Repositorio Universidad Europea del Atlántico
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Unveiling the truth: A systematic review of fact-checking and fake news research in social sciences
No full textThe current media ecosystem, marked by immediacy and social networks dynamics, has created a fertile field for disinformation. Faced with its exponential growth, since 2014, research has focused on combating false content in the media. From a descriptive approach, this study has analyzed 200 documents on fact-checking and fake news published between 2014 and 2022 in scientific journals indexed in Scopus. This study has found that Europe and the United States are leading the way in the number of journals and authors publishing on the subject. The United States universities are the ones that host the most significant number of authors working on fact-checking, while the methodologies used, mostly ad hoc due to the novelty of the topic, allow to reflect on the need to promote work focused on the design, testing, and evaluation of prototypes or real experiences within the field. The most common contributions analyzed include typologies of false content and media manipulation mechanisms, models for evaluating and detecting disinformation, proposals to combat false content and strengthen verification mechanisms, studies on the role of social media in the spread of disinformation, efforts to develop media literacy among the public and journalists, case studies of fact-checkers, identification of factors that influence the belief in fake news, and analysis of the relationship between disinformation, verification, politics, and democracy. It is concluded that it is essential to develop research that connects the academy with the industry to raise awareness of the need to address these issues among the different actors in the media scenario
Deep transfer learning-based bird species classification using mel spectrogram images
No full textThe classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet technique was utilized for the investigation. The study adapts the fine-tune model to learn the pertinent patterns from mel spectrogram images specific to this bird species classification task. The fine-tuned EfficientNet model combined with a type of Recurrent Neural Networks (RNNs) namely Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). RNNs are employed to capture the temporal dependencies in audio signals, thereby enhancing bird species classification accuracy. The dataset utilized in this work contains nearly 17,000 bird sound recordings across a diverse range of species. The experiment was conducted with several combinations of EfficientNet and RNNs, and EfficientNet-B7 with GRU surpasses other experimental models with an accuracy of 84.03% and a macro-average precision score of 0.8342
Evolving epidemiology, clinical features, and genotyping of dengue outbreaks in Bangladesh, 2000–2024: a systematic review
No full textBackground: The 2023 dengue outbreak has proven that dengue is not only an endemic disease but also an emerging health threat in Bangladesh. Integrated studies on the epidemiology, clinical characteristics, seasonality, and genotype of dengue are limited. This study was conducted to determine recent trends in the molecular epidemiology, clinical features, and seasonality of dengue outbreaks.
Methods: We analyzed data from 41 original studies, extracting epidemiological information from all 41 articles, clinical symptoms from 30 articles, and genotypic diversity from 11 articles. The study adhered to the standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement and Cochrane Collaboration guidelines.
Conclusion: This study provides integrated insights into the molecular epidemiology, clinical features, seasonality, and transmission of dengue in Bangladesh and highlights research gaps for future studies
Concordance of a new IMU in different small-sided games and real game tasks in indoor sports
No full textPurpose: The purpose of this study was to analyze the concordance of a new Inertial Measurement Unit (IMU) device called OLIVER in different specific training tasks and real futsal game.Methods: 10 elite futsal players competing in First National Division performed most of the typical futsal training tasks (game possession in 22×20m, 2vs2 in 20×20m, 3vs3, 4vs4 in 28×20m and 4vs4 in 40×20m). Players wore two tracking devices (OLIVER and WIMU Pro). Data were recorded with specific software systems to compare the concordance of data. After recording data, descriptive analysis was developed for each training task, as well as a one-way ANOVA to evaluate the concordance of OLIVER and WIMU devices.Results: The results reported good agreement for most variables, such as total distance, distance covered in different partials, as well as meters of accelerations and decelerations at different intensities and maximum speed (P> .05). However, the distance covered in high-speed running 12.1-18 km/h (m) and the number of runs above 12 km/h reported statistical differences between OLIVER and WIMU (P< .05) in some of the training tasks.Conclusion: The OLIVER system and WIMU system shows a high level of concordance in main variables of external load in different training tasks. OLIVER system is a valid and useful device to monitor external load in indoor sports, both small-sided games and real game
