Search Results (23,191)

Teacher innovation is critical for fostering student creativity, enhancing school effectiveness, and advancing national talent strategies. Grounded in the broaden-and-build theory of positive emotions and social information processing theory, this study develops a moderated mediation model to explore the motivational mechanisms underlying teachers’ innovative work behavior. Using survey data from 508 teachers in mainland China, the analysis reveals that teacher well-being positively influences innovative work behavior, and this relationship is mediated by perceived insider status. Furthermore, principal authentic leadership enhances the impact of perceived insider status on innovation and strengthens the indirect effect of well-being through this mediator. These findings underscore the importance of both emotional pathways and contextual signals in shaping teacher innovation, offering theoretical contributions to education leadership and teacher work behavior research while providing practical implications for creating supportive and innovation-conducive school environments. Full article

The raster is the most common type of spatio-temporal data, and it can be either regularly or irregularly spaced. Spatio-temporal prediction on regular raster data is crucial for modelling and understanding dynamics in disparate realms, such as environment, traffic, astronomy, remote sensing, gaming and video processing, to name a few. Historically, statistical and classical machine learning methods have been used to model spatio-temporal data, and, in recent years, deep learning has shown outstanding results in regular raster spatio-temporal prediction. This work provides a self-contained review about effective deep learning methods for the prediction of regular raster spatio-temporal data. Each deep learning technique is described in detail, underlining its advantages and drawbacks. Finally, a discussion of relevant aspects and further developments in deep learning for regular raster spatio-temporal prediction is presented. Full article

Cementation using Ordinary Portland Cement (OPC) remains the standard method for conditioning low- and intermediate-level radioactive waste, including Spent Ion Exchange Resins (SIERs). This work presents an integrated strategy involving thermal pretreatment to minimize waste volume and eliminate organic constituents, followed by encapsulation within three distinct binders: CEM I, CEM III, and a novel one-part geopolymer. The one-part geopolymer system represents a significant operational innovation, enabling safe and simple “just-add-water” processing and avoiding the need to handle alkaline solutions. The proposed geopolymer, synthesized from metakaolin, blast furnace slag, and solid sodium silicate, was systematically benchmarked against conventional OPC matrices (CEM I, CEM III) by assessing their capacity to immobilize thermally treated SIER ashes under accelerated leaching conditions. For benchmarking, leaching indices for Cs and Sr were determined following the ANSI/ANS 16.9 standard protocol in three representative environments simulating operational and long-term repository scenarios, providing a quantitative evaluation of radionuclide retention and matrix durability. Results indicate that the one-part geopolymer improved leaching indices for Cs and Sr compared to both cementitious binders and complied with regulatory waste acceptance criteria. The comparative results highlight the potential of geopolymer technology to increase waste loading efficiencies and improve long-term safety, establishing a robust framework for future radioactive waste management approaches. Full article

The development of high-performance MEMS-based eye trackers, crucial for next-generation medical diagnostics and human–computer interfaces, is often hampered by the mechanical instability and time-consuming recalibration of physical prototypes. To address this bottleneck, we present the development and rigorous validation of a high-fidelity digital twin (DT) designed to accelerate the design–test–refine cycle. We conducted a comparative study of a physical MEMS scanning system and its corresponding digital twin using a USAF 1951 test target under both static and dynamic conditions. Our analysis reveals that the DT accurately replicates the physical system’s behavior, showing a geometric discrepancy of <30 µm and a matching feature shift (1 µm error) caused by tracking dynamics. Crucially, the DT effectively removes mechanical vibration artifacts, enabling the precise analysis of system parameters in a controlled virtual environment. The validated model was then used to develop a pupil detection algorithm that achieved an accuracy of 1.80 arc minutes, a result that surpasses the performance of a widely used commercial system in our comparative tests. This work establishes a validated methodology for using digital twins in the rapid prototyping and optimization of complex optical systems, paving the way for faster development of critical healthcare technologies. Full article

