Search Results (4,193)

This study presents the validation of the Player Personality and Dynamics Scale (PPDS), designed to identify player profiles in educational gamification contexts with narrative elements. Through a sample of 635 participants, a questionnaire was developed and applied, covering sociodemographic data, lifestyle habits, gaming practices, and a classification system of 40 items on a six-point Likert scale. The results of the factorial analysis confirm a structure of five factors: Toxic Profile, Joker Profile, Tryhard Profile, Aesthetic Profile, and Coacher Profile, with high fit and reliability indices (RMSEA = 0.06; CFI = 0.95; TLI = 0.91). The resulting classification enables the design of personalized gamified experiences that enhance learning and interaction in the classroom, highlighting the importance of understanding players’ motivations to better adapt educational dynamics. Applying this scale fosters meaningful learning through the creation of narratives tailored to students’ individual preferences. Full article

The phase equilibria of the Si-C-Cu ternary system at 700 °C and 810 °C were experimentally investigated for the first time. Fifteen key alloys were prepared via powder metallurgy and analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and electron probe microanalysis (EPMA). Isothermal sections were constructed based on the identified equilibrium phases. At 700 °C, eight single-phase regions and six three-phase regions—(C)+(Cu)+hcp, (C)+hcp+γ-Cu₃₃Si₇, (C)+γ-Cu₃₃Si₇+SiC, γ-Cu₃₃Si₇+SiC+ε-Cu₁₅Si₄, SiC+ε-Cu₁₅Si₄+η-Cu₃Si(ht), and SiC+(Si)+η-Cu₃Si(ht)—were determined. At 810 °C, nine single-phase regions and seven three-phase regions were identified. The solubility of C and Si/Cu in the various phases was quantified and found to be significantly higher at 810 °C compared to 700 °C. Key differences include the presence of the bcc (β) and liquid phases at 810 °C. The results demonstrate that higher temperatures promote increased mutual solubility and reaction tendencies among Cu, C, and Si. Motivated by these findings, the influence of vacuum hot pressing parameters on SiC-fiber-reinforced Cu composites (SiC_f/Cu) was investigated. The optimal processing condition (1050 °C, 60 MPa, 90 min) yielded a high bending strength of 998.61 MPa, attributed to enhanced diffusion and interfacial bonding facilitated by the high-temperature phase equilibria. This work provides essential fundamental data for understanding interactions and guiding processing in SiC-reinforced Cu composites. Full article

The sustainable development of teaching expertise in research-intensive universities remains a critical global challenge. This study investigates the distinctive characteristics of expert teachers—exemplary faculty in research universities—addressing their developmental trajectories and motivational mechanisms within prevailing incentive systems that prioritize research productivity over pedagogical excellence. Employing grounded theory methodology, we conducted iterative coding of 20,000-word interview transcripts from 13 teaching-awarded professors at Chinese “Double First-Class” universities. Key findings reveal the following: (1) Compared to the original K-12 expert teacher model, university-level teaching experts exhibit distinctive disciplinary mastery—characterized by systematic knowledge structuring and cross-disciplinary integration capabilities. (2) Their developmental trajectory transcends linear expertise acquisition, instead manifesting as a problem-solving continuum across four nonlinear phases: career initiation, dilemma adaptation, theoretical consciousness, and leadership expansion. (3) Sustainable teaching excellence relies fundamentally on teachers’ professional passion, sustained through a virtuous cycle of high-quality instructional engagement and external validation (including positive student feedback, institutional recognition, and peer collaboration). Universities must establish comprehensive support systems—including (a) fostering a supportive and flexible learning atmosphere, (b) reforming evaluation mechanisms, and (c) facilitating interdisciplinary collaboration through teaching development communities—to institutionalize this developmental ecosystem. Full article

In this study, we investigate the nonlinear dynamical behavior of a one-dimensional linear piecewise-smooth discontinuous (LPSD) map with a negative slope, motivated by its occurrence in systems exhibiting discontinuities, such as power electronic converters. The objective of the proposed research is to develop an analytical approach. Analytical conditions are derived for the existence of stable period-1 and period-2 orbits within the third quadrant of the parameter space defined by slope coefficients $a<0$ and $b<0$. The coexistence of multiple attractors is demonstrated. We also show that a novel class of orbits exists in which both points lie entirely in either the left or right domain. These orbits are shown to eventually exhibit periodic behavior, and a closed-form expression is derived to compute the number of iterations required for a trajectory to converge to such orbits. This method also enhances the ease of analyzing system stability by mapping the state–variable dynamics using a non-smooth discontinuous map. The analytical findings are validated using bifurcation diagrams, cobweb plots, and basin of attraction visualizations. Full article

