The 100 most-cited articles in PLoS ONE, 2024

For the first time since 2015, the World Health Organization's (WHO) global Antimicrobial Resistance and Use Surveillance (GLASS) featured both global reports for antimicrobial resistance (AMR) and antimicrobial consumption (AMC) data in its annual reports. In this study we investigated the relationship of AMR with AMC within participating countries reported in the GLASS 2022 report. Our analysis found a statistically significant correlation between beta-lactam/cephalosporin and fluoroquinolones consumption and AMR to these antimicrobials associated with bloodstream E. coli and Klebsiella pneumoniae among the participating countries (P<0.05). We observed that for every 1 unit increase in defined daily dose DDD of beta-lactam/cephalosporins and quinolone consumptions among the countries, increased the recoveries of bloodstream-associated beta-lactam/cephalosporins-resistant E. coli/Klebsiella spp. by 11-22% and quinolone-resistant E. coli/Klebsiella spp. by 31-40%. When we compared the antimicrobial consumptions between the antimicrobial ATC (Alphanumeric codes developed by WHO)…

BACKGROUND: While different proposals exist for a guideline on reporting Delphi studies, none of them has yet established itself in the health and social sciences and across the range of Delphi variants. This seems critical because empirical studies demonstrate a diversity of modifications in the conduction of Delphi studies and sometimes even errors in the reporting. The aim of the present study is to close this gap and formulate a general reporting guideline. METHOD: In an international Delphi procedure, Delphi experts were surveyed online in three rounds to find consensus on a reporting guideline for Delphi studies in the health and social sciences. The respondents were selected via publications of Delphi studies. The preliminary reporting guideline, containing 65 items on five topics and presented for evaluation, had been developed based on a systematic review of the practice of Delphi studies and a systematic review of existing reporting guidelines for Delphi studies. Starting in the second Delphi round, the experts received feedback in the form of mean values, measures of…

Implementation of artificial intelligence systems for healthcare is challenging. Understanding the barriers and implementation strategies can impact their adoption and allows for better anticipation and planning. This study's objective was to create a detailed inventory of barriers to and strategies for AI implementation in healthcare to support advancements in methods and implementation processes in healthcare. A sequential explanatory mixed method design was used. Firstly, scoping reviews and systematic literature reviews were identified using PubMed. Selected studies included empirical cases of AI implementation and use in clinical practice. As the reviews were deemed insufficient to fulfil the aim of the study, data collection shifted to the primary studies included in those reviews. The primary studies were screened by title and abstract, and thereafter read in full text. Then, data on barriers to and strategies for AI implementation were extracted from the included articles, thematically coded by inductive analysis, and summarized. Subsequently, a direct qualitative content…

We assess the UK Biobank (UKB) Polygenic Risk Score (PRS) Release, a set of PRSs for 28 diseases and 25 quantitative traits that has been made available on the individuals in UKB, using a unified pipeline for PRS evaluation. We also release a benchmarking software tool to enable like-for-like performance evaluation for different PRSs for the same disease or trait. Extensive benchmarking shows the PRSs in the UKB Release to outperform a broad set of 76 published PRSs. For many of the diseases and traits we also validate the PRS algorithms in a separate cohort (100,000 Genomes Project). The availability of PRSs for 53 traits on the same set of individuals also allows a systematic assessment of their properties, and the increased power of these PRSs increases the evidence for their potential clinical benefit.

The utilization of digital educational infrastructure in schools has propelled digital educational games to the forefront of educational innovation. Despite an abundance of empirical studies on the relationship between digital educational games and student's motivation for learning, a consensus has yet to be reached. This study aims to bridge existing research gaps by adopting a mixed-methods approach grounded in behaviorist learning theory and contextual cognitive theory. A detailed questionnaire was disseminated to students from three distinct university in Thailand. After the exclusion of invalid responses, a robust sample of 434 valid responses was curated and utilized for analysis. Utilizing SPSS and MPLUS software, empirical analyses were conducted to explore the impact of digital educational games on student's motivation for learning. Research results indicate that: First, digital educational games positively influence student's motivation for learning; Second, learning engagement serves as a mediator between digital educational games and student's motivation for learning;…

Psychological well-being of students is an area of concern in higher education institutes across the world. Although several studies have explored the factors associated with students' psychological well-being, limited research has focused on the relation between the overall support for students and psychological well-being. Students of higher education may get formal support, in the form of team environment and institutional support; and informal support, in the form of family and friends' support. The purpose of this study is to examine the relation of these four kinds of support with psychological well-being of management students. We also examine the intervening role of academic engagement in this relationship. Analysis using structural equation modeling and hierarchical regression on data collected from 309 management students from Indian universities, shows that positive internal team environment, and institutional and family support positively relate to students' psychological well-being. Academic engagement partially mediates the relation between positive internal team…

BACKGROUND: Globally, there is growing attention towards health system strengthening, and the importance of quality in health systems. However, fragile and conflict-affected states present particular challenges. The aim of this study was to explore health system strengthening in fragile and conflict-affected states by synthesising the evidence from published literature. METHODS: We conducted a review of systematic reviews (Prospero Registration Number: CRD42022371955) by searching Ovid (Medline, Embase, and Global Health), Scopus, Web of Science, and the Cochrane Library databases. Only English-language publications were considered. The Joanna Briggs Institute (JBI) Critical Appraisal Tool was employed to assess methodological quality of the included studies. The findings were narratively synthesised and presented in line with the Lancet's 'high-quality health system framework'. RESULTS: Twenty-seven systematic reviews, out of 2,704 identified records, considered key dimensions of health systems in fragile and conflict-affected states, with the 'foundations' domain having most…

Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics,…

The proper use of model evaluation metrics is important for model evaluation and model selection in binary classification tasks. This study investigates how consistent different metrics are at evaluating models across data of different prevalence while the relationships between different variables and the sample size are kept constant. Analyzing 156 data scenarios, 18 model evaluation metrics and five commonly used machine learning models as well as a naive random guess model, I find that evaluation metrics that are less influenced by prevalence offer more consistent evaluation of individual models and more consistent ranking of a set of models. In particular, Area Under the ROC Curve (AUC) which takes all decision thresholds into account when evaluating models has the smallest variance in evaluating individual models and smallest variance in ranking of a set of models. A close threshold analysis using all possible thresholds for all metrics further supports the hypothesis that considering all decision thresholds helps reduce the variance in model evaluation with respect to…

The recent rise in artificial intelligence systems, such as ChatGPT, poses a fundamental problem for the educational sector. In universities and schools, many forms of assessment, such as coursework, are completed without invigilation. Therefore, students could hand in work as their own which is in fact completed by AI. Since the COVID pandemic, the sector has additionally accelerated its reliance on unsupervised 'take home exams'. If students cheat using AI and this is undetected, the integrity of the way in which students are assessed is threatened. We report a rigorous, blind study in which we injected 100% AI written submissions into the examinations system in five undergraduate modules, across all years of study, for a BSc degree in Psychology at a reputable UK university. We found that 94% of our AI submissions were undetected. The grades awarded to our AI submissions were on average half a grade boundary higher than that achieved by real students. Across modules there was an 83.4% chance that the AI submissions on a module would outperform a random selection of the same…

BACKGROUND AND OBJECTIVES: Transgender and gender diverse (trans) health research has grown rapidly, highlighting the need to characterize the scientific evidence base. We conducted a systematic review of peer-reviewed research on disease burden and correlates in trans adolescents and adults over a 20-month period to identify knowledge gaps and assess methodological characteristics including measurement of gender identity, community engagement, and study quality. DATA SOURCES, ELIGIBILITY CRITERIA, AND SYNTHESIS METHODS: We searched seven databases using terms related to (a) transgender populations and (b) health or disease. Eligible studies were in English, French, or Spanish and reported original quantitative data on mental health or substance use conditions, infectious diseases, or non-communicable conditions in at least 25 trans individuals aged 15+. Quality assessment was performed in duplicate on a 10% sample of articles and findings were summarized using narrative synthesis. RESULTS: The 328 included studies were conducted in 45 countries, with most from North America (54%)…

Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p<0.001 significance level. This performance superiority is consistent across both the model development and test phases. This study also used paired-sample t-tests for the subsets of the research datasets from disease prediction…

BACKGROUND: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience. PURPOSE: The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG. METHODS: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country.…

Authoring of help content within educational technologies is labor intensive, requiring many iterations of content creation, refining, and proofreading. In this paper, we conduct an efficacy evaluation of ChatGPT-generated help using a 3 x 4 study design (N = 274) to compare the learning gains of ChatGPT to human tutor-authored help across four mathematics problem subject areas. Participants are randomly assigned to one of three hint conditions (control, human tutor, or ChatGPT) paired with one of four randomly assigned subject areas (Elementary Algebra, Intermediate Algebra, College Algebra, or Statistics). We find that only the ChatGPT condition produces statistically significant learning gains compared to a no-help control, with no statistically significant differences in gains or time-on-task observed between learners receiving ChatGPT vs human tutor help. Notably, ChatGPT-generated help failed quality checks on 32% of problems. This was, however, reducible to nearly 0% for algebra problems and 13% for statistics problems after applying self-consistency, a "hallucination"…

