Search Results (15,953)

Core mathematics courses are fundamental to the academic success of engineering students in higher education. These courses equip students with skills and knowledge applicable to their specialized fields. However, first-year engineering students often face significant challenges in mathematics due to a range of factors, including insufficient preparation, mathematics anxiety, and difficulty connecting theoretical concepts to real-life applications. The transition from secondary to tertiary mathematics remains a key area of educational research, with ongoing discussions about effective pedagogical approaches for teaching engineering mathematics. This study utilized a belief survey to gain general insights into the attitudes of first-year mathematics students towards the subject. In addition, it employed the activity theory framework to conduct a deeper exploration of the experiences of first-year engineering students, aiming to identify contradictions, or “tensions,” encountered within a flipped-classroom learning environment. Quantitative data were collected using surveys that assessed students’ self-reported confidence, competence, and knowledge development. Results from Friedman’s and Wilcoxon’s Signed-Rank Tests, conducted with a sample of 20 participants in 10 flipped-classroom sessions, statistically showed significant improvements in all three areas. All of Friedman’s test statistics were above 50, with p-values below 0.05, indicating meaningful progress. Similarly, Wilcoxon’s Signed-Rank Test results supported these findings, with p values under 0.05, leading to the rejection of the null hypothesis. The qualitative data, derived from student questionnaire comments and one-to-one interviews, elucidated critical aspects of flipped-classroom delivery. The analysis revealed emerging contradictions (“tensions”) that trigger “expansive learning”. These tensions encompassed the following: student expectation–curriculum structure; traditional versus novel delivery systems; self-regulation and accountability; group learning pace versus interactive learning; and the interplay between motivation and anxiety. These tensions are vital for academic staff and stakeholders to consider when designing and delivering a first-year mathematics course. Understanding these dynamics can lead to more effective, responsive teaching practices and support student success during this crucial transition phase. Full article

(1) Aims: This review aims to investigate whether screen-based simulated learning experiences improve on traditional teaching strategies to bridge the theory–practice gap for biomedical scientists and enhance the readiness to practice of graduates. (2) Methods: This review adheres to the systematic–narrative hybrid literature review strategy with the scope of review defined according to Cochrane guidelines for systematic reviews. To identify the potentially relevant literature, the PUBMED, CINAHL, and Web of Science bibliographic databases were searched using the identified keywords from January 2020 to February 2025. Thematic analysis of the resultant literature was conducted in line with the Braun and Clarke framework. (3) Results: The original search and analysis of the online databases returned 45 papers. Collectively these sources explore global perspectives on biomedical science education, training, and professional practice. These include the identification of core competencies that may contribute to the theory–practice gap for biomedical scientists, as well as educational interventions that aim to address them. The poor quality of existing research on simulation-based learning, mostly from academic settings, makes it challenging to apply the findings to professional practice. This limitation is primarily due to an overreliance on self-reported data and perceived learning gains rather than direct, objective evaluations of competence. Future studies should focus on objective, validated outcome measures and longitudinal follow-up to assess real-world impacts and learning transfer. (4) Conclusions: Simulation-based learning experiences have the potential to address aspects of the theory–practice gap for biomedical scientists, but the current evidence base reflects a lack of understanding regarding specific targets and strategies for its design, evaluation, and integration in this context. There is a need for more robust evidence that evaluates their impacts on readiness to practice. This need is hindered by a lack of research directly investigating the impact of simulation-based teaching and training interventions in clinical settings. Full article