This study aims to clarify how different types of motivation influence employee retention by identifying the distinct roles of intrinsic and extrinsic factors in shaping job satisfaction, particularly under varying levels of urban stress and generational identity. Drawing on Herzberg’s Two-Factor Theory and Self-Determination Theory, we distinguish between intrinsic drivers (e.g., autonomy, achievement) and extrinsic hygiene factors (e.g., pay, stability). Using survey data from 356 Chinese employees and applying PLS-SEM with a moderated mediation design, we investigate how urbanization and Generation Z moderate these relationships. Results show that intrinsic motivation enhances satisfaction, especially in urban settings, while extrinsic factors negatively affect satisfaction when perceived as insufficient or unfair. Job satisfaction mediates the relationship between motivation and retention, although this effect is weaker among Generation Z employees. These findings refine motivational theories by demonstrating how environmental pressure and generational values jointly shape employee attitudes. The study contributes a context-sensitive framework for understanding retention by integrating individual motivation with macro-level moderators, offering practical implications for managing diverse and urbanizing labor markets. Full article

The organization of consciousness is described through increasingly rich theoretical models. We review evidence that working memory capacity—essential to generating consciousness in the cerebral cortex—is supported by dual limbic memory systems. These dorsal (Papez) and ventral (Yakovlev) limbic networks provide the basis for mnemonic processing and prediction in the dorsal and ventral divisions of the human neocortex. Empirical evidence suggests that the dorsal limbic division is (i) regulated preferentially by excitatory feedforward control, (ii) consolidated by REM sleep, and (iii) controlled in waking by phasic arousal through lemnothalamic projections from the pontine brainstem reticular activating system. The ventral limbic division and striatum, (i) organizes the inhibitory neurophysiology of NREM to (ii) consolidate explicit memory in sleep, (iii) operating in waking cognition under the same inhibitory feedback control supported by collothalamic tonic activation from the midbrain. We propose that (i) these dual (excitatory and inhibitory) systems alternate in the stages of sleep, and (ii) in waking they must be balanced—at criticality—to optimize the active inference that generates conscious experiences. Optimal Bayesian belief updating rests on balanced feedforward (excitatory predictive) and feedback (inhibitory corrective) control biases that play the role of prior and likelihood (i.e., sensory) precision. Because the excitatory (E) phasic arousal and inhibitory (I) tonic activation systems that regulate these dual limbic divisions have distinct affective properties, varying levels of elation for phasic arousal (E) and anxiety for tonic activation (I), the dual control systems regulate sleep and consciousness in ways that are adaptively balanced—around the entropic nadir of E–I criticality—for optimal self-regulation of consciousness and psychological health. Because they are emotive as well as motive control systems, these dual systems have unique qualities of feeling that may be registered as subjective experience. Full article

Introduction and Objectives: Risk-sharing agreements (RSAs) have emerged as a key strategy for financing high-cost medical technologies while ensuring financial sustainability. These payment mechanisms mitigate clinical and financial uncertainties, optimizing pricing and reimbursement decisions. Despite their widespread adoption globally, Latin America has reported limited implementation, particularly for high-cost medical devices. This study aims to share insights from designing RSAs in the form of Outcome Protection Programs (OPPs) for medical devices in Latin America from the perspective of a medical devices company. Methods: The report follows a structured approach, defining key OPP dimensions: payment base, access criteria, pricing schemes, risk assessment, and performance incentives. Risks were categorized as financial, clinical, and operational. The framework applied principles from prior models, emphasizing negotiation, program design, implementation, and evaluation. A multidisciplinary task force analyzed patient needs, provider motivations, and payer constraints to ensure alignment with health system priorities. Results: Over two semesters, a panel of seven experts from the manufacturer designed n = 105 innovative payment programs implemented in Argentina (n = 7), Brazil (n = 7), Colombia (n = 75), Mexico (n = 9), Panama (n = 4), and Puerto Rico (n = 3). The programs targeted eight high-burden conditions, including Coronary Artery Disease, atrial fibrillation, Heart Failure, and post-implantation arrhythmias, among others. Private providers accounted for 80% of experiences. Challenges include clinical inertia and operational complexities, necessitating structured training and monitoring mechanisms. Conclusions: Outcome Protection Programs offer a viable and practical risk-sharing approach to financing high-cost medical devices in Latin America. Their implementation requires careful stakeholder alignment, clear eligibility criteria and endpoints, and robust monitoring frameworks. These findings contribute to the ongoing dialogue on sustainable healthcare financing, emphasizing the need for tailored approaches in resource-constrained settings. Full article

