Search Results (33,692)

Education 4.0 and digital learning have led to a technology-driven transformation in educational methodologies and the roles of teachers, primarily at Higher Education Institutions (HEIs). From an educational standpoint, the extant literature on Education 4.0 highlights its technological features and benefits; however, there is a lack of studies that assess its impact on students’ learning outcomes. Seemingly, Education 4.0 features are taken for granted, as if the technology in itself were enough to guarantee students’ learning, self-efficacy, and engagement. Seeking to address this lack, this study describes the implications of tailoring Education 4.0 tenets and digital learning in an engineering curriculum. Four case studies conducted in the last four years with 119 students are presented, in which technologies such as digital twins, a Modular Production System (MPS), low-cost robotics, 3D printing, generative AI, machine learning, and mobile learning were integrated. With these case studies, an educational methodology with active learning, hands-on activities, and continuous teacher support was designed and deployed to foster cognitive and affective learning outcomes. A mixed-methods study was conducted, utilizing students’ grades, surveys, and semi-structured interviews to assess the approach’s impact. The outcomes suggest that including Education 4.0 tenets and digital learning can enhance discipline-based skills, creativity, self-efficacy, collaboration, and self-directed learning. These results were obtained not only via the technological features but also through the incorporation of reflective teaching that provided several educational resources and oriented the methodology for students’ learning and engagement. The results of this study can help complement the concept of Education 4.0, helping to find a student-centered approach and conceiving a balance between technology, teaching practices, and cognitive and affective learning outcomes. Full article

Guatemala’s intercountry adoptions were suspended in 2007 after widespread illicit procedures and the persistent trafficking of children. This article is a historical and policy analysis of the related social welfare systems. It uses Midgley’s framework to examine the past and the changes that have resulted in Guatemala’s reform era. Specific attention has been paid to non-formal systems, market-based or profit-oriented systems, non-profit and faith-based systems, and importantly, government-based systems. Previous (pre-reform) child welfare systems, particularly during the millennium adoption surge, are then compared to a relatively new and reformed system. An international child rights legal and policy context, to include the Hague Convention on Intercountry Adoption, frames the discussion that also considers the passage of the 2007 Adoption Law. The article has a child rights perspective and considers the role of women, particularly birth parents, during Guatemala’s peak adoption years. Full article

Photovoltaic installations are becoming an increasingly popular source of electricity around the world. The decision on where and how to install the modules and their location is made at the stage of building the installation and is crucial for obtaining the most beneficial effects of its operation. The choice of installation location and its geometry directly influence the following aspects, which determine maximum efficiency and thus economic benefits: solar irradiance, working cell temperature, shading, dust and soiling. Factors that have an unfavorable impact on the efficiency of a photovoltaic installation can be divided into those that should be taken into account at the design stage, such as the correct orientation and angle of inclination of the modules, and those that will play an important role during the use of the system: contamination of the front surface of the modules. This article discusses the impact of these factors and their importance for the proper operation of a photovoltaic installation. Full article

Solar-driven hydrogen production via photocatalytic water splitting represents a promising route toward sustainable and low-carbon energy systems. Among emerging photocatalysts, porphyrin-based framework materials, specifically porphyrinic metal–organic frameworks (PMOFs) and porphyrinic covalent organic frameworks (PCOFs), have attracted increasing attention owing to their strong visible-light absorption, tunable electronic structures, permanent porosity, and well-defined catalytic architectures. In these systems, porphyrins function as versatile photosensitizers whose photophysical properties can be precisely tailored through metalation, peripheral functionalization, and integration into ordered frameworks. This review provides a comprehensive, design-oriented overview of recent advances in PMOFs, PCOFs, and hybrid porphyrinic architectures for photocatalytic H₂ evolution. We discuss key structure–activity relationships governing light harvesting, charge separation, and hydrogen evolution kinetics, with particular emphasis on the roles of porphyrin metal centers, secondary building units, linker functionalization, framework morphology, and cocatalyst integration. Furthermore, we highlight how heterojunction engineering through coupling porphyrinic frameworks with inorganic semiconductors, metal sulfides, or single-atom catalytic sites can overcome intrinsic limitations related to charge recombination and limited spectral response. Current challenges, including long-term stability, reliance on noble metals, and scalability, are critically assessed. Finally, future perspectives are outlined, emphasizing rational molecular design, earth-abundant catalytic motifs, advanced hybrid architectures, and data-driven approaches as key directions for translating porphyrinic frameworks into practical photocatalytic hydrogen-generation technologies. Full article