Global policy actions to reduce the environmental and social impacts of natural gas flaring are primarily derived from voluntary arrangements. This paper evaluates stakeholder preferences amongst competing policies and regulatory options, optimizing environmental governance to eliminate routine gas flaring by 2030 and achieve net-zero greenhouse emissions by 2050, whilst addressing questions of justice and fair implementation. Using a mixed-methods social scientific approach, incorporating literature and document review, interviews, expert surveys, Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (G-TOPSIS), we derive two competing perspectives on gas flaring policy strategy, with differences revealed through the AHP ranking process of individual criteria. All identified criteria and sub-criteria were integral to achieving the flaring and emissions targets, with “policy and targets” and “enabling framework” being the most important individual criteria. The “background and the role of reductions in meeting environmental and economic objectives” and ‘’nonmonetary penalties” were the key emergent sub-criteria. G-TOPSIS showed that fully implementing gas flaring policies and regulatory framework criteria to limit warming to 1.5 °C is the most effective policy alternative. Globally coordinated, uniform, and reciprocal legally binding agreements between countries to supplement national initiatives are imperative for improving the effectiveness of country-specific gas flaring policy strategies. Full article

Immigrant and mixed-status families comprise a growing population in the United States, facing numerous barriers to accessing essential health and social services. This study examines service access barriers within the unique context of New Mexico’s borderlands, where constitutionally protected bilingualism and welcoming local policies contrast sharply with restrictive federal border enforcement. Using a qualitative approach, we conducted five focus groups with 36 immigrant caregivers in Doña Ana County, New Mexico, with the objective of understanding the factors that facilitate and hinder immigrant families’ access to health, behavioral health, and social services in this socio-politically complex border environment. Thematic analysis revealed three overarching themes: (1) structural and organizational limitations, including language barriers and transportation challenges exacerbated by border checkpoints; (2) the persistence of “chilling effects” on service use despite a Democratic presidency and post-pandemic policy shifts; and (3) community-defined recommendations for improving service access. The findings demonstrate how federal immigration enforcement undermines local inclusion efforts, creating enduring barriers to service access even in historically bilingual, immigrant-friendly regions. The participants proposed specific solutions, including mobile service units and integrated service centers, that account for both geographic and socio-political barriers unique to border regions. These community-generated recommendations offer practical strategies for improving immigrant service access in contexts where local welcome and federal enforcement create competing pressures on immigrant families. Full article

The relationship between sleep and epilepsy involves complex interactions between thalamocortical circuits, circadian mechanisms, and sleep architecture that fundamentally influence seizure susceptibility and cognitive outcomes. Epileptic activity disrupts essential sleep oscillations, particularly sleep spindles generated by thalamic circuits. Thalamic epileptic spikes actively compete with physiological sleep spindles, impairing memory consolidation and contributing to cognitive dysfunction in epileptic encephalopathy. This disruption explains why patients with epilepsy often experience learning difficulties despite adequate seizure control. Sleep stages show differential seizure susceptibility. REM sleep provides robust protection through enhanced GABAergic inhibition and motor neuron suppression, while non-REM sleep, particularly slow-wave sleep, increases seizure risk. These observations reveal fundamental mechanisms of seizure control within normal brain physiology. Circadian clock genes (BMAL1, CLOCK, PER, CRY) play crucial roles in seizure modulation. Dysregulation of these molecular timekeepers creates permissive conditions for seizure generation while being simultaneously disrupted by epileptic activity, establishing a bidirectional relationship. These mechanistic insights are driving chronobiological therapeutic approaches, including precisely timed antiseizure medications, sleep optimization strategies, and orexin/hypocretin system interventions. This understanding enables a paradigm shift from simple seizure suppression toward targeted restoration of physiological brain rhythms, promising transformative epilepsy management through sleep-informed precision medicine. Full article

