Search Results (5,470)

Introduction: The COVID-19 pandemic caused significant disruptions to medical training worldwide, particularly for junior doctors, as in-person clinical training was replaced by online education. This study aims to assess the impact of the pandemic on medical training in Greece, focusing on the perceptions of junior doctors across various specialties and exploring the implications for future clinical practice. Methods: We conducted a cross-sectional online survey of 465 junior doctors, all of whom were members of the Athens Medical Association, from 14 September to 14 October 2022. Participants completed a questionnaire assessing the perceived impact of the pandemic on their training, the effectiveness of online education, and potential consequences for clinical preparedness. Factor analysis was conducted to identify underlying patterns related to perceptions for the impact on medical training. Multiple linear regression models were used to assess potential associations among the extracted factors and participants’ sociodemographic characteristics. Results: Among the 465 participants, the mean age was 32.1 (SD = 7.0) years and 300 (64.5%) were female. Among the responders, the majority (n = 241, 51.8%) conducted training in Internal Medicine, 155 (33.3%) in a surgical specialty and 69 (14.8%) in other specialties, including Psychiatry, Radiology and Laboratory Medicine. Two out of five medical students reported that their medical training was mostly affected during the first wave of the pandemic, from March to June 2020 (n = 201, 43.2%). Factor analysis revealed the existence of two factors with high reliability and acceptable validity, interpreted as “perceptions towards online training” and “perceptions for the consequences of the pandemic on medical training”. Age and medical specialty were found to be significantly associated with both factors. Conclusion: Training was severely disrupted, with potential long-term implications for clinical competence; therefore Government and Universities should consider the lessons learned from the pandemic and compensate for the time and opportunities lost. Measures must be taken to safeguard medical education and training in the event of such outbreaks in the future. Full article

The study examines the impact of the COVID-19 pandemic on the digital competencies of teachers and the educational achievements of students, focusing on Serbia and comparisons with other countries. For this study, a survey was conducted in three phases, completed by teachers. The time periods during which the surveys were filled out are characteristic because they correspond to specific points in time (June 2019, June 2022, and May 2023). The aim of the first study, conducted in June 2019, was for every school in the Republic of Serbia to explore teachers’ digital competencies as a recommendation of the Ministry of Education. Later, this survey took on a different purpose with the onset of the pandemic. The pandemic exposed challenges such as insufficient teacher preparation for online teaching, educational inequalities affecting students from lower socio-economic backgrounds, and varying levels of adaptability among students. The hypothesis of this research is as follows: Teachers demonstrate a significantly higher level of digital literacy after the crisis caused by the COVID-19 virus than before the crisis. The findings reveal improvements in teachers’ digital skills after the crisis situation, particularly in hardware, software, and internet use, alongside a shift in the primary purpose of digital tools from entertainment to education. The study emphasizes the importance of continuous professional development, standardized e-learning devices, and improved digital infrastructure to enhance the quality of education. The research found that teachers in Serbia showed a significantly higher level of digital competencies after the crisis situation. Key recommendations include integrating digital skills into teacher training, fostering innovative pedagogical practices, and addressing the digital divide to ensure equitable access to education in the future. Full article

Children with severe motor and communication impairments (SMCIs) face significant challenges in expressing emotions, often leading to unmet needs and social isolation. This study investigated the potential of machine learning to identify emotions in children with SMCIs through the analysis of physiological signals. A model was created based on the data from the DEAP online dataset to identify the emotions of typically developing (TD) participants. The DEAP model was then adapted for use by participants with SMCIs using data collected within the Building and Designing Assistive Technology Lab (BDAT). Key adaptations to the DEAP model resulted in the exclusion of respiratory signals, a reduction in wavelet levels, and the analysis of shorter-duration data segments to enhance the model’s applicability. The adapted SMCI model demonstrated an accuracy comparable to the DEAP model, performing better than chance in TD populations and showing promise for adaptation to SMCI contexts. The models were not reliable for the effective identification of emotions; however, these findings highlight the feasibility of using machine learning to bridge communication gaps for children with SMCIs, enabling better emotional understanding. Future efforts should focus on expanding the data collection of physiological signals for diverse populations and developing personalized models to account for individual differences. This study underscores the importance of collecting data from populations with SMCIs for the development of inclusive technologies to promote empathetic care and enhance the quality of life of children with communication difficulties. Full article

