Search results for: institutional learning outcomes

Authors: Pornthip Seangsanga

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Sepsis or septic shock needs urgent care because it is a cause of the high mortality rate if patients do not receive timely treatment. Songkhla Hospital does not have a clear system or clinical practice guidelines for treatment of patients with severe sepsis or septic shock, which contributes to the said problem.To compare clinical outcomes based on the protocol after using the clinical guidelines between the Emergency Room, Intensive Care Unit, and the Ward. This quasi-experimental study was conducted on the population and 50 subjects who were diagnosed with severe sepsis or septic shock from December 2013 to May 2014. The data were collected using a nursing care and referring record form for patients with severe sepsis or septic shock at Songkhla Hospital. The record form had been tested for its validity by three experts, and the IOC was 1.The mortality rate in patients with severe sepsis or septic shock who were moved from the ER to the ICU was significantly lower than that of those patients moved from the Ward to the ICU within 48 hours. This was because patients with severe sepsis or septic shock who were moved from the ER to the ICU received more fluid within the first six hours according to the protocol which helped patients to have adequate tissue perfusion within the first six hours, and that helped improve blood flow to the kidneys, and the patients’ urine was found to be with a higher quantity of 0.5 cc/kg/hr, than those patients who were moved from the Ward to the ICU. This study shows that patients with severe sepsis or septic shock need to be treated immediately. Using the clinical practice guidelines along with timely diagnosis and treatment based on the sepsis bundle in giving sufficient and suitable amount of fluid to help improve blood circulation and blood pressure can clearly prevent or reduce severity of complications.

Keywords: clinical practice guidelines, caring, septic shock, sepsis bundle protocol

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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: Tadesse Melaku Abegaz, Akshaya Srikanth Bahagavathula, Abdulla Shehab Sheab, Asim Hassen

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Objectives: Non-adherence and discontinuation of anticoagulant therapy lead to increased ischemic stroke risk and contributes to suboptimal outcomes of the anticoagulant treatment. This systematic review and meta-analysis were aimed to investigate the adherence to NOACs and adverse events in patients with AF. Methods: Original research articles conducted on patients with AF and using any NOACs (dabigatran, rivoraxaban and apixaban) reporting adherence for at least 35 days were included. Scientific databases including PubMed, Web of Science, and Google Scholar were searched using MeSH keywords to obtaining literature researched between 2008 to till June, 2016. Study characteristics, patient’s sociodemographic and clinical characteristics, medication adherence levels and bleeding events reported were recorded. Results: The overall sample size of the six studies is 1,640,157, with CHADS2 scores < 2 in 551 patients, CHADS2-VASc ≥ 2 in 62,232 AF patients. Three-forth [75.6% (95%CI= 66.5-84.8), p < 0.001] are adherent to NOACs. However, a higher rate [72.7% (62.5-82.9), p < 0.001] of adherence was observed with Dabigatran than Apixaban [59.9% (3.2-123.1), p=0.063] and Rivaroxaban [59.3% (38.7-80.0), p<0.001]. Sub-group analysis revealed that nearly 57% of the AF patients on NOACs have CHADS2 scores < 2 and 20% of these patients were non-adherent to NOACs. Overall bleeding events rate associated with NOACs non-adherent AF patients was found to be 7.5% (0.2-14.8), p=0.045. However, nearly 11.2% of AF patients experienced bleeding events were non-adherent to NOAC medications. A higher proportion of bleeding events were noticed with Dabigatran (14.7%). Conclusions: Adherence rates, while uniformly suboptimal, nevertheless varied considerably, lowest at 59.3% for rivaroxaban and 59.9% for apixaban, followed by dabigatran (75.6%). Overall bleeding events associated with NOACs rates were 7.5%. However, lower adherence to NOACs was associated with worse outcomes among patients with greater stroke risk.

Keywords: atrial fibrillation, bleeding events, meta-analysis, novel oral anticoagulants

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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: B. J. Mahen, N. E. Lloyd-Gale, S. Johnson, W. P. Rakowicz, M. J. Harris, A. D. Miller