Background/Objectives: Advanced lung cancer is a highly distressing disease that negatively impacts older adults. Supportive care interventions designed for this population are scarce and often inaccessible due to competing demands and transportation access. We adapted and refined an evidence-based treatment, Self-System Therapy (SST), to address the unmet needs of older adults with advanced cancer. Methods: Guided by principles of implementation science, we conducted patient interviews, focus groups, and user testing to refine our new SST for the lung cancer (SST-LC) protocol. We then conducted a single-arm pilot trial (clinicaltrials.gov NCT04057196) for patients aged 65+ and above with Stage III or IV lung cancer (N = 30). Benchmarks for acceptability, feasibility, and preliminary changes in outcome measures were assessed. Results: Our study met the desired recruitment goals and demonstrated high treatment adherence rates (89%) and satisfaction rates (85%), indicating that SST-LC was feasible and well-received. Participants also showed reductions in distress and depression, and improvements in emotional and functional well-being from baseline to post-intervention, with effects mostly maintained at follow-up. Physical well-being, social well-being, and quality of life showed smaller, non-significant changes. Feedback from participants also suggested that SST enhanced their resilience and ability to cope with cancer-related challenges, but also indicated a preference for fewer sessions. Conclusions: SST for older adults living with advanced lung cancer is feasible and acceptable. Moreover, this supportive care intervention shows promise in addressing psychological distress, emotional well-being, and functional well-being in older adults. Future research will include testing the efficacy of SST in a larger randomized controlled trial. Full article

This study investigates how students of English Language and Literature Studies and those of Translation at the University of Maribor, Slovenia, perceive and engage with digital tools in academic and language learning contexts. Although students report high levels of confidence in their digital skills and express positive attitudes towards educational technologies, the survey results reveal a significant gap between perceived competence and actual usage. The study identifies the underutilization of institutional tools, limited awareness of resources available, and a reliance on general-purpose search engines rather than academic platforms. These findings highlight the need for improved digital literacy training, structured onboarding, and integration of digital tools into discipline-specific curricula. By focusing on a student population specializing in linguistics and translation in a Central and Eastern European context, this research contributes a localized perspective to broader discussions on digital transformation in higher education. The study offers applicable recommendations for enhancing institutional strategies and supporting students in becoming competent and critical users of educational technology. Full article

Nitrate pollution in water bodies has become a global environmental problem, and its excessive presence not only leads to eutrophication of water bodies but also threatens human health through the drinking water pathway. Therefore, it is urgent to develop new adsorbents with high adsorption capacity, good selectivity and excellent regeneration performance to solve the problem of nitrate pollution. In this study, reed straw (RS), trimethylamine-modified reed straw (MRS) and triethylamine-modified reed straw (ERS) were prepared by quaternary amination modification for nitrate removal. The adsorption performance, desorption performance, adsorption characteristics under disturbed environment and dynamic adsorption performance were investigated experimentally, and the adsorption mechanism was analyzed by various characterization means. The adsorption performance followed the order ERS (12.25 mg·g⁻¹) > MRS > RS, demonstrating that quaternary amination modification, particularly with triethylamine, significantly enhanced the NO₃⁻-N adsorption capacity. ERS exhibited excellent regeneration stability (over 80% after nine cycles) and high selectivity towards NO₃⁻-N in the presence of competing anions (Cl⁻, SO₄²⁻, humic acid). In the dynamic adsorption experiment, ERS had a breakthrough time of 290 min at a packing height of 3.3 cm, with an adsorption capacity of 10.74 mg·g⁻¹ and good adaptability to flow rate. In the actual wastewater application, the initial NO₃⁻-N removal rate was over 95%, the dynamic desorption rate reached 99.2% and the peak nitrate concentration of the desorbed solution reached 27 times of the initial value, confirming its high efficiency regeneration and enrichment ability. The study shows that the amine-modified reed straw adsorbent has a good potential for application and provides a new way for wastewater treatment plants to solve the problem of nitrate removal 12.25 mg·g⁻¹. Full article

