Search results for: academic learning stress

Authors: Jolanta Jonak

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Emotional wellbeing is crucial for healthy development of children, youth, and adolescence. Lack of healthy emotional development and functioning can lead to many challenges in academic, social, and physical domains. Unfortunately, mental health problems are steadily increasing over the past several years and significantly intensified during and after the Covid 19 pandemic. Some experts warn that globally, we are entering a mental health crisis pandemic. Prior to the Covid 19 pandemic in 2020, a considerable number of youth aged 6-17 experienced a mental health disorder each year, with half of all mental health conditions beginning by age 14. The most common mental health disorders in children are Attention-Deficit/Hyperactivity Disorder (ADHD), behavior problems, anxiety, and depression. In U.S. schools, youth with these diagnoses tend to be classified under the umbrella of Emotional Disability. Several concerning trends have been emerging about the mental health of U.S. high school students. These trends have been steadily increasing for the past decade, particularly having increasing numbers of high school students who experienced persistent feelings of sadness or hopelessness, seriously considered attempting suicide, made a suicide plan, and attempted suicide. The number of students who felt persistently sad or hopeless increased across every racial and ethnic group. Parents, educators, and mental health providers are key stakeholders that are responsible for actively and effectively supporting struggling youth. A commonly faced challenge is that mental wellbeing is not supported as effectively as it should be. When effective prevention is paired with enhanced treatment developed on a multidisciplinary approach, mental health and emotional wellbeing in young people can be positively enhanced. Major health organizations including the Center for Disease Control and the National Institutes of Health encourage health professionals and other stakeholders to focus on addressing emotional wellbeing and mental health of young people.

Keywords: social emotional wellbeing, disability, learning, prevention

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Sunico Armie Flores

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Early childhood education (ECE) is pivotal in shaping the future of individuals and society. This paper explores the collaborative efforts required among educators, families, and communities to create a collective impact on young children’s development. It delves into the importance of these partnerships, effective strategies for engagement, and the challenges and opportunities inherent in fostering such collaboration. By examining current research and practices, the paper aims to highlight the essential role of an integrated approach in achieving significant and sustainable improvements in early childhood outcomes.

Keywords: early childhood education, lifelong learning, cognitive development, socio-emotional development, educators, families, communities, collaborative efforts, collective impact, early learning environments, holistic development, high-quality ECE programs, investment in education

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Authors: Rachel Baruch

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This article describes the implementation of an experimental model for teaching ICT tools and digital environments in teachers training college. In most educational systems in the Western world, new programs were developed in order to bridge the digital gap between teachers and students. In spite of their achievements, these programs are limited due to several factors: The teachers in the schools implement new methods incorporating technological tools into the curriculum, but meanwhile the technology changes and advances. The interface of tools changes frequently, some tools disappear and new ones are invented. These conditions require an experimental model of training the pre-service teachers. The appropriate method for instruction within the domain of ICT tools should be based on exposing the learners to innovations, helping them to gain experience, teaching them how to deal with challenges and difficulties on their own, and training them. This study suggests some principles for this approach and describes step by step the implementation of this model.

Keywords: ICT tools, e-learning, pre-service teachers, new model

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Authors: Osagiede Mercy Afe

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After the internet explosion in the 90’s, Technology was immediately integrated into the school system. Technology which symbolizes advancement in human knowledge was seen as a setback by many educators many efforts have been made to help stem this erroneous believes and help educators realize the benefits of technology and ways of implementing it in the classrooms especially in the sciences. This advancement created a constantly expanding gap between the pupil’s perception on the use of technology within the learning atmosphere and the teacher’s perception and limitations hence the focus of this paper is on the need to refocus on the potentials of Science and Technology in enhancing children learning at school especially in science for sustainable development in Nigeria. The paper recommended measures for facilitating the sustenance of science and technology in Nigerian schools so as to enhance the potentials of our children in Science and Technology for a better tomorrow.

Keywords: children, information communication technology (ICT), potentials, sustainable development, science education

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Authors: Shady Adib, Vladimir Vinogradov, Peter Gosling

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Digital Twin (DT) technology is a new technology that appeared in the early 21st century. The DT is defined as the digital representation of living and non-living physical assets. By connecting the physical and virtual assets, data are transmitted smoothly, allowing the virtual asset to fully represent the physical asset. Although there are lots of studies conducted on the DT concept, there is still limited information about the ability of the DT models for monitoring and detecting unexpected changes in structural behaviour in real time. This is due to the large computational efforts required for the analysis and an excessively large amount of data transferred from sensors. This paper aims to develop the DT concept to be able to detect the abnormal changes in structural behaviour in real time using advanced modelling techniques, deep learning algorithms, and data acquisition systems, taking into consideration model uncertainties. finite element (FE) models were first developed offline to be used with a reduced basis (RB) model order reduction technique for the construction of low-dimensional space to speed the analysis during the online stage. The RB model was validated against experimental test results for the establishment of a DT model of a two-dimensional truss. The established DT model and deep learning algorithms were used to identify the location of damage once it has appeared during the online stage. Finally, the RB model was used again to identify the damage severity. It was found that using the RB model, constructed offline, speeds the FE analysis during the online stage. The constructed RB model showed higher accuracy for predicting the damage severity, while deep learning algorithms were found to be useful for estimating the location of damage with small severity.