This study examines how different programming paradigms are associated with learning experiences and cognitive challenges as encountered by non-computer science novice learners. Using a case-study approach situated within specific instructional contexts, we integrate survey data from undergraduate students with large-scale public question-and-answer data from Stack Overflow to explore paradigm-related difficulty patterns. Four instructional contexts—C, Java, Python, and Prolog—were examined as pedagogical instantiations of imperative, object-oriented, functional-style, and logic-based paradigms using text clustering, word embedding models, and interaction-informed complexity metrics. The analysis identifies distinct patterns of learning challenges across paradigmatic contexts, including difficulties related to low-level memory management in C-based instruction, abstraction and design reasoning in object-oriented contexts, inference-driven reasoning in Prolog-based instruction, and recursion-related challenges in functional-style programming tasks. Survey responses exhibit tendencies that are broadly consistent with patterns observed in public Q&A data, supporting the use of large-scale community-generated content as a complementary source for learner-centered educational analysis. Based on these findings, the study discusses paradigm-aware instructional implications for programming education tailored to non-major learners within comparable educational settings. The results provide empirical support for differentiated instructional approaches and offer evidence-informed insights relevant to curriculum-oriented teaching and future research on adaptive learning systems. Full article

Under the pressing imperative of achieving “dual carbon” goals and advancing urban low-carbon transitions, understanding how neighborhood spatial environments influence carbon emissions has become a critical challenge for enabling refined governance and precise planning in urban carbon reduction. Taking the central urban area of Xining as a case study, this research establishes a high-precision estimation framework by integrating Semantic Segmentation of Street View Images and Point of Interest data. This study employs a Geographically Weighted XGBoost model to capture the spatial non-stationarity of emission drivers, achieving a median R² of 0.819. The results indicate the following: (1) Socioeconomic functional attributes, specifically POI Density and POI Mixture, exert a more dominant influence on carbon emissions than purely visual features. (2) Lane Marking General shows a strong positive correlation by reflecting traffic pressure, Sidewalks exhibit a clear negative correlation by promoting active travel, and Building features display a distinct asymmetric impact, where the driving effect of high density is notably less pronounced than the negative association observed in low-density areas. (3) The development of low-carbon neighborhoods should prioritize optimizing functional mixing and enhancing pedestrian systems to construct resilient and low-carbon urban spaces. This study reveals the non-linear relationship between street visual features and neighborhood carbon emissions, providing an empirical basis and strategic references for neighborhood planning and design oriented toward low-carbon goals, with valuable guidance for practices in urban planning, design, and management. Full article

This study evaluates China’s National Smart Education Platform (NSEP) as a national digital reform aligned with SDG 4 (quality education) and SDG 10 (reduced inequalities), yet evidence remains limited on whether such platforms reduce urban–rural gaps in real-world use and outcomes. A quantitative, stratified, random survey of students, teachers, and administrators used validated scales to measure perceived ease of use (PEOU), perceived usefulness (PU), user satisfaction (US), behavioral intention (BI), engagement level (EL), learning outcomes (LO), and system quality (SQ). The measures demonstrated strong reliability. Hierarchical regression analyses supported an extended technology acceptance model (TAM): SQ, PEOU, and PU significantly predicted US and BI, with PU showing the strongest effect. Interaction effects indicated context-sensitive adoption and the results suggested a persistent rural disadvantage in adoption even after accounting for key predictors. Mediation analyses further showed that US and BI transmitted technology beliefs to LO. Nevertheless, urban–rural gaps remained evident, particularly in PEOU and SQ, and teachers consistently reported a lower PEOU than students and administrators. These findings suggest that NSEP has the potential to support SDG-oriented digital equity, but closing urban–rural gaps requires teacher-centered design, improved usability and system reliability, and targeted infrastructure and capacity-building support in rural contexts. Full article

