholder engagement mechanisms reducing project sustainability by 45%; and (5) insufficient monitoring frameworks limiting adaptive management. We introduce an Integrated Restoration Implementation Framework (IRIF) that uniquely integrates policy coordination, sustainable financing, technical capacity building, and community engagement within a unified adaptive management cycle, operationalized through empirically derived thresholds, to guide evidence-based interventions. Quantitative analyses demonstrate that multi-stakeholder governance models increase restoration success rates by 2.3-fold (p < 0.001), while integrated funding mechanisms improve long-term sustainability by 67%. Theoretically, this study advances socio-ecological systems scholarship by providing empirical evidence that multi-scalar governance configurations and integrated stakeholder engagement mechanisms are principal determinants of restoration success, advancing the evidence base for adaptive governance approaches in complex federal systems. Our findings provide actionable intelligence for policymakers and practitioners, while underscoring that sustainable FLR in complex federal systems depends on coherent multi-level governance architectures coordinating institutional mandates, financial resources, technical capacity, and community agency across jurisdictional scales. Full article

Historical water management infrastructures, often comprising underground environments, represent a significant example of the interplay between built heritage and the natural substrate. This study proposes an interdisciplinary, integrated and multi-scalar investigative methodology for such structures. Through the analysis of the case study of Palombaro Purgatoro Vecchio, a large historical public water cistern located in Matera in Italy, this paper presents a rigorous methodology replicable in different contexts. Bibliographic and archival research establish the knowledge base regarding the structure’s historical evolution; territorial and hydromorphic analyses, supported by GIS, highlight the dynamics of the surrounding watersheds. Meanwhile, a digital survey integrating SLAM and photogrammetry provides geometric-dimensional data, serving as the foundation for analysing construction techniques and materials. The selection of accessible and manageable technologies promotes a practical, replicable investigative methodology aimed at the protection, comprehension, enhancement and dissemination of water UBH. Full article

Unexpected degradation in servomotor-driven multi-mode industrial systems such as CNC feed drives and robotic machining cells compromises positioning accuracy, availability and operational safety, rendering early fault diagnosis and predictive maintenance essential in smart manufacturing environments. In this study, a predictive maintenance framework based on multi-sensor data fusion was developed to support condition monitoring, fault classification, and remaining useful life estimation of robot servomotors. Time- and frequency-domain features were extracted from synchronized electrical current, vibration, acoustic, and temperature signals using fixed-length sliding windows. Feature-level fusion was applied to combine complementary information from different sensor modalities. A data-driven health assessment approach was employed in which an autoencoder model trained on healthy operating data was used to generate a scalar Servomotor Health Score representing degradation progression. Fault types were identified using a Random Forest classifier, while remaining useful life was estimated in terms of operational cycles using a Gradient Boosting regression model. Experimental evaluations were carried out under repeated reference motion profiles, and representative mechanical and electrical fault conditions were introduced in a controlled manner. The results demonstrated that the proposed health score provided a smooth and monotonic degradation trend, enabling early fault detection without false alarms under healthy conditions. High classification performance was achieved for fault identification, and remaining useful life predictions showed low estimation error on previously unseen faulty servomotors. Feature contribution analysis indicated that electrical current and temperature signals provided the most robust indicators of degradation, while vibration and acoustic measurements offered complementary diagnostic information. The proposed framework was shown to be an effective and practical solution for predictive maintenance of servomotor-driven manufacturing systems such as CNC axes and robotic machining platforms operating under low-speed and variable-load conditions. Full article