This study investigates the relationship between democracy and innovation across 61 developing countries from 2013 to 2020, utilizing data from Global Innovation Index. Employing the Freedom House Index and Polity2 indicators as proxies for democracy, research employs Ordinary Least Squares (OLS), Fixed Effects and SystemGMM techniques to analyze their impact on innovation. The findings of the study reveal no statistically significant relationships between democracy and innovation in developing nations within specified timeframe. Through empirical analysis, including various econometric approaches, it is observed that the level of democracy as measured by these indicators, does not appear to exert a discernable impact on the innovation landscape of these countries. These results carry important implications for public policy. While the promotion of democracy remains a crucial goal, especially for societal development and political stability, this study suggests that solely focusing on enhancing democratic institutions might not necessarily yield immediate direct improvements in the…

This study examines the antecedents of environmental sustainability in small and medium enterprises (SMEs) of a developing country and explores the specific internal and external factors for environmental sustainability. The study focused on SMEs in Balochistan, Pakistan, utilizing convenience and purposive sampling techniques to select a sample size of 30 SMEs. In-depth qualitative interviews were conducted using a semi-structured questionnaire. The results of the study revealed that lack of finance and education are major barriers to recognizing and addressing environmental sustainability issues, along with the lack of government support and regulations to ensure compliance with environmental safety laws, hence leading to low concern for sustainability practices among SMEs. Awareness and attitude of SME owners/managers, along with customer demand and government policies, influence the adoption of environmental sustainability practices. Overcoming financial constraints and promoting cooperation among stakeholders are key to fostering sustainable practices in SMEs. This research…

With the development of network and simulation technology, virtual laboratories have been widely popularized in engineering education. However, few studies have systematically analyzed and summarized the impact of virtual labs on the effectiveness of engineering education. This study aims to conduct a meta-analysis of published data on the impact of virtual laboratories on engineering students' performance. A total of 709 peer-reviewed publications on this topic were gathered from Web of Science and Scopus, and after strict inclusion criteria were applied, 46 studies from 22 publications were included in this meta-analysis. These studies were controlled experiments and pre-post designs with virtual labs as the intervention, reporting necessary descriptive summary statistics such as mean score comparisons and standard deviations of the two comparison groups. The results indicate that virtual laboratories are a significant predictor of engineering education outcomes, with an effect size (Hedges' g) of 0.686 (95% CI 0.414-0.959). Among these, the effect sizes for "learning motivation"…

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This…

INTRODUCTION: The World Health Organization (WHO) called for the expansion of all nursing roles, including advanced practice nurses (APNs), nurse practitioners (NPs) and clinical nurse specialists (CNSs). A clearer understanding of the impact of these roles will inform global priorities for advanced practice nursing education, research, and policy. OBJECTIVE: To identify gaps in advanced practice nursing research globally. MATERIALS AND METHODS: A review of systematic reviews was conducted. We searched CINAHL, Embase, Global Health, Healthstar, PubMed, Medline, Cochrane Library, DARE, Joanna Briggs Institute EBP, and Web of Science from January 2011 onwards, with no restrictions on jurisdiction or language. Grey literature and hand searches of reference lists were undertaken. Review quality was assessed using the Critical Appraisal Skills Program (CASP). Study selection, data extraction and CASP assessments were done independently by two reviewers. We extracted study characteristics, country and outcome data. Data were summarized using narrative synthesis. RESULTS: We screened 5840…

Without fundamentally altering how humans generate and utilise energy, there is no effective strategy to safeguard the environment. The motivation behind this study was to analyse the effectiveness of renewable energy in addressing climate change, as it is one of the most pressing global issues. This study involved the analysis of panel data covering 138 nations over a 27 year period, from 1995 to 2021, making it the latest addition to the existing literature. We examined the extent of the impact of renewable energy on carbon dioxide over time using panel, linear, and non-linear regression approaches. The results of our analysis, revealed that the majority of countries with the exception of Canada, exhibited a downward trend, underscoring the potential of increasing renewable energy consumption as an effective method to reduce carbon dioxide emissions and combat climate change. Furthermore, to reduce emissions and combat climate change, it is advisable for nations with the highest carbon dioxide emissions to adopt and successfully transition to renewable energy sources.

Teachers play a pivotal role, both as educators and community leaders at the heart of any communities. This study seeks to address: "How do community-based interventions influence teacher resilience and well-being in the context of disaster education, and consequently, how does this affect overall community resilience?" Employing the rigorous PRISMA systematic review approach, we examined relevant studies, emphasizing the relationship between teacher resilience, well-being, and the efficacy of community-based disaster education interventions. 47 studies met the specific inclusion criteria and were included in in-depth analysis. This study identified a set of key interventions that have demonstrably boosted teacher resilience and well-being. There was a discernible positive relationship between teacher resilience and the effectiveness of community disaster education initiatives. The relationship between teacher resilience, their well-being, and effective community-based disaster education interventions is intricate and multifaceted. Enhanced teacher resilience contributes…

Inflammation is a temporary response of the immune system that can be treated using common anti-inflammatory drugs. However, prolonged use of these drugs increases the risk of adverse side effects. Accordingly, there is an increasing need for alternative treatments for inflammation with fewer side effects. Exosomes are extracellular vesicles secreted by most eukaryotic cells and have been studied as a candidate for cell-free therapy for inflammatory diseases due to their immunomodulatory and anti-inflammatory properties. In recent years, the focus of exosome research has shifted from animal cell-derived exosomes to plant-derived exosome-like nanoparticles (PDENs). Plant-derived exosome-like nanoparticles (PDENs) are easier to obtain, have minimal safety concerns, and can be produced in higher quantities and lower cost than exosomes derived from animal cells. In this study, the isolation and analysis of the anti-inflammatory potential of PDENs from black nightshade berries (Solanum nigrum L.) were carried out. The results of isolation and characterization showed that PDENs had a…

I report here a comprehensive analysis about the political preferences embedded in Large Language Models (LLMs). Namely, I administer 11 political orientation tests, designed to identify the political preferences of the test taker, to 24 state-of-the-art conversational LLMs, both closed and open source. When probed with questions/statements with political connotations, most conversational LLMs tend to generate responses that are diagnosed by most political test instruments as manifesting preferences for left-of-center viewpoints. This does not appear to be the case for five additional base (i.e. foundation) models upon which LLMs optimized for conversation with humans are built. However, the weak performance of the base models at coherently answering the tests' questions makes this subset of results inconclusive. Finally, I demonstrate that LLMs can be steered towards specific locations in the political spectrum through Supervised Fine-Tuning (SFT) with only modest amounts of politically aligned data, suggesting SFT's potential to embed political orientation in LLMs. With LLMs…

Waste management poses a major challenge for cities worldwide, with significant environmental, economic, and social impacts. This paper proposes a novel waste management system leveraging recent advances in the Internet of Things (IoT), algorithms, and cloud analytics to enable more efficient, sustainable, and eco-friendly waste collection and processing in smart cities. An ultrasonic sensor prototype is tailored for reliable fill-level monitoring. A LoRaWAN and cellular network architecture provides city-wide connectivity. A cloud platform handles sensor data storage, processing, and analytics. Dynamic route optimization algorithms minimize time, distance, and fuel use based on real-time bin data. Extensive pilot studies in 10 different locations across Lahore, Pakistan, validated the system, processing over 200 million data points. The results showed a 32% improvement in route efficiency, a 29% decrease in fuel consumption and emissions, a 33% increase in waste processing throughput, and 18% vehicle maintenance savings versus conventional practices. This demonstrates quantifiable…

Chinese traditional cultural symbols possess great aesthetic and cultural value, and are widely utilized in product design. In this study, we explore the relationship between metaphor design based on traditional cultural symbols, customer experience and cultural identity, and further estimate how these three variables stimulate consumers' perceived value to generate consumers' purchase intention. Based on existing traditional cultural literature and Stimulus-organism-response theory (SOR), we proposed a theoretical research model to characterize the relationship among metaphor design based on traditional cultural symbols, customer experience, cultural identity, perceived value and consumers' purchase intention. A research survey was conducted and 262 questionnaires were collected in total with 241 valid. We used Smart PLS graph version 3.0 for data analysis. Results indicate that the cognition of metaphor design based on traditional cultural symbols and customer experience has a direct and significant impact on the emotional value thereby, eliciting consumers' purchase intention,…

Gender roles, as social constructs, play a significant role in shaping individuals' beliefs and attitudes, influencing various aspects of life, including perceptions and expectations surrounding motherhood. These beliefs, acquired through culture and society, can have an impact on our mental well-being. This research consists of three independent studies conducted in the Mexican population. In the first and second studies, we extended the Attitudes Towards Gender Roles Scale and Motherhood Beliefs Scale and performed psychometric validation through exploratory and confirmatory factor analysis. The aim of including additional items in both scales was to update these attitudes and beliefs in Mexican culture to avoid the traditionalist bias in both instruments. Finally, the third study examined the relationship between the new versions of both scales and symptoms of depression, anxiety, and Positive Psychological Functioning as indicators of mental health in women and men with and without children. Our findings revealed a significant association between higher levels of traditional…

We examined if the therapeutic alliance between study participants and intervention facilitators in a psilocybin-assisted therapy (PAT) trial changed over time and whether there were relationships between alliance, acute psilocybin experiences, and depression outcomes. In a randomized, waiting list-controlled clinical trial for major depressive disorder in adults (N = 24), participants were randomized to an immediate (N = 13) or delayed (N = 11) condition with two oral doses of psilocybin (20mg/70kg and 30mg/70kg). Ratings of therapeutic alliance significantly increased from the final preparation session to one-week post-intervention (p = .03, d = .43). A stronger total alliance at the final preparation session predicted depression scores at 4 weeks (r = -.65, p = .002), 6 months (r = -.47, p = .036), and 12 months (r = -.54, p = .014) post-intervention. A stronger total alliance in the final preparation session was correlated with higher peak ratings of mystical experiences (r = .49, p = .027) and psychological insight (r = .52, p = .040), and peak ratings of mystical experience…