Background: Although digital cognitive behavioral therapy (dCBT) is considered effective for anxiety disorders, there is considerable heterogeneity in its efficacy across studies, and its varied treatment content and clinical components may explain such heterogeneity. Objective: This review aimed to identify the efficacy of digital cognitive behavioral therapy for panic disorder and agoraphobia, and examine whether applying relevant clinical components of interoceptive exposure, inhibitory-learning-based exposure, and personalization of treatment enhances its efficacy. Methods: Randomized controlled trials of dCBT for panic disorder and agoraphobia with passive or active controls were identified from OVID Medline, Embase, Cochrane Library, and PsycINFO. The overall effect sizes for dCBT groups (interventions through digital platforms based on the internet, mobile, computers, VR, etc.) were aggregated against passive control (placebo/sham) and active control (traditional CBT) groups. For subgroup analysis, key intervention components such as interoceptive exposure, inhibitory learning, and personalization were assessed dichotomously (0 or 1) along with other study characteristics. The stepwise meta-regression models were applied with traditional and Bayesian statistical testing. The risk of bias and publication bias of included studies were assessed. Results: Among the 31 selected studies, dCBT had an overall effect size of g = 0.70 against passive control and g = −0.05 against active control. In subgroup analysis, interoceptive exposure improved the clinical effects for both controls, and inhibitory learning and personalization increased the clinical effects for passive control along with therapist guide/support and the length of sessions. Many studies were vulnerable to therapist bias and attrition bias. No publication bias was detected. Conclusions: The heterogeneity in clinical effects of dCBT for panic and agoraphobia can be explained by the different intervention factors they include. For effective dCBT, therapists should consider the clinical components relevant to the treatment. Full article

Self-evaluation empowers students to progress independently and adapt their pace according to their unique circumstances. A critical facet of self-assessment and personalized learning lies in furnishing learners with formative feedback. This feedback, dispensed following their responses to self-assessment questions, constitutes a pivotal component of formative assessment systems. We hypothesize that it is possible to generate explanations that are useful as formative feedback using different techniques depending on the type of self-assessment question under consideration. This study focuses on a subject taught in a computer science program at a Spanish distance learning university. Specifically, it delves into advanced data structures and algorithmic frameworks, which serve as overarching principles for addressing complex problems. The generation of these explanatory resources hinges on the specific nature of the question at hand, whether theoretical, practical, related to computational cost, or focused on selecting optimal algorithmic approaches. Our work encompasses a thorough analysis of each question type, coupled with tailored solutions for each scenario. To automate this process as much as possible, we leverage natural language processing techniques, incorporating advanced methods of semantic similarity. The results of the assessment of the feedback generated for a subset of theoretical questions validate the effectiveness of the proposed methods, allowing us to seamlessly integrate this feedback into the self-assessment system. According to a survey, students found the resulting tool highly useful. Full article

Complex distributed parameter systems (DPSs) are prevalent in numerous industrial processes. However, the nonlinear spatiotemporal dynamics inherent in DPS present significant challenges for accurate modeling. In this paper, an innovative online three-dimensional (3D) fuzzy multi-output support vector regression learning method is proposed for DPS modeling. The proposed method employs spatial fuzzy basis functions from the 3D fuzzy model as kernel functions, enabling direct construction of a comprehensive fuzzy rule base. Parameters $C$ and $\epsilon$ in the 3D fuzzy model adaptively adjust according to data sequence variations, effectively responding to system dynamics. Furthermore, a stochastic gradient descent algorithm has been implemented for real-time updating of learning parameters and bias terms. The proposed method was validated through two typical DPS and an actual rotary hearth furnace industrial system. The experimental results show the effectiveness of the proposed modeling method. Full article

Teacher agency has been recognised as a relevant concept for understanding the role of teachers in the current uncertain and changing contexts. However, its study about inclusive education is recent, especially in the Global South. This study analysed how teachers exercised agency for inclusive education during the COVID-19 crisis and the conditions that enabled or inhibited agency. This article explores Chilean schools, where neoliberal policies particularly challenge teachers’ agency. A multiple case study was conducted based on mixed methods. Online questionnaires were carried out with 154 teachers from 5 schools. In addition, five teachers from each school participated in in-depth group interviews. The findings show how teachers promoted students’ learning and participation in response to the challenges of the pandemic. Teachers mobilised resources to adapt to the context of uncertainty and supported one another. Among the influential factors, education policy constraints and control were reduced during the pandemic, thus creating opportunities to achieve agency for inclusion across the schools. At the same time, leadership, collaboration, and vision influenced agency differently in each school. While not aiming for major transformations, this study shows how teachers develop initiatives to adapt their practices and contribute to building inclusive schools despite contextual constraints. Full article