Lightning rods, while essential for protecting weather radars from direct lightning strikes, act as persistent non-meteorological scatterers that can interfere with signal transmission and reception and thereby degrade detection accuracy and product quality. Existing studies have mainly focused on X-band and C-band systems, and robust, measurement-based quantitative assessments for S-band dual-polarization radars remain scarce. In this study, a controllable tilting lightning rod, a high-precision Far-field Antenna Measurement System (FAMS), and an S-band dual-polarization weather radar (SAD radar) are jointly employed to systematically quantify lightning-rod impacts on antenna electromagnetic parameters under different rod elevation angles and azimuth configurations. Typical precipitation events were analyzed to evaluate the influence of the lightning rods on dual-polarization parameters. The results show that the lightning rod substantially elevates sidelobe levels, with a maximum enhancement of 4.55 dB, while producing only limited changes in the antenna main-beam azimuth and beamwidth. Differential reflectivity () is the most sensitive polarimetric parameter, exhibiting a persistent positive bias of about 0.24–0.25 dB in snowfall and mixed-phase precipitation, while no persistent azimuthal anomaly is evident during freezing rain; the co-polar correlation coefficient () is only marginally affected. Collectively, these results provide quantitative, far-field evidence of lightning-rod interference in S-band dual-polarization radars and provide practical guidance for more reasonable lightning-rod placement and configuration, as well as useful references for -oriented polarimetric quality-control and correction strategies. Full article

Sepsis is a life-threatening syndrome characterized by marked clinical heterogeneity and complex host–pathogen interactions. Although traditional mechanistic studies have identified key molecular pathways, they remain insufficient to capture the highly dynamic, multifactorial, and systems-level nature of this condition. The advent of high-throughput omics technologies—particularly integrative multi-omics approaches encompassing genomics, transcriptomics, proteomics, and metabolomics—has profoundly reshaped sepsis research by enabling comprehensive profiling of molecular perturbations across biological layers. However, the unprecedented scale, dimensionality, and heterogeneity of multi-omics datasets exceed the analytical capacity of conventional statistical methods, necessitating more advanced computational strategies to derive biologically meaningful and clinically actionable insights. In this context, artificial intelligence (AI) has emerged as a powerful paradigm for decoding the complexity of sepsis. By leveraging machine learning and deep learning algorithms, AI can efficiently process ultra-high-dimensional and heterogeneous multi-omics data, uncover latent molecular patterns, and integrate multilayered biological information into unified predictive frameworks. These capabilities have driven substantial advances in early sepsis detection, molecular subtyping, prognosis prediction, and therapeutic target identification, thereby narrowing the gap between molecular mechanisms and clinical application. As a result, the convergence of AI and multi-omics is redefining sepsis research, shifting the field from descriptive analyses toward predictive, mechanistic, and precision-oriented medicine. Despite these advances, the clinical translation of AI-driven multi-omics approaches in sepsis remains constrained by several challenges, including limited data availability, cohort heterogeneity, restricted interpretability and causal inference, high computational demands, difficulties in integrating static molecular profiles with dynamic clinical data, ethical and governance concerns, and limited generalizability across populations and platforms. Addressing these barriers will require the establishment of standardized, multicenter datasets, the development of explainable and robust AI frameworks, and sustained interdisciplinary collaboration between computational scientists and clinicians. Through these efforts, AI-enabled multi-omics research may progress toward reproducible, interpretable, and equitable clinical implementation. Ultimately, the synergy between artificial intelligence and multi-omics heralds a new era of intelligent discovery and precision medicine in sepsis, with the potential to transform both research paradigms and bedside practice. Full article