Keywords: data acquisition system, deep learning, digital twin, model uncertainties, reduced basis, reduced order model

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

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Authors: Ageliki I. Katsarou, Andriana C. Kaliora, Antonia Chiou, Apostolos Papalois, Nick Kalogeropoulos, Nikolaos K. Andrikopoulos

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Oxidative stress is a major mechanism underlying CVDs while inflammation, an intertwined process with oxidative stress, is also linked to CVDs. Extra virgin olive oil (EVOO) is widely known to play a pivotal role in CVD prevention and CVD reduction. However, in most studies, olive oil constituents are evaluated individually and not as part of the native food, hence potential synergistic effects as drivers of EVOO beneficial properties may be underestimated. In this study, EVOO lipidic and polar phenolics fractions were evaluated for their effect on inflammatory (TNF-alpha) and oxidation (malondialdehyde/MDA) markers, in cholesterol-fed rats. Thereat, oils with discernible lipidic profile and polar phenolic content were used. Wistar rats were fed on either a high-cholesterol diet (HCD) or a HCD supplemented with oils, either commercially available, i.e. EVOO, sunflower oil (SO), or modified as to their polar phenol content, i.e. phenolics deprived-EVOO (EVOOd), SO enriched with the EVOO phenolics (SOe). After 9 weeks of dietary intervention, heart and blood samples were collected. HCD induced dylipidemia shown by increase in serum total cholesterol, low-density lipoprotein cholesterol (LDL-c) and triacylglycerols. Heart tissue has been affected by dyslipidemia; oxidation was indicated by increase in MDA in cholesterol-fed rats and inflammation by increase in TNF-alpha. In both cases, this augmentation was attenuated in EVOO and SOe diets. With respect to oxidation, SO enrichment with the EVOO phenolics brought its lipid peroxidation levels as low as in EVOO-fed rats. This suggests that phenolic compounds may act as antioxidant agents in rat heart. A possible mechanism underlying this activity may be the protective effect of phenolics in mitochondrial membrane against oxidative damage. This was further supported by EVOO/EVOOd comparison with the former presenting lower heart MDA content. As for heart inflammation, phenolics naturally present in EVOO as well as phenolics chemically added in SO, exhibited quenching abilities in heart TNF-alpha levels of cholesterol-fed rats. TNF-alpha may have played a causative role in oxidative stress induction while the opposite may have also happened, hence setting up a vicious cycle. Overall, diet supplementation with EVOO or SOe attenuated hypercholesterolemia-induced increase in MDA and TNF-alpha in Wistar rat hearts. This is attributed to phenolic compounds either naturally existing in olive oil or as fortificants in seed oil.

Keywords: extra virgin olive oil, hypercholesterolemic rats, MDA, polar phenolics, TNF-alpha

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Authors: Claude Nsobya, Alice Moncaster, Karen Potter, Jed Ramsay

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The shift in Flood Risk Governance (FRG) has moved away from traditional approaches that solely relied on centralized decision-making and structural flood defenses. Instead, there is now the adoption of integrated flood risk management measures that involve various actors and stakeholders. This new approach emphasizes people-centered approaches, including adaptation and learning. This shift to a diversity of FRG approaches has been identified as a significant factor in enhancing resilience. Resilience here refers to a community's ability to withstand, absorb, recover, adapt, and potentially transform in the face of flood events. It is argued that if the FRG merely focused on the conventional 'fighting the water' - flood defense - communities would not be resilient. The move to these people-centered approaches also implies that communities will be more involved in FRG. It is suggested that effective flood risk governance influences resilience through meaningful community involvement, and effective community engagement is vital in shaping community resilience to floods. Successful community participation not only uses context-specific indigenous knowledge but also develops a sense of ownership and responsibility. Through capacity development initiatives, it can also raise awareness and all these help in building resilience. Recent Flood Risk Management (FRM) projects have thus had increasing community involvement, with varied conceptualizations of such community engagement in the academic literature on FRM. In the context of overland floods, there has been a substantial body of literature on Flood Risk Governance and Management. Yet, groundwater flooding has gotten little attention despite its unique qualities, such as its persistence for weeks or months, slow onset, and near-invisibility. There has been a little study in this area on how successful community involvement in Flood Risk Governance may improve community resilience to groundwater flooding in particular. This paper focuses on a case study of a flood risk management project in the United Kingdom. Buckinghamshire Council is leading Project Groundwater, which is one of 25 significant initiatives sponsored by England's Department for Environment, Food and Rural Affairs (DEFRA) Flood and Coastal Resilience Innovation Programme. DEFRA awarded Buckinghamshire Council and other councils 150 million to collaborate with communities and implement innovative methods to increase resilience to groundwater flooding. Based on a literature review, this paper proposes a new paradigm for effective community engagement in Flood Risk Governance (FRG). This study contends that effective community participation can have an impact on various resilience capacities identified in the literature, including social capital, institutional capital, physical capital, natural capital, human capital, and economic capital. In the case of social capital, for example, successful community engagement can influence social capital through the process of social learning as well as through developing social networks and trust values, which are vital in influencing communities' capacity to resist, absorb, recover, and adapt. The study examines community engagement in Project Groundwater using surveys with local communities and documentary analysis to test this notion. The outcomes of the study will inform community involvement activities in Project Groundwater and may shape DEFRA policies and guidelines for community engagement in FRM.

