Search results for: impacting student learning outcomes

Authors: Troy A. Tyre

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Organizational behavior and organizational change are elements of the ever-changing global business environment. Leadership and organizational behavior are 21st century disciplines. Network marketing organizations need to understand the ever-changing nature of global business and be ready and willing to adapt to the environment. Network marketing organizations have a challenge keeping up with a rapid escalation in global growth. Network marketing growth has been steady and global. Network marketing organizations have been slow to develop a 21st century global strategy to manage the rapid escalation of growth degrading organizational behavior, job satisfaction, increasing attrition, and degrading customer service. Development of an organizational behavior and leadership theory for the 21st century to help network marketing develops a global business strategy to manage the rapid escalation in growth that affects organizational behavior. Managing growth means organizational leadership must develop and adapt to the organizational environment. Growth comes with an open mind and one’s departure from the comfort zone. Leadership growth operates in the tacit dimension. Systems thinking and adaptation of mental models can help shift organizational behavior. Shifting the organizational behavior requires organizational learning. Organizational learning occurs through single-loop, double-loop, and triple-loop learning. Triple-loop learning is the most difficult, but the most rewarding. Tools such as theory U can aid in developing a landscape for organizational behavioral development. Additionally, awareness to espoused and portrayed actions is imperatives. Theories of motivation, cross-cultural diversity, and communications are instrumental in founding an organizational behavior suited for the 21st century.

Keywords: global, leadership, network marketing, organizational behavior

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Authors: Joud Zagzoog, Amira Aldabbagh, Radiyah Hamidaddin

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This research aims to test out and observe whether artificial intelligence (AI) software and applications could actually be effective, useful, and time-saving for those who use them. Shaguf Bites, a web application that uses AI technology, claims to help students study and memorize information more effectively in less time. The website uses smart learning, or AI-powered bite-sized repetitive learning, by transforming documents or PDFs with the help of AI into summarized interactive smart flashcards (Bites, n.d.). To properly test out the websites’ effectiveness, both qualitative and quantitative methods were used in this research. An experiment was conducted on a number of students where they were first requested to use Shaguf Bites without any prior knowledge or explanation of how to use it. Second, they were asked for feedback through a survey on how their experience was after using it and whether it was helpful, efficient, time-saving, and easy to use for studying. After reviewing the collected data, we found out that the majority of students found the website to be straightforward and easy to use. 58% of the respondents agreed that the website accurately formulated the flashcard questions. And 53% of them reported that they are most likely to use the website again in the future as well as recommend it to others. Overall, from the given results, it is clear that Shaguf Bites have proved to be very beneficial, accurate, and time saving for the majority of the students.

Keywords: artificial intelligence (AI), education, memorization, spaced repetition, flashcards.

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Authors: Scott Ashby, Emily Granger, Mark Connellan

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Background: In-hospital management of Out-of-Hospital Cardiac Arrest (OHCA) is complex as the aetiologies are varied. Acute coronary angiography has been shown to improve outcomes for patients with coronary occlusion as the cause; however, these patients are difficult to identify. ECG results may help identify these patients, but the accuracy of this diagnostic test is under debate, and requires further investigation. Methods: Arrest and hospital management information was collated retrospectively for OHCA patients who presented to a single clinical site between 2009 and 2013. Angiography results were then collected and checked for significance with survival to discharge. The presence of a severe lesion (>70%) was then compared to categorised ECG findings, and the accuracy of the test was calculated. Results: 104 patients were included in this study, 44 survived to discharge, 52 died and 8 were transferred to other clinical sites. Angiography appears to significantly correlate with survival to discharge. ECG showed 54.8% sensitivity for detecting the presence of a severe lesion within the group that received angiography. A combined criterion including any ECG pathology showed 100% sensitivity and negative predictive value, however, a low specificity and positive predictive value. Conclusion: In the cohort investigated, ST elevation on ECG is not a sensitive enough screening test to be used to determine whether OHCA patients have coronary stenosis as the likely cause of their arrest, and more investigation into whether screening with a combined ECG criterion, or whether all patients should receive angiography routinely following OHCA is needed.

