Search Results (1,375)

Background: Nurses’ teaching skills in the clinical setting are crucial to ensuring that students receive high-quality training. Despite the growing importance of competency frameworks, there is little research on the relationship between nurses’ teaching competence and sociodemographic, professional, training, and research variables. Methods: This was a cross-sectional, descriptive, and correlational study conducted at nine hospitals linked to the clinical placement subjects of the Bachelor of Nursing of the University of Barcelona. The study population comprised all nurses directly involved in clinical teaching. Participants’ level of self-reported teaching competence was evaluated using the Spanish version of the Capabilities of Nurse Educators (S-CONE) questionnaire, and the sociodemographic, professional, and academic variables were collected in an ad hoc questionnaire. Descriptive statistics, non-parametric tests, and linear and logistic regression models were used to analyse the associations between the S-CONE total score and the variables collected. Results: The mean age of the participants (n = 596) was 41.9 years (standard deviation: 8.82), and 85.6% of them were women (n = 510). The overall mean S-CONE score was 3.81 (SD: 0.73). Higher scores were observed in those with advanced academic degrees, formal teacher training, and participation in academic activities. Professionals with mixed roles (clinical mentor and academic tutor) self-reported significantly higher competence levels. Multivariate analyses identified participation in conferences, tutoring of undergraduate theses, and involvement in research or development projects as the main predictors of higher teaching competence as measured by the S-CONE questionnaire. The lowest-scoring factor was research and evidence, which points to a potential area for improvement. No significant associations were found with age, sex, or years of clinical experience. Conclusions: Participants had a high self-reported level of teaching competence and rated themselves as competent overall, especially in professional practice and curriculum design. However, we identified areas for improvement related to pedagogical innovation and the use of evidence. The findings reinforce the importance of professional development and academic involvement to strengthen teacher competence. Full article

The nature of dislocation loops significantly influences their evolutionary behavior and, consequently, affects the material properties, particularly under irradiation conditions. Determining the habit plane of a dislocation loop is the key point to examining its nature using the inside–outside method. In the present study, we introduce a novel technique for determining the habit planes of dislocation loops in the transmission electron microscope (TEM). The traditional inside–outside technique requires an edge-on perspective of the dislocation loop for analysis of the habit plane. In contrast, our innovative method for the precise determination of the habit plane delves into the geometric correlations between the dislocation loop and its projections under different crystal zone axes in TEM without being bound by the restrictive requirement of an edge-on view. It also simplifies the procedure of the inside–outside method. Furthermore, we have discussed the advantages and limitations of various methodologies employed to examine the nature of dislocation loops, as well as the techniques for determining their habit planes. Full article

Recent years have witnessed performance improvements in Multimodal Large Language Models (MLLMs) on downstream natural image understanding tasks. However, when applied to the function graph reasoning task, which is highly information-dense and abundant in fine-grained structural details, these models face pronounced performance degradation. The challenges are primarily characterized by several core issues: the static projection bottleneck, inadequate cross-modal interaction, and insufficient visual context in text embeddings. To address these problems, this study proposes a multimodal feature fusion enhancement method for function graph reasoning and constructs the FuncFusion-Math model. The core innovation of this model resides in its design of a dual-path feature fusion mechanism for both image and text. Specifically, the image fusion module adopts cross-attention and self-attention mechanisms to optimize visual feature representations under the guidance of textual semantics, effectively mitigating fine-grained information loss. The text fusion module, through feature concatenation and Transformer encoding layers, deeply integrates structured mathematical information from the image into the textual embedding space, significantly reducing semantic deviation. Furthermore, this study utilizes a four-stage progressive training strategy and incorporates the LoRA technique for parameter-efficient optimization. Experimental results demonstrate that the FuncFusion-Math model, with 3B parameters, achieves an accuracy of 43.58% on the FunctionQA subset of the MathVista test set, outperforming a 7B-scale baseline model by 13.15%, which validates the feasibility and effectiveness of the proposed method. Full article