Keywords: flood risk governance, community, resilience, groundwater flooding

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Authors: Josephine Muchiri, Makena Muriithi

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Adverse mental health consequences are experienced by those that have been touched by Intimate Partner Violence (IPV), whether directly or indirectly. These negative effects are felt not only in the short term but in years to come. It is important to examine the prevalence and co-occurrence of mental disorders in order to provide strategic interventions for women who have experienced IPV. The aim of this study was to examine the prevalence and comorbidity of post-traumatic stress disorder (PTSD), Depression, and Anxiety among women who had experienced intimate Partner violence in two selected informal settlements in Nairobi County, Kenya. Participants were 116 women (15-60 years) selected through purposive and snowball sampling from the low social, economic settlements (Kawangware and Kibera) in Nairobi, Kenya. A social demographic questionnaire and the Woman Abuse Screening Tool (WAST) were used to collect data on intimate partner violence experiences. The PTSD Checklist for DSM-5 (PCL-5), Beck’s Depression Inventory, and the Beck’s Anxiety Inventory assessed for post-traumatic stress disorder, depression, and anxiety, respectively. Data analysis was conducted using the Statistical Package for Social Sciences (SPSS) version 29, utilizing descriptive and correlation analyses. Findings indicated that the women had undergone various forms of abuse from their intimate partners, which were physical abuse 111(92.5%), sexual abuse 70(88.6%), and verbal abuse 92(93.9%). The prevalence of the mental disorders was PTSD 47(32.4%); M= 44.11, S.D =14.67, depression was the highest at n=131(90.3%; M=33.37±9.98) with the levels of depression having varying prevalence rates where severe depression had the highest representation [moderate: n= 35; 24.1%, severe: n=69 (47.6%) and extremely severe: n=27(18.6%)]. Anxiety had the second highest prevalence of n=99 (68.8%; M= 28.55±13.63) with differing prevalence rates in the levels of anxiety which were normal anxiety: 45(31.3%), moderate anxiety n=62(43.1%) and severe anxiety: n=37(25.7%). Regarding comorbidities, the Pearson correlation test showed that there was a significant (p=0.000) positive relationship between PTSD and depression (r=0.379; p=.000), PTSD and anxiety (r=0.624; p=.000), and depression and anxiety (r=0.386; p=.000) such that increase in one disorder concomitantly led to increase of the other two disorders; hence comorbidity of the three disorders was ascertained. Conclusion: The study asserted the adverse impacts of IPV on women’s mental well-being, where the prevalence of PTSD, depression, and anxiety was established. Almost all the women had depressive symptoms; whereas more than half had anxiety and slightly more than a third had PTSD. Regarding the severity levels of anxiety and depression, almost half of the women with depression had severe depression whereas moderate anxiety was more prevalent for those with anxiety. The three disorders were found to co-occur where comorbidities of PTSD and anxiety had the highest probability of co-occurrence. It is thus recommended that mental health interventions with a focus on the three disorders be offered for women undergoing IPV.

Keywords: anxiety, comorbidity, depression, intimate partner violence, post-traumatic stress disorder

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Erica Cochran Hameen, Bobuchi Ken-Opurum, Shalini Priyadarshini, Berangere Lartigue, Sadhana Anath-Pisipati

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School districts throughout the United States are constantly seeking measures to improve test scores, reduce school absenteeism and improve indoor environmental quality. It is imperative to identify key building investments which will provide the largest benefits to schools in terms of improving the aforementioned factors. This study uses Analysis of Variance (ANOVA) tests to statistically evaluate the impact of a school building’s mechanical and plumbing characteristics on a child’s educational performance. The educational performance is measured via three indicators, i.e. test scores, suspensions, and absenteeism. The study investigated 125 New York City school facilities to determine the potential correlations between 50 mechanical and plumbing variables and the performance indicators. Key findings from the tests revealed that elementary schools with pneumatic systems in “good” condition have 48.8% lower percentages of students scoring at the minimum English Language Arts (ELA) competency level compared with those with no pneumatic system. Additionally, elementary schools with “unit heaters/cabinet heaters” in “good to fair” conditions have 1.1% higher attendance rates compared to schools with no “unit heaters/cabinet heaters” or those in inferior condition. Furthermore, elementary schools with air conditioning have 0.6% higher attendance rates compared to schools with no air conditioning, and those with interior floor drains in “good” condition have 1.8% higher attendance rates compared to schools with interior drains in inferior condition.