Digital transformation enables small and medium enterprises (SMEs) to reduce or overcome their reliance on resources and energy, thereby minimizing their environmental impact and providing them with sustainable green competitive advantages. However, the reasons for this phenomenon are not yet clear. To further investigate this issue, we selected 391 Chinese SMEs to examine the relationships among green transformation, green innovation, government regulation, and green competitive advantages. Green innovation includes green product innovation and green process innovation, while government regulation includes incentive regulation, constraint regulation, and guidance regulation. The empirical results show that digital transformation can enhance SMEs' green competitive advantages. Additionally, the hypothesized mediating effect of green product innovation and green process innovation between digital transformation and green competitive advantages is supported, while the moderating effect of incentive regulation, constraint regulation, and guidance regulation on the relationship between…

This study presents a surveillance system developed for early detection of forest fires. Deep learning is utilized for aerial detection of fires using images obtained from a camera mounted on a designed four-rotor Unmanned Aerial Vehicle (UAV). The object detection performance of YOLOv8 and YOLOv5 was examined for identifying forest fires, and a CNN-RCNN network was constructed to classify images as containing fire or not. Additionally, this classification approach was compared with the YOLOv8 classification. Onboard NVIDIA Jetson Nano, an embedded artificial intelligence computer, is used as hardware for real-time forest fire detection. Also, a ground station interface was developed to receive and display fire-related data. Thus, access to fire images and coordinate information was provided for targeted intervention in case of a fire. The UAV autonomously monitored the designated area and captured images continuously. Embedded deep learning algorithms on the Nano board enable the UAV to detect forest fires within its operational area. The detection methods produced the following…

PURPOSE: Smart home devices have great potential to improve the quality of life and independence of older people, positively impacting their health, safety, and comfort. However, Chinese research in this field is still in its early stages. Therefore, more comprehensive and in-depth studies are needed to comprehend the various aspects influencing the acceptance and use of smart homes by older users. PATIENTS AND METHODS: This study adopted the Technology Acceptance Model (TAM) and included perceived usefulness, perceived ease of use, usage intention, intergenerational technology support, perceived value, and perceived risk as extension variables to delve deeper into the behavioral intentions of older users in smart home services. The study used a convenience sampling method to randomly distribute 236 questionnaires among older adults over the age of 60 in the school's community and neighboring urban communities who have experience in smart home use and who can complete human-computer interactions either independently or with the help of others, mainly focusing on the four sections:…

Perception of sounds and speech involves structures in the auditory brainstem that rapidly process ongoing auditory stimuli. The role of these structures in speech processing can be investigated by measuring their electrical activity using scalp-mounted electrodes. However, typical analysis methods involve averaging neural responses to many short repetitive stimuli that bear little relevance to daily listening environments. Recently, subcortical responses to more ecologically relevant continuous speech were detected using linear encoding models. These methods estimate the temporal response function (TRF), which is a regression model that minimises the error between the measured neural signal and a predictor derived from the stimulus. Using predictors that model the highly non-linear peripheral auditory system may improve linear TRF estimation accuracy and peak detection. Here, we compare predictors from both simple and complex peripheral auditory models for estimating brainstem TRFs on electroencephalography (EEG) data from 24 participants listening to continuous speech. We also…

Despite the popularity of k-means clustering, feature scaling before applying it can be an essential yet often neglected step. In this study, feature scaling via five methods: Z-score, Min-Max normalization, Percentile transformation, Maximum absolute scaling, or RobustScaler beforehand was compared with using the raw (i.e., non-scaled) data to analyze datasets having features with different or the same units via k-means clustering. The results of an experimental study show that, for features with different units, scaling them before k-means clustering provided better accuracy, precision, recall, and F-score values than when using the raw data. Meanwhile, when features in the dataset had the same unit, scaling them beforehand provided similar results to using the raw data. Thus, scaling the features beforehand is a very important step for datasets with different units, which improves the clustering results and accuracy. Of the five feature-scaling methods used in the dataset with different units, Z-score standardization and Percentile transformation provided similar performances…

This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The Hippopotamus Optimizer (HO) is a novel approach in meta-heuristic methodology that draws inspiration from the natural behaviour of hippos. The HO is built upon a trinary-phase model that incorporates mathematical representations of crucial aspects of Hippo's behaviour, including their movements in aquatic environments, defense mechanisms against predators, and avoidance strategies. This conceptual framework forms the basis for developing the multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions and size constraints concerning stresses on individual sections and constituent parts, these problems also involved competing objectives, such as reducing the weight of the structure and the maximum nodal displacement. The findings of six popular optimization methods were used to compare the results. Four industry-standard performance measures were used…

INTRODUCTION: Vaccination during pregnancy protects both the mother and the foetus from vaccine-preventable diseases. However, uptake of the recommended vaccines (influenza, pertussis, COVID-19) by pregnant women remains low in Europe and the USA. Understanding the reasons for this is crucial to inform strategies to increase vaccination rates in pregnant women. This qualitative systematic review aimed to identify the barriers and facilitators to vaccination against influenza, pertussis/whooping cough and COVID-19 during pregnancy and identify possible strategies to increase vaccination rates. METHODS: We conducted a comprehensive search of electronic databases, including Medline, PsycINFO, CINAHL, Web of Science, WHO database, Embase and grey literature to identify qualitative studies that explored barriers and facilitators to vaccine uptake among pregnant women (PROSPERO CRD42023399488). The search was limited to studies published between 2012 and 2022 conducted in high-income countries with established vaccination programmes during pregnancy. Studies were thematically analysed and…

BACKGROUND: Modern patient care depends on the continuous improvement of community and clinical pharmacy services, and artificial intelligence (AI) has the potential to play a key role in this evolution. Although AI has been increasingly implemented in various fields of pharmacy, little is known about the knowledge, attitudes, and practices (KAP) of pharmacy students and faculty members towards this technology. OBJECTIVES: The primary objective of this study was to investigate the KAP of pharmacy students and faculty members regarding AI in six countries in the Middle East as well as to identify the predictive factors behind the understanding of the principles and practical applications of AI in healthcare processes. MATERIAL AND METHODS: This study was a descriptive cross-sectional survey. A total of 875 pharmacy students and faculty members in the faculty of pharmacy in Jordan, Palestine, Lebanon, Egypt, Saudi Arabia, and Libya participated in the study. Data was collected through an online electronic questionnaire. The data collected included information about socio-demographics,…

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transparency and trust of complex DL methods. However, XAI systems face challenges in gaining acceptance within the healthcare sector, mainly due to technical hurdles in utilizing these systems in practice and the lack of human-centered evaluation/validation. In this study, we focus on visual XAI systems applied to DL-enabled diagnostic system in chest radiography. In particular, we conduct a user study to evaluate two prominent visual XAI techniques from the human perspective. To this end, we created two clinical scenarios for diagnosing pneumonia and COVID-19 using DL techniques applied to chest X-ray and CT scans. The achieved accuracy rates were 90% for pneumonia and 98% for COVID-19. Subsequently, we employed two well-known XAI methods, Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic…

Insect monitoring is essential to design effective conservation strategies, which are indispensable to mitigate worldwide declines and biodiversity loss. For this purpose, traditional monitoring methods are widely established and can provide data with a high taxonomic resolution. However, processing of captured insect samples is often time-consuming and expensive, which limits the number of potential replicates. Automated monitoring methods can facilitate data collection at a higher spatiotemporal resolution with a comparatively lower effort and cost. Here, we present the Insect Detect DIY (do-it-yourself) camera trap for non-invasive automated monitoring of flower-visiting insects, which is based on low-cost off-the-shelf hardware components combined with open-source software. Custom trained deep learning models detect and track insects landing on an artificial flower platform in real time on-device and subsequently classify the cropped detections on a local computer. Field deployment of the solar-powered camera trap confirmed its resistance to high temperatures and humidity, which…

Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack basic security properties and are easily exploitable. A rapidly growing field of CAN security research has emerged that seeks to detect intrusions or anomalies on CANs. Producing vehicular CAN data with a variety of intrusions is a difficult task for most researchers as it requires expensive assets and deep expertise. To illuminate this task, we introduce the first comprehensive guide to the existing open CAN intrusion detection system (IDS) datasets. We categorize attacks on CANs including fabrication (adding frames, e.g., flooding or targeting and ID), suspension (removing an ID's frames), and masquerade attacks (spoofed frames sent in lieu of suspended ones). We provide a quality analysis of each dataset; an enumeration of each datasets' attacks, benefits, and drawbacks; categorization as real vs. simulated CAN data and real vs. simulated attacks; whether the data is raw CAN data or signal-translated; number of vehicles/CANs; quantity in terms of time; and finally a suggested use case of each dataset.…