The COVID-19 pandemic has significantly impacted and disrupted higher education worldwide, creating unprecedented challenges for university students. In New Zealand, universities operated under varying pandemic restrictions, requiring students to frequently transition between online and in-person learning. This distinctive context provides a valuable opportunity to examine students’ academic stress and coping strategies during these transitions. Grounded in the transactional model of Stress–Appraisal–Coping, this research investigates sources of academic stress, coping strategies, and their effectiveness among university students during the pandemic. A mixed-methods approach was employed, with 193 university students completing an online survey incorporating qualitative and quantitative components. The thematic analysis results indicate eight major sources of academic-related stress and three types of coping strategies. Hierarchical regression analysis revealed that proactive and assistance-seeking strategies were associated with effective stress management, while avoidant strategies were linked to poorer outcomes. However, the study is limited by a low response rate (39.68%), which may affect the generalisability of findings. The results underscore the importance of fostering adaptive coping mechanisms in university settings and highlight the need for targeted institutional support to enhance student wellbeing and resilience in the post-pandemic academic landscape. Full article

The role of experimental data and the use of IoT-based monitoring systems are gaining broader significance in research on bees across several aspects: bees as global pollinators, as biosensors, and as examples of swarm intelligence. This increases the demands on monitoring systems to obtain homogeneous, continuous, and standardized experimental data, which can be used for machine learning, enabling models to be trained on new online data. However, the continuous operation of monitoring systems introduces new risks, particularly the cumulative impact of electromagnetic radiation on bees and their behavior. This highlights the need to balance IoT energy consumption, functionality, and continuous monitoring. We present a novel IoT-based bee monitoring system architecture that has been operating continuously for several years, using solar energy only. The negative impact of IoT electromagnetic fields is minimized, while ensuring homogeneous and continuous data collection. We obtained experimental data on the adverse phenomenon of honey robbing, which involves elements of swarm intelligence. We demonstrate how this phenomenon can be predicted and illustrate the interactions between bee colonies and the influence of solar radiation. The use of criteria for detecting honey robbing will help to reduce the spread of diseases and positively contribute to the sustainable development of precision beekeeping. Full article

This systematic literature review aims to review studies on post-wildfire landslides. A thorough search of Web of Science, Scopus, and other online library sources identified 1580 research publications from 2003 to 2024. Following PRISMA protocols, 75 publications met the inclusion criteria. The analysis revealed a growing interest in research trends over the past two decades, with most publications being from 2021 to 2024. This study is divided into categories: (1) systematic review methods, (2) geographical distributions and research trends, and (3) the exploitation of post-wildfire landslides in terms of susceptibility mapping, monitoring, mitigation, modeling, and stability studies. The review revealed that post-wildfire landslides are primarily found in terrains that have experienced wildfires or bushfires and immediately occur after rainfall or a rainstorm—primarily within 1–5 years—which can lead to multiple forms of destruction, including the loss of life and infrastructure. Advanced technologies, including high-resolution remote sensing and machine learning models, have been used to map and monitor post-wildfire landslides, providing some mitigation strategies to prevent landslide risks in areas affected by wildfires. The review highlights the future research prospects for post-wildfire landslides. The outcome of this review is expected to enhance our understanding of the existing information. Full article

A drawback of fringe projection profilometry (FPP) is that it is still a challenge to perform efficient and accurate high-resolution absolute phase recovery with only a single measurement. This paper proposes a single-model self-recovering fringe projection absolute phase recovery method based on deep learning. The built Fringe Prediction Self-Recovering network converts a single fringe image acquired by a camera into four single mode self-recovering fringe images. A self-recovering algorithm is adopted to obtain wrapped phases and fringe grades, realizing high-resolution absolute phase recovery from only a single shot. Low-cost and efficient dataset preparation is realized by the constructed virtual measurement system. The fringe prediction network showed good robustness and generalization ability in experiments with multiple scenarios using different lighting conditions in both virtual and physical measurement systems. The absolute phase recovered MAE in the real physical measurement system was controlled to be 0.015 rad, and the reconstructed point cloud fitting RMSE was 0.02 mm. It was experimentally verified that the proposed method can achieve efficient and accurate absolute phase recovery under complex ambient lighting conditions. Compared with the existing methods, the method in this paper does not need the assistance of additional modes to process the high-resolution fringe images directly. Combining the deep learning technique with the self-recovering algorithm simplified the complex process of phase retrieval and phase unwrapping, and the proposed method is simpler and more efficient, which provides a reference for the fast, lightweight, and online detection of FPP. Full article