Keywords: academic attendance and performance, mechanical and plumbing systems, schools, student health

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Authors: Anum Rafiq

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Adolescence is a vulnerable period for youth across the world. It is a period of new learning with opportunities to understand and develop perspectives on health and well-being. With youth beginning to engage in intimate relationships at an earlier age in the 21st century, concentrating on the learning opportunity they have in school is paramount. The nature of what has been deemed important to teach in schools has changed throughout history, and the focus has shifted from home/family skills to teaching youth how to be competitive in the job market. Amidst this emphasis, opportunities for them exist to learn about building healthy intimate relationships, one of the foundational elements of most people’s lives. Using an Institutional Ethnography (IE), the lived experiences of youth in how they understand intimate relationships and how their learning experience is organized through the high school Health and Physical Education (H&PE) course is explored. An empirical inquiry into how the actual work of teachers and youth are socially organized by a biomedical, employment-related, and efficiency-based discourse is provided. Through thirty-two qualitative interviews with teachers and youth, a control of ruling relations such as institutional accountability circuits, performance reports, and timetabling over the experience of teachers and youth is found. One of the facets of the institutional accountability circuit is through the social organization of teaching and learning about healthy intimate relationships being framed through a biomedical discourse. In addition, the role of a hyper-focus on performance and evaluation is found as paramount in situating healthy intimacy discussions as inferior to neoliberally charged productivity measures such as employment skills. Lastly, due to the nature of institutional policies such as regulatory guidelines, teachers are largely influenced to avoid diving into discussions deemed risky or taboo by society, such as healthy intimacy in adolescence. The findings show how texts such as the H&PE curriculum, the Ontario College of Teachers (OCT) guidelines, Ministry of Education Performance Reports, and the timetable organize the day-to-day activities of teachers and students and reproduce different disjunctures for youth. This disjuncture includes some of their experiences being subordinated, difficulty relating to curriculum, and an experience of healthy living discussions being skimmed over across sites. The findings detail that the experience of youth in learning about healthy intimate relationships is not akin to the espoused vision outlined in policy documents such as the H&PE (2015) curriculum policy. These findings have implications for policymakers, activists, and school administration alike, which call for an investigation into who is in power when it comes to youth’s learning needs, as a pivotal period where youth can be equipped with life-changing knowledge is largely underutilized. A restructuring of existing institutional practices that allow for the social and institutional flexibility required to broach the topic of healthy intimacy in a comprehensive manner is required.

Keywords: health policy, intimate relationships, youth, education, ruling relations, sexual education, violence prevention

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Authors: Safaa H. Sindi, Michael Roe

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This project divides supply chain (SC) models into seven Eras, according to the evolution of the market’s needs throughout time. The five earliest Eras describe the emergence of supply chains, while the last two Eras are to be created. Research objectives: The aim is to generate the two latest Eras with their respective models that focus on the consumable goods. Era Six contains the Optimal Multidimensional Matrix (OMM) that incorporates most characteristics of the SC and allocates them into four quarters (Agile, Lean, Leagile, and Basic SC). This will help companies, especially (SMEs) plan their optimal SC route. Era Seven creates an Automated Multidimensional Model (AMM) which upgrades the matrix of Era six, as it accounts for all the supply chain factors (i.e. Offshoring, sourcing, risk) into an interactive system with Heuristic Learning that helps larger companies and industries to select the best SC model for their market. Methodologies: The data collection is based on a Fuzzy-Delphi study that analyses statements using Fuzzy Logic. The first round of Delphi study will contain statements (fuzzy rules) about the matrix of Era six. The second round of Delphi contains the feedback given from the first round and so on. Preliminary findings: both models are applicable, Matrix of Era six reduces the complexity of choosing the best SC model for SMEs by helping them identify the best strategy of Basic SC, Lean, Agile and Leagile SC; that’s tailored to their needs. The interactive heuristic learning in the AMM of Era seven will help mitigate error and aid large companies to identify and re-strategize the best SC model and distribution system for their market and commodity, hence increasing efficiency. Potential contributions to the literature: The problematic issue facing many companies is to decide which SC model or strategy to incorporate, due to the many models and definitions developed over the years. This research simplifies this by putting most definition in a template and most models in the Matrix of era six. This research is original as the division of SC into Eras, the Matrix of Era six (OMM) with Fuzzy-Delphi and Heuristic Learning in the AMM of Era seven provides a synergy of tools that were not combined before in the area of SC. Additionally the OMM of Era six is unique as it combines most characteristics of the SC, which is an original concept in itself.