China’s roof-cutting and pressure relief gob-side entry retaining (RCPR-GER) technology provides an efficient non-pillar mining solution that significantly enhances coal recovery. This paper presents a systematic review of the technological progress in Chinese coal mines from 2011 to 2023, based on an analysis of 1038 publications from CNKI, EI, and Web of Science using VOS viewer and Origin software. Four main technical approaches are examined: gob-side entry retaining without roadside filling, with roadside filling, with roof-cutting and pressure relief, and hybrid methods. Five key roof-cutting techniques are evaluated: dense drilling, high-pressure water-jet slotting, hydraulic fracturing, blasting, presplitting, and roof water injection softening. Successful applications have been documented in coal seams with thicknesses of 1.6–6.15 m and burial depths of 92–1037 m, demonstrating wide adaptability. The roof-cutting short-beam theory underpins the mechanism, which reduces roadway deformation, shortens the cantilever beam length, and alters stress transfer paths. Compared to previous reviews on general gob-side entry retaining, this study offers a dedicated synthesis and comparative analysis of RCPR-GER technologies, establishing a selection framework grounded in geological compatibility and engineering practice. Future research should focus on adaptive parameter design for deep hard composite roofs, quantitative modeling of passive roof-cutting effects, optimization of cutting timing and orientation, and floor-heave control technologies to extend applications under complex geological conditions. Full article

Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends with sharp local dynamics; and (ii) the graph structure required for forecasting is frequently latent, while learned graphs can be unstable when built from temporally derived node features alone. We propose SpeQNet, a query-enhanced spectral graph filtering framework that jointly strengthens node representations and graph construction while enabling frequency-selective spatial reasoning. SpeQNet injects global spatial context into temporal embeddings via lightweight learnable spatiotemporal queries, learns a task-oriented adaptive adjacency matrix, and refines node features with an enhanced ChebNetII-based spectral filtering block equipped with channel-wise recalibration and nonlinear refinement. Across twelve real-world benchmarks spanning traffic, electricity, solar power, and weather, SpeQNet achieves state-of-the-art performance and delivers consistent gains on large-scale graphs. Beyond accuracy, SpeQNet is interpretable and robust: the learned spectral operators exhibit a consistent band-stop-like frequency shaping behavior, and performance remains stable across a wide range of Chebyshev polynomial orders. These results suggest that query-enhanced spatiotemporal representation learning and adaptive spectral filtering form a complementary and effective foundation for effective spatiotemporal forecasting. Full article

Dynamic positioning (DP) systems play a critical role in maintaining vessel position and heading under environmental disturbances such as wind, waves, and currents. This study presents a model predictive control (MPC)-based DP system for a fireboat equipped with a rudder–propeller configuration, explicitly accounting for both environmental loads and the reaction force generated during water cannon operation. Unlike conventional DP architectures in which DP control and thrust allocation are treated as separate modules, the proposed framework integrates both functions within a unified MPC formulation, enabling real-time optimization under actuator constraints. Environmental loads are modeled by incorporating nonlinear second-order wave drift effects, while nonlinear rudder–propeller interaction forces are derived through computational fluid dynamics (CFD) analysis and embedded in a control-oriented dynamic model. This modeling approach allows operational constraints, including rudder angle limits and propeller thrust saturation, to be explicitly considered in the control formulation. Simulation results demonstrate that the proposed MPC-based DP system achieves improved station-keeping accuracy, enhanced stability, and increased robustness against combined environmental disturbances and water cannon reaction forces, compared to a conventional PID controller. Full article