This paper develops a practical computational framework for the Bayesian Cournot model with bilateral incomplete cost information, where each player is uncertain about the opponent’s marginal cost, drawn from a continuous compact interval $[c_{*}, c^{*}]$ with $0<c_{*}<c^{*}<\infty$. The infinite dimensionality of the functional strategy spaces (mappings from types to production quantities) renders analytical closed-form solutions infeasible in this continuous-type setting. To overcome this challenge, we restrict the strategy spaces to finite-dimensional differentiable sub-manifolds—specifically, one-parameter families of oscillatory functions (cosine, sine, and mixed forms). After suitable affine Q-rescaling to map the oscillatory range into the production interval $[0, Q]$, and with parameter ranges satisfying $\alpha , \beta >(\pi /2)/c_{*}$, these curves ensure near-exhaustivity: the joint production map $(\alpha , \beta )\mapsto (x_{\alpha }\left(s\right), y_{\beta }\left(t\right))$ covers ${[0, Q]}^2$ densely for every fixed cost pair $(s, t)$, thereby recovering (up to density and closure) the full ex-post payoff space. We introduce the ex-post payoff mapping $\Phi (s, t, x, y)=(e_s(x, y)\left(t\right), f_t(x, y)\left(s\right))$, which collects every realizable payoff pair once nature draws the types and players select their strategies. The image of $\Phi$ defines the general payoff space of the game, and its non-dominated points constitute the general ex-post Pareto frontier—all efficient realized outcomes across type-strategy realizations, without dependence on private probability measures over types. Using multi-objective genetic algorithms, we numerically approximate this frontier (and selected collusive compromises) within the restricted but representative sub-manifolds. The resulting frontiers are computationally accessible, robust to parameter variations, and validated through hypervolume convergence, sensitivity analysis, and comparisons with NSGA-II, PSO and scalarization methods. The findings are significant because they provide decision-makers in oligopolistic markets (e.g., electric vehicles) with viable, implementable production policies that explore efficient trade-offs under genuine cost uncertainty, without requiring explicit forecasts of the opponent’s type distribution—a limitation of traditional expected-utility approaches. By focusing on ex-post efficiency, the method reveals belief-independent compromise solutions that may guide tacit coordination or collusive outcomes in real-world strategic settings. Full article

Background: Anxiety symptoms among medical students often emerge at the intersection of sustained academic pressure, anticipatory uncertainty and early professional socialisation, complicating their distinction from transient stress responses. Instruments employed in this context are therefore expected to operate consistently across subgroups while preserving conceptual clarity under non-clinical conditions. The Generalized Anxiety Disorder scale (GAD-7), widely adopted as a brief screening measure, has shown variable factorial behaviour across populations, particularly when applied to student cohorts. Materials and methods: Using confirmatory factor analysis with robust weighted least squares estimation, the latent structure of a culturally adapted Spanish version of the GAD-7 was examined in a sample of medical students enrolled across all academic years at a public university. Model performance was evaluated through multiple fit indices suited for ordinal data, alongside estimates of convergent validity based on average variance extracted and reliability assessed via both Cronbach’s α and McDonald’s ω. Measurement invariance across sex was explored through a sequence of increasingly constrained multi-group models. Results: The unidimensional configuration originally proposed for the scale remained statistically coherent, despite minor tensions between absolute and incremental fit indicators commonly reported in comparable university-based samples. Convergent validity estimates suggested that the latent construct accounted for a substantial proportion of item variance, while reliability coefficients fell within the upper range observed internationally. Invariance testing supported comparability at the configurational and scalar levels, although full metric equivalence was less stable. Conclusions: Rather than resolving ongoing debates regarding the internal structure of the GAD-7, these findings situate its psychometric behaviour within the specific demands of medical education, where anxiety-related symptoms may fluctuate between normative adaptation and clinically relevant distress. This positioning invites further examination of how screening instruments perform when anxiety is shaped as much by institutional context as by individual psychopathology. Full article

Task-oriented dialogue systems face a tension between comprehensive constraint elicitation (task adequacy) and conversational efficiency (minimizing turns). Current preference learning frameworks treat preferences as static, unable to capture the dynamic evolution of interaction states that evolve across dialogue progression. We present Dual-DPO, a framework that embeds multi-objective preferences into data construction via turn-aware scoring. Our approach decouples objective balancing from policy updates through offline preference scalarization, addressing the optimization instability challenges in online multi-objective reinforcement learning. Experiments on MultiWOZ 2.4 demonstrate 28–35% dialogue turn reduction while maintaining Joint Goal Accuracy > 89% ($p<0.001$). Pareto frontier analysis shows 94% coverage with hypervolume $HV=0.847$. Independent expert evaluation by 10 PhD-level researchers ($n=300$ assessments, inter-rater agreement $\alpha =0.78$) confirms 32% user satisfaction improvement ($p<0.001$). Theoretical analysis demonstrates that offline scalarization, which correlates with improved optimization stability, achieves $3.2\times$ lower gradient variance than online multi-reward optimization by eliminating sampling stochasticity. Our approach enables balanced treatment of competing objectives through Pareto-optimal trade-offs. These results highlight a symmetric and balanced treatment of competing objectives within a Pareto-optimal optimization framework. Full article