BACKGROUND: Post-stroke depression (PSD) is a frequent problem in stroke patients, affecting their rehabilitation process and functional outcomes. Several studies have investigated the relationship between PSD and functional outcomes, but the results have been inconsistent. OBJECTIVE: This systematic review of non-experimental studies aims to investigate the prevalence of post-stroke depression and the association between post-stroke depression and functional outcomes. METHOD: A search of PubMed, MEDLINE, Web of Science, and CINAHL Plus with Full Text was carried out from inception until January 2024. The literature was screened using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with relevant papers included. We extracted data from non-experimental studies that examined associations between PSD and functional outcomes. The Joanna Briggs Institute for systematic reviews was used for critical appraisal. RESULTS: Twenty-one studies met the study criteria, including sixteen cohort studies, four cross-sectional studies, and one case-control…

This article thoroughly examines the influence of digital transformation on the efficiency of corporate supply chains. As global economic integration accelerates and technological innovations deepen, digital transformation has become key to enhancing core corporate competitiveness. This research, utilizing data from A-share listed companies in China between 2007 and 2022, analyzes how companies improve supply chain efficiency through digital transformation. Furthermore, the study establishes a theoretical framework that demonstrates how digital transformation facilitates supply chain efficiency from the perspectives of internal governance and external competition. The research indicates that digital transformation plays a key role in significantly enhancing supply chain efficiency. Furthermore, the results of the mechanism analysis confirmed that digital transformation contributes to enhancing corporate supply chain efficiency by improving the level of corporate governance and the degree of market competition. The study also finds that the effect of digital transformation on supply…

AIM: The aim of this umbrella review was to establish which biopsychosocial factors are associated with development of chronic musculoskeletal pain. METHODS: Ovid Medline, Embase, Web of Science Core Collection, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, PsycINFO, CINAHL, PEDro, PROSPERO, Google Scholar and grey literature were searched from database inception to 4th April 2023. Systematic reviews of observational prospective longitudinal studies, including populations with <3 months (not chronic) musculoskeletal pain, investigating biopsychosocial factors that contribute to development of chronic (>3 months) musculoskeletal pain. Two reviewers searched the literature, assessed risk of bias (Assessing the Methodological Quality of Systematic Reviews-2), and evaluated quality (Grading of Recommendations, Assessment, Development and Evaluation) to provide an overall statement on the certainty of evidence for each biopsychosocial factor. Data analysis was performed through random effects meta-analysis (including meta-analysis of meta-analyses…

Human behavior is a dominant factor in road accidents, contributing to more than 70% of such incidents. However, gathering detailed data on individual drivers' behavior is a significant challenge in the field of road safety. As a result, researchers often narrow the scope of their studies thus limiting the generalizability of their findings. Our study aims to address this issue by identifying demographic-related variables and their indirect effects on road accident frequency. The theoretical basis is set through existing literature linking demographics to risky driving behavior and through the concept of "close to home" effect, finding that the upwards of 62% of accidents happen within 11km of a driver's home. Using regression-based machine learning models, our study, looking at England, UK, explores the theoretical linkages between demographics of an area and road accident frequency, finding that census data is able to explain over 28% of the variance in road accident rates per capita. While not replacing more in-depth research on driver behavior, this research validates trends…

Achieving WHO cervical cancer elimination goals will necessitate efforts to increase HPV vaccine access and coverage in low-and-middle-income countries (LMICs). Although LMICs account for the majority of cervical cancer cases globally, scale-up of HPV vaccine programs and progress toward coverage targets in LMICs has been largely insufficient. Understanding the barriers and facilitators that stakeholders face in the introduction and scale-up of HPV vaccination programs will be pivotal in ensuring that LMICs are equipped to optimize the implementation of HPV vaccination programs. This qualitative study interviewed 13 global stakeholders categorized as either academic partners or global immunization partners to ascertain perspectives regarding factors affecting the introduction and scale-up of HPV vaccination programs in LMICs. Global stakeholders were selected as their perspectives have not been as readily highlighted within the literature despite their key role in HPV vaccination programming. The results of this investigation identified upstream (e.g., financial considerations,…

A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the various challenges faced in the agriculture sector and play a significant role in planning and decision-making. Machine learning algorithms provided enough belief and proved their ability to predict crop yield. The selection of the most suitable crop is influenced by various environmental factors such as temperature, soil fertility, water availability, quality, and seasonal variations, as well as economic considerations such as stock availability, preservation capabilities, market demand, purchasing power, and crop prices. The paper outlines a framework used to evaluate the performance of various machine-learning algorithms for forecasting crop yields. The models were based on a range of prime parameters including pesticides, rainfall and average temperature. The Results of three machine learning algorithms, Categorical Boosting (CatBoost),…

Generative AI can potentially support teachers in lesson planning by making the process of generating an outline more efficient. This qualitative study employed an exploratory case study design to examine a specific lesson design activity involving a series of prompts and responses from ChatGPT. The desired science lesson on heredity was aimed at first grade students. We analyzed the process's efficiency, finding that within 30 minutes we could generate and substantially refine a lesson plan that accurately aligned with the desired curriculum framework and the 5E model of instruction. However, the iterations of the lesson plan included questionable components, missing details, and a fake resource. We discussed the implications of these findings for faculty looking to train pre-service teachers to appropriately use generative AI in lesson planning.

BACKGROUND: The global outbreak of COVID-19 has brought to light the profound impact that large-scale disease outbreaks can have on healthcare systems and the dedicated professionals who serve within them. It becomes increasingly important to explore strategies for retaining nurses and physicians within hospital settings during such challenging times. This paper aims to investigate the determinants of retention among nurses and physicians during the COVID-19 pandemic. METHOD: A systematic review of other potential determinants impacting retention rates during the pandemic was carried out. Secondly, a meta-analysis on the prevalence of intention to leave for nurses and physicians during the COVID-19 pandemic. FINDINGS: A comprehensive search was performed within four electronic databases on March 17 2023. Fifty-five papers were included in the systematic review, whereas thirty-three papers fulfilled the eligibility criteria for the meta-analysis. The systematic review resulted in six themes of determinants impacting intention to leave: personal characteristics, job demands,…

A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model's performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we…

Inflammation is an immune system response that identifies and eliminates foreign material. However, excessive and persistent inflammation could disrupt the healing process. Plant-derived exosome-like nanoparticles (PDENs) are a promising candidate for therapeutic application because they are safe, biodegradable and biocompatible. In this study, papaya PDENs were isolated by a PEG6000-based method and characterized by dynamic light scattering (DLS), transmission Electron Microscopy (TEM), bicinchoninic acid (BCA) assay method, GC-MS analysis, total phenolic content (TPC) analysis, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. For the in vitro test, we conducted internalization analysis, toxicity assessment, determination of nitrite concentration, and assessed the expression of inflammatory cytokine genes using qRT-PCR in RAW 264.7 cells. For the in vivo test, inflammation was induced by caudal fin amputation followed by analysis of macrophage and neutrophil migration in zebrafish (Danio rerio) larvae. The result showed that papaya PDENs can be well isolated using the optimized…

BACKGROUND: The Mini-Cog is a rapid screening tool that can be administered to older adults to detect cognitive impairment (CI); however, the accuracy of the Mini-Cog to detect CI for older patients in various healthcare settings is unclear. OBJECTIVES: To evaluate the diagnostic accuracy of the Mini-Cog to screen for cognitive impairment in older patients across different healthcare settings. METHODS/DESIGN: We searched nine electronic databases (including MEDLINE, Embase) from inception to January 2023. We included studies with patients ≥60 years old undergoing screening for cognitive impairment using the Mini-Cog across all healthcare settings. A cut-off of ≤ 2/5 was used to classify dementia, mild cognitive impairment (MCI), and cognitive impairment (defined as either MCI or dementia) across various settings. The diagnostic accuracy of the Mini-Cog was assessed against gold standard references such as the Diagnostic and Statistical Manual of Mental Disorders (DSM). A bivariate random-effects model was used to estimate accuracy and diagnostic ability. The risk of bias was…

Computer-aided diagnosis systems based on deep learning algorithms have shown potential applications in rapid diagnosis of diabetic retinopathy (DR). Due to the superior performance of Transformer over convolutional neural networks (CNN) on natural images, we attempted to develop a new model to classify referable DR based on a limited number of large-size retinal images by using Transformer. Vision Transformer (ViT) with Masked Autoencoders (MAE) was applied in this study to improve the classification performance of referable DR. We collected over 100,000 publicly fundus retinal images larger than 224×224, and then pre-trained ViT on these retinal images using MAE. The pre-trained ViT was applied to classify referable DR, the performance was also compared with that of ViT pre-trained using ImageNet. The improvement in model classification performance by pre-training with over 100,000 retinal images using MAE is superior to that pre-trained with ImageNet. The accuracy, area under curve (AUC), highest sensitivity and highest specificity of the present model are 93.42%, 0.9853, 0.973…

Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper…

This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users.