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Background: Migraine is a leading cause of disability in the world. Calcitonin gene-related peptide (CGRP) receptor antagonists offer an approach to migraine prophylaxis by inhibiting the inflammatory and vasodilatory effects of CGRP. In recent years, NICE licensed the use of three CGRP-receptor antagonists: Fremanezumab, Galcanezumab, and Erenumab. Here, we present the outcomes of CGRP-antagonist treatment in a cohort of patients who suffer from episodic or chronic migraine and have failed at least three oral prophylactic therapies. Methods: We offered CGRP antagonists to 86 patients who met the NICE criteria to start therapy. We recorded the number of headache days per month (HDPM) at 0 weeks, 3 months, and 12 months. Of those, 26 patients were switched to an alternative treatment due to poor response or side effects. Of the 112 total cases, 9 cases did not sufficiently maintain their headache diary, and 5 cases were not followed up at 3 months. We have therefore included 98 sets of data in our analysis. Results: Fremanezumab achieved a reduction in HDPM by 51.7% at 3 months (p<0.0001), with 63.7% of patients meeting NICE criteria to continue therapy. Patients trialed on Galcanezumab attained a reduction in HDPM by 47.0% (p=0.0019), with 51.6% of patients meeting NICE criteria to continue therapy. Erenumab, however, only achieved a reduction in HDPM by 17.0% (p=0.29), and this was not statistically significant. Furthermore, 34.4%, 9.7%, and 4.9% of patients taking Fremanezumab, Galcanezumab, and Erenumab, respectively, continued therapy beyond 12 months. Of those who attempted drug holidays following 12 months of treatment, migraine symptoms relapsed in 100% of cases. Conclusion: We observed a significant improvement in HDPM amongst episodic and chronic migraine patients following treatment with Fremanezumab or Galcanezumab.

Keywords: migraine, CGRP, fremanezumab, galcanezumab, erenumab

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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: Pia Ulvenblad, Henrik Barth, Jennie Cederholm BjöRklund, Maya Hoveskog, Per-Ola Ulvenblad

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The importance of business model innovation (BMI) is widely recognized. This is also valid for firms in the agri-food industry, closely connected to global challenges. Worldwide food production will have to increase 70% by 2050 and the United Nations’ sustainable development goals prioritize research and innovation on food security and sustainable agriculture. The firms of the agri-food industry have opportunities to increase their competitive advantage through BMI. However, the process of BMI is complex and the implementation of new business models is associated with high degree of risk and failure. Thus, managers from all industries and scholars need to better understand how to address this complexity. Therefore, the research presented in this paper (i) explores different categories of barriers in research literature on business models in the agri-food industry, and (ii) illustrates categories of barriers with empirical cases. This study is addressing the rather limited understanding on barriers for BMI in the agri-food industry, through a systematic literature review (SLR) of 570 peer-reviewed journal articles that contained a combination of ‘BM’ or ‘BMI’ with agriculture-related and food-related terms (e.g. ‘agri-food sector’) published in the period 1990-2014. The study classifies the barriers in several categories and illustrates the identified barriers with ten empirical cases. Findings from the literature review show that barriers are mainly identified as outcomes. It can be assumed that a perceived barrier to growth can often be initially exaggerated or underestimated before being challenged by appropriate measures or courses of action. What may be considered by the public mind to be a barrier could in reality be very different from an actual barrier that needs to be challenged. One way of addressing barriers to growth is to define barriers according to their origin (internal/external) and nature (tangible/intangible). The framework encompasses barriers related to the firm (internal addressing in-house conditions) or to the industrial or national levels (external addressing environmental conditions). Tangible barriers can include asset shortages in the area of equipment or facilities, while human resources deficiencies or negative willingness towards growth are examples of intangible barriers. Our findings are consistent with previous research on barriers for BMI that has identified human factors barriers (individuals’ attitudes, histories, etc.); contextual barriers related to company and industry settings; and more abstract barriers (government regulations, value chain position, and weather). However, human factor barriers – and opportunities - related to family-owned businesses with idealistic values and attitudes and owning the real estate where the business is situated, are more frequent in the agri-food industry than other industries. This paper contributes by generating a classification of the barriers for BMI as well as illustrating them with empirical cases. We argue that internal barriers such as human factors barriers; values and attitudes are crucial to overcome in order to develop BMI. However, they can be as hard to overcome as for example institutional barriers such as governments’ regulations. Implications for research and practice are to focus on cognitive barriers and to develop the BMI capability of the owners and managers of agri-industry firms.

