Search Results (25,682)

In this case study, we examined a school in Northern Norway that has integrated outdoor learning as a core element of its pedagogical practices. To develop a comprehensive understanding of the role of outdoor learning and the factors contributing to the school’s success, we conducted semi-structured interviews with stakeholders of outdoor learning: six students, three teachers, one teaching assistant, and the principal. Our interviews were thematically analyzed using a whole-school approach framework, and our findings indicate that outdoor learning is embedded in the school’s identity. The regularity of outdoor learning for all students, with support from the school’s leadership and committed teachers, ensures predictability and continuity. Students and staff are broadly positive about outdoor learning and report that it strengthens student–teacher relationships. Outdoor learning is understood as interdisciplinary, and the practice enhances both academic learning and environmental awareness. Nevertheless, we identified limited opportunities and a weak culture for sharing outdoor learning practices among teachers. The school therefore aims to develop a progression plan for outdoor learning and to facilitate greater sharing to strengthen the professional community and improve coherence. This case study contributes to the literature by specifying organizational and contextual conditions for successful implementation and by highlighting the need to align outdoor and indoor teaching. Sustained outdoor learning requires holistic support from everyone involved in the school community. Full article

Europium (Eu) is a rare-earth element with unique optoelectronic properties that underpin its applications in displays and lighting, X-ray imaging, anti-counterfeiting, and biomedicine. Conventional methods typically involve the synthesis of europium-based luminescent materials in powder or crystalline form via high-temperature solid-state reactions or solution processes, followed by secondary processing such as spin coating or evaporation to fabricate films or devices. In this work, we report a direct approach to prepare trivalent europium-based luminescent materials using divalent europium bromide (EuBr₂) as the precursor via a gas-phase vacuum evaporation approach (GPVEA). This “deposition-as-synthesis” method enables the fabrication of the hybrid nanoscale films with various blending ratios, which exhibit changes in the fine structure of the emission peaks. The luminescence spectra remain nearly identical across the temperature range from 80 K to 320 K. The photoluminescence emission intensity is stronger in air than in a vacuum. The films show a maximum photoluminescence quantum yield (PLQY) of 8.27% and good photostability, with an emission decay of 3.44% over 50 min under continuous 300 nm excitation. Through patterned design, we demonstrate their value for anti-counterfeiting applications. This work thus provides guidance for the preparation of europium-based luminescent nanomaterials via GPVEA and their application in anti-counterfeiting. Full article

This paper studies multiplication operators and their associated strongly continuous semigroups acting on variable exponent Lebesgue spaces. We study the abstract Cauchy problem $\dot{u}\left(t\right)=Au\left(t\right)$, $u\left(0\right)=u_0$, in the space $L^{p\left(x\right)}(0,\ell )$ with $\ell >0$, where the generator A is given by the multiplication operator $A=M_q$. Using the modular ${\rho }_{p(·)}\left(u\right)={\int }_0^{\ell }{\left|u\left(x\right)\right|}^{p\left(x\right)}dx$, we establish the fundamental properties of $M_q$, including ${\rho }_{p(·)}$-closedness, density of its domain, and boundedness criteria in terms of the essential range of q.We show that $M_q$ generates a strongly continuous semigroup ${\left(S\left(t\right)\right)}_{t\ge 0}$ given explicitly by $S\left(t\right)=e^{tA}=M_{e^{tq}}$, and we derive modular growth estimates for the semigroup. We also obtain a complete characterization of the spectrum and resolvent of A, showing that $\sigma \left(A\right)=q_{\mathrm{ess}}(0,\ell )$ and $R(\lambda ,A)={(\lambda I-A)}^{-1}=M_{1/(\lambda -q)}$ for $\lambda \notin \sigma \left(A\right)$. The spectral mapping behavior of the associated semigroup is also analyzed, highlighting the validity of the weak spectral mapping theorem and the possible failure of the full spectral identity. As an application, we present a concrete example on $(0,4)$ involving a singular initial datum that does not belong to $L^2(0,4)$ but lies in $L^{p\left(x\right)}(0,4)$ due to a suitable spatial variation of the exponent. The corresponding evolution is explicitly given by $u(t,x)=e^{tq\left(x\right)}f\left(x\right)$ and remains well posed in $L^{p\left(x\right)}(0,4)$ for all $t\ge 0$. This shows that the variable exponent framework can accommodate singular behavior while preserving semigroup dynamics. These results show that multiplication operators provide an explicit model for semigroup theory in variable exponent spaces, connecting modular analysis with pointwise evolution equations. Full article