Keywords: Leagile, automation, heuristic learning, supply chain models

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: Danielle Page

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Foregoing studies, statistics, and medical evaluations have established a relationship between Posttraumatic stress disorder (PTSD) and criminal justice involvement in Vietnam veterans. War is highly trauma inducing and can leave combat veterans with mental disorders ranging from psychopathic thoughts to suicidal ideation. The majority of those suffering are unaware that they have PTSD, and as a coping mechanism, they often turn to self isolation. Beyond isolation, many veterans with symptomatic PTSD turn to aggression and substance abuse to cope with their internal agony. The most common crimes committed by veterans with PTSD fall into the assault and drug/alcohol abuse categories. Thus, a relationship is established between veteran populations and the criminal justice system. This research aims to define the relationship between PTSD and criminal justice involvement in veterans, explore the mediating factors in this relationship, and analyze numerous court cases in this subject area. Further, it will examine the ways in which crime rates can be reduced for veterans with symptoms of PTSD. This ranges from the improvement of healthcare systems to the implementation of special courts to handle veteran cases. The contribution of this work to the field of forensic psychology will be significant, as it will analyze preexisting case studies and experimental data in an effort to improve the ways in which veteran cases are handled in the criminal justice system. Military personnel involved in the criminal justice system are a vulnerable population in need of healthcare and legislative attention, and this work will bring us one step closer to providing them with just that.

Keywords: forensic psychology, psychotraumatology, PTSD, veterans

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Authors: Martin K. Steiger, Lukas Heisler, Hans-Georg Brachtendorf

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Deep neural networks and their variants form the backbone of many AI applications. Based on the so-called residual networks, a continuous formulation of such models as ordinary differential equations (ODEs) has proven advantageous since different techniques may be applied that significantly increase the learning speed and enable controlled trade-offs with the resulting error at the same time. For the evaluation of such models, high-performance numerical differential equation solvers are used, which also provide the gradients required for training. However, whether classical gradient-based methods are even applicable or which one yields the best results has not been discussed yet. This paper aims to redeem this situation by providing empirical results for different applications.

Keywords: deep neural networks, gradient-based learning, image processing, ordinary differential equation networks

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Authors: John Allbutt, Andrew Murray, Jonathan Ling, Thomas M. Heffernan

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Ultra-endurance events place unique physical and psychological pressures on participants. In this study, we examined the psychologically challenging aspects of taking part in a 3000 mile transatlantic rowing race using a qualitative approach. To date, more people have been into space than have rowed an ocean and only one psychological study has been conducted on this experience which had a specific research focus. The current study was a qualitative study using semi-structured interviews. Participants were an opportunity sample of seven competitors from a recent ocean rowing race. Participants were asked about the psychological aspects of the event after it had finished. The data were analysed using thematic analysis. Several themes emerged from the analysis. These related to: 1) preparation; 2) bodily aches/pains, 3) race setbacks; 4) boat conditions; 5) interpersonal factors and communication; 6) strategies for managing stress and interpersonal tensions. While participants were generally very positive about the event, the analysis showed that they experienced significant psychological challenges during their voyage. Competitors paid considerable attention to preparing for the physical challenges of the event. However, not all prospective competitors gave the same time to preparing for psychological factors or were aware how they might play out during their voyage. All Atlantic rowing crews should be aware of the psychological challenges they face, and have strategies in place to help cope with the psychological strain of taking part.

Keywords: confinement experiences, ocean rowing, stress, ultra-endurance sport

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Authors: Shavindrie Cooray

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It is vital that students are supported in having balanced conversations about topics that might be controversial. This process is crucial to the development of critical thinking skills. This can be difficult to attain in e-learning environments, with some research finding students report a perceived loss in the quality of knowledge exchange and performance. This research investigated if Systems Theory could be applied to structure the discussion, improve information sharing, and reduce conflicts when students are working in online environments. This research involved 160 participants across four categories of student groups at a college in the Northeastern US. Each group was provided with a shared problem, and each group was expected to make a proposal for a solution. Two groups worked face-to-face; the first face to face group engaged with the problem and each other with no intervention from a facilitator; a second face to face group worked on the problem using Systems tools to facilitate problem structuring, group discussion, and decision-making. There were two types of virtual teams. The first virtual group also used Systems tools to facilitate problem structuring and group discussion. However, all interactions were conducted in a synchronous virtual environment. The second type of virtual team also met in real time but worked with no intervention. Findings from the study demonstrated that the teams (both virtual and face-to-face) using Systems tools shared more information with each other than the other teams; additionally, these teams reported an increased level of disagreement amongst their members, but also expressed more confidence and satisfaction with the experience and resulting decision compared to the other groups.

Keywords: e-learning, virtual teams, systems approach, conflicts

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Authors: Li Jiuen Ong, Magdalin Cheong, Sri Rahayu, Lek Alexander, Pei Ting Tan

