Search Results (13,881)

Crafting STEM teaching into meaningful experiences can transform facts into knowledge. Immersive virtual reality (IVR) represents a significant pedagogical disruption, offering novel modalities of engagement with science content, extending beyond passive reception towards enhanced critical inquiry, reflective evaluation, and the cultivation of higher-order thinking skills. This study investigated how 20 Maltese students (mean age 12) adjusted their perceptions and acceptance of IVR when encountering it for the first time in formal STEM education. A quasi-experimental design was employed over six weeks, with data collected through pre- and post-intervention questionnaires. The analytical framework combined the Technological Frames of Reference (TFR) and Technology Acceptance Model (TAM) to capture perceptual snapshots and attitudinal shifts. While IVR initially stimulated enthusiasm, sustained exposure prompted critical reflections on its potential and limitations, particularly in relation to subject relevance, peer communication, and ease of use. Such deliberations are themselves suggestive indicators of reflective engagement. Rather than being demonstrated evidence of cognitive skill development, they are consistent with the early exercise of analytical and evaluative reasoning. These insights underscore the recursive dialog between technology-in-use and user contextualization, revealing how perceptions mature through experience. By examining how young learners engage with emergent technologies, this research highlights education’s role in cultivating adaptability, reflective judgment, and critical thinking capacities—central to innovative pedagogy and support for uncertain futures. Full article

When women choose a contraceptive method, the decision depends not only on what they prefer but also on the quality of the information they encounter online and on how confident they feel about using a method. We examined how exposure to inaccurate online contraceptive information (“digital misinformation”) and uncertainty about contraceptive decisions (“decisional conflict”) related to mental health and quality of life in women with and without a history of abortion. This was a cross-sectional study of 134 women aged 18–42 years attending obstetrics–gynecology or family-planning services at one Romanian tertiary center. Women were grouped as having no prior abortion (n = 41), one prior abortion (n = 53), or repeat abortion (n = 40). We measured a study-specific digital misinformation index, an access barrier index, contraceptive self-efficacy, reproductive autonomy, depression (PHQ-9), anxiety (GAD-7), well-being (WHO-5), quality of life (WHOQOL-BREF), and current use of highly effective contraception. We compared groups and used regression, mediation, and exploratory profiling. Women with repeat abortion reported the most online misinformation (10.0 ± 2.0), the most decisional conflict (43.9 ± 9.3), the lowest quality of life (61.3 ± 5.6), and the lowest use of highly effective contraception (45.0%). More misinformation and more access barriers were each associated with greater decisional conflict, while higher self-efficacy was associated with less. In this cross-sectional sample, online misinformation and decisional uncertainty were associated with reproductive vulnerability more closely than abortion count alone. Findings are associational and require prospective confirmation. Full article

Nystagmus, the involuntary rhythmic oscillation of the eyes, is a critical diagnostic marker in vestibular medicine, distinguishing life-threatening central disorders such as stroke from benign peripheral conditions including Benign Paroxysmal Positional Vertigo (BPPV). Despite its clinical importance, accurate nystagmus assessment has long been constrained by expensive infrared video-oculography equipment such as videonystagmography, specialist dependency, and the episodic nature of vestibular symptoms that are often resolved before a clinical encounter. This review synthesizes approximately 50 papers published between 1952 and 2026 across four thematic domains: AI-driven nystagmus analysis, clinical medicine, smartphone and portable hardware innovations, and telemedicine and remote monitoring. On the AI front, classical machine learning models achieve up to 98.77% nystagmus recognition accuracy using ensemble methods, while deep learning frameworks spanning CNNs, U-Nets, LSTMs, and optical flow networks demonstrate clinical-grade slow-phase velocity measurement equivalent to gold standard video-oculography on standard smartphone RGB video. Large language and vision models including GPT-4V and Gemini 2.0 show early-stage promise as zero-shot triage tools but currently fall well below specialist-level diagnostic accuracy. Concurrently, portable hardware innovations ranging from 3D-printed goggle systems to ARKit-based smartphone applications are narrowing the accessibility gap, while telemedicine frameworks enable ictal recording and cloud-based specialist review outside the clinic. Across all domains, the common barriers to clinical translation are dataset scarcity for rare BPPV subtypes, sensitivity to ambient conditions, and the absence of explainable AI mechanisms. This review maps the current state of the field and identifies multimodal data fusion, prospective clinical validation, and interpretable AI as the critical next steps toward equitable, specialist independent vestibular diagnostics. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

