Search results for: embedded learning support

Authors: S. Shahzadi, J. Rizk

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This paper describes the pulse width modulated control of a three phase, 4 polar DC brushless motor. To implement this practically the Atmel’s AVR ATmega 328 microcontroller embedded on an Arduino Eleven board is utilized. The microcontroller programming is done in an open source Arduino IDE development environment. The programming logic effectively manipulated a six MOSFET bridge which was used to energize the stator windings as per control requirements. The results obtained showed accurate, precise and efficient pulse width modulated operation. Another advantage offered by this pulse width modulated control was the efficient speed control of the motor. By varying the time intervals between successive commutations, faster energizing of the stator windings was possible thereby leading to quicker rotor alignment with these energized phases and faster revolutions.

Keywords: brushless DC motors, commutation, MOSFET, PWM

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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: Athanasia Kouroupa, Karen Irvine, Sivana Mengoni, Shivani Sharma

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Exploring parents’ preferences towards technology-based interventions for children on the autism spectrum can inform future research and support technology design. The study aimed to provide a comprehensive description of parents’ knowledge and preferences about innovative technology to support children on the autism spectrum. Survey data were collected from parents (n = 267) internationally. The survey included information about the use of conventional (e.g., smartphone, iPod, tablets) and non-conventional (e.g., virtual reality, robot) technologies. Parents appeared to prefer conventional technologies such as tablets and dislike non-conventional ones. They highlighted the positive contribution technology brought to the children’s lives during the pandemic. A few parents were equally concerned that the compulsory introduction of technology during the pandemic was associated with elongated time on devices. The data suggested that technology-based interventions are not widely known, need to be financially approachable and achieve a high standard of design to engage users.

Keywords: autism, intervention, preferences, technology

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Authors: Peter Tabatt, Christian Siemers

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Using FPGA devices to improve the behavior of time-critical parts of embedded systems is a proven concept for years. With reconfigurable FPGA devices, the logical blocks can be partitioned and grouped into static and dynamic parts. The dynamic parts can be reloaded 'on demand' at runtime. This work uses cellular automata, which are constructed through compilation from (partially restricted) ANSI-C sources, to determine the suitability of various metrics for optimal partitioning. Significant metrics, in this case, are for example the area on the FPGA device for the partition, the pass count for loop constructs and communication characteristics to other partitions. With successful partitioning, it is possible to use smaller FPGA devices for the same requirements as with not reconfigurable FPGA devices or – vice versa – to use the same FPGAs for larger programs.

Keywords: reconfigurable FPGA, cellular automata, partitioning, metrics, parallel computing

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Authors: D. Tedesco, G. Feletti, P. Trucco

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The present study aims to develop a Decision Support System (DSS) to support the operational decision of the Emergency Medical Service (EMS) regarding the assignment of medical emergency requests to Emergency Departments (ED). In the literature, this problem is also known as “hospital selection” and concerns the definition of policies for the selection of the ED to which patients who require further treatment are transported by ambulance. The employed research methodology consists of the first phase of revision of the technical-scientific literature concerning DSSs to support the EMS management and, in particular, the hospital selection decision. From the literature analysis, it emerged that current studies are mainly focused on the EMS phases related to the ambulance service and consider a process that ends when the ambulance is available after completing a request. Therefore, all the ED-related issues are excluded and considered as part of a separate process. Indeed, the most studied hospital selection policy turned out to be proximity, thus allowing to minimize the transport time and release the ambulance in the shortest possible time. The purpose of the present study consists in developing an optimization model for assigning medical emergency requests to the EDs, considering information relating to the subsequent phases of the process, such as the case-mix, the expected service throughput times, and the operational capacity of different EDs in hospitals. To this end, a Discrete Event Simulation (DES) model was created to evaluate different hospital selection policies. Therefore, the next steps of the research consisted of the development of a general simulation architecture, its implementation in the AnyLogic software and its validation on a realistic dataset. The hospital selection policy that produced the best results was the minimization of the Time To Provider (TTP), considered as the time from the beginning of the ambulance journey to the ED at the beginning of the clinical evaluation by the doctor. Finally, two approaches were further compared: a static approach, which is based on a retrospective estimate of the TTP, and a dynamic approach, which is based on a predictive estimate of the TTP determined with a constantly updated Winters model. Findings reveal that considering the minimization of TTP as a hospital selection policy raises several benefits. It allows to significantly reduce service throughput times in the ED with a minimum increase in travel time. Furthermore, an immediate view of the saturation state of the ED is produced and the case-mix present in the ED structures (i.e., the different triage codes) is considered, as different severity codes correspond to different service throughput times. Besides, the use of a predictive approach is certainly more reliable in terms of TTP estimation than a retrospective approach but entails a more difficult application. These considerations can support decision-makers in introducing different hospital selection policies to enhance EMSs performance.