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Background: Previous programme evaluations from the diabetes training programme conducted in Changi General Hospital revealed that healthcare professionals (HCPs) are keen to receive advance diabetes training and education, specifically in medical, nutritional therapy. HCPs also expressed a preference for interactive activities over didactic teaching methods to enhance their learning. Since the War on Diabetes was initiated by MOH in 2016, HCPs are challenged to be actively involved in continuous education to be better equipped to reduce the growing burden of diabetes. Hence, streamlining training to incorporate an element of fun is of utmost importance. Aim: The nutrition programme incorporates game play using an interactive board game that aims to provide a more conducive and less stressful environment for learning. The board game could be adapted for training of community HCPs, health ambassadors or caregivers to cope with the increasing demand of diabetes care in the hospital and community setting. Methodology: Stages for game’s conception (Jaffe, 2001) were adopted in the development of the interactive board game ‘Sweet Score™ ’ Nutrition concepts and topics in diabetes self-management are embedded into the game elements of varying levels of difficulty (‘Easy,’ ‘Medium,’ ‘Hard’) including activities such as a) Drawing/ sculpting (Pictionary-like) b)Facts/ Knowledge (MCQs/ True or False) Word definition) c) Performing/ Charades To study the effects of game play on knowledge acquisition and perceived experiences, participants were randomised into two groups, i.e., lecture group (control) and game group (intervention), to test the difference. Results: Participants in both groups (control group, n= 14; intervention group, n= 13) attempted a pre and post workshop quiz to assess the effectiveness of knowledge acquisition. The scores were analysed using paired T-test. There was an improvement of quiz scores after attending the game play (mean difference: 4.3, SD: 2.0, P<0.001) and the lecture (mean difference: 3.4, SD: 2.1, P<0.001). However, there was no significance difference in the improvement of quiz scores between gameplay and lecture (mean difference: 0.9, 95%CI: -0.8 to 2.5, P=0.280). This suggests that gameplay may be as effective as a lecture in terms of knowledge transfer. All the13 HCPs who participated in the game rated 4 out of 5 on the likert scale for the favourable learning experience and relevance of learning to their job, whereas only 8 out of 14 HCPs in the lecture reported a high rating in both aspects. 16. Conclusion: There is no known board game currently designed for diabetes training for HCPs.Evaluative data from future training can provide insights and direction to improve the game format and cover other aspects of diabetes management such as self-care, exercise, medications and insulin management. Further testing of the board game to ensure learning objectives are met is important and can assist in the development of awell-designed digital game as an alternative training approach during the COVID-19 pandemic. Learning through gameplay increases opportunities for HCPs to bond, interact and learn through games in a relaxed social setting and potentially brings more joy to the workplace.

Keywords: active learning, game, diabetes, nutrition

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Sung-Won Yoo, Chul-Hyeon Kang, Kyoung-Tae Park, Hae-Sik Woo

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Numerous studies were dedicated on the High Volume Fly-Ash (HVFA) concrete using high volume fly ash. The material properties of HVFA concrete have been the primordial topics of early studies, and interest shifted gradually toward the structural behavior of HVFA concrete such as elasticity modulus, stress-strain relationship, and structural behavior. However, structural studies consider small-scale members limited to the scope of reinforced concrete only. Therefore, in this paper, on the basis of recent studies on the structural behavior, 2 full-scale test members were manufactured with 7.5 m span length, fly ash replacement ratio of 50 % and concrete compressive strength of 50 MPa in order to evaluate the practicability of HVFA to real structures. In addition, 2 steel composite test members were also manufactured with span length of 3 m and using the same HVFA concrete for the same purpose. The test results of full-scale RC members showed that the practical use of HVFA on such structures is not hard despite small differences between test results and existing research results on the stress-strain relationship. The flexural test revealed very little difference between 50% fly ash concrete and general concrete in view of the similarity exhibited by the displacement and strain patterns. The experimental concrete shear strength being very close to that of design code, the existing design code can be applied. From the flexural test results of steel girder composite members, the composite behavior can be secured as much as that using normal concrete under the condition of sufficient arrangement of reinforcing bar.

Keywords: composite, fly ash, full-scale, high volume

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Authors: Nelson Kimeli Kemboi Yego, Juma Kasozi, Joseph Nkruzinza, Francis Kipkogei

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The study investigated the low uptake of health insurance in Kenya despite efforts to achieve universal health coverage through various health insurance schemes. Unsupervised machine learning techniques were employed to identify patterns in health insurance uptake based on sociodemographic factors among Kenyan households. The aim was to identify key demographic groups that are underinsured and to provide insights for the development of effective policies and outreach programs. Using the 2021 FinAccess Survey, the study clustered Kenyan households based on their health insurance uptake and sociodemographic features to reveal patterns in health insurance uptake across the country. The effectiveness of k-prototypes clustering, hierarchical clustering, and agglomerative hierarchical clustering in clustering based on sociodemographic factors was compared. The k-prototypes approach was found to be the most effective at uncovering distinct and well-separated clusters in the Kenyan sociodemographic data related to health insurance uptake based on silhouette, Calinski-Harabasz, Davies-Bouldin, and Rand indices. Hence, it was utilized in uncovering the patterns in uptake. The results of the analysis indicate that inclusivity in health insurance is greatly related to affordability. The findings suggest that targeted policy interventions and outreach programs are necessary to increase health insurance uptake in Kenya, with the ultimate goal of achieving universal health coverage. The study provides important insights for policymakers and stakeholders in the health insurance sector to address the low uptake of health insurance and to ensure that healthcare services are accessible and affordable to all Kenyans, regardless of their socio-demographic status. The study highlights the potential of unsupervised machine learning techniques to provide insights into complex health policy issues and improve decision-making in the health sector.