This study proposes a modification of the Weighted Sum Model (WSM) that formalizes the evaluation generation for alternatives. By integrating objective data with decision-makers’ (DMs’) subjective preferences, the modification addresses a gap in the classical WSM, where evaluations are traditionally assigned subjectively, despite the availability of measurable data describing the alternatives. The modification introduces a structured mechanism for handling heterogeneous data by distinguishing between numerically represented and matrix-represented criteria. The quantitative criteria are processed through normalization procedures aligned with individual DMs’ preferences. Meanwhile, the qualitative characteristics are decomposed into sets of options and structured as binary matrices. The applicability of the modified model is demonstrated through a case study on replacing photovoltaic modules in a public building. Results indicate that changes in DMs’ preferences lead to observable differences in the generated evaluations and in the ranking of the alternatives, even when identical objective data is used. Ultimately, these results demonstrate that the modified WSM improves the flexibility and transparency of the decision-making process, providing a more realistic representation of experts’ preferences. From a sustainability perspective, it facilitates more informed and balanced decisions in the management of energy systems and public infrastructure. Full article

How does artificial intelligence (AI) adoption shape administrative efficiency and e-citizen integration in local governments, and what role does professional development play in mediating these relationships? Drawing on a survey of 500 municipal employees across Spanish municipalities, this study employs partial least squares structural equation modelling (PLS-SEM), with formal non-linearity testing via Warp3 algorithms, to test a theoretically grounded model. The conceptual framework integrates Digital Transformation Theory and Public Value Theory as primary explanatory lenses, while drawing on the Technology Acceptance Model (TAM) and Total Factor Productivity (TFP) logic as complementary background perspectives that contextualise rather than directly operationalise the micro-level findings. Structural results reveal that AI adoption exerts a strong direct (and statistically linear) effect on perceived administrative efficiency (β = 1.04, p < 0.001; the standardised coefficient exceeding 1.0 and R² > 1 are a legitimate WarpPLS warp-model fit index rather than evidence of model misspecification: the Warp3 warp functions inflate the variance of predicted efficiency and break the additive identity SST = SSM + SSE, with the high AI–PD collinearity (r ≈ 0.84) as the contributing mechanism (RSCR = 1.000, SSR = 1.000); a comparative re-estimation without the moderation term yields β = 0.87 and R² = 0.76; we adopt this parsimonious specification (β ≈ 0.87, R² = 0.76) as the substantively interpretable estimate, with predictive relevance confirmed by a high Stone–Geisser Q² = 0.685, indicating that the model fits and predicts well rather than overfitting, while simultaneously stimulating professional development (β = 0.84, p < 0.001, R² = 0.70). Professional development positively predicted both efficiency (β = 0.27, p < 0.001) and e-citizen integration (β = 0.26, p < 0.01). Efficiency is the primary driver of e-citizen integration (β = 0.54, p < 0.001, R² = 0.53). The proposed moderation of AI adoption by professional development on efficiency was not supported (β = −0.01, p = 0.44), suggesting additive rather than synergistic effects. Model fit was robust (GoF = 0.701; ARS = 0.749; APC = 0.495); convergent and discriminant validity were confirmed by composite reliability, average variance extracted, Fornell–Larcker, and HTMT criteria; and common method bias diagnostics (Harman’s single-factor test, full-collinearity AFVIF, and marker-variable analysis) indicated that systematic method variance was not a material threat. These findings offer micro-empirical evidence of the mechanisms linking AI adoption to citizen service outcomes via a professional development pathway and provide actionable recommendations for Spanish and European municipalities navigating AI-driven governance reform. Full article