Virtual histopathology methods in medical imaging - a systematic review
No full textVirtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, often resulting in time-consuming processes and variability in diagnoses. Virtual histopathology offers a more consistent, and automated approach, employing techniques like machine learning, deep learning, and image processing to simulate staining and enhance tissue analysis. This review explores the strengths, limitations, and clinical applications of these methods, highlighting recent advancements in virtual histopathological approaches. In addition, important areas are identified for future research to improve diagnostic accuracy and efficiency in clinical settings
Side effects associated with homogenous and heterogenous doses of Oxford–AstraZeneca vaccine among adults in Bangladesh: an observational study
No full textAssessment of side effects associated with COVID-19 vaccination is required to monitor safety issues and acceptance of vaccines in the long term. We found a significant knowledge gap in the safety profile of COVID-19 vaccines in Bangladesh. We enrolled 1805 vaccine recipients from May 5, 2021, to April 4, 2023. Kruskal-Wallis test and χ2 test were performed. Multivariable logistic regression was also performed. First, second and third doses were administered among 1805, 1341, and 923 participants, respectively. Oxford–AstraZeneca (2946 doses) was the highest administered followed by Sinopharm BIBP (551 doses), Sinovac (214 doses), Pfizer-BioNTech (198 doses), and Moderna (160 doses), respectively. Pain at the injection site (80-90%, 3200–3600), swelling (85%, 3458), redness (78%, 3168), and heaviness in hand (65%, 2645) were the most common local effects, and fever (85%, 3458), headache (82%, 3336), myalgia (70%, 2848), chills (67%, 2726), muscle pain (60%, 2441) were the most prevalent systemic side effects reported within 48 h of vaccination. Thrombosis was only reported among the Oxford–AstraZeneca recipients (3.5-5.7%). Both local and systemic effects were significantly associated with the Oxford–AstraZeneca (p-value < 0.05), Pfizer–BioNTech (p-value < 0.05), and Moderna (p-value < 0.05) vaccination. Chronic urticaria and psoriasis were reported by 55-60% of the recipients after six months or later. The highest percentage of local and systemic effects after 2nd and 3rd dose were found among recipients of Moderna followed by Pfizer-BioNTech and Oxford–AstraZeneca. Homogenous doses of Oxford–AstraZeneca and heterogenous doses of Moderna and Pfizer-BioNTech were significantly associated with elevated adverse effects. Females, aged above 60 years with preexisting health conditions had higher risks. Vaccination with Pfizer-BioNTech (OR 4.34, 95% CI 3.95–4.58) had the highest odds of severe and long-term effects followed by Moderna (OR 4.15, 95% CI 3.92–4.69) and Oxford–AstraZeneca (OR 3.89, 95% CI 3.45–4.06), respectively. This study will provide an integrated insight into the safety profile of COVID-19 vaccines
Effectiveness of a Mindfulness-Based Professional Development Program for Primary School Teachers in the Czech Republic: A Quasi-Experimental Study
No full textBackground: Research has shown that 20% of Czech teachers suffer with burnout symptoms and 65% are at risk of burning out, which suggests that it is essential to continue addressing the issue of stress in Czech teachers. The main objective of this study was to evaluate a self-compassion and mindfulness-based professional development program for primary school teachers in the Czech Republic. Methods: Five schools were recruited, two as intervention schools (n of teachers = 47) and three as controls (n of teachers = 57). Teachers completed questionnaires at three time points: pre-test in September 2018, post-test in November 2018, and a follow-up in April 2019. Results: The results at post-test indicated that teachers in the intervention group scored significantly higher (p < 0.05) in self-efficacy and self-compassion, and significantly lower in depression, anxiety and emotional exhaustion, compared to the controls. The intervention teachers were marginally lower (p < 0.10) in perceived stress and marginally higher in subjective well-being, compared to the controls. At follow-up, teachers’ subjective well-being in the control group significantly worsened compared to the baseline. However, the intervention group did not show significant changes over time, which suggests a “protective effect” on the intervention group against worsening during the school year. Conclusions: The study suggests that providing teachers with self-compassion and mindfulness practices can lead to beneficial effects on several outcome variables. Further studies need to investigate if these benefits can be sustained and if they affect teachers’ physical health, their relationships with students, and the students’ outcomes
