Search Results (2,653)

There is minimal research concerning the role of custom-trained artificial intelligence (AI) tools in higher education, with a lack of research concerning the pedagogical qualities of an AI-based personal tutor. To fill this literature gap, we examined how a custom GPT personal tutor shapes key teaching and learning qualities. Using the mixed-methods approach, we aimed to demonstrate preliminary and exploratory empirical evidence concerning the contribution of custom-trained AI tutors to building up students’ competencies. Our research analyzed the subjective assessments of students related to the GPT tutor’s contribution to improving their competencies. Both the qualitative and quantitative empirical results confirmed the positive contribution. In addition, we triangulated the results to evaluate the potential of custom-trained AI chatbots in higher education, focusing on undergraduate business courses. However, the results of this study cannot be generalized to the entire student population of business schools, since the participation in the AI-assisted tutor program was voluntary, attracting only intrinsically motivated students. Full article

This study explores the perceptions of future educators, specifically Early Childhood Education students at the Universities of Cádiz and Gdansk, regarding the interconnections between critical thinking and sustainability. The work aims to provide valuable insights into general teacher training, examining how these students’ experiences are contextualized within their respective educational systems and cultural contexts. To achieve this, eleven group interviews (three in Cádiz, eight in Gdansk) were conducted using a structured and expert-validated script. The transcribed data were qualitatively analyzed using QDA MINER v.6 software. Key findings reveal divergent perceptions of critical thinking among pre-service teachers: while Spanish students leaned towards a subjective understanding, Polish students emphasized an objective, data-driven approach. This distinction has significant implications for the conceptualization and teaching of critical thinking in educator training. Despite these differences, both groups of participants highlighted the necessity of implementing active methodologies in higher education (such as cooperative learning, problem-solving, and debates) to foster critical thinking, both for their own development and for preparing for their future practice with young children. This study also identified an excessive emphasis on theoretical aspects of sustainability in these future teachers’ training and a limited understanding of their practical application in the classroom. Furthermore, explicit connections between critical thinking and sustainability were scarce in student responses, highlighting a gap in current educator training in these areas. Collectively, the results suggest significant weaknesses in current teacher training efforts regarding the development of critical thinking and its effective integration with sustainability competencies. Full article

Teacher education faces persistent challenges, including limited access to authentic field experiences and a disconnect between theoretical instruction and classroom practice. While virtual reality (VR) simulations offer an alternative, most are constrained by inflexible design and lack scalability, failing to mirror the complexity of real teaching environments. This study introduces TeacherGen@i, a generative AI (GenAI)-enhanced VR simulation designed to provide pre-service teachers with immersive, adaptive teaching practice through realistic GenAI agents. Using an explanatory case study with a mixed-methods approach, the study examines the simulation’s usability, design challenges, and instructional utility within a university-based teacher preparation course. Data sources included usability surveys and reflective journals, analyzed through thematic coding and computational linguistic analysis using LIWC. Findings suggest that TeacherGen@i facilitates meaningful development of teaching competencies such as instructional decision-making, classroom communication, and student engagement, while also identifying notable design limitations related to cognitive load, user interface design, and instructional scaffolding. This exploratory research offers preliminary insights into the integration of generative AI in teacher simulations and its potential to support responsive and scalable simulation-based learning environments. Full article

Hydrotreated ester and fatty acids to jet (HEFA-tJ) is presently the most developed and economically attractive pathway to produce sustainable aviation fuel (SAF). An ongoing systematic study of the critical variables of different pathways to SAF has revealed significantly lower greenhouse gas (GHG) reduction potential for the HEFA-tJ pathway compared to competing markets using the same resources for road diesel production. Moderate yield variations between air and road pathways lead to several hundred thousand tons less GHG reduction per project, which is generally not evaluated thoroughly in standard environmental assessments. This work demonstrates that, although the HEFA-tJ market seems to have more attractive features than biodiesel/renewable diesel, considerable viability risks might manifest as HEFA-tJ fuel market integration rises. The need for more transparent data and effort in this regard, before envisaging making decisions regarding the volume of HEFA-tJ production, is emphasized. Overall, reducing the carbon intensity of road diesel appears to be less capital-intensive, less risky, and several times more efficient in reducing GHG emissions. Full article