Electroluminescence (EL) imaging is widely used for photovoltaic (PV) defect inspection, yet fair comparison of deep learning backbones remains difficult because datasets, labels, and protocols vary across studies. This work presents a controlled image-level benchmark of six architectures (ConvNeXt-T, ViT-B/16, DeiT-B/16, Swin-T, DenseNet121, and MobileNetV3-Large) across five hierarchical tasks for monocrystalline and polycrystalline cells with binary and multi-class labels. A balanced proprietary dataset of 20,000 single-cell EL images was evaluated with identical preprocessing, augmentation, training, and stratified five-fold cross-validation, yielding 150 runs. ConvNeXt-T achieved the highest mean macro-F1 (93.12%) while using about one-third of the parameters of base ViT/DeiT models. On the four-class polycrystalline task, it reached 84.94 ± 0.45% macro-F1, compared with 70.08 ± 1.19% for DenseNet121 and 59.43 ± 1.71% for MobileNetV3-Large. Error analysis revealed conservative missed-defect behavior in lightweight CNNs, especially for surface-level degradation and crack categories. The results provide image-level cross-validation evidence for controlled benchmarking and motivate future module-level grouped validation. Full article

The gut microbiota plays a vital role in host health. In response to the scarcity of comparative studies examining wild and domesticated South American camelids under identical captive conditions, this study was conducted to compare the gut microbiota of 16 captive guanacos (Lama guanicoe) and 8 alpacas (Vicugna pacos) housed in the same zoo and fed identical diets, using 16S rRNA gene sequencing and multiple ecological metrics for analysis. Alpha diversity indices (Shannon, observed richness, and Shannoneven) did not differ between the two species, but beta diversity (principal component analysis) indicated significant separation (p < 0.05), and the guanacos exhibited significantly lower within-group Bray–Curtis dissimilarity, indicating more consistent microbial communities. Guanacos exhibited a lower average variation degree (AVD), indicating greater community stability, a broader niche, and a co-occurrence network with 81.1% positive edges and high modularity (0.691). In contrast, the alpacas showed a higher AVD (lower stability), a narrower niche, and a network with only 62.2% positive edges and lower modularity (0.534). Linear discriminant analysis effect size analysis revealed that Monoglobus and Bacteroides are enriched in guanacos, while Rikenellaceae_RC9_gut_group is enriched in alpacas. Functional predictions revealed that alpacas had higher predicted abundances of potentially pathogenic taxa and Kyoto Encyclopedia of Genes and Genomes pathways related to Staphylococcus aureus infection (p < 0.05). These findings demonstrate that, despite sharing environments, guanacos have a more stable, generalist-dominated gut microbiota with a higher proportion of positive co-occurrences, whereas alpacas exhibit a less stable, specialist-oriented community with a higher proportion of negative co-occurrences and greater predicted pathogenic potential. These results suggest that domestication may have contributed to the observed divergence in gut microbial ecology between the two species. Full article

Shallow eutrophic lakes recover from nutrient loading on time scales ranging from less than one year to many decades, yet whether this range is set by the lake or by the biological response group has rarely been quantified within a single monitoring framework. We assembled a five-year (2020–2025) quarterly monitoring panel from three shallow Yangtze-floodplain lakes (Lake Changhu, Lake Liangzihu, and Lake Honghu; 15 stations, 21 quarters) and applied a panel mixed-effect distributed lag model (PME-DLM) to estimate the lag-response windows of phytoplankton and benthic macroinvertebrate densities against five water-quality drivers. Cross-lake consistency was tested with a station-resampled bootstrap, and the contributions of water quality, season, and lake identity to community variation were resolved by three-table variation partitioning. The PME-DLM resolved a 3-month temperature window for phytoplankton and 9–15 month chlorophyll a and temperature windows for benthic communities, while total nitrogen and total phosphorus were non-significant in either group. Cross-lake bootstrap intervals on window width overlapped substantially across the three lakes, whereas cross-group differences in window centre and shape were an order of magnitude greater. Variation partitioning further showed a mirror-image structure in which phytoplankton variation was dominated by the pure water-quality fraction (12.2%) and benthic variation by the water-quality × season joint fraction (5.8%). Within the resolution of this five-year, three-lake panel, group-level differences in lag-response time scale were more apparent than lake-level differences and provide a quantitative basis for matching restoration assessment cadence to pelagic versus benthic recovery. Full article