Keywords: out of hospital cardiac arrest, coronary angiography, resuscitation, emergency medicine

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: Asma Alsaleh, Kara Makara

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Autistic spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties with communication and interaction. Besides presenting challenges for the ASD individual, the condition can entail negative outcomes for those who care for them, most often mothers. While this issue has been studied substantially in Western society, less is known about how mothers in the Arab world are affected by raising an ASD child. This study sought to gain insights into this area by assessing quality of life and stress in mothers with (n = 25) and without (n = 25) ASD children in Riyadh (Saudi Arabia) by using, respectively, the World Health Organization Quality of Life Assessment-BREF (WHOQOL-BREF) and the Parenting Stress Index-Short Form (PSI-SF). Data pertaining to income and education were also attained to investigate how socioeconomic factors interact with the above-mentioned variables. The analysis revealed that total stress scores and scores on the individual subscales of the PSI-SF were significantly higher for the mothers with an ASD child compared to those without an ASD child, though the opposite was true of quality of life scores. Moreover, increased income was associated with increased quality of life and decreased stress. While there were not main effects of education, there were interactions between education, whether children were ASD or non-ASD, and the outcome variables. These results suggest that mothers of ASD children in an Arab culture are at increased risk of negative outcomes relative to mothers of typically developing children, and, therefore, this study may act as a foundation for the delivery of interventions to assist mothers in this position.

Keywords: autism, education, income, mothers, quality of life, stress

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Authors: Hernández Yasmín, Ochoa Alberto, Hurtado Diego

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The aim of this study is developing an intelligent tutoring system for electrical operators training with virtual reality systems at the laboratory center of partials discharges LAPEM. The electrical domain requires efficient and well trained personnel, due to the danger involved in the partials discharges field, qualified electricians are required. This paper presents an overview of the intelligent tutor adaptive learning design and user interface with VR. We propose the develop of constructing a model domain of a subset of partial discharges enables adaptive training through a trainee model which represents the affective and knowledge states of trainees. According to the success of the intelligent tutor system with VR, it is also hypothesized that the trainees will able to learn the electrical domain installations of partial discharges and gain knowledge more efficient and well trained than trainees using traditional methods of teaching without running any risk of being in danger, traditional methods makes training lengthily, costly and dangerously.

Keywords: intelligent tutoring system, artificial intelligence, virtual reality, partials discharges, adaptive learning

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Authors: Yuanxiang Miao

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Couchsurfing is a free homestay and social networking service accessible via the website and mobile app. Couchsurfers can directly request free accommodations from others and receive offers from each other. However, it is typically difficult for people to make a decision that accepts or declines a request when they receive it from Couchsurfers because they do not know each other at all. People are expected to meet up with some Couchsurfers who are kind, generous, and interesting while it is unavoidable to meet up with someone unfriendly. This paper utilized classification algorithms of Machine Learning to help people to find out the Good Couchsurfers and Not Good Couchsurfers on the Couchsurfing website. By knowing the prior experience, like Couchsurfer’s profiles, the latest references, and other factors, it became possible to recognize what kind of the Couchsurfers, and furthermore, it helps people to make a decision that whether to host the Couchsurfers or not. The value of this research lies in a case study in Kyoto, Japan in where the author has hosted 54 Couchsurfers, and the author collected relevant data from the 54 Couchsurfers, finally build a model based on classification algorithms for people to predict Couchsurfers. Lastly, the author offered some feasible suggestions for future research.

Keywords: Couchsurfing, Couchsurfers prediction, classification algorithm, hospitality tourism platform, hospitality sciences, machine learning

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Authors: Ghada El-Husseiny, Dina Yousri, Christian Richter

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Social services are often characterized by market failures, which justifies government intervention in the provision of these services. It is widely acknowledged that government intervention breeds corruption since resources are being transferred from one party to another. However, what is still being extensively studied is the magnitude of the negative impact of corruption on publicly provided services and development outcomes. Corruption has the power to hinder development and cripple our march towards the Sustainable Development Goals. Corruption diminishes the efficiency and effectiveness of public health and education spending and directly impacts the outcomes of these sectors. This paper empirically examines the impact of Institutional Failure on public sector services provision, with the sole purpose of studying the impact of corruption on SDG3 and 4; Good health and wellbeing and Quality education, respectively. The paper explores the effect of corruption on these goals from various perspectives and extends the analysis by examining if the impact of corruption on these goals differed when it accounted for the current corruption state. Using Pooled OLS(Ordinary Least Square) and Fixed effects panel estimation on 22 corrupt and 22 clean countries between 2000 and 2017. Results show that corruption in both corrupt and clean countries has a more severe impact on Health than the Education sector. In almost all specifications, corruption has an insignificant effect on School Enrollment rates but a significant effect on Infant Mortality rates. Results further indicate that, on average, a 1 point increase in the CPI(Consumer Price Index) can increase health expenditures by 0.116% in corrupt and clean countries. However, the fixed effects model indicates that the way Health and Education expenditures are determined in clean and corrupt countries are completely country-specific, in which corruption plays a minimal role. Moreover, the findings show that School Enrollment rates and Infant Mortality rates depend, to a large extent, on public spending. The most astounding results-driven is that corrupt countries, on average, have more effective and efficient healthcare expenditures. While some insights are provided as to why these results prevail, they should be further researched. All in all, corruption impedes development outcomes, and any Anti-corrupt policies taken will bring forth immense improvements and speed up the march towards sustainability.