It is thought to be risk-free, environmentally benign, and safe for biological processes to produce zinc oxide nanoparticles from renewable resources. This study examined Cassia javanica's ability to create ZnONPs. The generated ZnONPs were analyzed using a variety of techniques, such as TEM, FTIR spectroscopy, UV-Vis spectroscopy, and XRD analysis. The antibacterial potential of ZnONPs has been investigated using both Agar well diffusion and microtitreplate (MTP) methods. One method used to evaluate ZnONPs' capacity to scavenge free radicals at different concentrations was the DPPH method. The permanent zinc oxide (ZnO) shape and the naturally occurring crystal structure of ZnONPs were validated by the XRD data. ZnONPs showed antibacterial activity with MICs of 31.7 μg/mL toward Bacillus subtilis, 62.5 μg/mL for Salmonella typhimurium, Escherichia coli while Clostridium sporogenes and Bacillus pumilus was 125μg/mL. Furthermore, ZnONPs demonstrated a range of antibiofilm activities toward Staphylococcus aureus (MRSA). ZnONPs showed an intriguing antioxidant capacity, achieving IC50…

OBJECTIVE: To comprehensively assess the global, regional and national burden of polycystic ovary syndrome (PCOS) in incidence, prevalence, and years lived with disability (DLYs) based on the Global Burden of Disease Study (GBD) 2019. METHODS: This was a cross-sectional descriptive study. Data on PCOS incidence, prevalence, and DLYs from 1990 to 2019 were obtained from the GBD study 2019. According to the commonwealth income, WHO region, and the sociodemographic index, the estimates were demonstrated along with the estimated annual percentage change (EAPC). The EAPC data were analyzed by four levels of hierarchical clustering and displayed in the world map. The Autoregressive Integrated Moving Average (ARIMA) and Bayesian age-period-cohort (BAPC) model was used to predict the PCOS burden in the next 20 years. RESULTS: From 1990 to 2019, the number of PCOS incidence in one year increased from 1.4 million in 1990 to 2.1 million in 2019 (54.3%). Only the EAPC estimates of incidence in the Region of the Americas decreased, and their aged-standardized incidence rate (ASIR) values were…

This paper presents Super Twisting Sliding Mode Control with a novel Fuzzy PID Surface for improved trajectory tracking of quadrotor unmanned aerial vehicles under external disturbances. First, quadrotor dynamic model with six degrees of freedom (6-DOF) is developed using Newton-Euler Method. Then, a robust Sliding Mode Control based on a new Fuzzy PID Surface is designed to be capable of automatically adjusting its gain parameters. The proposed SMC controller applies super twisting algorithm with PID surface to reduce chattering and a fuzzy logic controller to automatically adjust the gain parameters in order to enhance robustness. Furthermore, the solution to stability has been given by the Lyapunov method. The controller's performance is tested through various trajectories, parameter variations, and disturbance scenarios, comparing it with recent alternatives such as Sliding Mode Control, Fuzzy Sliding Mode Control, and Fuzzy Super Twisting Sliding Mode Control using numerical simulations. The simulation results show that the proposed controller has better tracking performance,…

In an era marked by pervasive digital connectivity, cybersecurity concerns have escalated. The rapid evolution of technology has led to a spectrum of cyber threats, including sophisticated zero-day attacks. This research addresses the challenge of existing intrusion detection systems in identifying zero-day attacks using the CIC-MalMem-2022 dataset and autoencoders for anomaly detection. The trained autoencoder is integrated with XGBoost and Random Forest, resulting in the models XGBoost-AE and Random Forest-AE. The study demonstrates that incorporating an anomaly detector into traditional models significantly enhances performance. The Random Forest-AE model achieved 100% accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (MCC), outperforming the methods proposed by Balasubramanian et al., Khan, Mezina et al., Smith et al., and Dener et al. When tested on unseen data, the Random Forest-AE model achieved an accuracy of 99.9892%, precision of 100%, recall of 99.9803%, F1 score of 99.9901%, and MCC of 99.8313%. This research highlights the effectiveness of the…

Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cataloging AEs within clinical reports. Utilizing data from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study particularly focuses on AEs to evaluate LLMs' capability for AE extraction. A variety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama2, were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5 model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match and 0.816 for relaxed match. The encouraging performance of the AE-GPT underscores LLMs' potential in processing medical data, indicating a significant stride towards advanced AE detection, thus presumably generalizable to other AE extraction tasks.

BACKGROUND: Digital transformation (DT) involves integrating digital technologies into organizations to improve productivity, efficiency, and quality. Investing in the workforce's skillsets is essential for successful DT. However, it remains unclear which skillsets are essential. OBJECTIVES: This study aims to identify and define the essential skillsets needed for exploiting the full potential of DT, and to consolidate the identified skills into a comprehensive framework of DT skills. METHOD: A systematic literature review was conducted using the PRISMA approach for selecting studies. This led to the selection of 36 articles that were examined using thematic analysis for identifying and consolidating skills into a framework. RESULTS: The Digital Transformation Skills Framework (DTSF) was developed, which contains six overarching skillsets and 44 underlying skills. The framework covers key skillsets in the areas of digital work, entrepreneurship, evidence-based work, collaboration, communication, and adaptation. CONCLUSION AND DISCUSSION: The DTSF offers a comprehensive understanding…

OBJECTIVE: The adoption of Generative AI in education presents both opportunities and challenges, particularly regarding its potential to foster student dependency. However, the psychological drivers of this dependency remain unclear. This study addresses this gap by applying the Interaction of Person-Affect-Cognition-Execution (I-PACE) model and Basic Psychological Needs (BPN) theory to explore how specific personality traits-neuroticism, self-critical perfectionism, and impulsivity-contribute to AI dependency through needs frustration, negative academic emotions, and reinforced performance beliefs. METHOD: Data were collected from 958 university students (Mage = 21.67) across various disciplines. Structural equation modeling (SEM) was used to analyze the relationships among the variables. RESULTS: Neuroticism, self-critical perfectionism, and impulsivity were found to be significantly associated with increase needs frustration and negative academic emotions, which in turn reinforced students' positive beliefs about performance of AI tools, deepening their dependency. The study…

Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutional Network (DenseNet)-121 and Visual Geometry Group (VGG)-16 as feature extractor networks, bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction, and the Support Vector Machine (SVM) and Random Forest (RF) algorithms to perform classification. For improved performance, the selected pre-trained CNN hyperparameters have been optimized using a modified grey wolf optimization method. The experimental analysis for the presented model on the Mammographic Image Analysis…

Cybercrime is a major challenge facing the world, with estimated costs ranging from the hundreds of millions to the trillions. Despite the threat it poses, cybercrime is somewhat an invisible phenomenon. In carrying out their virtual attacks, offenders often mask their physical locations by hiding behind online nicknames and technical protections. This means technical data are not well suited to establishing the true location of offenders and scholarly knowledge of cybercrime geography is limited. This paper proposes a solution: an expert survey. From March to October 2021 we invited leading experts in cybercrime intelligence/investigations from across the world to participate in an anonymized online survey on the geographical location of cybercrime offenders. The survey asked participants to consider five major categories of cybercrime, nominate the countries that they consider to be the most significant sources of each of these types of cybercrimes, and then rank each nominated country according to the impact, professionalism, and technical skill of its offenders. The outcome of…

In microbial community sequencing, involving bacterial ribosomal 16S rDNA or fungal ITS, the targeted genes are the basis for taxonomical assignment. The traditional bioinformatical procedure has for decades made use of a clustering protocol by which sequences are pooled into packages of shared percent identity, typically at 97%, to yield Operational Technical Units (OTUs). Progress in the data processing methods has however led to the possibility of minimizing technical sequencers errors, which were the main reason for the OTU choice, and to analyze instead the exact Amplicon Sequence Variants (ASV) which is a choice yielding much less agglomerated reads. We have tested the two procedures on the same 16S metabarcoded bacterial amplicons dataset encompassing a series of samples from 17 adjacent habitats, taken across a 700 meter-long transect of different ecological conditions unfolding in a gradient spanning from cropland, through meadows, forest and all successional transitions up to the seashore, within the same coastal area. This design allowed to scan a high biodiversity basin…

Environmental stewardship is a term describing both the philosophy and the actions required to protect, restore, and sustainably use natural resources for the future benefit of the environment and society. In this paper, we review the environmental science literature to map the types of practical actions that are identified as 'environmental stewardship' using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. We specifically mapped: 1) the type of actions and outcomes targeting the natural environment that have been categorized as environmental stewardship, 2) the main actors, and the underlying factors influencing their environmental stewardship actions, and 3) the methods used to mobilize environmental stewardship actions once these factors are known. From the 77 selected articles, we found the term environmental stewardship encompassed a multitude of different actions, undertaken by a range of actors and addressing an array of issues that impact biodiversity on the land and in the water. These stewardship actions were conducted…

Depression and anxiety are common comorbid conditions associated with cancer, however the risk factors responsible for the onset of depression and anxiety in cancer patients are not fully understood. Also, there is little clarity on how these factors may vary across the cancer phases: diagnosis, treatment and depression. We aimed to systematically understand and synthesise the risk factors associated with depression and anxiety during cancer diagnosis, treatment and survivorship. We focused our review on primary and community settings as these are likely settings where longer term cancer care is provided. We conducted a systematic search on PubMed, PsychInfo, Scopus, and EThOS following the PRISMA guidelines. We included cross-sectional and longitudinal studies which assessed the risk factors for depression and anxiety in adult cancer patients. Quality assessment was undertaken using the Newcastle-Ottawa assessment checklists. The quality of each study was further rated using the Agency for Healthcare Research and Quality Standards. Our search yielded 2645 papers, 21 of these were…

Diabetes mellitus (DM) poses a significant challenge to public health. Effective diabetes self-management education (DSME) interventions may play a pivotal role in the care of people with type 2 diabetes mellitus (T2DM) in low- and middle-income countries (LMICs). A specific up-to-date systematic review is needed to assess the effect of DSME interventions on glycaemic control, cardiometabolic risk, self-management behaviours, and psychosocial well-being among T2DM across LMICs. The MEDLINE, Embase, CINAHL, Global Health, and Cochrane databases were searched on 02 August 2022 and then updated on 10 November 2023 for published randomised controlled trials (RCTs) and quasi-experimental studies. The quality of the studies was assessed, and a random-effect model was used to estimate the pooled effect of diabetes DSME intervention. Heterogeneity (I2) was tested, and subgroup analyses were performed. Egger's regression test and funnel plots were used to examine publication bias. The risk of bias of the included studies was assessed using the Cochrane risk-of-bias tool for randomized trial…