Flexibility is increasingly recognized as a key competence in addressing current challenges and transitions. It is a multidimensional construct, discussed across various disciplines, encompassing cognitive, behavioral, and emotional dimensions. The European LifeComp framework offers one of the most recent and comprehensive definitions of this competence, emphasizing its role in enabling individuals to adapt to uncertainty, manage complexity, and foster transformative learning. This study investigates the assessment tools available to evaluate flexibility competence, focusing on their alignment with the LifeComp framework. A systematic literature review was conducted using the Scopus and WoS databases, based on inclusion criteria for language, publication type, disciplinary area, research topic, and target population, identifying 22 eligible articles. Following a quality assessment of the articles, a critical analysis revealed the presence of 22 tools and scales, including the actively open-minded thinking (AOT) scale, the resistance to change (RTC) scale, and the flexible thinking in learning (FTL) questionnaire. The findings show overlaps among flexibility and related constructs, such as learning agility and intellectual humility. However, most tools are context-specific and fail to address the multidimensional nature of flexibility competence. Future research should prioritize the development of comprehensive instruments to support educational initiatives, policy development, and professional training. Full article

Major powers compete over the 17 rare earth elements (REEs), which are strategic resources in traditional, green, and high-tech areas. The escalation of international trade conflicts poses a serious threat to the sustainable growth of the rare earth industry, triggering an investigation of the global trade landscape for diverse rare earth products. Taking cerium, the most abundant and widely traded REE, as an example, this study selected seven representative cerium products, constructed their global trade networks from 2000 to 2022, depicted macro, meso, and micro trade patterns, and revealed the impact of four major events on China’s trade influence. The findings demonstrate that (1) the trade volume of cerium products in green and high-tech sectors has increased significantly, surpassing that of cerium products in traditional sectors and upstream primary products, and (2) the global cerium trade networks are interconnected, regionalized, stable, and efficient. Germany, the U.S., and other European nations have long dominated mid- and downstream cerium product commerce, but China’s involvement has grown. (3) China’s cerium trade influence has significantly increased, positively shocked by major events. The research findings provide solid empirical support and policy insights for promoting the sustainable and high-quality development of the global cerium industry chain. Full article

Identifying knowledge gaps and predictors of performance are proven ways to implement changes to a curriculum. This cross-sectional study investigates the subjective and objective competency of 52 medical students at McGill University in musculoskeletal (MSK) medicine, with a focus on orthopaedic surgery. We surveyed medical students to assess their confidence levels in orthopaedic surgery and their perceptions of its teaching. The students then completed a 25-question orthopaedics-focused exam as an objective assessment of their knowledge. Descriptive statistics were calculated, exam performance was compared across academic years, predictors of exam scores were analyzed, and student self-assessment accuracy was evaluated. Students reported lower confidence in orthopaedic surgery than in many other specialties, exam scores varied significantly across academic years (p = 0.007), and predicted exam performance was the only significant predictor of test score in multiple linear regression (R² = 0.313, p = 0.025). Calibration analysis revealed a substantial miscalibration, where students with higher predicted scores tended to overestimate their performance, while those with lower predictions tended to underestimate themselves (intercept = 27.2, slope = 0.54). A Bland–Altman plot demonstrated wide limits of agreement between predicted and actual scores (mean bias −1.2%, 95% LoA −35.0% to +32.6%). These findings highlight meaningful orthopaedic knowledge gaps and miscalibrated self-assessment, emphasizing the need for targeted, structured educational interventions in the MSK curriculum. Full article

Introduction: Prescribing errors are the most common cause of preventable patient harm. In recent years, interprofessional education (IPE) has been increasingly utilised to improve knowledge and skills through promoting interprofessional collaboration. This scoping review aimed to evaluate the effectiveness of IPE interventions for pharmacological knowledge and prescribing skills in medical students and doctors-in-training. Methods: MEDLINE, EMBASE, CENTRAL, CINAHL, PsycINFO, ERIC and Scopus were searched on 18 February 2025 for studies published since 2020. Keywords included interprofessional education, medical student, medical trainee, pharmacology and prescribing. Results: Of the 2254 citations identified, 42 studies were included. There were four main types of IPE interventions: case-based learning, work-integrated-learning, didactic, and simulation and role-plays. Outcomes were spread across pharmacological knowledge, prescribing skills and interprofessional attitudes, and all studies reported one or more positive findings at Kirkpatrick IPE level 1, 2a, 2b, 3 or 4b. No study reported outcomes at Kirkpatrick IPE 4a. Conclusions: IPE interventions targeting pharmacology and prescribing are positively viewed by medical learners. IPE is effective in improving interprofessional attitudes and collaboration, as well as pharmacological knowledge and prescribing competency. Logistical challenges can be barriers to larger-group IPE implementation; nonetheless, IPE work-integrated learning in authentic clinical settings may overcome these challenges. Full article