Keywords: corruption, education, health, public spending, sustainable development

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Authors: Theddeus Tochukwu Akano, Omotayo Abayomi Fakinlede

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Proper understanding of the behavior of compliant mechanisms use by athletes is important in order to avoid catastrophic failure. Such compliant mechanisms like the flex-run require the knowledge of their dynamic response and deformation behavior under quickly varying loads. The modeling of finite deformations of the compliant athletic system is described by Neo-Hookean model under contact-impact conditions. The dynamic impact-contact governing equations for both the target and impactor are derived based on the updated Lagrangian approach. A method where contactor and target are considered as a united body is applied in the formulation of the principle of virtual work for the bodies. In this paper, methods of continuum mechanics and nonlinear finite element method were deployed to develop a model that could capture the behavior of the compliant athletic system under quickly varying loads. A hybrid system of symbolic algebra (AceGEN) and a compiled back end (AceFEM) were employed, leveraging both ease of use and computational efficiency. The simulated results reveal the effect of the various contact-impact conditions on the deformation behavior of the impacting compliant mechanism.

Keywords: eigenvalue problems, finite element method, robin boundary condition, sturm-liouville problem

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Derssie Mebratu

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As a large variety of micro services, web services, social graphic applications, and media applications are continuously developed, it is substantially vital to design and build a reliable, efficient, and faster memory tiering system. Despite limited design, implementation, and deployment in the last few years, several techniques are currently developed to improve a memory tiering system in a cloud. Some of these techniques are to develop an optimal scanning frequency; improve and track pages movement; identify pages that recently accessed; store pages across each tiering, and then identify pages as a hot, warm, and cold so that hot pages can store in the first tiering Dynamic Random Access Memory (DRAM) and warm pages store in the second tiering Compute Express Link(CXL) and cold pages store in the third tiering Non-Volatile Memory (NVM). Apart from the current proposal and implementation, we also develop a new technique based on a machine learning algorithm in that the throughput produced 25% improved performance compared to the performance produced by the baseline as well as the latency produced 95% improved performance compared to the performance produced by the baseline.

Keywords: machine learning, bayesian optimization, memory tiering, CXL, DRAM

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Authors: Jaspal Rana, Varinder Singh

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Oxidative stress has been identified as an underlying cause of ischemia-reperfusion (IR) related cognitive dysfunction and brain damage. Therefore, antioxidant based therapies to treat IR injury are being investigated. Allium cepa L. (onion) is used as culinary medicine and is documented to have marked antioxidant effects. Hence, the present study was designed to evaluate the effect of A. cepa outer scale extract (ACE) against IR induced cognition and biochemical deficit in mice. ACE was prepared by maceration with 70% methanol and fractionated into ethylacetate and aqueous fractions. Bilateral common carotid artery occlusion for 10 min, followed by 24 h reperfusion, was used to induce cerebral IR injury. Following IR injury, ACE (100 and 200 mg/kg) was administered orally to animals for 7 days once daily. Behavioral outcomes (memory and sensorimotor functions) were evaluated using Morris water maze and neurological severity score. Cerebral infarct size, brain thiobarbituric acid reactive species, reduced glutathione, and superoxide dismutase activity were also determined. Treatment with ACE significantly ameliorated IR mediated deterioration of memory and sensorimotor functions and rose in brain oxidative stress in animals. The results of the present investigation revealed that ACE improved functional outcomes after cerebral IR injury which may be attributed to its antioxidant properties.