It is a serious challenge for humanity to find an appropriate response to stop the accelerating rise in global temperature caused by atmospheric carbon dioxide emissions. After a methodological review of the literature, online and in-person modelling of education, work, and conferences, and relying on the results of life-cycle studies, we sought the answer to what reasonable solutions are available for decarbonization and energy reduction. During the research, the organizational carbon footprint of a selected office, educational institution and conference, and then the carbon footprint created by a person in 1 h, were examined. The two-day online education significantly reduced the daily commute load in transport by 402 tons of CO₂ equivalent per year. Still, the energy demand of home learning subtracts 136 tons from this, so the real benefit was 266 tons above in an institution educating nearly 3500 students. In a workplace of 180 people, where 52% of employees commute, 90% teleworking saved 222 tons of carbon dioxide emissions in one month, taking into account the carbon footprint of working from home. In the case of conferences, the online solution reduces the carbon footprint due to the absence of travel and catering. Comparing the three areas, for the in-person case, the conference’s carbon footprint per person per hour was the highest (11.91 kg CO₂ eq.). This value for education was 1.15 kg CO₂ eq.; for work, it was the lowest with a value of 0.90 kg CO₂ eq. Moving to an online space resulted in the most significant savings for the conference (11.55 kg CO₂ eq.), followed by working (0.54 kg CO₂ eq.), and minor savings were achieved in hybrid education (0.13 kg CO₂ eq.). The sensitivity analysis highlighted the impact of transport on carbon footprint in all three cases. However, the life cycle cost analysis showed that moving to a virtual space reduces the life cycle cost of de-carbonization by 42%. Full article

With the widespread use of credit cards in online and offline transactions, credit card fraud has become a significant challenge in the financial sector. The rapid advancement of payment technologies has led to increasingly sophisticated fraud techniques, necessitating more effective detection methods. While machine learning has been extensively applied in fraud detection, the application of deep learning methods remains relatively limited. Inspired by brain-like computing, this work employs the Continuous-Coupled Neural Network (CCNN) for credit card fraud detection. Unlike traditional neural networks, the CCNN enhances the representation of complex temporal and spatial patterns through continuous neuron activation and dynamic coupling mechanisms. Using the Kaggle Credit Card Fraud Detection (CCFD) dataset, we mitigate data imbalance via the Synthetic Minority Oversampling Technique (SMOTE) and transform sample feature vectors into matrices for training. Experimental results show that our method achieves an accuracy of 0.9998, precision of 0.9996, recall of 1.0000, and an F1-score of 0.9998, surpassing traditional machine learning models, which highlight CCNN’s potential to enhance the security and efficiency of fraud detection in the financial industry. Full article

With the wide adoption of Learning Management Systems (LMSs) in educational institutions, ample data have become available demonstrating students’ online behavior. Digital traces are widely applicable in Learning Analytics (LA). This study aims to explore and extract behavioral features from Moodle logs and examine their effect on undergraduate students’ performance. Additionally, traditional factors such as demographics, academic history, family background, and attendance data were examined, highlighting the prominent features that affect student performance. From January to April 2019, a total of 64,231 students’ Moodle logs were collected from a private university in Malaysia for analyzing students’ behavior. Exploratory Data Analysis, correlation, statistical tests, and post hoc analysis were conducted. This study reveals that age is found to be inversely correlated with student performance. Tutorial attendance and parents’ occupations play a crucial role in students’ performance. Additionally, it was found that online engagement during the weekend and nighttime positively correlates with academic performance, representing a 10% relative increase in the student’s exam score. Ultimately, it was found that course views, forum creation, overall assignment interaction, and time spent on the platform were among the top LMS variables that showed a statistically significant difference between successful and failed students. In the future, clustering analysis can be performed in order to reveal heterogeneous groups of students along with specific course-content-based logs. Full article

Many leading technology companies currently offer Machine Learning as a Service Platform, enabling developers and organizations to access the inference capabilities of pre-trained models via API calls. However, due to concerns over user data privacy, inter-enterprise competition, and legal and regulatory constraints, directly utilizing pre-trained models in the cloud for inference faces security challenges. In this paper, we propose communication-efficient secure three-party protocols for recurrent neural network (RNN) inference. First, we design novel three-party secret-sharing protocols for digit decomposition, B2A conversion, enabling efficient transformation of secret shares between Boolean and arithmetic rings. Then, we propose the lookup table-based secure three-party protocol. Unlike the intuitive way of directly looking up tables to obtain results, we compute the results by utilizing the inherent mathematical properties of binary lookup tables, and the communication complexity of the lookup table protocol is only related to the output bit width. We also design secure three-party protocols for key functions in the RNN, including matrix multiplication, sigmoid function, and Tanh function. Our protocol divides the computation into online and offline phase, and places most of the computations locally. The theoretical analysis shows that the communication round of our work was reduced from four rounds to one round. The experiment results show that compared with the current SOTA-SIRNN, the online communication overhead of sigmoid and tanh functions decreased by 80.39% and 79.94%, respectively. Full article