Keywords: discrete event simulation, emergency medical services, forecast model, hospital selection

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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: Navab Karimi, Tohid Alizadeh

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An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively.

Keywords: sun-dried organic raisin, genetic algorithm, feature extraction, ann regression, linear regression, support vector machine, south azerbaijan.

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Authors: Anum Rafiq

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

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

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Authors: Safaa H. Sindi, Michael Roe

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

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

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Authors: Che-Wei Lee, Bay-Erl Lai

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A new steganographic method via the use of numeric data on public websites with self-authentication capability is proposed. The proposed technique transforms a secret message into partial shares by Shamir’s (k, n)-threshold secret sharing scheme with n = k + 1. The generated k+1 partial shares then are embedded into the selected numeric items in a website as if they are part of the website’s numeric content. Afterward, a receiver links to the website and extracts every k shares among the k+1 ones from the stego-numeric-content to compute k+1 copies of the secret, and the phenomenon of value consistency of the computed k+1 copies is taken as an evidence to determine whether the extracted message is authentic or not, attaining the goal of self-authentication of the extracted secret message. Experimental results and discussions are provided to show the feasibility and effectiveness of the proposed method.

Keywords: steganography, data hiding, secret authentication, secret sharing

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Authors: Boudjedra Abderrahim, Toumi Salah, Boutalbi Mostefa, Frihi Mohammed

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Nowadays, using the advancement of technology in semiconductor device fabrication, many transistors can be integrated to a single chip (VLSI). Although the growth chip density potentially eases systems-on-chip (SoCs) integrating thousands of processing element (PE) such as memory, processor, interfaces cores, system complexity, high-performance interconnect and scalable on-chip communication architecture become most challenges for many digital and embedded system designers. Networks-on-chip (NoCs) becomes a new paradigm that makes possible integrating heterogeneous devices and allows many communication constraints and performances. In this paper, we are interested for good performance and low area for implementation and a behavioral modeling of network on chip mesh topology design using VHDL hardware description language with performance evaluation and FPGA implementation results.

Keywords: design, implementation, communication system, network on chip, VHDL

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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: Fatih Iscan, Ceren Yagci

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Rapid population growth, urbanization and industrialization are known as the most important factors of environment problems. Elimination and management of solid wastes are also within the most important environment problems. One of the main problems in solid waste management is the selection of the best site for elimination of solid wastes. Lately, Geographical Information System (GIS) has been used for easing selection of landfill area. GIS has the ability of imitating necessary economical, environmental and political limitations. They play an important role for the site selection of landfill area as a decision support tool. In this study; map layers will be studied for minimum effect of environmental, social and cultural factors and maximum effect for engineering/economical factors for site selection of landfill areas and using GIS for an decision support mechanism in solid waste landfill areas site selection will be presented in Aksaray/TURKEY city, Güzelyurt district practice.