Keywords: allium cepa, cerebral ischemia, memory, sensorimotor

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Authors: Adi Gielgun-Katz, Alina S. Rusu

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In the last two decades, education systems worldwide have integrated new pedagogical methods and strategies in lesson plans, such as innovative technologies, social-emotional learning (SEL), gamification, mixed learning, multiple literacies, and many others. Visual language, such as photographs, is known to transcend cultures and languages, and it is commonly used by youth to express positions and affective states in social networks. Therefore, visual language needs more educational attention as a linguistic and communicative component that can create connectedness among the students and their teachers. Nowadays, when SEL is gaining more and more space and meaning in the area of academic improvement in relation to social well-being, and taking and sharing pictures is part of the everyday life of the majority of people, it becomes natural to add the visual language to SEL approach as a reinforcement strategy for connecting education to the contemporary culture and language of the youth. This article presents a program conducted in a high school class in Israel, which combines the five SEL with photography techniques, i.e., Social-Emotional Learning Visual Empowerments (SELVEs) program (experimental group). Another class of students from the same institution represents the control group, which is participating in the SEL program without the photography component. The SEL component of the programs addresses skills such as: troubleshooting, uncertainty, personal strengths and collaboration, accepting others, control of impulses, communication, self-perception, and conflict resolution. The aim of the study is to examine the effects of programs on the level of the five SEL aspects in the two groups of high school students: Self-Awareness, Social Awareness, Self-Management, Responsible Decision Making, and Relationship Skills. The study presents a quantitative assessment of the SEL programs’ impact on the students. The main hypothesis is that the students’ questionnaires' analysis will reveal a better understanding and improvement of the five aspects of the SEL in the group of students involved in the photography-enhanced SEL program.

Keywords: social-emotional learning, photography, education program, adolescents

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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With the continuous increment of smart meter installations across the globe, the need for processing of the load data is evident. Clustering-based load profiling is built upon the utilization of unsupervised machine learning tools for the purpose of formulating the typical load curves or load profiles. The most commonly used algorithm in the load profiling literature is the K-means. While the algorithm has been successfully tested in a variety of applications, its drawback is the strong dependence in the initialization phase. This paper proposes a novel modified form of the K-means that addresses the aforementioned problem. Simulation results indicate the superiority of the proposed algorithm compared to the K-means.

Keywords: clustering, load profiling, load modeling, machine learning, energy efficiency and quality

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Authors: Pedro Martinez-Santos, Víctor Gómez-Escalonilla, Silvia Díaz-Alcaide, Esperanza Montero, Miguel Martín-Loeches

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Groundwater monitoring networks are critical in evaluating the vulnerability of groundwater resources to depletion and contamination, both in space and time. Groundwater monitoring networks typically grow over decades, often in organic fashion, with relatively little overall planning. The groundwater monitoring networks in the Madrid area, Spain, were reviewed for the purpose of identifying gaps and opportunities for improvement. Spatial analysis reveals the presence of various monitoring networks belonging to different institutions, with several hundred observation wells in an area of approximately 4000 km2. This represents several thousand individual data entries, some going back to the early 1970s. Major issues included overlap between the networks, unknown screen depth/vertical distribution for many observation boreholes, uneven time series, uneven monitored species, and potentially suboptimal locations. Results also reveal there is sufficient information to carry out a spatial and temporal analysis of groundwater vulnerability based on machine learning applications. These can contribute to improve the overall planning of monitoring networks’ expansion into the future.

Keywords: groundwater monitoring, observation networks, machine learning, madrid

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Authors: Cynthia Adlerstein

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Physical learning environment (PLE) has been considered, after family and educators, as the third teacher. There have been conflicting and converging viewpoints on the role of the physical dimensions of places to learn, in facilitating educational innovation and quality. Despite the different approaches, PLE has been widely recognized as a key factor in the quality of the learning experience , and in the levels of learning achievement in ECE . The conceptual frameworks of the field assume that PLE consists of a complex web of factors that shape the overall conditions for learning, and that much more interdisciplinary and complementary methodologies of research and development are required. Although the relevance of PLE attracts a broad international consensus, in Chile it remains under-researched and weakly regulated by public policy. Gaining deeper contextual understanding and more thoughtfully-designed recommendations require the use of innovative assessment tools that cross cultural and disciplinary boundaries to produce new hybrid approaches and improvements. When considering a PLE-based change process for ECE improvement, a central question is what dimensions, variables and indicators could allow a comprehensive assessment of PLE in Chilean kindergartens? Based on a grounded theory social justice inquiry, we adopted a mixed method design, that enabled a multivocal and interdisciplinary construction of data. By using in-depth interviews, discussion groups, questionnaires, and documental analysis, we elicited the PLE discourses of politicians, early childhood practitioners, experts in architectural design and ergonomics, ECE stakeholders, and 3 to 5 year olds. A constant comparison method enabled the construction of the dimensions, variables and indicators through which PLE assessment is possible. Subsequently, the instrument was applied in a sample of 125 early childhood classrooms, to test reliability (internal consistency) and validity (content and construct). As a result, an interdisciplinary and multivocal tool for assessing physical learning environments was constructed and validated, for Chilean kindergartens. The tool is structured upon 7 dimensions (wellbeing, flexible, empowerment, inclusiveness, symbolically meaningful, pedagogically intentioned, institutional management) 19 variables and 105 indicators that are assessed through observation and registration on a mobile app. The overall reliability of the instrument is .938 while the consistency of each dimension varies between .773 (inclusive) and .946 (symbolically meaningful). The validation process through expert opinion and factorial analysis (chi-square test) has shown that the dimensions of the assessment tool reflect the factors of physical learning environments. The constructed assessment tool for kindergartens highlights the significance of the physical environment in early childhood educational settings. The relevance of the instrument relies in its interdisciplinary approach to PLE and in its capability to guide innovative learning environments, based on educational habitability. Though further analysis are required for concurrent validation and standardization, the tool has been considered by practitioners and ECE stakeholders as an intuitive, accessible and remarkable instrument to arise awareness on PLE and on equitable distribution of learning opportunities.

