Search results for: young children with learning disabilities

Authors: Ibrahim Abubakar Mikugi

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Nigerian graduates could only be self-employed and marketable if they acquire relevant skills and knowledge for successful establishment in various occupation and gainful employment. Research has shown that entrepreneurship education will be successful through developing individual entrepreneurial attitudes, raising awareness of career options by integrating and inculcating a positive attitude in the mind of students through skill acquisition. This paper examined the student- led entrepreneurship education through skill acquisition with specific emphasis on analysis of David Kolb experiential learning cycle. This Model allows individual to review their experience through reflection and converting ideas into action by doing. The methodology used was theoretical approach through journal, internet and Textbooks. Challenges to entrepreneurship education through skill acquisition were outlined. The paper concludes that entrepreneurship education is recognised by both policy makers and academics; entrepreneurship is more than mere encouraging business start-ups. Recommendations were given which include the need for authorities to have a clear vision towards entrepreneurship education and skill acquisition. Authorities should also emphasise a periodic and appropriate evaluation of entrepreneurship and to also integrate into schools academic curriculum to encourage practical learning by doing.

Keywords: entrepreneurship, entrepreneurship education, active learning, Cefe methodology

Procedia PDF Downloads 505

Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

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Authors: Fatemeh Jannesarvatan, Ghazaal Parastooei, Jimmy frerejan, Saedeh Mokhtari, Peter Van Rosmalen

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Medical and dental education increasingly emphasizes the acquisition, integration, and coordination of complex knowledge, skills, and attitudes that can be applied in practical situations. Instructional design approaches have focused on using real-life tasks in order to facilitate complex learning in both real and simulated environments. The Four component instructional design (4C/ID) model has become a useful guideline for designing instructional materials that improve learning transfer, especially in health profession education. The objective of this study was to apply the 4C/ID model in the creation of virtual patients (VPs) that dental students can use to practice their clinical management and clinical reasoning skills. The study first explored the context and concept of complication factors and common errors for novices and how they can affect the design of a virtual patient program. The study then selected key dental information and considered the content needs of dental students. The design of virtual patients was based on the 4C/ID model's fundamental principles, which included: Designing learning tasks that reflect real patient scenarios and applying different levels of task complexity to challenge students to apply their knowledge and skills in different contexts. Creating varied learning materials that support students during the VP program and are closely integrated with the learning tasks and students' curricula. Cognitive feedback was provided at different levels of the program. Providing procedural information where students followed a step-by-step process from history taking to writing a comprehensive treatment plan. Four virtual patients were designed using the 4C/ID model's principles, and an experimental design was used to test the effectiveness of the principles in achieving the intended educational outcomes. The 4C/ID model provides an effective framework for designing engaging and successful virtual patients that support the transfer of knowledge and skills for dental students. However, there are some challenges and pitfalls that instructional designers should take into account when developing these educational tools.

Keywords: 4C/ID model, virtual patients, education, dental, instructional design

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Authors: Muhammed Kök & Adem Kantar

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Learning a foreign language can be affected by various factors such as age, aptitude, motivation, L2 disposition, etc. Among these factors, masculine gender roles stress (MGRS) that male learners possess is the least touched area that has been examined so far.MGRS can be defined as the traditional male role stress when the male learners feel the masculinity threat against their traditionally adopted masculinity norms. Traditional masculine norms include toughness, accuracy, completeness, and faultlessness. From this perspective, these norms are diametrically opposed to the language learning process since learning a language, by its nature, involves stages such as making mistakes and errors, not recalling words, pronouncing sounds incorrectly, creating wrong sentences, etc. Considering the potential impact of MGRS on the language learning process, the main purpose of this study is to investigate the mediating role of MGRS on the relationship between the EFL learners’ self-disclosure and English class anxiety. Data were collected from Turkish EFL learners (N=282) who study different majors in various state universities across Turkey. Data were analyzed by means of the Bootstraping method using the SPSS Process Macro plugin. The findings show that the indirect effect of self-disclosure level on the English Class Anxiety via MGRS was significant. We conclude that one of the reasons why Turkish EFL learners have English class anxiety might be the pressure that they feel because of their traditional gender role stress.