Keywords: GIS, landfill, solid waste, spatial analysis

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Authors: Sakul Jariyachamsit

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The objectives of this research were to study the behaviour of inbound tourist who visited Ratanakosin Island and to study their needs concerning support facilities. The independent variables included gender, age, levels of education, occupation, and income while the dependent variables were classified into two groups: tourists’ behaviour variables and tourists’ need of supporting facilities. A simple random sampling method was utilized to get 225 respondents. The majority of respondents were both male and female in the same proportion but most were between 21-30 years old. Most were married with a graduated degree. The average income of the respondents was between $20,000-30,000. The findings revealed that the majority of respondents came to Thailand for the first time and spent about 8 days in Thailand and preferred to travel in small groups. Their decision to come to Thailand was influenced by word of mouth. When they first thought of Thailand, they thought of Thai food. In terms of the needs for tourists around the Ratanakosin Island, and ranked in importance, are as follows: a tourist centre, somebody who can speak English, a trustable agency, police patrol, and the availability of maps and brochures.

Keywords: Rattanakosin Island, tourist, travelling behaviour, media engineering

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Authors: Shiau-Fang Chao, Yu-Chih Chen

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Owing to physical limitations and restrained lifestyle, older long-term care (LTC) residents are more likely to be affected by their environment than their community-dwelling counterparts. They also participate fewer activities and experience worse mental health than healthy older adults. This study adopts the ICF model to determine the extent to which the clustered patterns of LTC environment and activity participation are associated with older residents’ mental health. Method: Data were collected from a stratified equal probability sample of 634 older residents in 155 LTC institutions in Taiwan. Latent profile analysis (LPA) and latent class analysis (LCA) were conducted to explore the profiles for environment and activity participation. Multilevel modeling was performed to elucidate the relationships among environment profiles, activity profiles, and mental health. Results: LPA identified three mutually exclusive environment profiles (Low-, Moderate-, and High-Support Environment) based on the physical, social, and attitudinal environmental domains, consolidated from 12 environmental measures. LCA constructed two distinct activity profiles (Low- and High-Activity Participation) across seven activity domains (outdoor, volunteer-led leisure, spiritual, household chores, interpersonal exchange, social, and sedentary activity) that were factored from 20 activities. Compared to the Low-Support Environment class, older adults in the Moderate- and High-Support Environment classes had better mental health. Older residents in the Moderate- and High-Support Environment classes were more likely to be in the “High Activity” class, which in turn, exhibited better mental health. Conclusion: This study advances the current knowledge through rigorous methods and study design. The study findings lead to several conclusions. First, this study supports the use of ICF framework to institutionalized older individuals with functional limitations and demonstrates that both measures of environment and activity participation can be refined from multiple indicators. Second, environmental measures that encompass the physical, social, and attitudinal domains would provide a more comprehensive assessment on the place where an older individual embeds. Third, simply counting activities in which an older individual participates or considering a certain type of activity may not capture his or her way of life. Practitioners should not only focus on group or leisure activities within the institutions; rather, more efforts should be made to consider residents’ preferences for everyday life and support their remaining ability by encouraging continuous participation in activities they still willing and capable to perform. Fourth, environment and activity participation are modifiable factors which have greater potential to strengthen older LTC residents’ mental health, and activity participation should be considered in the link between environment and mental health. A combination of enhanced physical, social, and attitudinal environments, and continual engagement in various activities may optimize older LTC residents’ mental health.

Keywords: activity, environment, mental health, older LTC residents

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Authors: Gia Surguladze, Nino Topuria, Lily Petriashvili, Giorgi Surguladze

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Modeling of building processes of a multimodal freight transportation support information system is discussed based on modern CASE technologies. Functional efficiencies of ports in the eastern part of the Black Sea are analyzed taking into account their ecological, seasonal, resource usage parameters. By resources, we mean capacities of berths, cranes, automotive transport, as well as work crews and neighbouring airports. For the purpose of designing database of computer support system for Managerial (Logistics) function, using Object-Role Modeling (ORM) tool (NORMA – Natural ORM Architecture) is proposed, after which Entity Relationship Model (ERM) is generated in automated process. The software is developed based on Process-Oriented and Service-Oriented architecture, in Visual Studio.NET environment.