Keywords: Chilean kindergartens, early childhood education, physical learning environment, third teacher

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Authors: Stephanie Giordano, Eric Lam, Rosa Plasencia

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An important piece of active aging is ensuring people have the right support to meet individual needs. Using NCI-AD data, we will look at measures of satisfaction with community access and needed services among people with physical disabilities receiving LTSS in the US. National Core Indicators—Aging and Disabilities (NCI-AD) is a voluntary effort by State Medicaid, aging, and disability agencies across the US to measure and track their own performance. NCI-AD uses a standardized survey – the Adult Consumer Survey (ACS), to hear directly from people receiving services about the quality of services and supports they receive. Data from the 2018-19 ACS found that compared to people without a physical disability, those with a physical disability were more likely to make choices about the services they receive, including when and how often they receive those services. Yet people with a physical disability were less likely to report they get enough assistance with everyday activities (e.g., shopping, housework, and taking medications) and self-care (e.g., dressing or bathing) and more likely to report that services and supports do not fully meet their needs and goals. A further breakdown by age shows that people 40-65 years old with a physical disability experienced even greater barriers to being as active in the community as they would like to be, indicating a need to better support people as they age with or into a disability. We will explore how these and other outcomes were affected by COVID-19, take a closer look at outcomes by demographics (e.g., race/ethnicity, gender, and mental health diagnoses) and discuss implications on the future needs of service systems.

Keywords: quality-of-life, long-term services and supports, person-centered, community

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Authors: Aisha Zubair, Anila Kamal

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Leadership styles and practices greatly influence the organizational effectiveness and productivity. It also plays an important role in employees’ experiences of positive emotions at workplace and creative work behaviors. Authentic leadership as a newly emerging concept has been found as a significant predictor of various desirable work related outcomes. However, leadership practices and its work related outcomes, to a great extent, are determined by the very nature of the organizational structures (tall and flat). Tall organizations are characterized by multiple hierarchical layers with predominant vertical communication patterns, and narrow span of control; while flat organizations are featured by few layers of management employing both horizontal and vertical communication styles, and wide span of control. Therefore, the present study was undertaken to determine the work related outcomes of perceived authentic leadership; that is work related flow and creative work behavior among employees of flat and tall organizations. Moreover, it was also intended to determine the moderating role of organizational structure (flat and tall) in the relationship between perceived authentic leadership with work related flow and creative work behavior. In this regard, two types of companies have been considered; that is, banks as a form of tall organizational structure with multiple hierarchical structures while software companies have been considered as flat organizations with minimal layers of management. Respondents (N = 1180) were full time regular employees of marketing departments of banks (600) and software companies (580) including both men and women with age range of 22-52 years (M = 33.24; SD = 7.81). Confirmatory Factor Analysis yielded factor structures of measures of work related flow and creative work behavior in accordance to the theoretical models. However, model of authentic leadership exhibited variation in terms of two items which were not included in the final measure of the perceived authentic leadership. Results showed that perceived authentic leadership was positively associated with work related flow and creative work behavior. Likewise, work related flow was positively aligned with creative work behavior. Furthermore, type of organizational structure significantly moderated the relationship of perceived authentic leadership with work related flow and creative work behavior. Results of independent sample t-test showed that employees working in flat organization reflected better perceptions of authentic leadership; higher work related flow and elevated levels of creative work behavior as compared to those working in tall organizations. It was also found that employees with extended job experience and more job duration in the same organization displayed better perceptions of authentic leadership, reported more work related flow and augmented levels of creative work behavior. Findings of the present study distinctively highlighted the similarities as well as differences in the interactions of major constructs which function differentially in the context of tall (banks) and flat (software companies) organizations. Implications of the present study for employees and management as well as future recommendations were also discussed.