The retina offers a unique window into both ocular and systemic health, motivating the development of AI-based tools for disease screening and risk assessment. In this study, we present a comprehensive evaluation of six state-of-the-art deep neural networks, including convolutional neural networks and vision transformer architectures, on the Brazilian Multilabel Ophthalmological Dataset (BRSET), comprising 16,266 fundus images annotated for multiple clinical and demographic labels. We explored seven classification tasks: Diabetes, Diabetic Retinopathy (2-class), Diabetic Retinopathy (3-class), Hypertension, Hypertensive Retinopathy, Drusen, and Sex classification. Models were evaluated using precision, recall, F1-score, accuracy, and AUC. Among all models, the Swin-L generally delivered the best performance across scenarios for Diabetes (AUC = 0.88, weighted F1-score = 0.86), Diabetic Retinopathy (2-class) (AUC = 0.98, weighted F1-score = 0.95), Diabetic Retinopathy (3-class) (macro AUC = 0.98, weighted F1-score = 0.95), Hypertension (AUC = 0.85, weighted F1-score = 0.79), Hypertensive Retinopathy (AUC = 0.81, weighted F1-score = 0.97), Drusen detection (AUC = 0.93, weighted F1-score = 0.90), and Sex classification (AUC = 0.87, weighted F1-score = 0.80). These results reflect excellent to outstanding diagnostic performance. We also employed gradient-based saliency maps to enhance explainability and visualize decision-relevant retinal features. Our findings underscore the potential of deep learning, particularly vision transformer models, to deliver accurate, interpretable, and clinically meaningful screening tools for retinal and systemic disease detection. Full article

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

In distance learning environments, learner engagement directly impacts attention, motivation, and academic performance. Signs of fatigue, negative affect, or critical remarks can warn of growing disengagement and potential dropout. However, most existing approaches rely on a single modality, visual or text-based, without providing a general view of learners’ cognitive and affective states. We propose a multimodal system that integrates three complementary analyzes: (1) a CNN-LSTM model augmented with warning signs such as PERCLOS and yawning frequency for fatigue detection, (2) facial emotion recognition by EmoNet and an LSTM to handle temporal dynamics, and (3) sentiment analysis of feedback by a fine-tuned BERT model. It was evaluated on three public benchmarks: DAiSEE for fatigue, AffectNet for emotion, and MOOC Review (Coursera) for sentiment analysis. The results show a precision of 88.5% for fatigue detection, 70% for emotion detection, and 91.5% for sentiment analysis. Aggregating these cues enables an accurate identification of disengagement periods and triggers individualized pedagogical interventions. These results, although based on independently sourced datasets, demonstrate the feasibility of an integrated approach to detecting disengagement and open the door to emotionally intelligent learning systems with potential for future work in real-time content personalization and adaptive learning assistance. Full article

Obstructive sleep apnea (OSA) is a prevalent disorder linked to metabolic complications such as diabetes and cardiovascular disease. By fragmenting normal sleep architecture, OSA perturbs the growth hormone/insulin-like growth factor (GH/IGF) axis and alters circulating levels of IGF-binding proteins (IGFBPs). A prior clinical observation of elevated IGFBP4 in OSA patients motivated the present investigation in a controlled animal model. Building on the previously reported protocol, OSA was induced in male C57BL/6 mice (9–12 weeks old) through intralingual injection of polytetrafluoroethylene (PTFE), producing tongue hypertrophy, intermittent airway obstruction, and hypoxemia. After 8–10 weeks, the study assessed (1) hypoxia biomarkers—including HIF-1α and VEGF expression—and (2) neurobehavioral outcomes in anxiety and cognition using the open-field and novel object recognition tests. PTFE-treated mice exhibited a significant increase in circulating IGFBP4 versus both baseline and control groups. Hepatic Igfbp4 mRNA was also upregulated. Behaviorally, PTFE mice displayed heightened anxiety-like behavior and impaired novel object recognition, paralleling cognitive deficits reported in human OSA. These findings validate the PTFE-induced model as a tool for studying OSA-related hypoxia and neurocognitive dysfunction, and they underscore IGFBP4 as a promising biomarker and potential mediator of OSA’s systemic effects. Full article