Keywords: health insurance, unsupervised learning, clustering algorithms, machine learning

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Authors: Marcelo Costa Cardoso, Luciano Pessanha Moreira

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In this work, the plastic behaviour of cold-rolled zinc coated dual-phase steel sheets DP600 and DP800 grades is firstly investigated with the help of uniaxial, hydraulic bulge and Forming Limit Curve (FLC) tests. The uniaxial tensile tests were performed in three angular orientations with respect to the rolling direction to evaluate the strain-hardening and plastic anisotropy. True stress-strain curves at large strains were determined from hydraulic bulge testing and fitted to a work-hardening equation. The limit strains are defined at both localized necking and fracture conditions according to Nakajima’s hemispherical punch procedure. Also, an elasto-plastic localization model is proposed in order to predict strain and stress based forming limit curves. The investigated dual-phase sheets showed a good formability in the biaxial stretching and drawing FLC regions. For both DP600 and DP800 sheets, the corresponding numerical predictions overestimated and underestimated the experimental limit strains in the biaxial stretching and drawing FLC regions, respectively. This can be attributed to the restricted failure necking condition adopted in the numerical model, which is not suitable to describe the tensile and shear fracture mechanisms in advanced high strength steels under equibiaxial and biaxial stretching conditions.

Keywords: advanced high strength steels, forming limit curve, numerical modelling, sheet metal forming

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Authors: Chui Ka Shing

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This case study research attempts to examine the use of the Bar Model Method approach in learning and teaching mathematics in a primary school in Hong Kong. The objectives of the study are to find out to what extent (a) the Bar Model Method approach enhances the construction of students’ mathematics concepts, and (b) the school-based mathematics curriculum development with adopting the Bar Model Method approach. This case study illuminates the effectiveness of using the Bar Model Method to solve mathematics problems from Primary 1 to Primary 6. Some effective pedagogies and assessments were developed to strengthen the use of the Bar Model Method across year levels. Suggestions including school-based curriculum development for using Bar Model Method and further study were discussed.

Keywords: bar model method, curriculum development, mathematics education, problem solving

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Authors: Austin Oduor Otieno

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There is an ingrained belief that the use of performance-enhancing drugs by athletes enable them to perform better. While this has been found to be truth, it also raises ethical and health issues. This paper analyzes the health hazards associated with performance enhancing drugs. It seeks to achieve this through the analysis of different academic journals as well as publications on the relationship between doping in sports and health. It concludes that there are inherent health hazards associated with the use of performance-enhancing drugs as they affect the physical and psychological health and wellbeing of a user (athlete).

Keywords: doping, health hazards, athletes, drugs

Procedia PDF Downloads 138

Authors: Victoria Eugenia Garnacho-Garnacho, Elena Sonsoles Rodriguez-Lopez, Raquel Delgado-Delgado, Alvaro Otero-Campos, Jesus Guodemar-Perez, Angelo Michelle Vagali, Juan Pablo Hervas-Perez

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Asthma is a heterogeneous disease which has always had a drug treatment. Osteopathic treatment that we propose is aimed, seen through a dorsal manipulation (Dog Technic D1-D4) and a technique for diaphragm (Longitudinal Stroke) forced expiratory flow in spirometry changes there are in particular that there is an increase in the volumes of the Peak Flow and Post intervention and effort and that the application of these two techniques together is more powerful if we applied only a Longitudinal (Stroke). Also rating if this type of treatment will have repercussions on breathlessness, a very common symptom in asthma. And finally to investigate if provided vertebra pain decreased after a manipulation. Methods—Participants were recruited between students and professors of the University, aged 18-65, patients (n = 18) were assigned randomly to one of the two groups, group 1 (longitudinal Stroke and manipulation dorsal Dog Technic) and group 2 (diaphragmatic technique, Longitudinal Stroke). The statistical analysis is characterized by the comparison of the main indicator of obstruction of via area PEF (peak expiratory flow) in various situations through the peak flow meter Datospir Peak-10. The measurements were carried out in four phases: at rest, after the stress test, after the treatment, after treatment and the stress test. After each stress test was evaluated, through the Borg scale, the level of Dyspnea on each patient, regardless of the group. In Group 1 in addition to these parameters was calculated using an algometer spinous pain before and after the manipulation. All data were taken at the minute. Results—12 Group 1 (Dog Technic and Longitudinal Stroke) patients responded positively to treatment, there was an increase of 5.1% and 6.1% of the post-treatment PEF and post-treatment, and effort. The results of the scale of Borg by which we measure the level of Dyspnea were positive, a 54.95%, patients noted an improvement in breathing. In addition was confirmed through the means of both groups group 1 in which two techniques were applied was 34.05% more effective than group 2 in which applied only a. After handling pain fell by 38% of the cases. Conclusions—The impact of the technique of Dog-Technic for D1-D4 and the Longitudinal Stroke technique for diaphragm in the volumes of peak expiratory flow (PEF) in asthmatic patients were positive, there was a change of the PEF Post intervention and post-treatment, and effort and showed the most effective group in which only a technique was applied. Furthermore this type of treatment decreased facilitated vertebrae pain and was efficient in the improvement of Dyspnea and the general well-being of the patient.