Background: Intraoral radiography remains highly operator-dependent, with small deviations in beam angulation or receptor placement leading to geometric distortions, diagnostic inaccuracies, and repeated exposures. This pilot study introduces and evaluates a gyroscopic-guided, laser-assisted radiographic device designed to standardize cone positioning and improve the geometric reliability of bisecting-angle intraoral radiographs. Methods: Eighteen dental graduates and practitioners performed periapical radiographs on phantom models using a charge-coupled device (CCD) sensor over six months. Each participant obtained six standardized projections with and without the device, yielding 200 analysable radiographs. Radiographic linear measurements included tooth height (occluso–apical dimension) and tooth width (mesio-distal diameter), which were compared with reference values obtained using the paralleling technique. Radiographic errors—including cone cut, elongation, proximal overlap, sliding occlusal plane deviation, and apical cut—were recorded and compared between groups. Results: Use of the gyroscopic-guided device significantly enhanced geometric accuracy. Height measurements showed a strong correlation with reference values in the device group (r = 0.942; R² = 0.887) compared with the non-device technique (r = 0.767; R² = 0.589; p < 0.0001). Width measurements demonstrated similar improvement (device: r = 0.878; R² = 0.770; non-device: r = 0.748; R² = 0.560; p < 0.0001). Overall, the device reduced technical radiographic errors by approximately 62.5%, with significant reductions in cone cut, elongation, proximal overlap, sliding occlusal plane errors, and tooth-centering deviations. Conclusions: Integrating gyroscopic stabilization with laser trajectory guidance substantially improves the geometric fidelity, reproducibility, and diagnostic quality of intraoral radiographs. By minimizing operator-dependent variability, this innovation has the potential to reduce repeat exposures and enhance clinical diagnostics. Further clinical trials are recommended to validate performance in patient-based settings. Full article

Sub-Saharan Africa has the potential to achieve sustainable development through facilitating green transition projects, leveraging the revenue generated from its abundant natural resources. However, the resource curse hypothesis suggests that developing nations often face problems with corruption that hinder economic development in these countries. The present study aims to investigate how environmental sustainability can be advanced in Sub-Saharan Africa using revenue from natural resources in the presence of green financial technology and clean energy. Therefore, data for Sub-Saharan Africa from 2000 to 2023 are employed in the analysis. The analysis of these data is undertaken with the ‘Method of Moments Quantile Regression’ technique, and the ‘Panel Correlated Standard Errors’ is used for robustness checks. The key findings presented in this research depict the importance of natural resource rents in supporting sustainable environments in Sub-Saharan Africa. Therefore, the revenue from natural resources can be used to support green transition projects in developing nations with high natural resource endowments. Moreover, renewable energy and green finance foster a reduction in ecological footprint, hence supporting environmental sustainability. Consequently, technological innovation and financial development do not promote the achievement of environmental sustainability, raising questions about the environmental policies and regulations in Sub-Saharan Africa. To this end, there is a need for policy reforms and corruption control in order to prevent the misallocation and misuse of resources designed to support green transition projects. Full article

The automated localization of the flange interface in LNG tanker loading and unloading imposes stringent requirements for accuracy and illumination robustness. Traditional monocular vision methods are prone to localization failure under extreme illumination conditions, such as intense glare or low light, while LiDAR, despite being unaffected by illumination, suffers from limitations like a lack of texture information. This paper proposes an illumination-robust localization method for LNG tanker flange interfaces by fusing monocular vision and LiDAR, with three scenario-specific innovations beyond generic multi-sensor fusion frameworks. First, an illumination-adaptive fusion framework is designed to dynamically adjust detection parameters via grayscale mean evaluation, addressing extreme illumination (e.g., glare, low light with water film). Second, a multi-constraint flange detection strategy is developed by integrating physical dimension constraints, K-means clustering, and weighted fitting to eliminate background interference and distinguish dual flanges. Third, a customized fusion pipeline (ROI extraction-plane fitting-3D circle center solving) is established to compensate for monocular depth errors and sparse LiDAR point cloud limitations using flange radius prior. High-precision localization is achieved via four key steps: multi-modal data preprocessing, LiDAR-camera spatial projection, fusion-based flange circle detection, and 3D circle center fitting. While basic techniques such as LiDAR-camera spatiotemporal synchronization and K-means clustering are adapted from prior works, their integration with flange-specific constraints and illumination-adaptive design forms the core novelty of this study. Comparative experiments between the proposed fusion method and the monocular vision-only localization method are conducted under four typical illumination scenarios: uniform illumination, local strong illumination, uniform low illumination, and low illumination with water film. The experimental results based on 20 samples per illumination scenario (80 valid data sets in total) show that, compared with the monocular vision method, the proposed fusion method reduces the Mean Absolute Error (MAE) of localization accuracy by 33.08%, 30.57%, and 75.91% in the X, Y, and Z dimensions, respectively, with the overall 3D MAE reduced by 61.69%. Meanwhile, the Root Mean Square Error (RMSE) in the X, Y, and Z dimensions is decreased by 33.65%, 32.71%, and 79.88%, respectively, and the overall 3D RMSE is reduced by 64.79%. The expanded sample size verifies the statistical reliability of the proposed method, which exhibits significantly superior robustness to extreme illumination conditions. Full article