This paper develops a boundary-sensitive asymptotic theory for nonparametric conditional U-statistics smoothed by support-adapted asymmetric kernels when the response variable is subject to Missing-at-Random observation. The problem lies at the intersection of three well-established but traditionally separate lines of research: conditional U-statistics, asymmetric smoothing on constrained supports, and incomplete-data inference under MAR sampling. The contribution of the paper is not a novelty claim concerning any of these components in isolation. Rather, it consists in deriving a kernel-specific and MAR-aware limit theory for their simultaneous occurrence, where the estimators are nonlinear complete-case ratios of localized U-statistics and the localization devices are point-dependent approximate identities adapted to the geometry of the covariate support. The analysis covers three principal classes of support-respecting smoothers: Dirichlet kernels on the simplex, Bernstein polynomial smoothers, and multivariate beta kernels on hypercubes, with an additional extension to mixed continuous–categorical regressors. These smoothing schemes are not translation-invariant, and their local moments, effective support, normalizing constants and $L^2$-masses vary with the evaluation point, especially near the boundary. Consequently, their incorporation into conditional U-statistics requires more than a direct transfer of ordinary asymmetric-kernel regression theory. The numerator and denominator of the estimators are localized U-statistics whose stochastic expansions are governed by Hoeffding projections, including canonical components that must be controlled uniformly over the conditioning domain. Under regularity, smoothness and positivity assumptions adapted to the MAR setting, we establish uniform consistency, weak and strong uniform convergence rates, stochastic expansions and asymptotic normality. The results are obtained both on fixed compact subsets and on interior regions approaching the boundary, thereby identifying how support geometry enters the bias and stochastic normalizations. A central feature of the theory is the separation between the deterministic effect of complete-case sampling and its stochastic effect. For the complete-case estimator, the natural deterministic equivalent is obtained by replacing the design density f with the effective complete-case density $pf$, where p is the propensity score. Thus, the MAR mechanism may enter higher-order deterministic bias constants through the local design tilt, whereas the leading stochastic dispersion reflects the loss of effective information through propensity score factors. The precise variance constants and normalizing rates remain kernel-specific, depending on the local $L^2$-structure of the Dirichlet, Bernstein or beta smoothing device. The paper should therefore be viewed as a MAR extension and refinement of the complete-data asymmetric-kernel conditional U-statistic theory. It provides a common probabilistic architecture for several boundary-adapted smoothing schemes while retaining the kernel-dependent bias operators, variance constants, boundary regimes and Hoeffding-projection structures required for sharp asymptotic interpretation. Numerical experiments illustrate the finite-sample behavior predicted by the theory and highlight the interaction between support-adapted smoothing, boundary effects and incomplete response observation. Full article