Albendazole (ABZ) is a broad-spectrum anthelmintic drug whose residual presence in food and the environment raises public health concerns, requiring rapid and sensitive methods of detection. In this work, a sensitive Fabry–Pérot interferometer (FPI) probe was fabricated by realizing a cavity located at the tip of a single-mode optical fiber core with a molecularly imprinted polymer (MIP) for ABZ detection. The fabrication process involved the development of a photoresist-based micro-hole filled by the specific MIP via thermal polymerization. Interferometric measurements obtained using the proposed sensor system have demonstrated a limit of detection (LOD) of 27 nM, a dynamic concentration range spanning from 27 nM (LOD) to 250 nM, and a linear response at the nanomolar level (27 nM–100 nM). The selectivity test demonstrated no signal when interfering molecules were present, and the application of the sensor for ABZ quantification in a commercial pharmaceutical sample provided good recovery, in accordance with bioanalytical validation standard methods. These results demonstrate the capability of a MIP layer-based FPI probe to provide low-cost and selective optical-sensing strategies, proposing a competitive approach to traditional analytical techniques for ABZ monitoring. Full article

Estuaries represent quintessential coupled human–natural systems (CHNS) where the dynamic interplay between ecological processes and anthropogenic pressures (e.g., shipping, water use exploitation) challenges conventional static spatial planning approaches. Focusing on the Yangtze River Estuary—a globally significant yet intensely utilized ecosystem—this study develops an adaptive management (AM)-oriented spatial planning framework for estuarine protected areas. Our methodology integrates systematic identification of optimal zones using multi-criteria assessments of biodiversity indicators (e.g., flagship species habitats), ecological metrics (e.g., ecosystem services), and management considerations; delineation of a three-tier adaptive zoning system (Control–Functional–Seasonal) to address spatiotemporal pressures; and dynamic management strategies to mitigate human-environment conflicts. The proposed phased conservation boundary (Phase I: 664.38 km²; Phase II: 1721.94 km²) effectively balances ecological integrity with socio-economic constraints. Spatial–temporal analysis of shipping activities over five years demonstrates minimal operational interference, confirming the framework’s efficacy in reconciling conservation and development priorities. By incorporating ecological feedback mechanisms into spatial planning, this work advances a transferable model for governing contested seascapes, contributing to CHNS theory through practical tools for adaptive, conflict-sensitive conservation. The framework’s implementation in the Yangtze context provides empirical evidence that science-driven, flexible spatial planning can reduce sectoral conflicts while maintaining ecosystem functionality, offering a replicable pathway for sustainable water management of similarly complex human–natural systems worldwide. Full article

Computer viruses remain a persistent technological challenge in information security. They require mathematical frameworks that realistically capture their propagation in digital networks. Classical continuous-time, integer-order models often overlook two key aspects of cyber environments: their inherently discrete nature and the memory-dependent effects of networked interactions. In this work, we introduce a fractional-order discrete computer virus (FDCV) model, derived from a three-dimensional continuous integer-order formulation and reformulated into a two-dimensional fractional discrete framework. We analyze its rich dynamical behaviors under both commensurate and incommensurate fractional orders. Leveraging a comprehensive toolbox including bifurcation diagrams, Lyapunov spectra, phase portraits, the 0–1 test for chaos, spectral entropy, and $C_0$ complexity measures, we demonstrate that the FDCV system exhibits persistent chaos and high dynamical complexity across broad parameter regimes. Our findings reveal that fractional-order discrete models not only enhance the dynamical richness compared to integer-order counterparts but also provide a more realistic representation of malware propagation. These insights advance the theoretical study of fractional discrete systems, supporting the development of potential technologies for cybersecurity modeling, detection, and prevention strategies. Full article

This study explores how Brazilian logistics companies manage road safety by identifying key practices, motivators, and barriers. While traffic safety has been widely studied, few investigations adopt an organizational perspective, especially in the logistics sector. Addressing this gap, we applied a mixed-methods approach combining expert input (qualitative phase) and a structured survey of industry professionals (quantitative phases). The findings reveal that practices such as infrastructure development, driver training, and compliance monitoring are perceived as most effective. Motivators include operational planning and economic incentives, while major barriers involve lack of internal motivation, awareness, and resource constraints. Factor analysis confirmed the structure of practices, motivators, and barriers, while a SWOT framework provided strategic insights into internal strengths and external challenges. This study offers practical recommendations for integrating safety into strategic planning, improving training, and strengthening collaboration with public actors. By aligning safety efforts with long-term business goals, logistics providers can enhance both operational performance and social responsibility. These results contribute to global discussions on sustainable logistics by supporting key Sustainable Development Goals, including SDG 3.6 (road safety), SDG 8.8 (safe working environments), SDG 9.1 (sustainable infrastructure), and SDG 11.2 (safe and accessible transport). Full article