Keywords: masculine, gender role stress, english class anxiety, self-disclosure, masculinity norms

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Authors: Bernardo Cortez, Florian Peter, Thomas Lausenhammer, Paulo Oliveira

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Designing missiles’ autopilot controllers have been a complex task, given the extensive flight envelope and the nonlinear flight dynamics. A solution that can excel both in nominal performance and in robustness to uncertainties is still to be found. While Control Theory often debouches into parameters’ scheduling procedures, Reinforcement Learning has presented interesting results in ever more complex tasks, going from videogames to robotic tasks with continuous action domains. However, it still lacks clearer insights on how to find adequate reward functions and exploration strategies. To the best of our knowledge, this work is a pioneer in proposing Reinforcement Learning as a framework for flight control. In fact, it aims at training a model-free agent that can control the longitudinal non-linear flight dynamics of a missile, achieving the target performance and robustness to uncertainties. To that end, under TRPO’s methodology, the collected experience is augmented according to HER, stored in a replay buffer and sampled according to its significance. Not only does this work enhance the concept of prioritized experience replay into BPER, but it also reformulates HER, activating them both only when the training progress converges to suboptimal policies, in what is proposed as the SER methodology. The results show that it is possible both to achieve the target performance and to improve the agent’s robustness to uncertainties (with low damage on nominal performance) by further training it in non-nominal environments, therefore validating the proposed approach and encouraging future research in this field.

Keywords: Reinforcement Learning, flight control, HER, missile autopilot, TRPO

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Authors: Swarna Pundir, Prabuddha Hans

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As AI has already stepped into the daily life of human society, one cannot be ignorant about the data it collects and used it to provide a quality of services depending up on the individuals’ choices. It also helps in giving option for making decision Vs choice selection with a calculation based on the history of our search criteria. Over the past decade or so, the way Artificial Intelligence (AI) has impacted society is undoubtedly large.AI has changed the way we shop, the way we entertain and challenge ourselves, the way information is handled, and has automated some sections of our life. We have answered as to what AI is, but not why one may see it as useful. AI is useful because it is capable of learning and predicting outcomes, using Machine Learning (ML) and Deep Learning (DL) with the help of Artificial Neural Networks (ANN). AI can also be a system that can act like humans. One of the major impacts be Joblessness through automation via AI which is seen mostly in manufacturing sectors, especially in the routine manual and blue-collar occupations and those without a college degree. It raises some serious concerns about AI in regards of less employment, ethics in making moral decisions, Individuals privacy, human judgement’s, natural emotions, biased decisions, discrimination. So, the question is if an error occurs who will be responsible, or it will be just waved off as a “Machine Error”, with no one taking the responsibility of any wrongdoing, it is essential to form some rules for using the AI where both machines and humans are involved.

Keywords: AI, ML, DL, ANN

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Marike Venter de Villiers, Jessica Ramoshaba

