Search Results (3,917)

Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment. Full article

Instructor clarity is a central component of instructional communication and has been consistently associated with positive academic outcomes; however, less evidence exists regarding the mechanisms through which it influences student interest in higher education contexts. From a sustainability perspective, understanding these mechanisms is essential for promoting inclusive, equitable, and high-quality learning environments in line with global educational goals. This study fills a gap in the literature by examining, through multivariate models, the relationship between instructor clarity and student interest as mediated by academic satisfaction and state motivation, within the framework of the Rhetorical/Relational Goals Theory in the Spanish higher education context. A quantitative, cross-sectional, ex post facto research design was employed using a survey method. A non-probabilistic convenience sampling approach was used. A total of 258 undergraduate students from the University of Extremadura enrolled in the Bachelor’s Degree in Early Childhood Education and the Bachelor’s Degree in Primary Education participated in the study. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), including an assessment of the model’s predictive capability. The results indicated that instructor clarity was positively associated with academic satisfaction, state motivation, and student interest, with the first two variables acting as complementary mediators in these relationships. Among the predictors, state motivation emerged as the strongest determinant of student interest, whereas the direct effect of instructor clarity was comparatively weaker, highlighting the relevance of indirect pathways. The model demonstrated high predictive power and strong predictive validity with respect to student interest. Overall, the findings indicate that instructor clarity influences student interest primarily through its indirect effects on academic satisfaction and state motivation, emphasizing the importance of fostering motivational processes as key mechanisms linking teaching practices with students’ learning outcomes in higher education. Finally, it should be noted that the findings are directly aligned with Sustainable Development Goal (SDG) 4, contributing to Target 4.3 by enhancing the effectiveness and equity of teaching in higher education, as well as supporting the development of sustainable learning environments that foster long-term student engagement and academic persistence. Full article

Background: Professional identity formation (PIF) and wellbeing are increasingly being recognised in postgraduate Family Medicine (FM) education. Role models are central to both, yet traditional learning activities often struggle to implement them effectively. Podcasts offer a flexible medium that may support these goals. This study examines the potential of postgraduate medical education (PGME) podcasts, such as the European Young Family Doctor’s Movement (EYFDM) podcast, to promote PIF and wellbeing. Methods: This mixed-methods study analyses podcast use, role modelling effects, and PIF among young general practitioners (GPs). In 2024, 57 participants, including students, FM trainees, and specialists, completed an online questionnaire with quantitative and qualitative items. Descriptive and analytical statistics were combined with qualitative content analysis (Kuckartz). Sentiment analysis was conducted using artificial intelligence, and triangulation enhanced credibility. Results: Within the trainees and specialists of the study population, most participants (70%; 32/46 SPs) reported regularly using podcasts for PGME, and particularly young female GPs in Western Europe. In our study population, 90% (27/30 SPs) agreed that the podcasts broadened their perspective on professional opportunities in FM. Many participants reported reflections on potential career pathways and PIF. Exposure to role models significantly increased motivation to work in FM (χ² (1) = 10.7, p < 0.001). Conclusions: Podcasts may help address gaps in affective competency training, including wellbeing and PIF, while integrating easily into busy routines. Findings suggest a positive influence on career attitudes, with role modelling supporting PIF and motivation in FM. Full article