This article addresses the task of building personalized educational recommendations based on a heterogeneous knowledge graph that integrates data from university curricula, job vacancies, and online courses. To solve the problem of course recommendations by their relevance to a user’s competencies, a graph neural network (GNN)-based approach is proposed, specifically utilizing and comparing the Heterogeneous Graph Transformer (HGT) architecture, Graph Sample and Aggregate network (GraphSAGE), and Heterogeneous Graph Attention Network (HAN). Experiments were conducted on a heterogeneous graph comprising various node and relation types. The models were evaluated using regression and ranking metrics. The results demonstrated the superiority of the HGT-based recommendation model as a link regression task, especially in terms of ranking metrics, confirming its suitability for generating accurate and interpretable recommendations in educational systems. The proposed approach can be useful for developing adaptive learning recommendations aligned with users’ career goals. Full article

Background: Point-of-care ultrasound (POCUS) is gaining recognition as a valuable diagnostic tool in various fields of medicine, including pediatrics. Its application at the point of care enables real-time clinical decision-making, which is particularly advantageous in pediatric settings. Although global interest in POCUS is growing, many European countries—including Poland—still lack formal training programs for POCUS at both the undergraduate and postgraduate levels. Nevertheless, the number of pediatricians incorporating POCUS into their daily clinical practice in Poland is increasing. However, the extent of its use and perceived value among pediatricians remains largely unknown. This study aimed to evaluate the current level of POCUS utilization in pediatric care in Poland, focusing on pediatricians’ self-assessed competencies, perceptions of its clinical utility, and key barriers to its implementation in daily practice. Methods: This cross-sectional study was conducted between July and August 2024 using an anonymous online survey distributed to pediatricians throughout Poland via national professional networks, with a response rate of 7.3%. Categorical variables were analyzed using the chi-square test of independence to assess the associations between key variables. Quantitative data were analyzed using descriptive statistics, and qualitative data from open-ended responses were subjected to a thematic analysis. Results: A total of 210 pediatricians responded. Among them, 149 (71%) reported access to ultrasound equipment at their workplace, and 89 (42.4%) reported having participated in some form of POCUS training. Only 46 respondents (21.9%) reported frequently using POCUS in their clinical routine. The self-assessed POCUS competence was rated as low or very low by 136 respondents (64.8%). While POCUS was generally perceived as a helpful tool in facilitating and accelerating clinical decisions, the main barriers to implementation were a lack of formal training and limited institutional support. Conclusions: Although POCUS is perceived as clinically valuable by the surveyed pediatricians in Poland, its routine use remains limited due to training and systemic barriers. Future efforts should prioritize the development of a validated, competency-based training framework and the implementation of a larger, representative national study to guide the structured integration of POCUS into pediatric care. Full article

This paper presents the expected and unexpected, but typically substituent-dependent, ferrocene-catalyzed DDQ-mediated oxidative transformations of a series of 5,8-bis(methylthio)-1-aryl-1,2-dihydropyridazino[4,5-d]pyridazines and 8-(3,5-dimethyl-1H-pyrazol-1-yl)-5-(methylthio)-1-aryl-1,2-dihydropyridazino[4,5-d]pyridazines. Under noncatalytic conditions the reactions were sluggish, mainly producing a substantial amount of undefined tarry materials; nevertheless, the ferrocene-catalyzed reactions of the 5,8-bis(methylthio)-substituted precursors gave the aromatic products the expected aromatic products in low yields. Their formation was accompanied by ring transformations proceeding via aryne-generating fragmentation/Diels–Alder (DA)/N₂-releasing retro Diels–Alder (rDA) sequence to construct arene-fused phthalazines. On the other hand, neither the noncatalytic nor the catalytic reactions of the 8-pyrazolyl-5-methylthio-substituted dihydroaromatics yielded the expected aromatic products. Instead, depending on their substitution pattern, the catalytic reactions of these pyrazolyl-substituted precursors also led to the formation of dearylated arene-fused phthalazines competing with an unprecedented multistep fragmentation sequence terminated by the hydrolysis of cationic intermediates to give 4-(methylthio)pyridazino[4,5-d]pyridazin-1(2H)-one and the corresponding 3,5-dimethyl-1-aryl-1H-pyrazole. When 0.6 equivalents of DDQ were applied in freshly absolutized THF, a representative pyrazolyl-substituted model underwent an oxidative coupling to give a dimer formed by the interaction of the cationic intermediate, and a part of the N-nucleophilic precursor remained intact. A systematic computational study was conducted on these intriguing reactions to support their complex mechanisms proposed on the basis of the structures of the isolated products. Full article