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In recent years, the fashion industry has been associated with the exploitation of both people and resources. This is largely due to the emergence of the fast fashion concept, which entails rapid and continual style changes where clothes quickly lose their appeal, become out-of-fashion, and are then disposed of. This cycle often entails appalling working conditions in sweatshops with low wages, child labor, and a significant amount of textile waste that ends up in landfills. Although the awareness of the negative implications of ‘mindless fashion production and consumption’ is growing, fast fashion remains to be a popular choice among the youth. This is especially prevalent in South Africa, a poverty-stricken country where a vast number of young adults are unemployed and living in poverty. Despite being in poverty, the celebrity conscious culture and fashion products frequently portrayed on the growing intrusive social media platforms in South Africa pressurizes the consumers to purchase fashion and luxury products. Young adults are therefore more vulnerable to the temptation to purchase fast fashion products. A possible solution to the detrimental effects that the fast fashion industry has on the environment is the revival of the secondhand clothing trend. Although the popularity of secondhand clothing has gained momentum among selected consumer segments, the adoption rate of such remains slow. The main purpose of this study was to explore consumers’ perceptions of the secondhand clothing trend and to gain insight into factors that inhibit the adoption of secondhand clothing. This study also aimed to investigate whether consumers are aware of the negative implications of the fast fashion industry and their likelihood to shift their clothing purchases to that of secondhand clothing. By means of a quantitative study, fifty young females were asked to complete a semi-structured questionnaire. The researcher approached females between the ages of 18 and 35 in a face-to-face setting. The results indicated that although they had an awareness of the negative consequences of fast fashion, they lacked detailed insight into the pertinent effects of fast fashion on the environment. Further, a number of factors inhibit their decision to buy from secondhand stores: firstly, the accessibility to the latest trends was not always available in secondhand stores; secondly, the convenience of shopping from a chain store outweighs the inconvenience of searching for and finding a secondhand store; and lastly, they perceived secondhand clothing to pose a hygiene risk. The findings of this study provide fashion marketers, and secondhand clothing stores, with insight into how they can incorporate the secondhand clothing trend into their strategies and marketing campaigns in an attempt to make the fashion industry more sustainable.

Keywords: eco-friendly fashion, fast fashion, secondhand clothing, eco-friendly fashion

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Authors: Annelyn Fatima Lopez, Ermenilda Avendano, Aileen Marie Vargas, Lara Baylon, Rorilee Angeles

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BACKGROUND: The COVID-19 resulted in a public health emergency and quarantine measures which may negatively impact psychosocial and environmental aspects of vulnerable populations. OBJECTIVES: This study intended to determine the quality of life, situations and emerging concerns of parents of children with neurodevelopmental disorders during the ongoing coronavirus pandemic. METHODOLOGY: Parents of patients seen in the PCMC Neurodevelopmental Pediatrics OPD clinic were recruited to fill out questionnaires on parent and child characteristics, survey on situations and emerging concerns during the coronavirus pandemic and WHOQOL-BREF (Filipino version) for parental quality of life. RESULTS: Data from 115 respondents showed a lower score in the environmental domain. The child characteristics that are statistically comparable with the QoL scores include sex, severity of ID and ADHD while the parent characteristics that are statistically comparable with the QoL scores include educational attainment, monthly family income, father’s employment status and family structure (P-value <0.05). Most respondents reported physical distancing (82.61%) and curfew (80.87%) as measures implemented due to the pandemic. Inability to access essential services (43.48-74.48%) were further compounded by limited financial resources (51.30%) and public transport (60%). Government responses received include quarantine pass (90.43%), food allowance or relief package (86.09%), disinfection (60.87%), DSWD-SAP (42.61%) and cash distribution (41.74%). Concerns encountered include socio-environmental issues (i.e. no available transportation, effect on the ability to earn, inadequate food/medicine rations, disruptions in basic social services) and patient concerns (i.e. access to education, medical, developmental and behavioral services, nutrition and sleep). RECOMMENDATIONS: Programs and policies should be planned accordingly to provide improvement of quality of life for both parents and the child with a neurodevelopmental disorder.

Keywords: covid-19, neurodevelopmental disorder, parental quality of life, whoqol-bref

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Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: fall detection, machine learning, deep learning, pose estimation, tracking

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Authors: Mutiara Amalina Khairisa, Purnama Arafah, Rahayu Listiana Ramli

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In recent years, so many cases of divorce increasingly occur. Betrayal in form of infidelity, less communication one another, economically problems, selfishness of two sides, intervening parents from both sides which frequently occurs in Asia, especially in Indonesia, the differences of both principles and beliefs, “Sense of Romantism” depletion, role confict, a large difference in the purpose of marriage,and sex satisfaction are expected as the primary factors of the causes of divorce. Every couple of marriage wants to reach happy life in their family but severe problems brought about by either of those main factors come as a reasonable cause of failure marriage. The purpose of this study is to find out how marital adjustment and supporting factors in ensuring the success of that previous marital adjusment are inseparable two things assumed as a framework can affect the success in marriage becoming a resolution to reduce the desires to divorce. Those two inseparable things are able to become an aspect of learning from the success of the different ethnics marriage to keep holding on wholeness.