Keywords: agri-food, barriers, business model, innovation

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Authors: Amina Ottozbeer, Nazanin Derakhshan

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Emotion regulation, a transdiagnostic process, is often impaired in individuals with high trait anxiety due to compromised executive functioning and attentional control. Recent research underscores the importance of studying individual differences and contextual factors in understanding the adaptability of emotion regulation processes, particularly in those with high trait anxiety. Prior studies have emphasized the role of self-efficacy in promoting positive cognitive flexibility outcomes and mitigating executive function impairments in highly anxious individuals. Accordingly, the objective of this study was to examine the moderating influence of attentional control, cognitive flexibility, and self-efficacy on the relationship between trait anxiety and emotion regulation. Using a correlational design, an online study was conducted with a sample of 82 participants (mean age: 22 years). Self-report questionnaires measured individual difference variables. The Classic Stroop Task assessed attentional control as an objective measure of cognitive flexibility . The findings revealed three significant interactions. Firstly, high cognitive flexibility and self-efficacy were linked to reduced expressive suppression in individuals with low trait anxiety. Secondly, elevations in cognitive flexibility and self-efficacy were associated with increased suppression in those with high trait anxiety. Thirdly, high trait anxiety was associated with reduced attentional control. The results suggest that typically adaptive processes can yield different outcomes in highly anxious populations, highlighting the need to explore additional variables that could alter the impact of cognitive flexibility and self-efficacy on emotion regulation in individuals with high anxiety. These findings have significant clinical implications, emphasizing the need to consider individual differences in emotion regulation and trait anxiety to inform more effective psychological treatments.

Keywords: attentional control, trait anxiety, emotional dysregulation, transdiagnostic, individual differences

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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: 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: Severino Machingambi

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Most academics entering Higher education as lecturers in South Africa do not have qualifications in Education or teaching. This creates serious problems since they are not sufficiently equipped with pedagogical approaches and theories that inform their facilitation of learning strategies. This, arguably, is one of the reasons why higher education institutions are experiencing high student failure rate. In order to mitigate this problem, it is critical that higher education institutions devise internal academic staff development programmes to capacitate academics with pedagogical skills and competencies so as to enhance the quality of student learning. This paper reported on how the Teaching and Learning Development Centre of a university used design-based research methodology to conceptualise and implement an academic staff development programme for new academics at a university of technology. This approach revolves around the designing, testing and refining of an educational intervention. Design-based research is an important methodology for understanding how, when, and why educational innovations work in practice. The need for a professional development course for academics arose due to the fact that most academics at the university did not have teaching qualifications and many of them were employed straight from industry with little understanding of pedagogical approaches. This paper examines three key aspects of the programme namely, the preliminary phase, the teaching experiment and the retrospective analysis. The preliminary phase is the stage in which the problem identification takes place. The problem that this research sought to address relates to the unsatisfactory academic performance of the majority of the students in the institution. It was therefore hypothesized that the problem could be dealt with by professionalising new academics through engagement in an academic staff development programme. The teaching experiment phase afforded researchers and participants in the programme the opportunity to test and refine the proposed intervention and the design principles upon which it was based. The teaching experiment phase revolved around the testing of the new academics professional development programme. This phase created a platform for researchers and academics in the programme to experiment with various activities and instructional strategies such as case studies, observations, discussions and portfolio building. The teaching experiment phase was followed by the retrospective analysis stage in which the research team looked back and tried to give a trustworthy account of the teaching/learning process that had taken place. A questionnaire and focus group discussions were used to collect data from participants that helped to evaluate the programme and its implementation. One of the findings of this study was that academics joining university really need an academic induction programme that inducts them into the discourse of teaching and learning. The study also revealed that existing academics can be placed on formal study programmes in which they acquire educational qualifications with a view to equip them with useful classroom discourses. The study, therefore, concludes that new and existing academics in universities should be supported through induction programmes and placement on formal studies in teaching and learning so that they are capacitated as facilitators of learning.

Keywords: academic staff, pedagogy, programme, staff development

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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: 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: Gulnaz Erkomaishvili, Eteri Kharaishvili, Marina Chavleishvili

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The paper discusses the economic policy of increasing national competitiveness, the tools, and means which help the country to improve its competitiveness. The sectors of the economy, in which the country can achieve a competitive advantage, are studied. It is noted that the country’s economic policy plays an important role in obtaining and maintaining a competitive advantage - authority should take measures to ensure a high level of education; scientific and research activities should be funded by the state; foreign direct investments should be attracted mainly in science-intensive industries; adaptation with the latest scientific achievements of the modern world and deepening of scientific and technical cooperation. Stable business environment and export-oriented strategy is the basis for the country’s economic growth. The studies have shown that institutional reforms in Georgia are not enough to significantly improve the country's competitiveness.