Keywords: creative work behavior, organizational structure, perceived authentic leadership, work related flow

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Authors: Narvada Ramdhany, Reena Bhattu Babajee

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The 2007-2008 Global Financial Crisis which initiated in the US had a global outreach, impacting the financial and banking sectors of several economies; such as European countries, developing and emerging countries in Asia, Latin America and Africa. European countries represent one of the main sources of export earnings for Mauritius and given that Europe has been quite profoundly affected by the crisis, the Mauritian economy also could have been negatively affected. This study is being undertaken to see if the crisis had a spill-over effect on the Mauritian banking system. It will also enable to determine if the measures put in place to counteract the crisis by regulatory authorities have been effective. The study will be carried out on 17 banks and data will be collected over a time frame of seven years; with a pre-crisis period from 2005 to 2007 and a post-crisis period from 2009 to 2011. The impact of the crisis as such will be measured through the financial performance of the banks, using financial ratios and regression analysis. The results show that during the period concerned Mauritian banks have remained solvent and relatively stable. One of the main explanations put forward to explain the resilience of the banking sector to the crisis is that foreign exposure was relatively low. Another explanation put forward is that Mauritian banks normally transact mainly with prime borrowers unlike most the banks which were affected by the financial crisis.  

Keywords: global financial crisis, banking sector, financial performance, Mauritian banks

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Zhe Li, Aihua Cai

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Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.

Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Secil Kaya Gulen

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Face to face and virtual classrooms that came up with different conditions and environments, but similar purposes have different characteristics. Although virtual classrooms have some similar facilities with face-to-face classes such as program, students, and administrators, they have no walls and corridors. Therefore, students can attend the courses from a distance and can control their own learning spaces. Virtual classrooms defined as simultaneous online environments where students in different places come together at the same time with the guidance of a teacher. Distance education and virtual classes require different intellectual and managerial skills and models. Therefore, for effective use of virtual classrooms, the virtual property should be taken into consideration. One of the most important factors that affect the spread and effective use of the virtual classrooms is the perceptions and opinions of students -as one the main participants-. Student opinions and recommendations are important in terms of providing information about the fulfillment of expectation. This will help to improve the applications and contribute to the more efficient implementations. In this context, ideas and perceptions of the students related to the virtual classrooms, in general, were determined in this study. Advantages and disadvantages of virtual classrooms expected contributions to the educational system and expected characteristics of virtual classrooms have examined in this study. Students of an online distance education graduate program in which all the courses offered by virtual classrooms have asked for their opinions. Online Distance Education Graduate Program has totally 19 students. The questionnaire that consists of open-ended and multiple choice questions sent to these 19 students and finally 12 of them answered the questionnaire. Analysis of the data presented as frequencies and percentages for each item. SPSS for multiple-choice questions and Nvivo for open-ended questions were used for analyses. According to the results obtained by the analysis, participants stated that they did not get any training on virtual classes before the courses; but they emphasize that newly enrolled students should be educated about the virtual classrooms. In addition, all participants mentioned that virtual classroom contribute their personal development and they want to improve their skills by gaining more experience. The participants, who mainly emphasize the advantages of virtual classrooms, express that the dissemination of virtual classrooms will contribute to the Turkish Education System. Within the advantages of virtual classrooms, ‘recordable and repeatable lessons’ and ‘eliminating the access and transportation costs’ are most common advantages according to the participants. On the other hand, they mentioned ‘technological features and keyboard usage skills affect the attendance’ is the most common disadvantage. Participants' most obvious problem during virtual lectures is ‘lack of technical support’. Finally ‘easy to use’, ‘support possibilities’, ‘communication level’ and ‘flexibility’ come to the forefront in the scope of expected features of virtual classrooms. Last of all, students' opinions about the virtual classrooms seems to be generally positive. Designing and managing virtual classrooms according to the prioritized features will increase the students’ satisfaction and will contribute to improve applications that are more effective.