Keywords: marital adjustment, marital sustainability, learning process, successful ethnicity differences marriage, basical cultural values

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Authors: Yu-Chi Chou

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The purpose of this study was to establish a logical path of an instructional model of empathy and social regulation, providing feasibility evidence on the model implementation in students with autism spectrum disorder (ASD). This newly developed Emotional Bug-Out Bag (BoB) curriculum was designed to enhance the empathy and socially shared regulation of students with ASD. The BoB model encompassed three instructional phases of basic theory lessons (BTL), action plan practices (APP), and final theory practices (FTP) during implementation. Besides, a learning flow (teacher-directed instruction, student self-directed problem-solving, group-based task completion, group-based reflection) was infused into the progress of instructional phases to deliberately promote the social regulatory process in group-working activities. A total of 23 junior high school students with ASD were implemented with the BoB curriculum. To examine the logical path for model implementation, data was collected from the participating students’ self-report scores on the learning nodes and understanding questions. Path analysis using structural equation modeling (SEM) was utilized for analyzing scores on 10 learning nodes and 41 understanding questions through the three phases of the BoB model. Results showed (a) all participants progressed throughout the implementation of the BoB model, and (b) the models of learning nodes and phases were positive and significant as expected, confirming the hypothesized logic path of this curriculum.

Keywords: autism spectrum disorder, empathy, regulation, socially shared regulation

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: Heewon Jeong, Seongpyo Kim, Joon Ha Kim

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Over the last few tens of years, the coastal regions of Korea have experienced red tide algal blooms, which are harmful and toxic to both humans and marine organisms due to their potential threat. It was accelerated owing to eutrophication by human activities, certain oceanic processes, and climate change. Previous studies have tried to monitoring and predicting the algae concentration of the ocean with the bio-optical algorithms applied to color images of the satellite. However, the accurate estimation of algal blooms remains problems to challenges because of the complexity of coastal waters. Therefore, this study suggests a new method to identify the concentration of red tide algal bloom from images of geostationary ocean color imager (GOCI) which are representing the water environment of the sea in Korea. The method employed GOCI images, which took the water leaving radiances centered at 443nm, 490nm and 660nm respectively, as well as observed weather data (i.e., humidity, temperature and atmospheric pressure) for the database to apply optical characteristics of algae and train deep learning algorithm. Convolution neural network (CNN) was used to extract the significant features from the images. And then artificial neural network (ANN) was used to estimate the concentration of algae from the extracted features. For training of the deep learning model, backpropagation learning strategy is developed. The established methods were tested and compared with the performances of GOCI data processing system (GDPS), which is based on standard image processing algorithms and optical algorithms. The model had better performance to estimate algae concentration than the GDPS which is impossible to estimate greater than 5mg/m³. Thus, deep learning model trained successfully to assess algae concentration in spite of the complexity of water environment. Furthermore, the results of this system and methodology can be used to improve the performances of remote sensing. Acknowledgement: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017.