Keywords: ANS, asthma, manipulation, manual therapy, osteopathic

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Authors: Mohsine Jebbour

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Motivation and classroom interaction are of prime significance for second/foreign language learning to take place effectively. Thus, a considerable amount of motivation and classroom interaction helps ensure students’ success in and continuation of learning the TL. One way to enhance students’ motivation and classroom interaction in the Moroccan EFL classroom then is through the use of self-disclosure. For the purposes of this study, self-disclosure has been defined as the verbal communication of positive personal information including opinions, feelings, experiences, family and friendship stories to classmates and teachers. This paper is meant to demonstrate that positive self-disclosure can serve as an effective tool for helping students develop favorable attitudes toward the EFL classroom (i.e., English courses, teacher of English, and classroom activities) and promoting their intrinsic motivation (IM to know and IM toward stimulation). A further objective is that since self-disclosure is reciprocal, when teachers of English reveal their personal information, students will uncover their personal matters in return. This will help ensure effective classroom participation, foster teacher-student communication, and encourage students to practice and hence improve their oral proficiency (i.e., the speaking skill). A questionnaire was used to collect data in this study. 164 undergraduate students (99 females and 65 males) from the department of English at the faculty of letters and humanities, Dher el Mehraz, Sidi Mohammed Ben Abd Allah University completed a questionnaire that assessed self-disclosure in relation to motivation (i.e., attitudes toward the learning situation and intrinsic motivation) and classroom interaction (i.e., teacher-student interaction, participation, and out-of-class communication) on a 1 to 5 scale with (1) Strongly Disagree and (5) Strongly Agree. The level of agreement on the positive dimension of self-disclosure was ranked first by the respondents. The hypothesis set at the very beginning of the study, which posited that positive self-disclosure is essential to enhancing motivation and classroom interaction in the EFL context, was confirmed. In this regard, the findings suggest that implementing self-disclosure in the Moroccan EFL classroom may serve as an effective tool to have positive affect of teacher, class and classroom activities. This in turn will encourage the learners to attend classes, enjoy the language learning activity, complete classroom assignments, participate in class discussions, and interact with their teachers and classmates. It is hoped that teachers benefit from the results of this study and hence encourage the use of positive self-disclosure to develop English language learning in the Moroccan context where opportunities of using English outside the classroom are limited.

Keywords: EFL classroom, classroom interaction, motivation, self-disclosure

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Authors: Cristy Stagnar, Nina Hubig, Diana Ivankovic

Abstract:

The epigenome is a compelling candidate for mediating long-term responses to environmental effects modifying disease risk. The main goal of this research is to develop a machine learning-based DNA methylation score, which will be valuable in delineating the unique contribution of paternal epigenetic modifications to the germline impacting childhood health outcomes. It will also be a useful tool in validating self-reports of nonsmoking and in adjusting epigenome-wide DNA methylation association studies for this early-life exposure. Using secondary data from two population-based methylation profiling studies, our DNA methylation score is based on CpG DNA methylation measurements from cord blood gathered from children whose fathers smoked pre- and peri-conceptually. Each child’s mother and father fell into one of three class labels in the accompanying questionnaires -never smoker, former smoker, or current smoker. By applying different machine learning algorithms to the accessible resource for integrated epigenomic studies (ARIES) sub-study of the Avon longitudinal study of parents and children (ALSPAC) data set, which we used for training and testing of our model, the best-performing algorithm for classifying the father smoker and mother never smoker was selected based on Cohen’s κ. Error in the model was identified and optimized. The final DNA methylation score was further tested and validated in an independent data set. This resulted in a linear combination of methylation values of selected probes via a logistic link function that accurately classified each group and contributed the most towards classification. The result is a unique, robust DNA methylation score which combines information on DNA methylation and early life exposure of offspring to paternal smoking during pregnancy and which may be used to examine the paternal contribution to offspring health outcomes.

Keywords: epigenome, health outcomes, paternal preconception environmental exposures, supervised machine learning

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Authors: Janet Holland

Abstract:

Eye tracking is a great way to triangulate multiple data sources for deeper, more complete knowledge of how instructional materials are really being used and emotional connections made. Using sensor based biometrics provides a detailed local analysis in real time expanding our ability to collect science based data for a more comprehensive level of understanding, not previously possible, for teaching and learning. The knowledge gained will be used to make future improvements to instructional materials, tools, and interactions. The literature has been examined and a preliminary pilot test was implemented to develop a methodology for research in Instructional Design and Technology. Eye tracking now offers the addition of objective metrics obtained from eye tracking and other biometric data collection with analysis for a fresh perspective.

Keywords: area of interest, eye tracking, biometrics, fixation, fixation count, fixation sequence, fixation time, gaze points, heat map, saccades, time to first fixation

Procedia PDF Downloads 114