This paper presents a unified analytical and computational framework for the study of typhoid fever transmission dynamics governed by a Caputo fractional-order compartmental model of order $\kappa \in (0,1]$. The population is stratified into five epidemiological classes, namely susceptible $\left(\mathcal{S}\right)$, asymptomatic $\left(\mathcal{A}\right)$, symptomatic $\left(\mathcal{I}\right)$, hospitalised $\left(\mathcal{H}\right)$, and recovered $\left(\mathcal{R}\right)$, and the governing system explicitly incorporates asymptomatic transmission, treatment dynamics, and temporary immunity with waning. The use of the Caputo fractional derivative is motivated by the well-documented existence of chronic asymptomatic Salmonella Typhi carriers, whose heavy-tailed sojourn times in the carrier state are naturally encoded by the Mittag–Leffler waiting-time distribution arising from the fractional operator. A complete qualitative analysis of the fractional system is carried out: the basic reproduction number ${\mathcal{R}}_0$ is derived via the next-generation matrix method; local and global asymptotic stability of both the disease-free equilibrium ${\mathcal{E}}_0$ (when ${\mathcal{R}}_0\le 1$) and the endemic equilibrium ${\mathcal{E}}^{*}$ (when ${\mathcal{R}}_0>1$) are established using fractional Lyapunov theory and the LaSalle invariance principle; and the normalised sensitivity indices of ${\mathcal{R}}_0$ are computed to identify transmission-amplifying and transmission-suppressing parameters. Existence, uniqueness, and Ulam–Hyers stability of solutions are established via Banach and Leray–Schauder fixed-point arguments. To complement the analytical results, a fractional physics-informed neural network ($\mathcal{PINN}$) framework is developed to simultaneously reconstruct compartmental trajectories and identify unknown biological parameters from sparse synthetic observations. $\mathcal{PINN}$ embeds the L1-Caputo discretisation directly into the training residuals and employs a four-stage Adam–L-BFGS optimisation strategy to recover five trainable parameters $\Theta$ = $\{\varphi ,\mu ,\sigma ,\psi ,\beta \}$ across three fractional orders $\kappa \in \{1.0,0.95,0.9\}$. The estimated parameters show strong agreement with the true values at the classical limit $\kappa =1.0$ ($\mathrm{MAPE}=2.27\%$), with the natural mortality rate $\mu$ recovered with $\mathrm{APE}\le 0.51\%$ and the transmission rate $\beta$ with $\mathrm{APE}\le 3.63\%$ across all fractional orders, confirming the structural identifiability of the model. Pairwise correlation analysis of the learned parameters establishes the absence of equifinality, validating that $\beta$ can be reliably included in the trainable set. Noise robustness experiments under Gaussian perturbations of $1\%$, $3\%$, and $5\%$ demonstrate graceful degradation ($\mathrm{MAPE}$: $0.82\%\to 3.10\%\to 7.31\%$), confirming the reliability of the proposed framework under realistic observational conditions. Full article

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) provides a powerful framework to probe and modulate human cortical and corticospinal excitability. In recent years, brain state-dependent EEG–TMS paradigms have gained increasing interest by synchronizing stimulation to ongoing neural activity. However, traditional approaches relying on single oscillatory features or fixed thresholds have yielded heterogeneous and often inconsistent results, motivating the adoption of machine learning (ML) and artificial intelligence (AI) methods to model brain state in a multivariate, data-driven manner. This review synthesizes current ML and deep learning (DL) approaches aimed at predicting cortical and corticospinal excitability from pre-stimulus EEG. We contextualize these methods within brain state-dependent EEG–TMS frameworks based on oscillatory phase, power, and network-level features, and within evolving definitions of brain state that move beyond local biomarkers toward distributed, large-scale, and dynamically evolving neural representations. The reviewed studies span feature-engineered models, data-driven decoding approaches, and emerging adaptive closed-loop frameworks. Finally, we discuss key methodological challenges, translational barriers, and future directions toward personalized, interpretable, and fully closed-loop neuromodulation systems. Full article

Generative artificial intelligence (AI) is increasingly used in higher education as an interactive tutoring partner rather than a passive information tool. While AI offers opportunities to support learning, concerns remain regarding cognitive offloading, reduced engagement, and unreflective use. Although instructional scaffolding is a well-established design principle for supporting complex learning, its role in shaping cognitive and metacognitive processes in AI-supported settings remains underexplored. This quasi-experimental pre–post study examined how varying levels of scaffolding influence learning outcomes and motivational, cognitive and metacognitive processes during AI-tutored learning. A total of 175 first-semester students from two faculties and diverse academic backgrounds completed the same academic task within a four-hour university session under one of three conditions: (1) full scaffolding, including a structured prompting template based on the Goal–Context–Constraints (GCC) strategy, iterative refinement, and reflective guidance; (2) light scaffolding, including the GCC prompting template; or (3) no scaffolding template as the control condition. Measures included knowledge gain, motivation, cognitive load, critical thinking, and reflective use. Data were analysed using ANOVAs, ANCOVAs, regression models, and PROCESS moderation and mediation analyses. Across the conditions, students showed significant gains in knowledge, critical thinking, and reflective use, while motivation remained stable and intrinsic and extraneous cognitive load decreased; no significant differences between scaffolding conditions were observed. The scaffolding conditions did not produce significant interaction effects, although descriptive trends suggested higher gains in higher-order knowledge under scaffolded conditions. Overall, the findings suggest that short-term learning gains in AI-supported settings may not depend on scaffolding intensity alone, but rather on how learners engage with AI during the learning process. Full article