Keywords: deep learning, algae concentration, remote sensing, satellite

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Authors: Nir Wittenberg, Moshe Farhi

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This mixed-methods study examines the integration of artificial intelligence (AI) tools in a first-year social work course to assess their potential for enhancing professional knowledge and skills. The incorporation of digital technologies, such as AI, in social work interventions, training, and research has increased, with the expectation that AI will become as commonplace as email and mobile phones. However, policies and ethical guidelines regarding AI, as well as empirical evaluations of its usefulness, are lacking. As AI is gradually being adopted in the field, it is prudent to explore AI thoughtfully in alignment with pedagogical goals. The outcomes assessed include professional identity, course satisfaction, and motivation. AI offers unique reflective learning opportunities through personalized simulations, feedback, and queries to complement face-to-face lessons. For instance, AI simulations provide low-risk practices for situations such as client interactions, enabling students to build skills with less stress. However, it is essential to recognize that AI alone cannot ensure real-world competence or cultural sensitivity. Outcomes related to student learning, experience, and perceptions will help to elucidate the best practices for AI integration, guiding faculty, and advancing pedagogical innovation. This strategic integration of selected AI technologies is expected to diversify course methodology, improve learning outcomes, and generate new evidence on AI’s educational utility. The findings will inform faculty seeking to thoughtfully incorporate AI into teaching and learning.

Keywords: artificial intelligence (AI), social work education, students, developing a professional identity, ethical considerations

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Authors: Irum Naz

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In the Arabian higher education context, linguistic proficiency in the English language is considered crucial for the developmental sustainability, economic growth, and stability of communities and societies. Qatar’s educational reforms package, through the 2030 vision, identifies the acquisition of English at K-12 as an essential survival communication tool for globalization, believing that Qatari students need better preparation to take on the responsibilities of leadership and to participate effectively in the country’s surging economy. The idea of introducing Qatari students to modern curricula benchmarked to high-student-performance curricula in developed countries is one of the components of reformatory design principles of Education for New Era reform project that is mutually consented to and supported by the Office of Shared Services, Communications Office, and Supreme Education Council. In appreciation of the government’s vision, the English Language Centre (ELC) at the Community College of Qatar ran an internal educational audit and conducted evaluative research to understand and appraise the value, impact, and practicality of the existing ELC language development program. This study sought to identify the type of change that could identify and improve the quality of Foundation Program courses and the manners in which second language learners could be assisted to transit smoothly between (ELC) levels. Following the interpretivist paradigm and mixed research method, the data was gathered through a bicyclic research model and a triangular design. The analyses of the data suggested that there was a need for improvement in the ELC program as a whole, and particularly in terms of curriculum, student learning outcomes, and the general learning environment in the department. Key findings suggest that the target program would benefit from significant revisions, which would include narrowing the focus of the courses, providing sets of specific learning objectives, and preventing repetition between levels. Another promising finding was about the assessment tools and process. The data suggested that a set of standardized assessments that more closely suited the programs of study should be devised. It was also recommended that students undergo a more comprehensive placement process to ensure that they begin the program at an appropriate level and get the maximum benefit from their learning experience. Although this ties into the idea of curriculum revamp, it was expected that students could leave the ELC having had exposure to courses in English for specific purposes. The idea of a more reliable exit assessment for students was raised frequently so ELC could regulate itself and ensure optimum learning outcomes. Another important recommendation was the provision of a Student Learning Center for students that would help them to receive personalized tuition, differentiated instruction, and self-driven and self-evaluated learning experience. In addition, an extra study level was recommended to be added to the program to accommodate the different levels of English language proficiency represented among ELC students. The evidence collected in the course of conducting the study suggests that significant change is needed in the structure of the ELC program, specifically about curriculum, the program learning outcomes, and the learning environment in general.

Keywords: educational audit, ESL, optimum learning outcomes, Qatar’s educational reforms, self-driven and self-evaluated learning experience, Student Learning Center

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Authors: Atakan Aral Ormancı, Tuğçe Arslantaş, Murat Özcü