BACKGROUND: Achilles tendon rupture (ATR) account for 10.7% of all tendon and ligament injuries and causes lasting muscular deficits and have a profound impact on patients' quality of life. The incidence, characteristics and management of ATR in the United Kingdom (UK) is poorly understood. This investigation aims to understand the incidence of ATR in the UK. METHODS: Prospective data collection of ATR incidence from a United Kingdom Emergency department. Retrospective review of management protocols and immobilisation duration from electronic medical records. RESULTS: ATR incidence is 8 per 100,000 people per annum. Participants were predominately male (79.2%) and primarily reported a sporting mechanism of injury (65.2%). Mean immobilisation duration was 63.1 days. 97.1% were non-surgically managed post ATR. 46.2% of participants had experienced a previous ATR or Achilles tendinopathy prior to their current ATR. CONCLUSION: The incidence of ATR found was 8. cases per 100,000 people per annum. Most ATR were managed non-surgically in this cohort. The majority of ruptures occurred…

The world's digital information ecosystem continues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content-so-called superspreaders-are at the center of this problem. We quantitatively confirm this hypothesis and introduce simple metrics to predict the top superspreaders several months into the future. We then conduct a qualitative review to characterize the most prolific superspreaders and analyze their sharing behaviors. Superspreaders include pundits with large followings, low-credibility media outlets, personal accounts affiliated with those media outlets, and a range of influencers. They are primarily political in nature and use more toxic language than the typical user sharing misinformation. We also find concerning evidence that suggests Twitter may be overlooking prominent superspreaders. We hope this work will further public understanding of bad actors and promote steps to mitigate their negative impacts on healthy digital discourse.

CONTEXT: Cardiovascular diseases (CVDs) and cancer are the two main leading causes of death and disability worldwide. Suboptimal diet, poor in vegetables, fruits, legumes and whole grain, and rich in processed and red meat, refined grains, and added sugars, is a primary modifiable risk factor. Based on health, economic and ethical concerns, plant-based diets have progressively widespread worldwide. OBJECTIVE: This umbrella review aims at assessing the impact of animal-free and animal-products-free diets (A/APFDs) on the risk factors associated with the development of cardiometabolic diseases, cancer and their related mortalities. DATA SOURCES: PubMed and Scopus were searched for reviews, systematic reviews, and meta-analyses published from 1st January 2000 to 31st June 2023, written in English and involving human subjects of all ages. Primary studies and reviews/meta-analyses based on interventional trials which used A/APFDs as a therapy for people with metabolic diseases were excluded. DATA EXTRACTION: The umbrella review approach was applied for data extraction and analysis. The…

This paper investigates the capacity of ChatGPT, an advanced language model created by OpenAI, to mitigate the side effects encountered by learners in Personal Learning Environments (PLEs) within higher education. A series of semi-structured interviews were conducted with six professors and three Information and Communication Technology (ICT) experts. Employing thematic analysis, the interview data were assessed, revealing that the side effects stemming from the learner and learning perspectives could be primarily categorized into cognitive, non-cognitive, and metacognitive challenges. The findings of the thematic analysis indicate that, from a cognitive standpoint, ChatGPT can generate relevant and trustworthy information, furnish personalized learning resources, and facilitate interdisciplinary learning to fully actualize learners' potential. Moreover, ChatGPT can aid learners in cultivating non-cognitive skills, including motivation, perseverance, self-regulation, and self-efficacy, as well as metacognitive abilities such as self-determination, self-efficacy, and self-regulation,…

Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patients. Given this, early intervention is particularly crucial, as it can not only significantly improve the prognosis of patients but also provide valuable reference information for clinical treatment. This study selected the BRFSS (Behavioral Risk Factor Surveillance System) dataset, which is publicly available on the Kaggle platform, as the research object, aiming to provide a scientific basis for the early diagnosis and treatment of diabetes through advanced machine learning techniques. Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. The results show: (1) Random oversampling, ADASYN, SMOTE, and SMOTEENN were used for data balance processing, among which SMOTEENN showed better efficiency and effect in dealing with data imbalance. (2) The…

INTRODUCTION: The neutrophil-percentage-to-albumin ratio (NPAR), a novel inflammatory biomarker, has been used to predict the prognosis of patients with cancer and cardiovascular disease. However, the relationship between NPAR and chronic kidney disease (CKD) remains unknown. The purpose of this study was to investigate the possible association between NPAR and CKD. METHODS: The cross-sectional study included participants with complete information on NPAR, serum creatinine (Scr), or urinary albumin-to-creatinine ratio (UACR) from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). CKD was defined as the presence of either low estimated glomerular filtration rate (eGFR) or albuminuria. Univariate and multivariate logistic regression and restricted cubic spline regression were used to assess the linear and nonlinear associations between NPAR and renal function. Subgroup and interactive analyses were performed to explore potential interactive effects of covariates. Missing values were imputed using random forest. RESULTS: A total of 25,236 participants were…

OBJECTIVE: At some point in their career, many healthcare workers will experience psychological distress associated with being unable to take morally or ethically correct action, as it aligns with their own values; a phenomenon known as moral distress. Similarly, there are increasing reports of healthcare workers experiencing long-term mental and psychological pain, alongside internal dissonance, known as moral injury. This review examined the triggers and factors associated with moral distress and injury in Health and Social Care Workers (HSCW) employed across a range of clinical settings with the aim of understanding how to mitigate the effects of moral distress and identify potential preventative interventions. METHODS: A systematic review was conducted and reported according to recommendations from Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Searches were conducted and updated regularly until January 2024 on 2 main databases (CENTRAL, PubMed) and three specialist databases (Scopus, CINAHL, PsycArticles), alongside hand searches of…

BACKGROUND: Illicitly manufactured fentanyls and stimulants are implicated in the escalating US mortality from drug overdose. San Francisco, California (SF) has seen declining fentanyl injection while smoking has increased. Beliefs and behaviors surrounding this development are not well understood. METHODS: The study used rapid ethnography to explore fentanyl and methamphetamine use in SF. The team conducted semi-structured interviews (n = 34) with participants recruited from syringe service programs. Video-recorded smoking sequences (n = 12), photography and daily field notes supplemented interview data. RESULTS: Difficulty injecting and fear of overdose motivated transitions from injecting to smoking. Fentanyl was extremely cheap-$10/gram-with variability in quality. Foil was the most commonly used smoking material but glass bubbles, bongs and dabbing devices were also popular. No reliable visible methods for determining fentanyl quality existed, however, participants could gauge potency upon inhalation, and developed techniques to regulate dosage. Several participants reported at…

Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Quick diagnosis and treatment planning can be facilitated by deep learning models' ability to process images rapidly and deliver outcomes instantly. Our research aims to provide a non-invasive method for early detection and timely eye disease treatment using a Convolutional Neural Network (CNN). We used a dataset Retinal Fundus Multi-disease Image Dataset (RFMiD), which contains various categories of fundus images representing different eye diseases, including Media Haze (MH), Optic Disc Cupping (ODC), Diabetic Retinopathy (DR), and healthy images (WNL). Several pre-processing techniques were applied to improve the model's performance, such as data augmentation, cropping, resizing, dataset splitting, converting images to arrays, and one-hot encoding. CNNs have extracted extract pertinent features from the input color fundus images. These…

The outbreak of SARS-CoV-2, which causes COVID-19, has significantly impacted the psychological and physical health of a wide range of individuals, including healthcare professionals (HCPs). This umbrella review aims provide a quantitative summary of meta-analyses that have investigated the prevalence of stress, anxiety, depression, and sleep disturbance among HCPs during the COVID-19 pandemic. An umbrella review of systematic reviews and meta-analyses reviews was conducted. The search was performed using the EMBASE, PubMed, CINAHL, MEDLINE, PsycINFO, and Google Scholar databases from 01st January 2020 to 15th January 2024. A random-effects model was then used to estimate prevalence with a 95% confidence interval. Subgroup analysis and sensitivity analyses were then conducted to explore the heterogeneity of the sample. Seventy-two meta-analyses involved 2,308 primary studies were included after a full-text review. The umbrella review revealed that the pooled prevalence of stress, anxiety, depression, and sleep disturbance among HCPs during the COVID-19 pandemic was 37% (95% CI…

BACKGROUND: The mental health of healthcare workers during the coronavirus-2019 pandemic was seriously affected, and the risk of mental health problems was high. The present study sought to systematically evaluate the mental health problems of healthcare workers worldwide during the pandemic and to determine the latest global frequency of COVID-19 associated mental health problems. METHODS: Data in the Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, Elsevier, MEDLINE, PubMed, PsycINFO and the Web of Science before November 11, 2022, were systematically searched. Cohort, case-control and cross-sectional studies were included. The meta-analysis used a random effects model to synthesize the comprehensive prevalence rate of mental health problems. Subgroup analyses were performed based on time of data collection; whether the country was or was not developed; continent; doctors and nurses; doctors/nurses vs. other healthcare workers; and psychological evaluation scale. RESULTS: A total of 161 studies were included, including 341,014 healthcare workers…