Vegetation carbon stock is a key component of the terrestrial carbon cycle and supports climate-change mitigation and carbon-neutrality strategies. While field inventories provide accurate references, they are constrained by cost and limited scalability, motivating the rapid adoption of remote sensing for large-scale spatial estimation and mapping. However, the literature lacks a consolidated bibliometric and critical synthesis focused on above-ground vegetation carbon stock estimation. Therefore, this review aims to provide a quantitative overview of publication trends, synthesise methodological developments, and identify key research gaps in remote-sensing-based above-ground vegetation carbon stock estimation. A total of 1825 Web of Science records (2015–2024) were retrieved, of which 763 were included for bibliometric mapping using VOSviewer version 1.6.20 and CiteSpace version 6.3.R2, complemented by a critical review of 32 high-quality studies. Results indicate a shift from passive optical and single-index approaches toward active sensing and multi-sensor, multi-platform integration, alongside broad uptake of machine learning and an emerging dominance of deep learning for nonlinear modelling and feature learning. Research attention is expanding beyond forests to non-forest ecosystems, yet challenges persist in spatial resolution, validation data availability, and cross-biome generalizability. This review summarizes methodological trajectories and identifies priorities for robust, transferable above-ground carbon estimation. Full article

Background: In 2021, an Australian Hospital Nursing Research Hub sponsored 13 healthcare staff to complete the Graduate Certificate (Clinical Redesign), to build capability in health service improvement though work-integrated learning (WIL). Healthcare professionals undertaking workplace-based WIL likely experience significant challenges including balancing professional and student roles and aligning work with academic requirement. These pressures were likely intensified during the Coronavirus disease 2019 (COVID-19) pandemic. This study aimed to explore and understand the experiences of hospital healthcare staff completing WIL redesign projects, including the impacts of COVID-19. Methods: A qualitative descriptive inquiry approach was used to explore individual student experiences. Thirteen staff, mostly nurses, who enrolled in the 2021 course were invited to participate. Online semi-structured interviews were conducted. Data were analyzed using a general inductive thematic analysis approach. Results: Four participants (36%) took part; all were female and working full-time. Five main themes were identified that centered around: COVID-19, Support, Motivation, Alignment and Relevance, and Success. Conclusions: Novel insights include the need to reconceptualize “success” to improve student experience, the critical role of organizational–university–student alignment in enabling WIL studies, and the unique pressures of completing WIL during crisis conditions that direct impact the health sector, such as COVID-19. Although not generalizable, these findings are likely to be important considerations more broadly to strengthen WIL design, support and student experiences, ultimately enhancing health service staff capability to lead quality improvement in the workplace. Full article

In recent years, Visual Education has emerged as an innovative and interdisciplinary teaching approach aimed at promoting meaningful learning through the conscious use of visual tools and languages. This educational paradigm helps to facilitate the understanding of complex concepts, translating them into clear and intuitive visual representations, while enhancing memorisation skills, critical information processing and the practical application of acquired knowledge. This systematic review, conducted according to the PRISMA (2020) protocol, analyses the most recent empirical evidence on the effectiveness of Visual Education in educational contexts. The main objective is to assess how the intentional use of visual tools—images, concept maps, educational videos, interactive digital materials, and virtual manipulatives—contributes to enhancing learning processes and developing transversal skills. Through a comparative analysis of fourteen international contributions published between 2020 and 2025, selected from the Scopus, Web of Science and EBSCO databases, the research highlights how Visual Education significantly influences the improvement of academic performance, motivation and cognitive and emotional engagement of students. The results also confirm the inclusive function of visual teaching, which can encourage participation, self-esteem and cooperation even in individuals with special educational needs. The discussion emphasises the need for the systematic integration of Visual Education into school curricula as a strategy to enhance soft skills and promote more equitable, effective learning geared towards the integral development of the individual. Full article