Mitochondrial reactive oxygen species (mtROS) are central regulators of cellular function, yet their biological roles are often reduced to an oxidative-stress/antioxidant dichotomy. This review reframes mtROS through the concept of mitohormesis, in which outcomes are neither inherently harmful nor beneficial but are determined by a defined set of contextual variables. We present a mechanistic framework in which mtROS effects depend on chemical species identity, sub-mitochondrial site of production, temporal dynamics, redox-buffering capacity, and metabolic state; together, these variables determine whether mtROS promote adaptive eustress or pathological distress. We then show that, across polyphenols, isothiocyanates, terpenoids, alkaloids, and quinones, the biologically relevant effects of natural redox-modulating compounds are mediated less by direct radical scavenging than by pro-hormetic mechanisms, including mild electron transport chain perturbation, nuclear factor erythroid 2-related factor 2/Kelch-like ECH-associated protein 1 (NRF2/KEAP1) activation, modulation of mitochondrial membrane potential, mitochondrial quality control, and NAD⁺/NADPH regulation. Applying this framework to disease reveals strong tissue and state dependence: neurodegeneration favors buffering expansion and mitophagy; metabolic disease may benefit from exercise-mimetic and NRF2-activating strategies; cardiovascular disease illustrates mitohormesis through ischemic preconditioning and CoQ10 supplementation; and cancer requires distinction between prevention and therapy because redox buffering can either protect normal tissue or support tumor survival. Finally, we argue that the failure of non-specific antioxidant supplementation is mechanistically predictable and propose context-aware, biomarker-guided, temporally optimized, and compartment-targeted redox interventions as a more rational translational path. Full article

Multimodal biometric systems have become essential components of modern electronic identity and authentication platforms where robustness under real-world degradation is critical. However, opaque face masks impose severe facial occlusion and attenuate high-frequency spectral components. Conversely, transparent face shields introduce complex specular reflections and act as an acoustic channel distortion source. Addressing these asymmetric degradation challenges, this paper proposes a reliability-aware Dynamic Score Fusion (DSF) for multimodal biometric identification. The proposed method performs sample-level reliability estimation for both face and voice modalities at the input stage. This enables sample-wise adaptive weighting of modality scores based on their estimated reliability. The framework integrates an ElasticFace-Arc backbone for face recognition with an Emphasized Channel Attention, Propagation and Aggregation—Time Delay Neural Network (ECAPA-TDNN) for speaker identification. The proposed approach is evaluated on the FaciaVox dataset, comprising face images and voice recordings acquired under multiple face-covering conditions. Experiments under the Standard to Cross-Condition Protocol (SCCP) and Multi-Condition Protocol (MCP) demonstrate that the proposed DSF consistently outperforms conventional score-level fusion methods, including Weighted Sum Fusion (WSF) and Logistic Regression Fusion (LRF). It achieves average Rank-1 accuracies of 89.6% (SCCP) and 93.7% (MCP), with gains of up to 9.3 percentage points over these baselines. The reliability estimators further demonstrate strong predictive capability, yielding Area Under the Curve (AUC) values above 0.95 for both modalities in distinguishing correctly and incorrectly identified samples under the closed-set identification setting. These findings confirm that sample-wise reliability modeling provides an effective mechanism for enhancing multimodal biometric performance under challenging mask and shield conditions, supporting the deployment of robust AI-driven electronic identification systems. Full article

Macrophages are highly plastic innate immune cells that adopt context-dependent phenotypes along a continuum, integrating developmental origin with local microenvironmental cues rather than conforming to discrete M1/M2 states. This review delineates the molecular circuits shaping macrophage identity—TLR/cytokine signaling, microRNA networks, metabolic rewiring, and epigenetic mechanisms including histone lactylation—and traces how circulating monocyte subsets contribute to tissue macrophage diversity. We examine macrophage plasticity across a broad disease spectrum—oncology, autoimmune and rheumatic diseases, inflammatory bowel disease, infectious diseases, metabolic disorders, and neurological conditions—showing that the pathogenic phenotype is strikingly context-dependent: for instance, M2-like tumor-associated macrophages promote immune evasion in solid tumors, whereas M1-skewed programs drive tissue damage in autoimmunity. Soluble markers (sCD163, sCD14, soluble mannose receptor) are emerging biomarkers of disease activity and prognosis. High-dimensional flow cytometry and mass cytometry (CyTOF) bridge molecular biology and clinical phenotyping, enabling integrated readouts of surface phenotype, intracellular signaling, and metabolic state. Therapeutic strategies discussed include selective tumor-associated macrophage (TAM) reprogramming, chimeric antigen receptor (CAR)-M cell therapies, and biomaterial-based platforms. Future priorities encompass spatially resolved multi-omics, epigenetic and metabolic targeting, and macrophage-centered vaccine approaches. Standardized cytometry panels will be essential for biomarker-guided stratification and context-specific interventions. Full article