With the rapid development of the Internet of Things and 5G communication technologies, the demand for the random access of a massive number of user equipment in burst scenarios has significantly increased. Traditional fixed-frame-length mechanisms, due to their inability to dynamically adapt to fluctuations in access traffic, are prone to exacerbating channel resource competition, increasing the probability of preamble collisions, and significantly elevating access delays, thereby constraining the system performance of large-scale machine-type communications. To address these issues, this paper proposes a dynamic frame length optimization algorithm based on Q-learning. By leveraging reinforcement learning algorithms to autonomously perceive access traffic characteristics, this algorithm can dynamically adjust frame length parameters without relying on estimates of the number of user equipment. It optimizes the frame length to improve random access performance, reduces collisions among user equipment competing for preambles, and enhances the utilization ratio of preamble resources. Full article

Internal waves (IWs) play a crucial role in the transport of energy and matter within the ocean while also posing significant risks to marine engineering, navigation, and underwater communication systems. Consequently, effective segmentation methods are essential for mitigating their adverse impacts and minimizing associated hazards. A promising strategy involves applying remote sensing image segmentation techniques to accurately identify IWs, thereby enabling predictions of their propagation velocity and direction. However, current IWs segmentation models struggle to balance computational efficiency and segmentation accuracy, often resulting in either excessive computational costs or inadequate performance. Motivated by recent developments in the Mamba2 architecture, this paper introduces the state-space model meets linear attention (SMLA), a novel segmentation framework specifically designed for IWs. The proposed hybrid architecture effectively integrates three key components: a feature-aware serialization (FAS) block to efficiently convert spatial features into sequences; a state-space model with linear attention (SSM-LA) block that synergizes a state-space model with linear attention for comprehensive feature extraction; and a decoder driven by hierarchical fusion and upsampling, which performs channel alignment and scale unification across multi-level features to ensure high-fidelity spatial detail recovery. Experiments conducted on a dataset of 484 synthetic-aperture radar (SAR) images containing IWs from the South China Sea achieved a mean Intersection over Union (MIoU) of 74.3%, surpassing competing methods evaluated on the same dataset. These results demonstrate the superior effectiveness of SMLA in extracting features of IWs from SAR imagery. Full article

Background: The COVID-19 pandemic has claimed thousands of lives worldwide. While infection rates have declined in recent years, emerging variants remain a deadly threat. Accurate diagnosis is critical to curbing transmission and improving treatment outcomes. However, the similarity of COVID-19 symptoms to those of the common cold and flu has spurred the development of automated diagnostic methods, particularly through lung computed-tomography (CT) scan analysis. Methodology: This paper proposes a novel deep learning-based approach for detecting diverse COVID-19 variants using advanced textural feature extraction. The framework employs a dual-channel convolutional neural network (CNN), where one channel processes texture-based features and the other analyzes spatial information. Unlike existing methods, our model dynamically learns textural patterns during training, eliminating reliance on predefined features. A modified local binary pattern (LBP) technique extracts texture data in matrix form, while the CNN’s adaptable internal architecture optimizes the balance between accuracy and computational efficiency. To enhance performance, hyperparameters are fine-tuned using the Adam optimizer and focal loss function. Results: The proposed method is evaluated on two benchmark datasets, COVID-349 and Italian COVID-Set, which include diverse COVID-19 variants. Conclusions: The results demonstrate its superior accuracy (94.63% and 95.47%, respectively), outperforming competing approaches in precision, recall, and overall diagnostic reliability. Full article