Keywords: seaport resources, business-processes, multimodal transportation, CASE technology, object-role model, entity relationship model, SOA

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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: Víctor Cecilio, Roberto Gómez, J. Alberto Escobar, Héctor Guerrero

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The study of the structural behavior of a support/pier of an elevated viaduct in Mexico City is presented. Detection of foundation piles with uncertain integrity prompted the review of possible situations that could jeopardy the structural safety of the pier. The objective of this paper is to evaluate the structural conditions of the support, taking into account the type of anomaly reported and the depth at which it is located, the position of the pile with uncertain integrity in the foundation system, the stratigraphy of the surrounding soil and the geometry and structural characteristics of the pier. To carry out the above, dynamic analysis, spectral modal, and step-by-step, with elastic and inelastic material models, were performed. Results were evaluated in accordance with the standards used for the design of the original structural project and with the Construction Regulations for Mexico’s Federal District (RCDF-2017, 2017). Comments on the response of the analyzed models are issued, and the conclusions are presented from a structural point of view.

Keywords: dynamic analysis, inelastic models, dubious foundation, bridge pier

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Authors: Jacob Dangerfield, Jacob Pollard, Jennifer DeCou

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Introduction: Burnout, anxiety, and depression are three conditions that are prevalent in medical providers. This is especially the case in the field of anesthesia, which has a high number of providers suffering from burnout and burnout syndrome. A major contributor to this issue is isolation in the workplace, with a perceived lack of peer support as a major risk factor for burnout. Two organizational interventions that can be done to help improve this issue are small group sessions and providing affordable mental health services. Per American College of Graduate Medical Education (ACGME) Guidelines, these affordable mental health services are a requirement of all residency programs, but for a variety of reasons, many residents do not access them. As physicians, we are often not good at asking for help. With this in mind, we hypothesized that carrying out small group resiliency sessions facilitated by Graduate Medical Education (GME) Wellness Counselors would improve both resident peer support as well as the likelihood that a resident will reach out to GME Wellness in a time of need. Methods: We held small group resiliency sessions with the GME Wellness Mental Health Professionals during protected didactic time. These sessions were small groups, including the members of one’s class (i.e., first-year residents on their own), and were facilitated by 1-2 mental health professionals. After these sessions, we surveyed residents who attended using a short Google Forms survey and using a 5-point Likert Scale, asked residents about some outcomes from the session. A “strongly agree” or “agree” was considered a positive response. Results: Results from our survey showed that the resident sessions had multiple positive outcomes. This survey was sent to 29 residents, and we had a 62% response rate. We found out through this survey that these small group sessions had a perceived positive impact on resident personal well-being, increased perceived peer support from classmates, and made residents more likely to reach out to GME Wellness in the future. Perceived positive impact on well-being was found in 83% of resident respondents, improved perceived peer support in 83% of respondents, and 78% of resident respondents stated that this session increased their likelihood of reaching out to mental health professionals. Conclusions: Through this study, we can conclude that our hypothesis was correct in that Small Group Resiliency Sessions that are facilitated by GME Wellness Counselors improve both resident peer support as well as the likelihood a resident reaches out to these mental health professionals in time of need. We believe these findings are very important as they address two important factors that can aid in decreasing a provider’s risk of experiencing burnout. Through this simple means, we believe other residency programs can help the well-being of their residents, and together, we can decrease the number of cases of burnout in anesthesia.

Keywords: anesthesiology, burnout, wellness, depression, residents, trainees, mental health

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Authors: C. N. Okafor, M. Maaza, T. A. E. Mokrani

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A proton exchange membrane has been developed for Direct Methanol Fuel Cell (DMFC). The nanofiber network composite membranes were prepared by interconnected network of Nafion (perfuorosulfonic acid) nanofibers that have been embedded in an uncharged and inert polymer matrix, by electro-spinning. The spinning solution of Nafion with a low concentration (1 wt. % compared to Nafion) of high molecular weight poly(ethylene oxide), as a carrier polymer. The interconnected network of Nafion nanofibers with average fiber diameter in the range of 160-700nm, were used to make the membranes, with the nanofiber occupying up to 85% of the membrane volume. The matrix polymer was cross-linked with Norland Optical Adhesive 63 under UV. The resulting membranes showed proton conductivity of 0.10 S/cm at 25°C and 80% RH; and methanol permeability of 3.6 x 10-6 cm2/s.