Background/Objectives: The objective was to describe the one-year outcomes of salvage trabeculectomy (TLE) in eyes with persistent elevation of intraocular pressure (IOP) requiring early surgical intervention after failed minimally invasive glaucoma surgery (MIGS). Methods: This retrospective observational study included 38 eyes of 38 consecutive Japanese patients who underwent TLE within 100 days after Trabectome (TOM) or microhook ab interno trabeculotomy (μTLO) because of uncontrolled IOP despite maximally tolerated medical therapy. Surgical success was defined as (1) IOP reduction ≥30% from baseline, (2) 5 < IOP < 18 mmHg, (3) no additional glaucoma surgery, and (4) no loss of light perception. The Kaplan–Meier method was used to estimate the one-year success rate. Changes in IOP, medication use, best-corrected visual acuity (BCVA), and mean deviation (MD) were analyzed using the Wilcoxon matched-pairs signed-rank test and a linear mixed-effects model. Results: The median interval between MIGS and TLE was 41.5 days (interquartile range, 28–70 days). The one-year surgical success rate was 86.8% (Kaplan–Meier estimate). IOP and medication use were significantly reduced after TLE (p < 0.0001) and remained stable throughout the 12-month follow-up. BCVA did not differ significantly between baseline and 12 months after TLE, whereas a small but statistically significant difference in MD was observed. No serious vision-threatening complications were encountered. Conclusions: TLE performed shortly after failed MIGS achieved substantial IOP reduction with acceptable safety over a one-year follow-up period. TLE may be considered as one of the surgical options in cases where sufficient IOP reduction cannot be achieved after failed MIGS, and no effective alternative treatments are available. Full article

Background/Objectives: Head and neck cancer (HNC) is associated with unique risks of catastrophic health events throughout the care continuum. This scoping review evaluated distinct domains of catastrophic health events in HNC and the associated risk factors to inform the development of risk prediction models. Methods: Five electronic databases were searched to identify peer-reviewed journal articles published from 2015 to 2025. Eligible studies were primary observational studies involving adults (≥18) with HNC that reported a catastrophic (or similar term) health event and assessed the associated risk factors using multivariable regression modelling. Results: Fifty-six studies involving 941,329 patients with HNC were included. Half (n = 28, 50.0%) of these studies were conducted in North America, followed by Asia and Europe (n = 13 each, 23.2%). Four domains of catastrophic health events were identified: sudden or premature deaths (n = 22, 39.3%), severe treatment-related complications (n = 19, 33.9%), unplanned acute care encounters (n = 22, 39.3%), and severe patient-reported symptoms (n = 6, 10.7%). Across all domains, comorbidity was the most repeatedly identified risk factor, followed by treatment characteristics (e.g., surgery duration), older age, and advanced cancer stage. Conclusions: Risk prediction models in HNC should always include variables of comorbidity. The varied measurability of risk factors requires strategic efforts when developing prediction algorithms for different domains of catastrophic health events. Full article

The rapid emergence of generative AI tools is transforming software development. Consequently, software engineering education must adapt to ensure that students not only learn traditional development methods but also understand how to use these new technologies effectively and responsibly. In particular, project-based courses provide an effective setting in which to explore and evaluate the integration of AI assistance into real-world development practices. This paper presents our approach and a user study conducted in the context of a university programming project in which students collaboratively developed computer games. The study investigates how participants used generative AI tools across different phases of the software development process, identifies the tasks for which these tools were perceived as most useful, and analyzes the challenges students encountered. Building on these insights, we further examine a repository-aware, locally deployed large language model (LLM) assistant designed to provide project-contextualized support. The system employs retrieval-augmented generation (RAG) to ground its responses in relevant documentation and source code, thereby enabling a qualitative analysis of model behavior, parameter sensitivity, and common failure modes. These findings deepen our understanding of context-aware AI support in educational software projects and inform the future integration of AI-based assistance into software engineering curricula. Full article