INTRODUCTION: There is a resurgence of interest in the therapeutic potential of psychedelic substances such as 3,4-methylenedioxymethamphetamine (MDMA). Primary findings from our randomized, double-blind, placebo-controlled, multi-site Phase 3 clinical trial of participants with severe PTSD (NCT03537014) showed that MDMA-assisted therapy induced significant attenuation in the Clinician-Administered PTSD Scale for DSM-5 compared to Therapy with placebo. Deficits in emotional coping skills and altered self-capacities constitute major obstacles to successful completion of available treatments. The current analysis evaluated the differential effects of MDMA-assisted therapy and Therapy with placebo on 3 transdiagnostic outcome measures and explored the contribution of changes in self-experience to improvement in PTSD scores. METHODS: Participants were randomized to receive manualized therapy with either MDMA or placebo during 3 experimental sessions in combination with 3 preparation and 9 integration therapy visits. Symptoms were measured at baseline and 2 months after the last…

BACKGROUND: Falls contribute to impairments in activities of daily living (ADLs), resulting in significant declines in the quality of life, safety, and functioning of older adults. Understanding the magnitude and duration of the effect of falls on ADLs, as well as identifying the characteristics of older adults more likely to have post-fall ADL impairment is critical to inform fall prevention and post-fall intervention. The purpose of this study is to 1) Quantify the association between falls and post-fall ADL impairment and 2) Model trajectories of ADL impairment pre- and post-fall to estimate the long-term impact of falls and identify characteristics of older adults most likely to have impairment. METHOD: Study participants were from the Ginkgo Evaluation of Memory Study, a randomized controlled trial in older adults (age 75+) in the United States. Self-reported incident falls and ADL scores were ascertained every 6 months over a 7-year study period. We used Cox proportional hazards analyses (n = 2091) to quantify the association between falls and ADL impairment and latent class…

Augmented Reality (AR) offers a rich business format, convenient applications, great industrial potential, and strong commercial benefits. The integration of AR technology with online shopping has brought tremendous changes to e-commerce. The Technology Acceptance Model (TAM) is a mature model for assessing consumer acceptance of new technologies, and applying it to evaluate the impact of AR online shopping experiences on consumer purchase intention is an urgently needed area of research. Firstly, the typical applications of AR in online shopping were reviewed, and the connotations and experiences of AR online shopping were summarized. Secondly, using the five types of AR online shopping experiences as antecedent variables, and perceived ease of use and perceived usefulness as intermediate variables, a theoretical model was constructed to explore the impact of AR online shopping experiences on customer purchase intentions, followed by an empirical study. Finally, suggestions were proposed for optimizing the online shopping experience to enhance purchase intentions. The article…

This manuscript investigates bifurcation, chaos, and stability analysis for a significant model in the research of shallow water waves, known as the second 3D fractional Wazwaz-Benjamin-Bona-Mahony (WBBM) model. The dynamical system for the above-mentioned nonlinear structure is obtained by employing the Galilean transformation to fulfill the research objectives. Subsequent analysis includes planar dynamic systems techniques to investigate bifurcations, chaos, and sensitivities within the model. Our findings reveal diverse features, including quasi-periodic, periodic, and chaotic motion within the governing nonlinear problem. Additionally, diverse soliton structures, like bright solitons, dark solitons, kink waves, and anti-kink waves, are thoroughly explored through visual illustrations. Interestingly, our results highlight the importance of chaos analysis in understanding complex system dynamics, prediction, and stability. Our techniques' efficiency, conciseness, and effectiveness advance our understanding of this model and suggest broader applications for exploring nonlinear…

The rapid advances in Generative AI tools have produced both excitement and worry about how AI will impact academic writing. However, little is known about what norms are emerging around AI use in manuscript preparation or how these norms might be enforced. We address both gaps in the literature by conducting a survey of 271 academics about whether it is necessary to report ChatGPT use in manuscript preparation and by running GPT-modified abstracts from 2,716 published papers through a leading AI detection software to see if these detectors can detect different AI uses in manuscript preparation. We find that most academics do not think that using ChatGPT to fix grammar needs to be reported, but detection software did not always draw this distinction, as abstracts for which GPT was used to fix grammar were often flagged as having a high chance of being written by AI. We also find disagreements among academics on whether more substantial use of ChatGPT to rewrite text needs to be reported, and these differences were related to perceptions of ethics, academic role, and English language…

BACKGROUND: Adolescent idiopathic scoliosis (AIS) is a spinal deformity that affects approximately 4% of the world's population. Several hypotheses regarding the etiology of AIS have been investigated. In the last decades, impaired visual-spatial perception, alterations in spatial body orientation and sensory integration deficits have been documented. OBJECTIVE: We aimed to summarize the neurophysiological, balance, and motion evidence related to AIS published in the last fifteen years, between January 2008 and April 2023. Both observational and interventional studies were considered. Only studies using quantitative assessment methods, such as electroencephalography (EEG), electromyography (EMG), magnetic resonance imaging (MRI), somatosensory evoked potentials, force platform, or motion capture, were included. METHODS: 1250 eligible records identified from online database searching were filtered by duplicate removal, title and abstract screening, and qualitative analysis. 61 articles met the inclusion criteria (i.e., Cobb range 10°-35°, age range 10-18 years) and were summarized.…

As ChatGPT emerges as a potential ally in healthcare decision-making, it is imperative to investigate how users leverage and perceive it. The repurposing of technology is innovative but brings risks, especially since AI's effectiveness depends on the data it's fed. In healthcare, ChatGPT might provide sound advice based on current medical knowledge, which could turn into misinformation if its data sources later include erroneous information. Our study assesses user perceptions of ChatGPT, particularly of those who used ChatGPT for healthcare-related queries. By examining factors such as competence, reliability, transparency, trustworthiness, security, and persuasiveness of ChatGPT, the research aimed to understand how users rely on ChatGPT for health-related decision-making. A web-based survey was distributed to U.S. adults using ChatGPT at least once a month. Bayesian Linear Regression was used to understand how much ChatGPT aids in informed decision-making. This analysis was conducted on subsets of respondents, both those who used ChatGPT for healthcare decisions and those who did…

Ethiopia's healthcare system relies on traditional medicinal practices that use medicinal plants to treat human and livestock ailments. However, the lack of empirical validation regarding the efficacy of these treatments against specific infectious diseases necessitates rigorous scientific investigations. The objective of this study was to investigate the antibacterial activity and phytochemical screening on five selected medicinal plant species, namely Solanum somalense Franchet., Verbascum sinaiticum Benth., Rumex nervosus Vahl, Withania somnifera (L.) Dunal and Calpurnia aurea (Ait.) Benth. The plants were first identified jointly with local informants and later considering mainly their high informant consensus and fidelity level values for their efficacy in treating infectious diseases in the area. Ethanol and aqueous extracts were prepared from the plant materials, and their antibacterial activities were evaluated against standard bacterial strains, representing both gram-positive and gram-negative types. To assess the antibacterial activity of the extracts, the minimum…

Transgender individuals face stigma, discrimination, and other barriers impacting their ability to engage in physical activity (PA). We aim to review current literature on PA among transgender individuals. A systemic literature search of research studies from 2010-2023 was conducted. Studies must have reported a measure of PA and gender, be original research, and focus on transgender participants' PA. Rates of PA for transgender individuals were lower compared to cisgender or sexual minority individuals. Transgender women were less likely to engage in PA than other groups. Qualitative results suggest transgender oppression, stigma, discrimination, body image, unwelcoming environments (gyms, locker rooms, swimming pools), and the dichotomous structure of sport contribute to lower rates of PA among transgendered individuals. Disparities in PA for transgender individuals exist. Policy, environment, and system changes are needed to reduce transgender stigma in sport and PA settings. Current legislation is being developed and implemented in the United States regarding the place of…

High-dimensional data is widely used in many fields, but selecting key features from it is challenging. Feature selection can reduce data dimensionality and weaken noise interference, thereby improving model efficiency and enhancing model interpretability. In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. The algorithm simulates the process of natural selection, searches for possible subsets of feature, and finds the subsets of feature that optimizes the performance of the model. The results show that when the value of K is less than 4 or more than 8, the recognition rate is very low. After adaptive bias filtering, 724 features are filtered to 372, and the accuracy is improved from 0.9352 to 0.9815. From 714 features to 406 Gaussian codes, the accuracy is improved from 0.9625 to 0.9754. Among all tests, the colon has the highest average accuracy, followed by small round blue cell tumor(SRBCT), lymphoma, central nervous system(CNS) and ovaries. The green curve…

The proliferation of cyber threats necessitates robust security measures to safeguard critical assets and data in today's evolving digital landscape. Small and Medium Enterprises (SMEs), which are the backbone of the global economy are particularly vulnerable to these threats due to inadequate protection for critical and sensitive information, budgetary constraints, and lack of cybersecurity expertise and personnel. Security Information and Event Management (SIEM) systems have emerged as pivotal tools for monitoring, detecting, and responding to security incidents. While proprietary SIEM solutions have historically dominated the market, open-source SIEM systems have gained prominence for their accessibility and cost-effectiveness for SMEs. This article presents a comprehensive study focusing on the evaluation of open-source SIEM systems. The research investigates the capabilities of these open-source solutions in addressing modern security challenges and compliance with regulatory requirements. Performance aspects are explored through empirical testing in simulated enterprise-grade…