Few-shot image classification requires models to generalize from limited labeled examples. While metric-based approaches such as Prototypical Networks have demonstrated strong performance, they rely exclusively on visual features and ignore the rich semantic information encoded in class names. This paper presents a systematic empirical study investigating the interaction between visual and semantic modalities in few-shot learning. We present Asymmetric Cross-Modal Prototypical Networks(ACM-ProtoNet), a controlled experimental framework which augments standard prototypical learning with frozen CLIP text encoders to incorporate zero-cost linguistic priors. Our method explicitly models the symmetric relationshipbetween visual and semantic modalities through learnable projection heads that map both image and text features into a shared embedding space. Image and text prototypes are fused via a learnable scalar gate $\alpha \in (0,1)$, allowing adaptive balancing of modalities. Under our experimental setup (frozen CLIP encoders, scalar fusion gate, simple template-based prompts), we observe an asymmetric pattern in comprehensive ablation studies on miniImageNet: cross-modal integration yields a statistically significant improvement in five-shot ($+2.12$ pp, $p=0.03125$, Wilcoxon signed-rank test over five seeds) but not in one-shot ($-0.09$ pp, n.s.) learning. Our key contribution is not achieving state-of-the-art accuracy but rather providing controlled empirical evidence about cross-modal interaction patterns under specific design constraints. Further analysis shows that: (1) structured semantic information is essential—random text features harm performance by $7.4$–$8.1$ percentage points; (2) projection heads provide asymmetric benefits, more critical in one-shot ($-2.85$ pp when removed) than in five-shot learning ($-0.74$ pp); (3) text-only prototypes achieve near-random performance (≈20%), suggesting that semantics alone are insufficient in our setup; (4) shuffled-class-name ablation confirms genuine semantic binding, where randomly permuting class-name assignments causes consistent degradation (five-shot: $-5.74$ pp, $p<0.001$; one-shot: $-3.83$ pp, $p<0.001$ across five seeds). These findings, specific to our simple fusion design, reveal an asymmetric pattern that is equally consistent with two hypotheses: (i) semantic priors may require sufficient visual context to be useful, or (ii) our scalar fusion gate may lack the capacity to leverage text in the extreme low-data regime of one-shot learning. This ambiguity motivates future work with more expressive fusion mechanisms and stronger text representations. Full article

Background: The literature shows inconclusive results from utilizing motivational interviewing (MI) in indigenous populations to address early childhood caries (ECC). Great Beginnings for Healthy Native Smiles (GBHNS) (NIDCR U01DE028508), a community focused oral health (OH) intervention, was utilized alongside adapted MI techniques to promote OH care and education at home. Methods: The intervention was conducted by local Community Health Representatives (CHRs) from the two partnered indigenous communities. Reflecting on the years-long MI training and CHRs’ concerns, GBHNS conducted post-intervention semi-structured interviews with all MI staff regarding their experiences with MI. This paper uses participant observation, semi-structured interviewing, and inductive and deductive qualitative coding and analysis. Results: Thematic analysis was used to explore lessons learned and future research recommendations for interventions considering the use of MI. Generally considered a person-centered approach, MI reinforces Western psychological frameworks and practices which may disrupt local communicative practices and values. Conclusions: Specifically, interdisciplinary pre-intervention community assessments are recommended to ensure acceptability, relevance and appropriateness through attention to local communicative practices. Full article

In this paper, we propose a Newton-inspired black-box optimization algorithm for multiobjective optimization in constrained ergodic Markov chain environments. The method is motivated by challenges in application areas, where decision-making under uncertainty and limited access to structural information is pervasive. A central contribution of the proposed algorithm is the complexity analysis, which yields substantial computational advantages over conventional optimization approaches. Operating in a purely black-box setting, the algorithm relies exclusively on function evaluations and derivative approximations, without requiring explicit knowledge of the objective function’s internal structure. To approximate system dynamics, we employ an Euler-based scheme that enhances the scalability and adaptability of convex optimization problems. While Markov chains are seldom leveraged in black-box optimization, we demonstrate that constrained ergodic Markov chains constitute a powerful and underexplored modeling framework for learning and decision-making under structural constraints. We provide a complexity analysis and illustrate the effectiveness of the proposed method through a numerical example, highlighting its potential to advance applications in multiobjective optimization and decision-making. Full article