Keywords: distance education, virtual classrooms, higher education, e-learning

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Authors: Gezahegn Bekele Welldgiyorgise

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It is obvious that the economics, politics and social conditions of a country are determined by the quality and standard of its education. Indeed, education plays a vital role in changing the consciousness and awareness of society and transforming it on a large scale. Moreover, education contributes a lot to the advancement of science and technology, information and communication, and above all, it speeds up its progress in no time if it focuses mainly on the qualitative approach to education. Education brings about universal change and transformation and lightens mankind in all dimensions. It creates an educated, enlightened and brightened generation in society. The generation will be sharped, sharpened and well-oriented if it gets modern, sophisticated and standardized education in its field of study. The main goal of education is to produce well-qualified, well-trained and disciplined young offers in a given community. If the youth is well trained and well-mannered, he will certainly be enlightened, problem solvers and solution seekers, researchers, and innovators. In this respect, we have to provide the youth with modern education, a teaching-learning process led by active learning and a participatory approach with a new curriculum preparation for the age of children supported by modern facilities (ICT).In addition to that, the curriculum should have to give attention to mathematics and science lessons that include international experience in a comfortable school and classrooms. Therefore, the generation that will be created through such kinds of the guided education system will make the students active participants, self-confident, researchers and problem solvers, besides that result in changed life standards and a developed country. Similarly, our country, Ethiopia, has aimed to get such change in youth (generation) through modern education, designing a new educational policy and curriculum which was implemented for many years, although the goal of education has not reached the required level. To get the main idea of the article, I should have answered the question of why our country's educational goal had not reached the desired level because it is necessary to lay the foundation for research in finding out problems seen through students learning performance, the first task is selecting primary-school as a sample. Therefore, we selected “Dilla primary school (5-8)” which is a workplace for a teacher and gives me a chance to recognize students’ learning performance to recognize their learning grades (internal and external) and measure performance (achievement) of students easily’.

Keywords: curriculum, performance, innovation, learning

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Authors: Brandon Knopp, Matthew Harris

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Purpose: Tip-apex distance (TAD) has long been discussed as a metric for determining risk of failure in the fixation of peritrochanteric fractures. TAD measurements over 25 millimeters (mm) have been associated with higher rates of screw cut out and other complications in the first several months after surgery. However, there is limited evidence for the efficacy of this measurement in predicting the long-term risk of negative outcomes following hip fixation surgery. The purpose of our study was to investigate risk factors including TAD for hospital readmission, loss of pre-injury ambulation and development of complications within 1 year after hip fixation surgery. Methods: A retrospective review of proximal hip fractures treated with single screw intramedullary devices between 2016 and 2020 was performed at a 327-bed regional medical center. Patients included had a postoperative follow-up of at least 12 months or surgery-related complications developing within that time. Results: 44 of the 67 patients in this study met the inclusion criteria with adequate follow-up post-surgery. There was a total of 10 males (22.7%) and 34 females (77.3%) meeting inclusion criteria with a mean age of 82.1 (± 12.3) at the time of surgery. The average TAD in our study population was 19.57mm and the average 1-year readmission rate was 15.9%. 3 out of 6 patients (50%) with a TAD > 25mm were readmitted within one year due to surgery-related complications. In contrast, 3 out of 38 patients (7.9%) with a TAD < 25mm were readmitted within one year due to surgery-related complications (p=0.0254). Individual TAD measurements, averaging 22.05mm in patients readmitted within 1 year of surgery and 19.18mm in patients not readmitted within 1 year of surgery, were not significantly different between the two groups (p=0.2113). Conclusions: Our data indicate a significant improvement in hospital readmission rates up to one year after hip fixation surgery in patients with a TAD < 25mm with a decrease in readmissions of over 40% (50% vs 7.9%). This result builds upon past investigations by extending the follow-up time to 1 year after surgery and utilizing hospital readmissions as a metric for surgical success. With the well-documented physical and financial costs of hospital readmission after hip surgery, our study highlights a reduction of TAD < 25mm as an effective method of improving patient outcomes and reducing financial costs to patients and medical institutions. No relationship was found between TAD measurements and secondary outcomes, including loss of pre-injury ambulation and development of complications.

Keywords: hip fractures, hip reductions, readmission rates, open reduction internal fixation

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Authors: Manal Sultan Alhussein, Xiang Michelle Liu

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Telehealth is an advanced technology using digital information and telecommunication facilities that provide access to health services from a distance. It slows the transmission factor of COVID-19, especially for elderly patients and patients with chronic diseases during the pandemic. Therefore, understanding patient perspectives on telehealth services and the factors impacting their option of telehealth service will shed light on the measures that healthcare providers can take to improve the quality of telehealth services. This study aimed to evaluate perceptions of telehealth services among different patient groups and explore various aspects of telehealth utilization in the United States during the COVID-19 pandemic. An online survey distributed via social media platforms was used to collect research data. In addition to the descriptive statistics, both correlation and regression analyses were conducted to test research hypotheses. The empirical results highlighted that the factors such as accessibility to telehealth services and the type of specialty clinics that the patients required play important roles in the effectiveness of telehealth services they received. However, the results found that patients’ waiting time to receive telehealth services and their annual income did not significantly influence their desire to select receiving healthcare services via telehealth. The limitations of the study and future research directions are discussed.