INTRODUCTION: ChatGPT, a sophisticated large language model (LLM), has garnered widespread attention for its ability to mimic human-like communication. As recent studies indicate a potential supportive role of ChatGPT in academic writing, we assessed the LLM's capacity to generate accurate and comprehensive scientific abstracts from published Randomised Controlled Trial (RCT) data, focusing on the adherence to the Consolidated Standards of Reporting Trials for Abstracts (CONSORT-A) statement, in comparison to the original authors' abstracts. METHODOLOGY: RCTs, identified in a PubMed/MEDLINE search post-September 2021 across various medical disciplines, were subjected to abstract generation via ChatGPT versions 3.5 and 4, following the guidelines of the respective journals. The overall quality score (OQS) of each abstract was determined by the total number of adequately reported components from the 18-item CONSORT-A checklist. Additional outcome measures included percent adherence to each CONOSORT-A item, readability, hallucination rate, and regression analysis of reporting quality…

BACKGROUND: Computed tomography (CT) scan is a common imaging technique used to evaluate the severity of a head injury. The overuse of diagnostic interventions in the health system is a growing concern worldwide. Objectives: The aim of this systematic review is to investigate the rate of CT scan overuse in cases of mild head injury. METHODS: Eligibility criteria: We encompassed observational studies-either designed as cohort, case-control, or cross-sectional investigations-that reported on CT scan overuse rates for mild head injuries. Studies had to be published in peer-reviewed, English-language sources and provide full content access Information sources: Web of Sciences, Scopus, Medline via PubMed, the Cochrane Library and Embase were searched from inception until April 1, 2023. Studies were included if reporting the overuse of CT scans for mild head injuries using validated criteria. Risk of bias: We used the Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tool to evaluate the risk bias assessment of included studies. Two independent reviewers evaluated the…

Cervical cancer remains a leading cause of female mortality, particularly in developing regions, underscoring the critical need for early detection and intervention guided by skilled medical professionals. While Pap smear images serve as valuable diagnostic tools, many available datasets for automated cervical cancer detection contain missing data, posing challenges for machine learning models' efficacy. To address these hurdles, this study presents an automated system adept at managing missing information using ADASYN characteristics, resulting in exceptional accuracy. The proposed methodology integrates a voting classifier model harnessing the predictive capacity of three distinct machine learning models. It further incorporates SVM Imputer and ADASYN up-sampled features to mitigate missing value concerns, while leveraging CNN-generated features to augment the model's capabilities. Notably, this model achieves remarkable performance metrics, boasting a 99.99% accuracy, precision, recall, and F1 score. A comprehensive comparative analysis evaluates the proposed model against…

Cervical cancer is a leading cause of women's mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification-handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of…

BACKGROUND: The COVID-19 pandemic has accelerated the transition to remote work, leading to increased attention on presenteeism and absenteeism among remote workers. Understanding the implications of these phenomena on worker health and productivity is crucial for optimizing remote work arrangements and developing policies to improve employee well-being. OBJECTIVES: This scoping review aims to examine the occurrence of presenteeism and absenteeism among remote workers during the COVID-19 pandemic and the interrelated physical and mental health issues during these periods. METHODS: PsycINFO, Medline, Embase, CINAHL, Eric, Business Source Premier, SCOPUS, and sociological abstracts were searched resulting in 1792 articles. Articles were included if the population of interest was 18+ (i.e., working age), engaged in full or part-time work, and the employees shifted from in-person to remote work due to the COVID-19 pandemic. All study designs, geographical areas, and papers written post-onset of the COVID-19 pandemic were included; however, systematic reviews were excluded. Data was…

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n =…

BACKGROUND: Approximately 10 to 20% of pregnant women worldwide experience perinatal depression (PND), a depressive episode with onset during pregnancy or after childbirth. We performed a systematic review to identify, summarize and discuss studies on inflammatory biomarkers described in relation to PND. METHOD: Inclusion criteria defined the selection of observational studies written in English, French, Spanish or Portuguese, that evaluate analytical levels of inflammatory molecules (protein levels) in biological fluids in women, with a diagnosis of depression using ICD/DSM diagnostic criteria or depressive symptoms assessed by standardized psychometric instruments, during pregnancy and/or postpartum. Case reports, experimental studies, reviews, qualitative analysis, meta-analysis, gray literature or replicated data were excluded. Three electronic databases were used for search (Pubmed, Web of Science and PsychInfo) and quality assessment of selected studies were performed using the Newcastle-Ottawa Scale. Data extraction included study design; number of subjects; obstetric…

This research aimed to investigate the mediating function of Green Employee Empowerment (GEE) in the relationship between Green Human Resource Management practices (GHRM) and the environmental performance of small and medium-sized enterprises (SMEs) in Ghana, drawing on the Ability-Motivation-Opportunity (AMO) theory. This study assessed the hypotheses in the established research model using structural equation modeling based on data collected from 320 participants from small and medium-sized firms in Ghana. The study's results revealed that GHRM practices were significantly correlated with the firm's environmental performance. The study found significant GHRM's indirect consequences on environmental performance through GEE in all models examined. These findings suggest that GEE plays a crucial role in translating the impact of GHRM practices into improved environmental performance. The study overlooked other potential mediators or moderators in the relationship between GHRM practices and environmental performance, focusing on GEE. To better understand the complex dynamics behind…

BACKGROUND: Stress during pregnancy is detrimental to maternal health, pregnancy and birth outcomes and various preventive relaxation interventions have been developed. This systematic review and meta-analysis aimed to evaluate their effectiveness in terms of maternal mental health, pregnancy and birth outcomes. METHOD: The protocol for this review is published on PROSPERO with registration number CRD42020187443. A systematic search of major databases was conducted. Primary outcomes were maternal mental health problems (stress, anxiety, depression), and pregnancy (gestational age, labour duration, delivery mode) and birth outcomes (birth weight, Apgar score, preterm birth). Randomized controlled trials or quasi-experimental studies were eligible. Meta-analyses using a random-effects model was conducted for outcomes with sufficient data. For other outcomes a narrative review was undertaken. RESULT: We reviewed 32 studies comprising 3,979 pregnant women aged 18 to 40 years. Relaxation interventions included yoga, music, Benson relaxation, progressive muscle relaxation (PMR), deep…

This paper presents an improved nonsingular adaptive super twisting sliding mode control for tracking of a quadrotor system in the presence of external disturbances and uncertainty. The initial step involves developing a dynamic model for the quadrotor that is free from singularities, achieved through the utilization of the Newton-Quaternion formalism. Then, the super twisting algorithm is used to develop a novel sliding mode control that mitigates chattering. Particle Swarm Optimization (PSO) is employed for the adjustment of the controller gains. Moreover, to maintain stable control of the quadcopter, even in scenarios where the upper limit of disturbances is unknown, an adaptive rule grounded in Lyapunov stability is applied. Simulation results demonstrate that the proposed controller reduces tracking errors to 0.1% for roll, 0.05% for pitch, and 2.2% for altitude, outperforming other state-of-the-art sliding mode controllers. Additionally, the proposed controller effectively rejects disturbances, maintaining minimal steady-state errors of 0.01° for roll, 0.02° for pitch, and…

With rising environmental concerns, accurate air quality predictions have become paramount as they help in planning preventive measures and policies for potential health hazards and environmental problems caused by poor air quality. Most of the time, air quality data are time series data. However, due to various reasons, we often encounter missing values in datasets collected during data preparation and aggregation steps. The inability to analyze and handle missing data will significantly hinder the data analysis process. To address this issue, this paper offers an extensive review of air quality prediction and missing data imputation techniques for time series, particularly in relation to environmental challenges. In addition, we empirically assess eight imputation methods, including mean, median, kNNI, MICE, SAITS, BRITS, MRNN, and Transformer, to scrutinize their impact on air quality data. The evaluation is conducted using diverse air quality datasets gathered from numerous cities globally. Based on these evaluations, we offer practical recommendations for practitioners dealing…

Due to the high incidence of smartphone addiction and its harmful effects on health in recent years, it has received widespread attention from society. This study aims to examine the association between smartphone addiction and sleep quality among college students, and assess the correlation with physical exercise in a non-interventional, cross-sectional study design. The study utilized data from the 2022 Chinese College Health Tracking Survey. A total of 4670 students participated in and completed the questionnaire. The test tools comprised the smartphone addiction tendency scale, the Pittsburgh Sleep Quality Index, and the physical activity rating scale. The average score of the college students' smartphone addiction was 39.230±14.931, and the proportion of college students with average and or very poor sleep quality was 52.6%. Mobile phone addiction among college students is negatively correlated with physical exercise (r = -0.101, p<0.01), and positively correlated with sleep quality (r = 0.287, p<0.01. Physical exercise had a significant regulatory effect on the behavior…

OBJECTIVE: This study investigates the association between physical exercise and emotion regulation abilities among college students, introducing self-efficacy as a mediating variable to analyze the pathway mechanism through which physical exercise affects emotion regulation abilities. METHODS: A cross-sectional study design was employed, utilizing a stratified random sampling method to survey three colleges in Jiangsu Province, China. Physical Activity Rating Scale, Physical Activity Self-efficacy Scale, and Emotional Intelligence Scale were used to measure the college student population. Regression analysis and mediation tests assessed whether self-efficacy mediates the relationship between physical exercise and college students' emotion regulation abilities. A total of 5,430 valid questionnaires were collected. RESULTS: The distribution of college students' physical activities was 77.0% for low, 13.1% for medium, and 9.3% for high levels. Physical activities were significantly and positively correlated with self-efficacy and emotional management abilities (r =…