This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using quintic spline functions and a dynamic speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. The theoretical distinction of LSPP lies in its modular integration of: (1) a finite state machine (FSM)-based decision-making layer that selects maneuver-specific goal states (e.g., keep lane, change lane left/right); (2) quintic spline optimization to generate smooth, jerk-minimized, and kinematically consistent trajectories; (3) a multi-objective cost evaluation framework that ranks competing paths according to safety, comfort, and efficiency; and (4) a closed-loop MPC controller to ensure real-time trajectory execution with robustness. Extensive simulations conducted in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, safe, and computationally efficient trajectories. Results show consistent improvements in lane-keeping accuracy, collision avoidance, enhanced materials wear performance, and planning responsiveness compared to traditional path-planning methods. These findings confirm LSPP’s potential as a practical and high-performance solution for autonomous highway driving. Full article

This study focuses on analyzing progressive Type-II right censoring competing risks datasets. The latent causes of failures are assumed to follow independent generalized linear exponential distributions. The maximum likelihood and maximum product of spacing methods are employed to estimate the unknown parameters and survival indices. Furthermore, approximate confidence intervals are derived using the asymptotic normality of the maximum likelihood and the maximum product of spacing estimators. Additionally, bootstrap methods are employed to construct confidence intervals. A comprehensive simulation study is carried out to evaluate the effectiveness of these estimation approaches. Finally, real-world datasets are analyzed to illustrate the practical applicability of the proposed model. Full article

This research addresses the challenges of proactive scheduling and routing in manufacturing systems governed by the Material Requirement Planning (MRP) method. Such systems often face capacity constraints, difficulties in resource balancing, and limited traceability of component requirements. The lack of seamless integration between customer orders and production tasks, combined with the manual and time-consuming nature of schedule adjustments, highlights the need for an automated and optimized scheduling method. We propose a novel optimization-based approach that leverages mixed-integer linear programming (MILP) combined with a proprietary procedure for reducing the size of the modeled problem to generate feasible and/or optimal production schedules. The model incorporates dynamic routing, partial resource utilization, limited additional resources (e.g., tools, workers), technological breaks, and time quantization. Key results include determining order feasibility, identifying unfulfilled order components, minimizing costs, shortening deadlines, and assessing feasibility in the absence of available resources. By automating the generation of data from MRP/ERP systems, constructing an optimization model, and exporting the results back to the MRP/ERP structure, this method improves decision-making and competes with expensive Advanced Planning and Scheduling (APS) systems. The proposed innovation solution—the integration of MILP-based optimization with the proprietary PT (data transformation) and PR (model-size reduction) procedures—not only increases operational efficiency but also enables demand source tracking and offers a scalable and economical alternative for modern production environments. Experimental results demonstrate significant reductions in production costs (up to 25%) and lead times (more than 50%). Full article

Surface defects of photovoltaic (PV) cells can seriously affect power generation efficiency. Accurately detecting such defects and handling them in a timely manner can effectively improve power generation efficiency. Aiming at the high-precision and real-time requirements for surface defect detection during the use of PV cells, this paper proposes a PV cell surface defect detection algorithm based on SEC-YOLOv8. The algorithm first replaces the Spatial Pyramid Pooling Fast module with the SPPELAN pooling module to reduce channel calculations between convolutions. Second, an ECA attention mechanism is added to enable the model to pay more attention to feature extraction in defect areas and avoid target detection interference from complex environments. Finally, the upsampling operator CARAFE is introduced in the Neck part to solve the problem of scale mismatch and enhance detection performance. Experimental results show that the improved model achieves a mean average precision (mAP@0.5) of 69.2% on the PV cell dataset, which is 2.6% higher than the original network, which is designed to achieve a superior balance between the competing demands of accuracy and computational efficiency for PV defect detection. Full article

Nipah virus (NiV) is a highly pathogenic bat-borne zoonotic pathogen that poses a significant threat to human and animal health, with fatality rates exceeding 70% in some outbreaks. Despite its significant public health impact, there are currently no licensed vaccines or specific therapeutics available. Various virological tools—such as reverse genetics systems, replicon particles, VSV-based pseudoviruses, and recombinant Cedar virus chimeras—have been widely used to study the molecular mechanisms of NiV and to support vaccine development. Building upon these platforms, we developed a replication-competent recombinant vesicular stomatitis virus (rVSVΔG-eGFP-NiV_BD F/G) expressing NiV attachment (G) and fusion (F) glycoproteins. This recombinant virus serves as a valuable tool for investigating NiV entry mechanisms, cellular tropism, and immunogenicity. The virus was generated by replacing the VSV G protein with NiV F/G through reverse genetics, and protein incorporation was confirmed via immunofluorescence and electron microscopy. In vitro, the virus exhibited robust replication, characteristic cell tropism, and high viral titers in multiple cell lines. Neutralization assays showed that monoclonal antibodies HENV-26 and HENV-32 effectively neutralized the recombinant virus. Furthermore, immunization of golden hamsters with inactivated rVSVΔG-eGFP-NiV_BD F/G induced potent neutralizing antibody responses, demonstrating its robust immunogenicity. These findings highlight rVSVΔG-eGFP-NiV_BD F/G as an effective platform for NiV research and vaccine development. Full article