The use of Unmanned Aerial Vehicles (UAVs) has become quite popular in several applications during the last few years. Their spread is motivated by the flexibility of usage of UAVs and by their ability to automatically execute several tasks, mostly thanks to the availability of Global Navigation Satellite Systems (GNSSs), which usually allow reliable outdoor localization of aerial vehicles. However, the extension of task automatic execution indoors, and in other challenging working conditions for the GNSS, requires an alternative positioning system able to compensate for the unreliability or unavailability of GNSS in those cases. To this end, additional sensors are usually considered. Among them, cameras are probably the most popular ones. The most common case of a vision-based positioning system is a camera mounted on a moving platform used to determine its ego-motion in a dead-reckoning approach, i.e., visual odometry. Although this solution is affordable and does not require the installation of any infrastructure, it enables absolute positioning of the camera, i.e., of the UAV, only if certain landmarks, with known position, are visible in the flying area. In contrast, this work considers the use of external cameras installed in the flying area to track the UAV movements. This approach is similar to the one implemented in motion capture systems as well, where a set of static cameras is used to triangulate some target positions using calibrated cameras. Instead, this work investigates the use of vision and machine learning tools to (i) extract the UAV position from each video frame and (ii) estimate its 3D position. Estimation of the 3D UAV position is performed with a single camera, exploiting machine learning tools in order to avoid the need for camera calibration. Performance analysis is provided for a dataset collected at the Agripolis campus of the University of Padua. Full article

Gamification is widely used to enhance student engagement in higher education, yet its effects in English for Specific Purposes (ESP) contexts, particularly from a Self-Determination Theory perspective, remain underexplored. This study examines whether gamification in a Moodle-based ESP course is associated with differences in students’ course satisfaction. A total of 94 undergraduate students participated in a quasi-experimental study, enrolling in either a gamified or non-gamified course format. Gamification was implemented using the Level Up plugin and H5P interactive activities. Students’ perceptions were measured using selected items from the Course Satisfaction Questionnaire. Non-parametric analyses (Mann–Whitney U tests with Holm–Bonferroni correction) were applied. Results show statistically significant differences favoring the gamified format in engagement and enjoyment, motivation to participate, and willingness to recommend the course. Differences in perceived competence and support for individual learning were positive but not statistically significant. These findings suggest that gamification in ESP is associated with more favorable motivational and affective dimensions of course satisfaction, while effects on broader learning-related perceptions remain less conclusive. This study contributes by providing evidence from a controlled LMS-based implementation and highlights the importance of theoretically grounded gamification design. Full article

Social media environments and meme-based communication are increasingly incorporated into nursing education, yet it remains unclear whether students respond uniformly to digitally embedded instructional strategies. This study examined whether alignment between meme-based instruction and perceived social media learning utility differentiates motivation, perceived academic impact, and demand for educator presence among undergraduate nursing students. A cross-sectional study was conducted with 458 nursing students from Spanish universities who completed a structured questionnaire assessing perceptions of meme-based instruction, social media learning utility, motivation, perceived academic impact, and expectations of educator presence. Hierarchical regression models examined interaction effects, quadrant comparisons were analysed using Kruskal–Wallis tests, and a sequential mediation model evaluated indirect pathways. Students reported high endorsement of meme-based instruction (M = 4.44, SD = 0.80) and social media learning utility (M = 4.15, SD = 0.80). However, substantial divergence emerged across alignment profiles. Students showing high alignment between meme endorsement and perceived social media utility tended to report higher motivation and different expectations of educator presence, whereas perceived academic impact was primarily explained by additive effects. These findings suggest that digital instructional innovations may not generate entirely homogeneous responses across students and that alignment between instructional format and perceived learning utility is associated with differences in motivational activation and instructional expectations in undergraduate nursing education. Full article