Steel surface defect inspection plays an essential role in maintaining product quality and production safety in industrial manufacturing. However, existing detection methods still encounter difficulties in accurately identifying tiny defects, suppressing interference from complex backgrounds, and balancing detection accuracy with computational cost. To address these challenges, this paper proposes CNS-YOLOv8, an improved defect detection model based on YOLOv8n. First, a C2f_SCConv module is introduced to enhance multi-scale feature extraction and spatial representation capability. Second, a Normalization-based Attention Module (NAM) is embedded after the high-level semantic feature layer to improve the model’s sensitivity to critical defect regions. Third, a SlimNeck structure is adopted to strengthen feature fusion while reducing computational overhead. Experimental results on the NEU-DET dataset demonstrate that CNS-YOLOv8 achieves 83.1% mAP@0.5 and 49.6% mAP@0.5:0.95, surpassing YOLOv8n by 3.9 and 1.2 percentage points, respectively. In addition, comparative experiments show that CNS-YOLOv8 outperforms Faster R-CNN and YOLOv7 in terms of mAP@0.5 while requiring substantially fewer GFLOPs. In general, the proposed method balances detection accuracy and computational efficiency effectively, highlighting its potential for real-time industrial surface defect detection. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

Magnesium–rich environments are frequently encountered in cementitious systems, including the use of high–Mg raw materials in clinker production, cement–clay interfaces relevant to nuclear waste disposal, and exposure of cement–based materials to seawater, where progressive decalcification can substantially alter the structure and durability of calcium aluminosilicate hydrate (C–A–S–H) phases. In this study, density functional theory (DFT) calculations were employed to investigate the combined effects of interlayer and intralayer partial decalcification, Mg²⁺ substitution, and reinforcement with epoxy– and hydroxyl–functionalized reduced graphene oxide (rGO) on the structural stability and elastic properties of alkali charge–balanced C–A–S–H under dry and hydrated conditions. Adsorption–energy calculations reveal thermodynamically favorable interactions between functionalized rGO and silicate hydrate species in the presence of Mg²⁺, with hydroxyl/rGO promoting stronger interfacial stabilization and epoxy/rGO preserving greater graphene lattice integrity. The results demonstrate that Mg²⁺ substitution together with rGO intercalation generally enhances the mechanical response of partially decalcified structures through structural densification and interfacial cohesion. Relative to dry systems, hydration further improves elastic performance, increasing Young’s modulus and bulk modulus by 1–11% and 4–19%, respectively, for interlayer decalcified nanocomposites, while intralayer configurations exhibit stronger but model–dependent enhancements of up to ≈22% and ≈33%. Compared with untreated systems, rGO–treated nan–composites exhibit enhanced stiffness, with Young’s modulus and bulk modulus increasing by up to ≈22% and ≈15%, respectively. Overall, these findings provide atomistic insights into stabilization mechanisms in partially decalcified alkali charge–balanced C–A–S–H systems and identify Mg²⁺–rGO incorporation as a promising strategy for mitigating decalcification–induced degradation in durable low–carbon cementitious nanocomposites. Full article

This study consists of two main components. The first part establishes a seakeeping assessment method, while the second part focuses on hull-form optimization with seakeeping performance as the objective. For the seakeeping analysis, the Lewis conformal mapping method is used to calculate the sectional hydrodynamic coefficients. Strip theory is then applied to obtain the global hydrodynamic coefficients of the hull. The coupled heave and pitch motion responses are calculated and compared with nonlinear time-domain simulation results and experimental data, showing good agreement. A multivariate linear regression model is established to approximate the relationship between the principal hull-form parameters and the heave and pitch RAOs. The comparison between the regression model and strip theory results shows that the prediction error remains within 5%, indicating that the regression model can provide an efficient surrogate objective function for hull-form optimization. The particle swarm optimization (PSO) algorithm is then employed to optimize the hull form, with the ship length, breadth, draft, and block coefficient considered as design variables. To further evaluate the optimized hull, additional calculations are conducted under different Froude numbers and encounter angles. Under head sea conditions with varying Froude numbers, the optimized hull reduces the peak heave RAO by 11.6–31.1% and the peak pitch RAO by 8.6–17.9%. Under different encounter angles at F_r = 0.3, the reductions in peak heave and pitch RAOs are 31.1–33.9% and 16.5–18.8%, respectively. These results demonstrate that the proposed regression assisted PSO optimization framework can effectively reduce the heave and pitch responses of the Wigley hull under the investigated regular wave conditions. Full article