Detecting underwater targets is crucial for ecological evaluation and the sustainable use of marine resources. To enhance environmental protection and optimize underwater resource utilization, this study proposes AGS-YOLO, an innovative underwater small-target detection model based on YOLO11. Firstly, this study proposes AMSA, a multi-scale attention module, and optimizes the C3k2 structure to improve the detection and precise localization of small targets. Secondly, a streamlined GSConv convolutional module is incorporated to minimize the parameter count and computational load while effectively retaining inter-channel dependencies. Finally, a novel and efficient cross-scale connected neck network is designed to achieve information complementarity and feature fusion among different scales, efficiently capturing multi-scale semantics while decreasing the complexity of the model. In contrast with the baseline model, the method proposed in this paper demonstrates notable benefits for use in underwater devices constrained by limited computational capabilities. The results demonstrate that AGS-YOLO significantly outperforms previous methods in terms of accuracy on the DUO underwater dataset, with mAP@0.5 improving by 1.3% and mAP@0.5:0.95 improving by 2.6% relative to those of the baseline YOLO11n model. In addition, the proposed model also shows excellent performance on the RUOD dataset, demonstrating its competent detection accuracy and reliable generalization. This study proposes innovative approaches and methodologies for underwater small-target detection, which have significant practical relevance. Full article

Background/Objectives: Effective teamwork is crucial for minimizing human error in healthcare settings. Medical teams, typically composed of physicians and nurses, supported by auxiliary professionals, achieve better outcomes when they possess strong collaborative competencies. High-quality teamwork is associated with fewer adverse events and complications and lower mortality rates. Based on this background, the objective of this study is to analyze the perception of non-technical skills and immediate learning outcomes in interprofessional simulation settings based on E-CRM items. Methods: A cross-sectional observational study was conducted involving participants from the official postgraduate Medicine and Nursing programs at the Catholic University of Murcia (UCAM) during the 2024–2025 academic year. Four interprofessional E-CRM simulation sessions were planned, involving randomly assigned groups with proportional representation of medical and nursing students. Teams worked consistently throughout the training and participated in clinical scenarios observed via video transmission by their peers. Post-scenario debriefings followed INACSL guidelines and employed the PEARLS method. Results: Findings indicate that 48.3% of participants had no difficulty identifying the team leader, while 51.7% reported minor difficulty. Role assignment posed moderate-to-high difficulty for 24.1% of respondents. Communication, situation awareness, and early help-seeking were generally managed with ease, though mobilizing resources remained a challenge for 27.5% of participants. Conclusions: This study supports the value of interprofessional education in developing essential competencies for handling urgent, emergency, and high-complexity clinical situations. Strengthening interdisciplinary collaboration contributes to safer, more effective patient care. Full article

Background: Intermediate care units face growing complexity due to aging populations and chronic illnesses. Non-technical skills such as empathy and communication are crucial for quality care. We aimed to examine the relationship between communication skills, self-efficacy, and sense of coherence among intermediate care nurses. Methods: We conducted an observational, cross-sectional study with 60 intermediate care nurses from three units in a Catalan hospital, Spain. Participants engaged in high-fidelity simulation using geriatric end-of-life scenarios with an ultra-realistic manikin representing a geriatric patient at the end of life. NTSs were measured using validated tools: the Health Professionals Communication Skills Scale (HP-CSS), the General Self-Efficacy Scale, and the Sense of Coherence Questionnaire (OLQ-13). Sessions followed INACSL standards, including prebriefing, simulation, and debriefing phases. Results: Post-simulation outcomes revealed significant gains in interpersonal competencies, with men reporting higher assertiveness (p = 0.015) and greater satisfaction with both the simulation experience (p = 0.003) and the instructor (p = 0.008), underscoring gender-related perceptions in immersive training. Conclusions: Ultra-realistic clinical simulation is effective in enhancing NTS among intermediate care nurses, contributing to improved care quality and clearer professional profiles in geriatric nursing. Full article