Keywords: competitiveness, economic policy, competitiveness improvement strategy, competitiveness of Georgia

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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: 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: 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: 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

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Authors: Bruno Biagianti, Angela Tseng, Kathy Wannaviroj, Allison Corlett, Megan DuBois, Kyu Lee, Suma Jacob

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Background: ASD is characterized by pervasive Sensory Processing Abnormalities (SPA) and social cognitive deficits that persist throughout the course of the illness and have been linked to functional abnormalities in specific neural systems that underlie the perception, processing, and representation of sensory information. SPA and social cognitive deficits are associated with difficulties in interpersonal relationships, poor development of social skills, reduced social interactions and lower academic performance. Importantly, they can hamper the effects of established evidence-based psychological treatments—including PEERS (Program for the Education and Enrichment of Relationship Skills), a parent/caregiver-assisted, 16-weeks social skills intervention—which nonetheless requires a functional brain capable of assimilating and retaining information and skills. As a matter of fact, some adolescents benefit from PEERS more than others, calling for strategies to increase treatment response rates. Objective: We will present interim data on CICADAS (Care Improving Cognition for ADolescents on the Autism Spectrum)—a clinician-assisted, closed-loop technology mobile application for adolescents with ASD. Via ten mobile assessments, CICADAS captures data on sensory processing abnormalities and associated cognitive deficits. These data populate a machine learning algorithm that tailors the delivery of ten neuroplasticity-based social cognitive training (NB-SCT) exercises targeting sensory processing abnormalities. Methods: In collaboration with the Autism Spectrum and Neurodevelopmental Disorders Clinic at the University of Minnesota, we conducted a fully remote, three-arm, randomized crossover trial with adolescents with ASD to document the acceptability of CICADAS and evaluate its potential as a stand-alone treatment or as a treatment enhancer of PEERS. Twenty-four adolescents with ASD (ages 11-18) have been initially randomized to 16 weeks of PEERS + CICADAS (Arm A) vs. 16 weeks of PEERS + computer games vs. 16 weeks of CICADAS alone (Arm C). After 16 weeks, the full battery of assessments has been remotely administered. Results: We have evaluated the acceptability of CICADAS by examining adherence rates, engagement patterns, and exit survey data. We found that: 1) CICADAS is able to serve as a treatment enhancer for PEERS, inducing greater improvements in sensory processing, cognition, symptom reduction, social skills and behaviors, as well as the quality of life compared to computer games; 2) the concurrent delivery of PEERS and CICADAS induces greater improvements in study outcomes compared to CICADAS only. Conclusion: While preliminary, our results indicate that the individualized assessment and treatment approach designed in CICADAS seems effective in inducing adaptive long-term learning about social-emotional events. CICADAS-induced enhancement of processing and cognition facilitates the application of PEERS skills in the environment of adolescents with ASD, thus improving their real-world functioning.

Keywords: ASD, social skills, cognitive training, mobile app

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Authors: Meruyert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova, Madina Ermaganbetova

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This research paper explores the process of creating a proctoring system by evaluating the implementation of practical face recognition algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As an outcome, a proctoring system will be created, enabling the conduction of tests and ensuring academic integrity checks within the system. Due to the correct operation of the system, test works are carried out. The result of the creation of the proctoring system will be the basis for the automation of the informational, educational portal developed by machine learning.

Keywords: artificial intelligence, education portal, face recognition, machine learning, proctoring

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Authors: Mahdi Bazarganigilani

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Computer-aided diagnosis (CAD) systems can play crucial roles in diagnosing crucial diseases such as breast cancer at the earliest. In this paper, a CAD system for the diagnosis of breast cancer was introduced and evaluated. This CAD system was developed by using spatio-temporal analysis of data on a set of consecutive thermographic images by employing wavelet transformation. By using this analysis, a very accurate machine learning model using random forest was obtained. The final results showed a promising accuracy of 91% in terms of the F1 measure indicator among 200 patients' sample data. The CAD system was further extended to obtain a detailed analysis of the effect of smaller sub-areas of each breast on the occurrence of cancer.

Keywords: computer-aided diagnosis systems, thermographic analysis, spatio-temporal analysis, image processing, machine learning

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Authors: Assel Jaxylykova, Alexnder Pak

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This study utilized a Self-Organizing Map (SOM) for unsupervised learning on the CONLL-2003 dataset for Named Entity Recognition (NER). The process involved encoding words into 300-dimensional vectors using FastText. These vectors were input into a SOM grid, where training adjusted node weights to minimize distances. The SOM provided a topological representation for identifying and clustering named entities, demonstrating its efficacy without labeled examples. Results showed an F1-measure of 0.86, highlighting SOM's viability. Although some methods achieve higher F1 measures, SOM eliminates the need for labeled data, offering a scalable and efficient alternative. The SOM's ability to uncover hidden patterns provides insights that could enhance existing supervised methods. Further investigation into potential limitations and optimization strategies is suggested to maximize benefits.