Osteoarticular infections (OAIs) present a significant diagnostic and therapeutic challenge to paediatric clinical practice. When evaluating suspected paediatric OAIs, the principal pathogens commonly considered are Staphylococcus aureus, Streptococcus pyogenes, Streptococcus pneumoniae and Kingella kingae. However, advances in molecular diagnostic techniques, particularly polymerase chain reaction assays and next-generation sequencing, have considerably improved the detection of less commonly identified pathogens responsible for OAIs. Consequently, familiarity with fastidious, emerging, exposure-related and uncommon bacterial pathogens is essential to ensure accurate diagnoses and appropriate therapeutic interventions. This review summarises bacterial pathogens responsible for OAIs in immunocompetent children that are fastidious, emerging, exposure-related or otherwise less commonly encountered. We describe their microbiological characteristics, clinical phenotypes, diagnostic pitfalls and organism-specific diagnostic strategies. Full article

For students in animal science-related disciplines, animal welfare education may be associated with their understanding of human–animal relationships and willingness to pursue animal-related careers. However, empirical research from developing countries such as China remains limited. To address this gap, we conducted a survey among undergraduates at Northeast Agricultural University to examine the associations of demographic characteristics, educational exposure, and animal-related experiences with animal welfare awareness and career aspirations. A total of 346 valid responses were obtained. The results showed that students who encountered animal welfare through school demonstrated significantly higher levels of self-reported animal welfare awareness (p < 0.001). Binary logistic regression further confirmed that the channel remained significantly associated with the level of awareness (OR = 8.714, p < 0.001). Furthermore, gender and pet-keeping experience were significantly associated with career aspirations in both univariate and logistic regression analysis. In addition, although 50.00% of respondents considered primary school to be the optimal stage for animal welfare education, 81.48% of those exposed through school channels reported their first exposure at university. These findings highlight the need for a progressive animal welfare education framework, transitioning from life education and human–animal relationship training at the primary level to professional practice education in higher education. This approach may help students develop more stable and scientifically informed understanding of animal welfare, as well as a stronger sense of professional identity. Full article

Background: University counselling services provide essential support for students navigating critical academic phases. These services have proven to be successful in promoting long-term psychological well-being and student retention. Methods: In a clinical and health psychology perspective, this study aims to analyze the effectiveness of the Narrative Mediation Path (NMP) counselling groups involving 85 underachieving university students, lagging behind in their studies. The intervention aims at promoting psychological well-being, Reflective Functioning, emotion regulation, and academic engagement in order to improve students’ academic performance and prevent university dropouts. At the beginning and end of counselling the following measures were administered: (a) Clinical Outcomes in Routine Evaluation—Outcome Measure, (b) SInAPSi Academic Engagement Scale, (c) Academic Performance Inventory, (d) Reflective Functioning Questionnaire, (e) Emotion Regulation Questionnaire. Results: The results showed that counselling group participation was associated with significant and clinical improvement in all the outcomes considered. Reflective Functioning showed a trend toward improvement, but this change did not reach statistical significance. Conclusions: Overall, the present study suggested that group counselling could represent a useful service supporting students’ psychological wellbeing and in facing the difficulties encountered during the academic career. Full article

Mosquito control personnel work within health departments, public works, private companies, and other agencies. These essential outdoor workers have highly specialized training and are faced with a variety of potential health and safety hazards (e.g., arthropod bites and stings, exposure to insecticides and other chemicals, working with heavy equipment, noise, heat, solar ultraviolet radiation, slips, trips, and/or falls). Mosquito control personnel undergo employer-provided and other types of training on a variety of topics from regulatory updates to new surveillance and control techniques that are required for safety purposes and to maintain their applicator license. Here, an exploratory baseline survey was conducted among members of the North Carolina Mosquito and Vector Control Association (NCMVCA) and the Virginia Mosquito Control Association (VMCA). There was a 28% response rate so results should be interpreted with caution in this pilot study. Most respondents reported utilizing ultra-low volume insecticide application equipment for controlling adult mosquitoes. Backpack sprayers were utilized by less than half of respondents. Those who reported using respirators showed higher concern about insecticide-related health effects than those who did not use respirators. Outdoor workers encounter various potential hazards and utilize several forms of personal protective equipment to reduce risks. This baseline work can be considered a starting point for implementing and strengthening occupational safety and health awareness and preventive measures for mosquito control workers. Knowledge of health and safety hazards can reduce workplace risk. Full article