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This study presents the design and implementation of an experimental chassis-level system for various control applications. Specifically, the height level of the chassis is controlled using proportional integrated derivative, fuzzy logic, and deep learning control methods. Real-time data obtained from height and pressure sensors installed in a 6x2 truck chassis, in combination with pulse-width modulation signal values, are utilized during the tests. A prototype pneumatic system of a 6x2 truck is added to the setup, which enables the Smart Pneumatic Actuators to function as if they were in a real-world setting. To obtain real-time signal data from height sensors, an Arduino Nano is utilized, while a Raspberry Pi processes the data using Matlab/Simulink and provides the correct output signals to control the Smart Pneumatic Actuator in the truck chassis. The objective of this research is to optimize the time it takes for the chassis to level down and up under various loads. To achieve this, proportional integrated derivative control, fuzzy logic control, and deep learning techniques are applied to the system. The results show that the deep learning method is superior in optimizing time for a non-linear system. Fuzzy logic control with a triangular membership function as the rule base achieves better outcomes than proportional integrated derivative control. Traditional proportional integrated derivative control improves the time it takes to level the chassis down and up compared to an uncontrolled system. The findings highlight the superiority of deep learning techniques in optimizing the time for a non-linear system, and the potential of fuzzy logic control. The proposed approach and the experimental results provide a valuable contribution to the field of control, automation, and systems engineering.

Keywords: automotive, chassis level control, control systems, pneumatic system control

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Authors: Para Jansuwan, Kerstin K. Zander

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Most people in rural Thailand still depend on agriculture. The rural areas are undergoing changes in their demographic structures with an increasing older population, out migration of younger people and a shift away from work in the agricultural sector towards manufacturing and service provisioning. These changes may lead to a decline in agricultural productivity and food insecurity. Our research aims to examine perceptions of older farmers on how rural demographic change affects them, to investigate how farmers may change their agricultural practices to cope with their ageing and to explore the factors affecting these changes, including the opportunities and challenges arising from them. The data were collected through a household survey with 368 farmers in the Prachin Buri province in central Thailand, the main area for agricultural production. A series of binomial logistic regression models were applied to analyse the data. We found that most farmers suffered from age-related diseases, which compromised their working capacity. Most farmers attempted to reduce labour intense work, by either stopping farming through transferring farmland to their children (41%), stopping farming by giving the land to the others (e.g., selling, leasing out) (28%) and continuing farming with making some changes (e.g., changing crops, employing additional workers) (24%). Farmers’ health and having a potential farm successor were positively associated with the probability of stopping farming by transferring the land to the children. Farmers with a successor were also less likely to stop farming by giving the land to the others. Farmers’ age was negatively associated with the likelihood of continuing farming by making some changes. The results show that most farmers base their decisions on the hope that their children will take over the farms, and that without successor, farmers lease out or sell the land. Without successor, they also no longer invest in expansion and improvement of their farm production, especially adoption of innovative technologies that could help them to maintain their farm productivity. To improve farmers’ quality of life and sustain their farm productivity, policies are needed to support the viability of farms, the access to a pension system and the smooth and successful transfer of the land to a successor of farmers.

Keywords: rural demographic change, older farmer, stopping farming, continuing farming, health and age, farm successor, Thailand

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Authors: Shakirat Abimbola Adesola, Shuaib Akintunde Asifat, Jelili Olalekan Amoo

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This paper presented the impact of students’ emotions on learning when receiving lectures and when taking tests. It was observed that students experience different types of emotions during the study, and this was found to have a significant effect on their academic performance. A total of one thousand six hundred and seventy-five (1675) students from the department of Computer Science in two Colleges of Education in South-West Nigeria took part in this study. The students were randomly selected for the research. Sample comprises of 968 males representing 58%, and 707 females representing 42%. A structured questionnaire, of Motivated Strategies for Learning Questionnaire (MSLQ) was distributed to the participants to obtain their opinions. Data gathered were analyzed using the IBM SPSS 20 to obtain ANOVA, descriptive analysis, stepwise regression, and reliability tests. The results revealed that emotion moderately shape students’ motivation and engagement in learning; and that self-regulation and self-determination do have significant impact on academic performance. It was further revealed that test anxiety has a significant correlation with academic performance.