In the context of Poland’s commitments under the European Union’s climate policy, including the European Green Deal and the Fit for 55 package, as well as the decision to ban imports of hard coal from Russia and Belarus, ensuring the stability of the domestic market for energy commodities is becoming a key challenge. The response to these needs is the Coal Platform concept developed by the KOMAG Institute of Mining Technology (KOMAG), which aims to integrate data on hard coal resources, production, and demand. The most important problem is not the just-in-time (JIT) strategy itself, but the lack of accurate, up-to-date data and the high technological and organizational inertia on the production side. The JIT strategy assumes an ability to predict future demand well in advance, which requires advanced analytical tools. Therefore, the Coal Platform project analyses the use of artificial intelligence algorithms to forecast demand and adjust production to actual market needs. The developed mathematical model (2024–2030) takes into account 12 variables, and the tested forecasting methods (including ARX and FLNN) exhibit high accuracy, which together make it possible to reduce overproduction, imports, and CO₂ emissions, supporting the country’s responsible energy transition. This article describes approaches to issues related to the development of the Coal Platform and, above all, describes the concept, preliminary architecture, and data model. As an additional element, a mathematical model and preliminary results of research on forecasting methods in the context of historical data on hard coal production and consumption are presented. The core innovation lies in integrating the just-in-time (JIT) philosophy with AI-driven forecasting and scenario-based planning within a cloud-ready Coal Platform architecture, enabling dynamic resource management and compliance with decarbonization targets. Full article

HiRo-SLAM is a visual-inertial SLAM system developed to achieve high accuracy and enhanced robustness. To address critical limitations of conventional methods, including systematic biases from imperfect camera models, uneven spatial feature distribution, and the impact of outliers, we propose a unified optimization framework that integrates four key innovations. First, Precise Camera Projection Modeling (PCPM) embeds a fully differentiable camera model in nonlinear optimization, ensuring accurate handling of camera intrinsics and distortion to prevent error accumulation. Second, Visibility Pyramid-based Adaptive Non-Maximum Suppression (P-ANMS) quantifies feature point contribution through a multi-scale pyramid, providing uniform visual constraints in weakly textured or repetitive regions. Third, Robust Optimization Using Graduated Non-Convexity (GNC) suppresses outliers through dynamic weighting, preventing convergence to local minima. Finally, the Point-Line Feature Fusion Frontend combines XFeat point features with SOLD2 line features, leveraging multiple geometric primitives to improve perception in challenging environments, such as those with weak textures or repetitive structures. Comprehensive evaluations on the EuRoC MAV, TUM-VI, and OIVIO benchmarks show that HiRo-SLAM outperforms state-of-the-art visual-inertial SLAM methods. On the EuRoC MAV dataset, HiRo-SLAM achieves a 30.0% reduction in absolute trajectory error compared to strong baselines and attains millimeter-level accuracy on specific sequences under controlled conditions. However, while HiRo-SLAM demonstrates state-of-the-art performance in scenarios with moderate texture and minimal motion blur, its effectiveness may be reduced in highly dynamic environments with severe motion blur or extreme lighting conditions. Full article

Against the backdrop of global climate change and China’s “dual-carbon” goals, carbon emissions from the construction phase of transportation infrastructure, particularly the rapidly expanding railway network, have garnered significant attention. However, systematic research and general evaluation models targeting the factors influencing carbon emissions during the railway bridge construction phase remain insufficient. To address this gap, this study presents a novel low-carbon evaluation model that integrates the analytic network process (ANP) and the fuzzy comprehensive evaluation (FCE) method. First, a carbon accounting model covering four stages—material production, transportation, construction, and maintenance—is established based on life cycle assessment (LCA) theory, providing a data foundation. Second, an innovative low-carbon evaluation index system for railway bridges, comprising 5 criterion layers and 23 indicator layers, is constructed. The ANP method is employed to calculate weights, effectively capturing the interdependencies among indicators, while the FCE method handles assessment ambiguities, forming a comprehensive evaluation framework. A case study of the bridge demonstrates the model’s effectiveness, yielding an evaluation score of 82.38 (“excellent” grade), which is consistent with expert judgement. The ranking of indicator weights from the model is highly consistent with the actual carbon emission inventory ranking (Spearman coefficient of 0.714). Key indicators—C21 (use of high-performance materials), C22 (concrete consumption), and C25 (transportation energy consumption)—collectively account for approximately 60% of the total impact, accurately identifying the major emission sources. This research not only verifies the model’s efficacy in pinpointing critical carbon sources but also provides a scientific theoretical basis and practical tool for low-carbon decision-making and optimization in the planning and design stages of railway bridge projects. Full article