Despite the extensive cultivation of Moringa trees in tropical regions, understanding of accession-specific performance across diverse agroecological zones remains inadequate. Thus, this study evaluated the growth, adaptability, and leaf yield performance of 12 Moringa accessions (11 M. oleifera and 1 M. stenopetala) over three years in a subtropical climate (Pretoria, South Africa). Seeds were planted in seedling trays in the glasshouse at the University of Pretoria’s experimental farm. Vigorous seedlings were transplanted to the field at the Roodeplaat experimental site of the Agricultural Research Council two months after establishment, following a randomized complete block design (RCBD). Data were measured on establishment (emergence, survival), growth and yield parameters, and monitored plant health via leaf greenness, vigour, chlorosis, and pest and disease incidence. Accessions exhibited substantial variation for most traits, except for stem diameter. Moringa stenopetala showed the highest initial emergence rate but later displayed lower survival rates than most M. oleifera accessions. Survival rates, morphological features (plant height, canopy diameter, and branching), visual scores for leaf greenness and plant vigour, and leaf yield (fresh and dry) varied considerably among the accessions. Moringa oleifera A2 consistently performed well, exhibiting vigorous growth, the maximum survival rate (78%), and fresh leaf production (6206 kg ha⁻¹). Accessions A3 and A8 showed intermediate yield and longevity, indicating potential for cultivation or breeding. Conversely, M. oleifera A10 and M. stenopetala markedly underperformed in most traits, limiting their cultivation potential. Based on multi-year performance, A2 is suggested for large-scale cultivation due to its vigour, yield, and stress tolerance, while A3 and A8 hold breeding potential. The study emphasizes the critical role of genetic variation and selection in enhancing Moringa productivity under subtropical environments. Future work should focus on genetic characterization and agronomic practices optimization of superior accessions. Full article

This study establishes quantitative relationships between neighborhood layouts, as evaluated by key neighborhood morphological parameters and pedestrian wind environments across China’s five major climate zones. We analyzed 3204 residential neighborhoods using satellite imaging and simulated 281 scenarios by CFD simulations, identifying six typical neighborhood layouts and quantifying their performance in terms of climate specific wind comfort criteria. This work takes an approach that takes into account mechanical wind effects and region-specific criteria for evaluating pedestrian-level wind environment performance, going beyond previous studies that utilize universal evaluation standards. The most influential parameter is building enclosure ratio with sensitivity indices of 0.844 for winter wind proofing. Closed perimeter layout confers 15–20% better winter wind proofing in cold climates and semi-open design enhances summer ventilation by 12–18% in hot climates according to our cross-climate analysis. Quantitative optimization adopting regression technique (R² = 0.727–0.810) points to an optimal enclosure ratio of 0.25–0.28 or 0.52–0.61 with aspect ratio of 1.75–2.75. The results can provide evidence-based design guidelines for high-rise residential neighborhood planning and pedestrian wind environment, aiming to improve urban livability and support climate adaptation strategies across a broad range of climate zones. Full article

Despite digital evidence (DE) now being a major component of most criminal investigations, very few studies have examined how police officers themselves evaluate and use DE over the course of an investigation. Drawing on in-depth interviews with N = 13 police officers from England and Wales, four themes are presented: (i) Sense-making and handling of digital devices and DE in investigations; (ii) The interpretation and reliability of DE; (iii) Strategic use of DE in investigative interviews with suspects, with a subtheme of Digital devices and DE in victim-centered interviews; and (iv) DE in the courtroom. While often seen as objective and infallible, DE is fragile, volatile, and legally complex, highlighting the cognitive and interpretive work that officers must do when dealing with DE. This is important because this work has a direct impact on how investigations proceed, including what is taken from crime scenes and how it is used in investigative interviews. Findings show how DE creates unique challenges and opportunities within investigative interviewing, extending research on strategic disclosure into the digital domain. Future directions include setting up better communication workflows to reduce epistemic drift and offering more DE interpretation training to help officers in an increasingly digital environment. Full article