Resource-efficient federated learning over IoAT for rice leaf disease classification
No full textRice is an important staple food in Asia. It is produced and consumed in large quantities. It contributes to 15 % of protein intake and 21 % of total per capita energy intake in the region, underscoring its important role as a primary global food source. Conversely, rice plants are heavily affected by bacterial, fungal and other microbial diseases, resulting in reduced plant health and crop yields and posing a major challenge for rice farmers. Manual diagnosis of these diseases is particularly problematic in regions with a shortage of agricultural experts. Farmers with insufficient experience sometimes incorrectly identify these diseases by hand. Recent advances in deep learning (DL) models offer a promising solution through automatic image recognition systems that can be very helpful in accurately identifying these diseases. This manuscript presents a resource-efficient federated learning IoAT (Internet of Agriculture Things) approach for rice leaf disease classification. This approach incorporates two key strategies, namely federated transfer learning and feature extraction, and evaluates their performance on various metrics such as accuracy, loss, precision, recall, AUC, and resource-related parameters such as GPU memory consumption, virtual memory, CPU and GPU process utilization. The dataset used in the study includes 5932 images of rice leaf diseases categorized as bacterial leaf blight, blast, brown spot and tungro. The research investigates the application of federated transfer learning and federated feature extraction techniques to classify rice leaf disease images. It performs a comparative analysis of their performance and resource utilization. EfficientNetB3, with an impressive validation accuracy of 99 %, is identified as the base model for the federated learning (FL) environment. Furthermore, we implement FL using both transfer learning and feature extraction methods and compare their results on a number of performance and resource related parameters. It is shown that the federated feature extraction approach has lower GPU memory and process utilization compared to the federated transfer learning approach and is thus more resource efficient. Extracting features before model training results in a lightweight model, which is especially beneficial for FL, IoAT devices that require efficient execution on edge devices. Therefore, the proposed approach is presented as a lightweight, privacy-friendly and resource-efficient solution for rice leaf disease classification
A Lightweight Energy-Efficient Routing Scheme for Real-Time WSN-VANET-Based Applications
No full textAn extensive examination reveals that Wireless Sensor Networks (WSNs) offer a promising solution for essential sensing and event-driven data communication needs. WSNs hold significant potential for event-driven communication, primarily owing to their decentralized and infrastructure-free operational characteristics. However, the traditional WSN’s inherent static nature imposes limitations on its applicability, particularly in scenarios requiring generic operating characteristics or routing protocols for Vehicle-to-Vehicle (V2V) communication. This constraint arises from the immobility of sensor nodes within the network. Nonetheless, adopting a forward-looking perspective that incorporates mobility into WSNs opens up opportunities to create a mobile-WSN solution tailored for V2V communication. In response to the challenges posed by mobile-WSNs and the pursuit of a cost-effective V2V communication solution, a Lightweight Energy-Efficient Cross-layer Routing (LRECR) scheme has been proposed for WSN-VANET-based networks. The proposed routing model aims to enhance the timely delivery of Real-Time Data (RTD) with low latency, provide optimal resources for Non-Real Time (NRT) data delivery, optimize resource allocation, minimize delay, reduce energy consumption, and lower buffer and holding costs. These comprehensive parameters empower mobile-WSNs to fulfill the requirements of a Quality of Service (QoS)-oriented and energy-efficient V2V communication system
The preventive and inhibitory effects of red raspberries on cancer
No full textRed raspberries are gaining attention more and more for their nutritional and bioactive components, with potential health effects such as antitumor properties. This review aims to describe the antioxidant activities of red raspberries, emphasizing the role of anthocyanins and ellagitannins as primary contributors among red raspberry polyphenols; it also outlined the connection between red raspberries and their role in inhibiting cancer cell growth by regulating oxidative stress. Numerous studies suggest that red raspberries are able to block cancer cell progression by inhibiting proliferation, migration, and autophagy, as well as regulating the cell cycle, angiogenesis, and DNA damage repair. This review sheds light to the growing evidence supporting antioxidants as a crucial link between fruit consumption and cancer prevention