Background and Objectives: There is a lack of comprehensive, focused reviews on the topic of open bite in the literature. This study aims to quantitatively reveal publication productivity, annual trends, publication sources, key themes, and citation patterns in the field of open bite. Materials and Methods: A total of 1208 articles and reviews published between 1973 and 2025, obtained from the Web of Science database, were analyzed using bibliometric and network analysis methods. Results: A significant increase in the number of publications after 2010, acceleration particularly after 2015, and high productivity observed in the 2018–2024 period. A clear increasing trend was observed over time. 71.5% of publications are included in SCI-Expanded. Journal distribution is centralized, with the American Journal of Orthodontics and Dentofacial Orthopedics and Angle Orthodontist being the dominant publications. Keyword and cluster analyses showed that the literature is concentrated on four main thematic axes: (1) etiology and biomechanical processes, (2) surgical approaches and orthognathic interventions, (3) early intervention and habit control, (4) post-treatment stability and relapse. Furthermore, treatment-oriented concepts such as “miniscrew/temporary anchorage device,” “molar intrusion,” and “cephalometric analysis” are central. Conclusions: The study reveals that open bite has become an increasingly prevalent and thematically diverse area of research in the orthodontic literature. The current distribution indicates that research focuses on both clinical application and treatment outcomes; however, it also highlights the importance of long-term comparative data and studies on treatment stability. In the future, methodological standardization and comparable long-term data will contribute to the maturation of the literature. Full article

This study addresses a key limitation of conventional clay constitutive models, which often assume linear stress paths at low stress ratios and lack a systematic link between plasticity and yield surface shape. A symmetry-consistent bounding surface plasticity framework is proposed, introducing two shape parameters, Ψ and Ω, to control curvature and scaling of the yield surface under low stress ratios. The formulation preserves a unified, smooth yield function with continuous gradients, ensuring compatibility with standard numerical integration schemes. To enhance practical applicability, a three-level calibration strategy is established, ranging from direct triaxial interpretation to empirical correlations based on oedometer-derived indices. Model performance is validated against experimental data for clays with varying plasticity, demonstrating improved representation of curved stress paths without increasing formulation complexity. The proposed approach provides a transparent and reproducible extension to existing frameworks, bridging the gap between theoretical consistency and engineering-oriented calibration. Full article

Glass wool waste constitutes a rapidly increasing fraction of construction and demolition residues, yet it remains one of the most challenging insulation materials to recycle. Its non-combustible nature, extremely low bulk density, and high fibre elasticity preclude energy recovery and severely limit conventional mechanical recycling routes, resulting in long-term landfilling and loss of mineral resources. Converting glass wool waste into a fine mineral powder represents a potentially viable pathway for its integration into low-carbon construction materials, provided that industrial scalability, particle-size control, and chemical compatibility with cementitious binders are ensured. This study investigates the industrial-scale milling of end-of-life glass wool waste in a ventilated horizontal ball mill. It compares two grinding routes: a corundum-free route (BK) and an abrasive-assisted route (ZK) employing α-Al₂O₃ corundum to intensify fibre fragmentation. Particle size distribution was quantified by laser diffraction using cumulative and differential analyses, as well as characteristic diameters. The results confirm that abrasive-assisted milling significantly enhances fragmentation efficiency and reduces the coarse fibre fraction. However, the study demonstrates that this gain in fineness is inherently coupled with the incorporation of α-Al₂O₃ into the milled powder, introducing a chemically foreign crystalline phase that cannot be removed by post-processing. From a cement-oriented perspective, this contamination represents a critical limitation, as α-Al₂O₃ may interfere with hydration reactions, aluminate–sulfate equilibria, and microstructural development in Portland and calcium sulfoaluminate binders. In contrast, the corundum-free milling route yields a slightly coarser, chemically unmodified powder, offering improved process robustness, lower operational complexity, and greater compatibility with circular economy objectives. The study establishes that, for the circular reuse of fibrous insulation waste in cementitious systems, particle fineness alone is insufficient as an optimization criterion. Instead, the combined consideration of fineness, chemical purity, and binder compatibility governs the realistic and sustainable reuse potential of recycled glass wool powders. Full article