This study examines the relationships between intrinsic motivation factors—task autonomy, personal growth and development opportunities, self-actualization, and decision-making participation—and employee retention, as well as the mediating role of perceived organizational pride. Using a quantitative cross-sectional survey, data were collected from 154 academic staff members at Taif University, Saudi Arabia. CFA supported the measurement model, and the hypotheses were tested using Hayes’ PROCESS macro. The findings show that all intrinsic motivation factors are positively associated with employee retention. Perceived organizational pride also mediates these relationships, suggesting that intrinsically motivating work conditions may support retention by strengthening employees’ pride in institutional membership. The results further indicate that developmental and participative factors show stronger associations with retention than task autonomy. This study contributes to employee retention research by integrating intrinsic motivation and identity-based explanations in the context of Saudi higher education. However, given the cross-sectional design and single-university sample, causal interpretation and generalizability should be treated with caution. The findings highlight the importance of growth-oriented, participative, and pride-enhancing work environments for supporting academic staff retention. Full article

Hypogeous ascomycetous fungi (truffles) are challenging to study because they produce underground sporocarps that may not be encountered during traditional fungal surveys. Genea is one such genus that has garnered considerable attention over the past decades due to its role as an ectomycorrhizal partner and contribution to nutrient cycling and ecosystem stability. Yet, very limited information is available about its taxonomy, phylogeny and ecology worldwide. The current study aims to expand the known distribution of Genea species in Central Europe by integrating morphological, molecular and ecological analyses of new collections as well as the assessment of herbarium materials. Light microscopy and SEM were used to determine morphological characteristics along with FT-IR (Fourier transform infrared) spectroscopy measurements, which proved to be a powerful tool for species differentiation. Molecular phylogenetic analyses were conducted using the internal transcribed spacer (ITS1-5.8S-ITS2 = ITS) and D1/D2 domain of the large subunit (28S) of nuclear ribosomal DNA sequences to confirm species identity. In this study, a new species, Genea szemereiensis, along with the first report of Genea pinicola from Hungary, was made. In addition, the ecological parameters of the species, including habitat, altitude, soil nutrients and pH, were revised, which has not been reported previously in detail for this genus. Full article

Phishing email remains a persistent and operationally critical cybersecurity threat, yet existing detection approaches, including traditional machine learning and single-pass large language model systems, either lack native interpretability or provide explanations that are difficult to standardize and audit. This paper introduces an explainable multi-agent LLM framework that decomposes phishing evidence across three role-specialized agents focused on linguistic patterns, psychological manipulation, and sender identity consistency. The framework then aggregates specialist outputs through schema-governed synthesis, enabling each intermediate and final decision to be structured, comparable, and auditable. The central contribution is the treatment of role-specialized evidence decomposition and explanation structure as first-class design constraints rather than post hoc additions. The framework is evaluated on a fixed 1000-email subset drawn from a unified TREC/Nazario corpus of 56,212 emails under controlled zero-shot conditions. The full multi-agent Meta-Judge system achieves Macro-F1 = 98.28% and phishing recall = 99.45%, improving Macro-F1 by 6.3 percentage points over a zero-shot single-model GPT-4o-mini baseline. Paired statistical testing confirms that this improvement is significant and is driven primarily by reduced false positives on legitimate emails while preserving high phishing recall. Additional evaluation on an independent LLM-attributed email benchmark shows a consistent Macro-F1 improvement of 0.0773 over the zero-shot baseline under distribution shift. Ablation results show that role-specialized decomposition is the primary performance driver, while deterministic voting provides a competitive raw-classification aggregator and Meta-Judge synthesis provides structured, analyst-facing explanations. These results indicate that role-specialized evidence decomposition combined with schema-governed explanation can improve both detection reliability and auditability in phishing classification workflows. Full article