Purpose: Research drives clinical advancement in oral and craniomaxillofacial surgery by generating evidence that guides practice and innovation. However, limited literature exists describing research engagement among surgeons within AO CMF in the Middle East and North Africa. This study evaluated awareness, participation, and perceived barriers to research among AO CMF members and affiliated surgeons in the MENA region. Methods: A cross-sectional, questionnaire-based survey was distributed electronically to AO CMF members, affiliates, and professional CMF surgeon networks between October and December 2024. The 14-item survey assessed demographics, research awareness, attitudes, productivity, and barriers. Responses were anonymized and analyzed descriptively using SurveyPlanet analytics. Results: A total of 144 surgeons from 21 countries completed the survey. Pakistan (35%), Morocco (9.8%), Kuwait (7.7%), and the United Arab Emirates (7%) contributed the largest proportions. Most respondents (47.6%) expressed strong interest in research but reported difficulty initiating projects, while 32.2% cited lack of time as a major constraint. The most frequently reported barriers included challenges in research methodology (14.6%), publishing (14.6%), and manuscript writing (14.1%). Only 18.9% of participants had published more than ten articles, while 29.4% had none. Mentorship demand was high (94.4%), but awareness of the AO PEER program remained limited (37.8%). Conclusion: Surgeons expressed strong enthusiasm for research yet face substantial barriers. Strengthening research methodology training, establishing structured mentorship, expanding AO PEER engagement, and facilitating multicenter collaboration are key strategies to enhance research productivity across the region. Full article

Production of dairy products is a crucial component of food security. The situation in the dairy sector affects not only the supply of the population with dairy products but also the overall sustainable development of the country. The main purpose of this publication is to determine the forecasted need for investment in innovations for Ukrainian enterprises engaged in the production of milk and cream in order to achieve sustainable development goals. The study employed the following economic research methods: the inductive method—for collecting, systematizing, and processing information; the deductive method—for theoretical interpretation of the problem; analysis and synthesis—for assessing the investment attractiveness of dairy enterprises and examining the components of sustainable development and their interrelationships. The primary data on enterprises engaged in the production of milk and cream were collected and systematized for large, medium, and small enterprises based on the information from the State Statistics Service of Ukraine. The study substantiates the impact of investments in the modernization of the dairy industry on achieving sustainable development goals. Integral indicators of the investment attractiveness of Ukrainian milk and cream producers were calculated, revealing that large enterprises are the most suitable for absorbing investments aimed at production modernization. An analysis of milk and cream production volumes by large enterprises in Ukraine for 2014–2024 was conducted, demonstrating that in 2023–2024, production began to grow after the crisis of 2021–2022. Based on historical production dynamics, a forecast for 2026–2030 was developed. It was determined that under the pessimistic scenario, production will reach 291.79 thousand tons in 2030, under the realistic scenario, 349.84 thousand tons, and under the optimistic scenario, 407.88 thousand tons. The key factors influencing the pessimistic, realistic, and optimistic projections were identified. Since the realistic scenario enables the most comprehensive consideration of influencing factors, the calculation of investment needs for the modernization of large milk and cream producers was based on this scenario. It was established that to meet EU product quality standards, comply with sustainable development goals, and accommodate the projected increase in production, the total investment required for the modernization of large enterprises engaged in the production of milk and cream in Ukraine should amount to 126 million euros by 2030. Full article

The current debate on digital education is at a crossroads between the need for technological innovation and the growing concern about the impact of passive screen use. In this context, identifying sustainable pedagogical models that integrate Information and Communication Technologies (ICT) in a meaningful and inclusive way is an urgent need. This article presents a case study of the Amara Berri System (ABS), aiming to analyze how inclusive and sustainable digital innovation is operationalized within the system and whether teachers’ length of service is associated with the implementation and perceived impact of inclusive ICT practices. The investigation is based on a mixed-methods sequential design. A questionnaire was administered to a sample of 292 teachers to collect data on their practices and perceptions. Subsequently, a focus group with eight teachers was conducted to further explore the meaning of their practices. Quantitative results show that the implementation and positive evaluation of inclusive ICT practices correlate significantly with teachers’ seniority within the system, which suggests that the model is formative in itself. Qualitative analysis shows that ICTs are not an end in themselves within the ABS, but an empowering tool for the students. The “Audiovisual Media Room”, managed by students, functions as a space for social and creative production that gives technology a pedagogical purpose. The study concludes that the sustainability of digital innovation requires coherence with the pedagogical project. Findings offer valuable implications for the design of teacher training contexts that foster the integration of technology within a framework of truly inclusive education. Full article