Keywords: composite membrane, electrospinning, fuel cell, nanofibers

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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

Procedia PDF Downloads 206

Authors: Sajjad Hassanpour, Vicente A. González, Yang Zou, Jiamou Liu

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Earthquakes pose a significant threat to both structural and non-structural elements in buildings, putting human lives at risk. Effective post-earthquake evacuation is critical for ensuring the safety of building occupants. However, current design practices often neglect the integration of post-earthquake evacuation considerations into the early-stage architectural design process. To address this gap, this paper presents a novel automated internal architectural layout generation tool that optimizes post-earthquake evacuation performance. The tool takes an initial plain floor plan as input, along with specific requirements from the user/architect, such as minimum room dimensions, corridor width, and exit lengths. Based on these inputs, firstly, the tool randomly generates different architectural layouts. Secondly, the human post-earthquake evacuation behaviour will be thoroughly assessed for each generated layout using the advanced Agent-Based Building Earthquake Evacuation Simulation (AB2E2S) model. The AB2E2S prototype is a post-earthquake evacuation simulation tool that incorporates variables related to earthquake intensity, architectural layout, and human factors. It leverages a hierarchical agent-based simulation approach, incorporating reinforcement learning to mimic human behaviour during evacuation. The model evaluates different layout options and provides feedback on evacuation flow, time, and possible casualties due to earthquake non-structural damage. By integrating the AB2E2S model into the automated layout generation tool, architects and designers can obtain optimized architectural layouts that prioritize post-earthquake evacuation performance. Through the use of the tool, architects and designers can explore various design alternatives, considering different minimum room requirements, corridor widths, and exit lengths. This approach ensures that evacuation considerations are embedded in the early stages of the design process. In conclusion, this research presents an innovative automated internal architectural layout generation tool that integrates post-earthquake evacuation simulation. By incorporating evacuation considerations into the early-stage design process, architects and designers can optimize building layouts for improved post-earthquake evacuation performance. This tool empowers professionals to create resilient designs that prioritize the safety of building occupants in the face of seismic events.

Keywords: agent-based simulation, automation in design, architectural layout, post-earthquake evacuation behavior

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Authors: Mohsine Jebbour

Abstract:

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: Sekou Kangoye, Alexis Todoskoff, Mihaela Barreau

Abstract:

Modified Condition/Decision Coverage (MC/DC) is a structural coverage criterion that aims to prove that all conditions involved in a Boolean expression can influence the result of that expression. In the context of automotive, MC/DC is highly recommended and even required for most security and safety applications testing. However, due to complex Boolean expressions that often embedded in those applications, generating a set of MC/DC compliant test cases for any of these expressions is a nontrivial task and can be time consuming for testers. In this paper we present an approach to automatically generate MC/DC test cases for any Boolean expression. We introduce novel techniques, essentially based on binary trees to quickly and optimally generate MC/DC test cases for the expressions. Thus, the approach can be used to reduce the manual testing effort of testers.

Keywords: binary trees, MC/DC, test case generation, nontrivial task

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Authors: Cristy Stagnar, Nina Hubig, Diana Ivankovic

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

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

Procedia PDF Downloads 175

Authors: Janet Holland

Abstract:

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

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

Procedia PDF Downloads 118

Authors: Yu Qin

Abstract:

In order to tackle the ever-growing problem of the lack of parking space, this paper presents the design and implementation of a smart unmanned parking system that is based on RFID (radio frequency identification) technology and Wireless communication technology. This system uses RFID technology to achieve the identification function (transmitted by 2.4 G wireless module) and is equipped with an STM32L053 micro controller as the main control chip of the smart vehicle. This chip can accomplish automatic parking (in/out), charging and other functions. On this basis, it can also help users easily query the information that is stored in the database through the Internet. Experimental tests have shown that the system has the features of low power consumption and stable operation, among others. It can effectively improve the level of automation control of the parking lot management system and has enormous application prospects.

Keywords: RFID, embedded system, unmanned, parking management

Procedia PDF Downloads 316