Keywords: telehealth, patient satisfaction, pandemic, healthcare, survey

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Abiodun Ezekiel Adesina, Alice M. Olagunju

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This study determined the effects of Computer Assisted Instructions (CAI) and Academic Self-Concepts (ASC) on pre-service teachers’ achievement in AE concepts in CoE in Southwest, Nigeria. The study adopted pretest-posttest, control group, quasi-experimental design. Six CoE with e-library facilities were purposively selected. Two hundred and thirty-two intact 200 level Agricultural education students offering introduction to AE course across the six CoE were participants. The participants were assigned to three groups (D&PM, 77, TM, 73 and control, 82). Treatment lasted eight weeks. The AE achievement test (r=0.76), pre-service teachers’ ASC Scale (r=0.81); instructional guides for tutorial (r=0.76), drill and practice (r=0.81) and conventional lecture modes (r=0.83), and teacher performance assessment sheet were used for data collection. Data were analysed using analysis of covariance and Scheffe post-hoc at 0.05 level of significance. The participants were 55.6% female with mean age of 20.8 years. Treatment had significant main effects on pre-service teachers’ achievement (F(2,207)=60.52; η²=0.21; p < 0.05). Participants in D&PM (x̄ =27.83) had the best achievement compared to those in TM (x̄ =25.41) and control (x̄ =18.64) groups. ASC had significant main effect on pre-service teachers’ achievement (F(1,207)=22.011; η²=0.166; p < 0.05). Participants with high ASC (x̄ =27.52) had better achievement compared to those with low ASC (x̄ =22.37). The drill and practice and tutorial instructional modes enhanced students’ achievement in Agricultural Economics concepts. Therefore, the two instructional modes should be adopted for improved learning outcomes in agricultural economics concepts among pre-service teachers.

Keywords: achievement in agricultural economics concepts, colleges of education in southwestern Nigeria, computer-assisted instruction, drill and practice instructional mode, tutorial instructional mode

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Authors: Josephine Aikpitanyi, Friday Okonofua, Lorrettantoimo, Sandy Tubeuf

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Every day, 800 women die from conditions related to pregnancy and childbirth, resulting in an estimated 300,000 maternal deaths worldwide per year. Over 99 percent of all maternal deaths occur in developing countries, with more than half of them occurring in sub-Saharan Africa. Nigeria being the most populous nation in sub-Saharan Africa bears a significant burden of worsening maternal and child health outcomes with a maternal mortality rate of 917 per 100,000 live births and child mortality rate of 117 per 1,000 live births. While several studies have documented that financial barriers disproportionately discourage poor women from seeking needed maternal and child healthcare, other studies have indicated otherwise. Evidence shows that there are instances where health facilities with skilled healthcare providers exist, and yet maternal, and child health outcomes remain abysmally low, indicating the presence of non-cognitive and behavioural factors that may affect the utilization of healthcare services. This study investigated the influence of locus of control and self-esteem on utilization of maternal and child healthcare services in Nigeria. Specifically, it explored the differences in utilization of antenatal care, skilled birth care, postnatal care, and child vaccination by women having an internal and external locus of control and women having high and low self-esteem. We collected information on non-cognitive traits of 1411 randomly selected women, along with information on utilization of the various indicators of maternal and child healthcare. Estimating logistic regression models for various components of healthcare services utilization, we found that women’s internal locus of control was a significant predictor of utilization of antenatal care, skilled birth care, and completion of child vaccination. We also found that having high self-esteem was a significant predictor of utilization of antenatal care, postnatal care, and completion of child vaccination after adjusting for other control variables. By improving our understanding of non-cognitive traits as possible barriers to maternal and child healthcare utilization, our findings offer important insights for enhancing participant engagement in intervention programs that are initiated to improve maternal and child health outcomes in low-and-middle-income countries.

Keywords: behavioural economics, health-seeking behaviour, locus of control and self-esteem, maternal and child healthcare, non-cognitive traits, and healthcare utilization

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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