This paper presents the Living Lab Assessment Method (LLAM), a context-sensitive framework for assessing impact and value creation in Living Labs (LLs). While LLs have become established instruments for Open and Urban Innovation, systematic and transferable approaches to evaluate their impact remain scarce and still show theoretical and practical barriers. This study proposes a new methodological approach that aims to address these challenges through the development of the LLAM, the Living Lab Assessment Method. This study reports a five-year iterative development process embedded in Ghent’s urban and social innovation ecosystem through the combination of three complementary methodological pillars: (1) co-creation and co-design with lead users, ensuring alignment with practitioner needs and real-world conditions; (2) multiple case study research, enabling iterative refinement across diverse Living Lab projects, and (3) participatory action research, integrating reflexive and iterative cycles of observation, implementation, and adjustment. The LLAM was empirically developed and validated across four use cases, each contributing to the method’s operational robustness and contextual adaptability. Results show that LLAM captures multi-level value creation, ranging from individual learning and network strengthening to systemic transformation, by linking participatory processes to outcomes across stakeholder, project, and ecosystem levels. The paper concludes that LLAM advances both theoretical understanding and practical evaluation of Living Labs by providing a structured, adaptable, and empirically grounded methodology for assessing their contribution to sustainable and inclusive urban innovation. Full article

Under the background of “Emerging Engineering Education”, promoting reform in the practical teaching of Agricultural Hydraulic Engineering is a crucial task for cultivating water conservancy professionals with sustainability competencies in the new era. This study addresses current issues in the practical education of Agricultural Hydraulic Engineering, including fragmented practical content, disjointed tiered training, superficial teaching models, and simplified assessment methods. Guided by the Outcome-Based Education (OBE) concept and incorporating sustainability education principles, and integrating the distinctive features and course orientation of the university’s programme, this study implements a multidimensional practical teaching reform characterized by “three level–four integration–five dimension–three objective” framework in Agricultural Hydraulic Engineering. This reform has achieved significant outcomes: teaching quality has been notably enhanced, with students demonstrating substantially improved practical and innovative capabilities, earning over ten national and provincial competition awards in the past two years. Faculty teaching research capabilities have strengthened, resulting in multiple provincial-level teaching reform projects and top-tier course approvals. The proportion of courses achieving a satisfactory level of target attainment stands at 66.7% of the total practical courses. Graduate and employer satisfaction rates reached 96.2% and 100%, respectively. The results demonstrate the strong applicability and effectiveness of this multidimensional practical teaching model in fostering talent equipped for sustainable water conservancy development, providing an important reference for practical teaching reforms in agricultural universities during the new era. Full article

Problem: Entrepreneurship education continues to expand, yet it remains fragmented across disciplines and loosely connected to the knowledge, innovation, and venture ecosystems that shape entrepreneurial success. At the same time, AI is transforming research, collaboration, and venture development, but its use in education is typically limited to narrow, task-specific applications rather than ecosystem-level integration. Objective: This paper seeks to develop a comprehensive conceptual model for integrating AI into entrepreneurship education by positioning AI as a connective infrastructure that links and activates the knowledge, innovation, and venture ecosystems. Methods: The model is derived through an integrative synthesis of literature, programs, and activities on entrepreneurship education, ecosystem-based learning, and AI-enabled research and innovation practices, combined with an analysis of gaps in current educational approaches. Key Findings: The proposed model defines a progressive learning pathway consisting of (1) AI competency training that builds foundational capacities in critical judgment, responsible application, and creative adaptation; (2) AI praxis labs that use AI-curated ecosystem data to support iterative, project-based learning; and (3) venture studios where students scale outputs into innovations and ventures through structured ecosystem engagement. This pathway demonstrates how AI can function as a structural mediator of problem definition, research design, experimentation, analysis, and narrative translation. Contributions: This paper reframes entrepreneurship education as an iterative, inclusive, and ecosystem-connected process enabled by AI infrastructure. It offers a new theoretical lens for understanding AI’s educational role and provides actionable implications for curriculum design, institutional readiness, and policy development while identifying avenues for future research on competency development and ecosystem impacts. Full article