In this work, the advection-dispersion model (ADM) is time-fractionalized by the exploitation of Atangana-Baleanu (AB) differential operator to describe contaminant transport in a geological environment. Dispersion, adsorption, and decay, which are known as the foremost transport mechanisms, are considered. The exact solutions of the suggested Atangana-Baleanu advection-dispersion models (AB-ADMs) are acquired using Fourier sine transform and Laplace transform. The classical ADMs are demonstrated to be the special limiting cases of the suggested models. The high consistency among the suggested models and experimental data denotes that the AB-ADMs characterize contaminant transport more effectively. Additionally, the corresponding numerical and graphical results are explored to demonstrate the necessity, effectiveness, and suitability of the suggested models. Full article

Accurate depth image inpainting in complex urban environments remains a critical challenge due to occlusions, reflections, and sensor limitations, which often result in significant data loss. We propose a hybrid deep learning framework that explicitly combines local and global modelling through Convolutional Neural Networks (CNNs) and Transformer modules. The model employs a multi-branch parallel architecture, where the CNN branch captures fine-grained local textures and edges, while the Transformer branch models global semantic structures and long-range dependencies. We introduce an optimized attention mechanism, Agent Attention, which differs from existing efficient/linear attention methods by using learnable proxy tokens tailored for urban scene categories (e.g., façades, sky, ground). A content-guided dynamic fusion module adaptively combines multi-scale features to enhance structural alignment and texture recovery. The frame-work is trained with a composite loss function incorporating pixel accuracy, perceptual similarity, adversarial realism, and structural consistency. Extensive experiments on the Paris StreetView dataset demonstrate that the proposed method achieves state-of-the-art performance, outperforming existing approaches in PSNR, SSIM, and LPIPS metrics. The study highlights the potential of multi-scale modeling for urban depth inpainting and discusses challenges in real-world deployment, ethical considerations, and future directions for multimodal integration. Full article

This work focuses on research into innovative lead-free perovskite materials to be employed as a sensitive layer for a new generation of solar cells, exploiting their potential applications in covering greenhouses to move toward an eco-friendly environment. Two types of lead-free perovskites—yellow and orange double-cation Cs₂AgBiBr₆, synthesized with an innovative method without chemical thinners—have been used, for the first time, as a cover for greenhouses in indoor experiments by analyzing the incident electromagnetic radiation. Two plant species, Solanum lycopersicum L. and Artemisia annua L., were cultivated indoors under controlled light, temperature, and humidity, covering the greenhouses with yellow (PY+) and orange (PO+) panels for comparison with control plants (P−) roofed by a glass panel. The growth and development parameters of all plants were investigated, referring to the aerial and root parts. Significant differences were found in terms of the plant growth parameters and photosynthetic pigments of both PY+ and PO+ compared to P− and also between them, with the yellow panel being less invasive. These results, dealing with two different plant species, confirm the feasibility of using perovskite-based panels for indoor cultivation and pave the way for outdoor application in greenhouses under sunlight. Full article

The highly pathogenic avian influenza (HPAI) H5N1 virus poses a growing global health threat, particularly following its unprecedented spillover into dairy cattle and subsequent transmission to more than 1000 dairy farms in 18 states. This study investigates the host cell responses to distinct H5N1 strains (bovine- and mink-derived H5N1) in the presence and absence of raw milk across diverse mammalian cell lines (MDCK, MDBK, A549, Vero, MV1). Our findings reveal that the bovine-derived H5N1 strain exhibits more robust replication than the mink-derived H5N1 and demonstrates intra-host viral evolution with emerging amino acid substitutions detectable by deep sequencing. Although raw milk supplementation did not directly enhance viral replication in vitro, it significantly modulated host gene expression, often dampening key antiviral interferon-stimulated gene (ISG) responses and disrupting essential host cellular processes like intracellular trafficking and sialic acid biosynthesis. These host gene modulations are cell-type- and strain-specific, suggesting a complex interplay that may theoretically influence virus–host dynamics, though the biological significance of these in vitro observations requires validation through infectious virus assays and in vivo studies. This hypothesis-generating work provides preliminary insights into H5N1-milk interactions, highlighting the need for further mechanistic investigation to assess potential implications for viral transmission in dairy environments. Full article