Introduction: The main purpose of this study was to test a hierarchical model of motivation that integrates Achievement Goal Theory and Self-Determination Theory to explain and predict exercise adherence. Method: In total, 2180 exercisers (1020 female, 1160 male) aged between 18 and 60 years, from different gyms and health clubs, completed several scales validated in exercise settings, regarding perceived motivational climate, basic psychological need satisfaction, behavioral regulation, and exercise adherence. For the last measure, weekly computer access to a control system over a 6-month period before and after data collection was consulted. Results: Through structural equation models (SEM), it was verified that (1) task-involving climate positively predicted basic psychological needs. In turn, the satisfaction of these needs predicted autonomous motivation, which led to a positive prediction of adherence; (2) a small variation in exercise adherence was explained by the motivational model under analysis. Nevertheless, models significantly improved their analytical power when past adherence was inserted in the model increasing the explained variance in future behavior from 9.2% to 64%. Conclusions: In conclusion, autonomous motivation can predict people’s exercise adherence, and past behavior increases that predictive effect. The present study brings scientific evidence to the popular saying “tell me what you’ve done and, and I’ll predict what you’ll do”. Full article

Self-regulated learning (SRL) has been widely recognized as a critical skill for academic success in online and blended learning contexts. However, many students experience difficulty in effectively applying SRL strategies in the absence of structured instructional guidance. To address this challenge, this study developed and implemented a learning management system integrated with SRL support (SRL-LMS), specifically designed for the online component of a blended learning course. The SRL-LMS consisted of two sections: a conventional course content section and a SRL training section designed to support students in applying SRL strategies. A quasi-experimental design was adopted with 69 college students assigned to either an experimental group, with access to both course and SRL sections, or a control group, which accessed only the course section. Results indicated that students in the experimental group reported higher levels of self-regulation and showed more frequent and diverse application of SRL strategies compared to the control group. In terms of academic performance, the experimental group performed significantly better than the control group on the first exam, though no significant difference was observed on the second exam. These results highlight the effectiveness of structured SRL interventions within digital learning platforms for improving students’ self-regulatory behaviors. Future implementations should address cognitive load and incorporate strategic approaches to sustain student motivation. This study advances current SRL intervention designs and offers valuable insights for educators and instructional designers aiming to integrate targeted SRL supports in online and blended learning environments. Full article

In today’s fast-changing world, the higher education system must evolve to enhance the quality of learning and teaching. Fulfilling the role of a university is a major challenge. Universities must implement strategies that place the student at the center of their concerns; so, these strategies must be designed for and by the student. However, the high university dropout rate is one of the current problems faced by many universities. This suggests that there are some issues that hinder the learning process. Several studies have highlighted the advantage of artificial intelligence (AI) technologies in providing explorative and predictive analyses that explain why students are dropping out, with the aim of improving the quality of teaching and providing an integrated learning environment. This paper proposes a framework that predicts student dropout rates using machine learning techniques, based on data collected from various sources. Data collection was carried out between 2022 and 2024. We used a quantitative analysis method employed through a questionnaire distributed to 120 students (aged 18–26) from open access faculties of a Moroccan public university to identify the factors leading to an increase in university dropout rates. We discuss the impact of selected variables, and the findings show that several factors are related to university dropout rates, such as social background, psychological and health problems, insufficient motivation of professors, limited perspective on educational programs, changes in language and teaching methodologies, absenteeism, student attitude, and a lack of interaction between professors and students. Full article

Background/Objectives: Legislative changes in educational systems have influenced how student learning is understood and promoted. In physical education (PE), there has been a shift from behaviorist models to more holistic approaches. In this context, physical literacy (PL) is presented as an emerging pedagogical model in school PE, aimed at fostering students’ motor competence in a safe, efficient, and meaningful way. The aim of this study is to analyze the origins, foundations, methodological elements, and educational value of PL, highlighting its potential to promote holistic and inclusive learning as the basis for an emerging PL model. Methods: A narrative review was conducted through a literature search in the Web of Science, PubMed, Scopus, and SportDiscus databases up to June 2025, focusing on scientific literature related to PL and PE. The analysis included its historical background, philosophical and theoretical foundations, and the key methodological elements and interventions that support its use as a pedagogical model. Results/Discussion: The findings indicate that the PL model can be grounded in key principles, such as student autonomy, teacher training, connection with the environment, inclusion, and collaboration. Additionally, motivation, enjoyment, creativity, and continuous assessment are identified as essential components for effective implementation. Moreover, this model not only guides and supports teachers in the field of PL but also promotes comprehensive benefits for students at the physical, cognitive, affective, and social levels, while encouraging increased levels of physical activity (PA). Conclusions: PL is understood as a dynamic and lifelong process that should be cultivated from early childhood to encourage sustained and active participation in PA. As a pedagogical model, PL represents an effective tool to enhance student learning and well-being in PE classes. Full article