Keywords: named entity recognition, natural language processing, self-organizing map, CONLL-2003, semantics

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Authors: Mohammed Islam, Jungyong Wang, Dong Cheol Seo

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The Smoothed Particle Hydrodynamics (SPH) method is a novel, meshless, and Lagrangian technique based numerical method that has shown promises to accurately predict the hydrodynamics of water and structure interactions in violent flow conditions. The main goal of this study is to build confidence on the versatility of the Smoothed Particle Hydrodynamics (SPH) based tool, to use it as a complementary tool to the physical model testing capabilities and support research need for the performance evaluation of ships and offshore platforms exposed to an extreme and harsh environment. In the current endeavor, an open-sourced SPH-based tool was used and validated for modeling and predictions of the hydrodynamic interactions of a 6-DOF ship and bergy bits. The study involved the modeling of a modern generic drillship and simplified bergy bits in floating and towing scenarios and in regular and irregular wave conditions. The predictions were validated using the model-scale measurements on a moored ship towed at multiple oblique angles approaching a floating bergy bit in waves. Overall, this study results in a thorough comparison between the model scale measurements and the prediction outcomes from the SPH tool for performance and accuracy. The SPH predicted ship motions and forces were primarily within ±5% of the measurements. The velocity and pressure distribution and wave characteristics over the free surface depicts realistic interactions of the wave, ship, and the bergy bit. This work identifies and presents several challenges in preparing the input file, particularly while defining the mass properties of complex geometry, the computational requirements, and the post-processing of the outcomes.

Keywords: SPH, ship and bergy bit, hydrodynamic interactions, model validation, physical model testing

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Authors: Varun Agarwal

Abstract:

Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Julia Wimmers Klick

Abstract:

Interprofessional Education (IPE) is widely recognized as a valuable approach in healthcare education, despite the challenges it presents. This study explores IP surface anatomy lab sessions, with a focus on fostering trust and collaboration among healthcare students. The research is conducted within the context of rural healthcare settings in British Columbia (BC), where a medical school and a physical therapy (PT) program operate under the Faculty of Medicine at the University of British Columbia (UBC). While IPE sessions addressing soft skills have been implemented, the integration of hard skills, such as Anatomy, remains limited. To address this gap, a pilot feasibility study was conducted with a positive outcome, a follow-up study involved these IPE sessions aimed at exploring the influence of bonding and trust between medical and PT students. Data were collected through focus groups comprising participating students and faculty members, and a structured SWOC (Strengths, Weaknesses, Opportunities, and Challenges) analysis was conducted. The IPE sessions, 3 in total, consisted of a 2.5-hour lab on surface anatomy, where PT students took on the teaching role, and medical students were newly exposed to surface anatomy. The focus of the study was on the relationship-building process and trust development between the two student groups, rather than assessing the acquisition of surface anatomy skills. Results indicated that the surface anatomy lab served as a suitable tool for the application and learning of soft skills. Faculty members observed positive outcomes, including productive interaction between students, reversed hierarchy with PT students teaching medical students, practicing active listening skills, and using a mutual language of anatomy. Notably, there was no grade assessment or external pressure to perform. The students also reported an overall positive experience; however, the specific impact on the development of soft skill competencies could not be definitively determined. Participants expressed a sense of feeling respected, welcomed, and included, all of which contributed to feeling safe. Within the small group environment, students experienced becoming a part of a community of healthcare providers that bonded over a shared interest in health professions education. They enjoyed sharing diverse experiences related to learning across their varied contexts, without fear of judgment and reprisal that were often intimidating in single professional contexts. During a joint Christmas party for both cohorts, faculty members observed students mingling, laughing, and forming bonds. This emphasized the importance of early bonding and trust development among healthcare colleagues, particularly in rural settings. In conclusion, the findings emphasize the potential of IPE sessions to enhance trust and collaboration among healthcare students, with implications for their future professional lives in rural settings. Early bonding and trust development are crucial in rural settings, where healthcare professionals often rely on each other. Future research should continue to explore the impact of content-concentrated IPE on the development of soft skill competencies.

Keywords: interprofessional education, rural healthcare settings, trust, surface anatomy

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