Keywords: motivation, self-determination, test anxiety, academic performance, and academic engagement

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Authors: Saugata Bose, Ritambhra Korpal

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The internet has increased the copy-paste scenarios amongst students as well as amongst researchers leading to different levels of plagiarized documents. For this reason, much of research is focused on for detecting plagiarism automatically. In this paper, an initiative is discussed where Natural Language Processing (NLP) techniques as well as supervised machine learning algorithms have been combined to detect plagiarized texts. Here, the major emphasis is on to construct a framework which detects external plagiarism from monolingual texts successfully. For successfully detecting the plagiarism, n-gram frequency comparison approach has been implemented to construct the model framework. The framework is based on 120 characteristics which have been extracted during pre-processing the documents using NLP approach. Afterwards, filter metrics has been applied to select most relevant characteristics and then supervised classification learning algorithm has been used to classify the documents in four levels of plagiarism. Confusion matrix was built to estimate the false positives and false negatives. Our plagiarism framework achieved a very high the accuracy score.

Keywords: lexical matching, shallow NLP, supervised machine learning algorithm, word n-gram

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Authors: A. Gagat-Matuła

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The article is concerned in the issue of social support and quality of life of youth suffering from cerebral palsy, who are temporarily orphaned due to the emigration of a parent. Migration causes multi-aspect consequences in various spheres of life. They are particularly severe for the functioning of families. Temporal parting of parents and children, especially the disabled, is a difficult situation. In this case, the family structure is changed, as well as the quality of life of its members. Children can handle migration parting in a better or worse way; these can be divided into properly functioning and manifesting behaviour disorders. In conditions of the progressing phenomenon of labour migration of Poles and a wide spectrum of consequences for the whole social life, it is essential to undertake actions aimed at support of migrants and their families. This article focuses mainly on social support and quality of families members, of which, are the labour migrants perceived by youth suffering from cerebral palsy. The quantitative method was used in this study. In the study, the Satisfaction with Life Scale (SWLS) by Diener, was used. The analysed group consisted of 50 persons (37 girls and 13 boys), aged 16 years to 18 years, whose parents are labour migrants. The results indicate that the quality of life and social support for youth suffering from cerebral palsy who are temporarily orphaned is at a low and average level.

Keywords: social support, quality of life, migration, cerebral palsy

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Authors: Ahmet Karakuş, Akif Can Kilic, Emre Alptekin

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A growing number of studies have been conducted to determine how well-being may be predicted using well-designed models. It is necessary to investigate the backgrounds of features in order to construct a viable Subjective Well-Being (SWB) model. We have picked the suitable variables from the literature on SWB that are acceptable for real-world data instructions. The goal of this work is to evaluate the model by feeding it with SWB characteristics and then categorizing the stress levels using machine learning methods to see how well it performs on a real dataset. Despite the fact that it is a multiclass classification issue, we have achieved significant metric scores, which may be taken into account for a specific task.

Keywords: machine learning, multiclassification problem, subjective well-being, perceived stress scale

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Authors: Inmaculada Yuste Martinez

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Since July 5, 2014, Abubaker al Bagdadi, leader of the Islamic State since 2010 climbed the pulpit of the Great Mosque of Al Nuri of Mosul and proclaimed the Caliphate, the number of fighters who have travelled to Syria to join the Caliphate has increased as never before. Although it is true that the phenomenon of foreign fighters is not a new phenomenon, as it occurred after the Spanish Civil War, Republicans from Ireland and the conflict of the Balkans among others, it is highly relevant the fact that in this case, it has reached figures unknown in Europe until now. The approval of the resolution 2178 (2014) of the Security Council, foreign terrorist fighters placed the subject a priority position on the International agenda. The available data allow us to affirm that women have increasingly assumed operative functions in jihadist terrorism and in the activities linked to it in the development of attacks in the European Union, including minors and young adults. In the case of Spain, one in four of the detainees in 2016 were women, a significant increase compared to 2015. This contrasts with the fact that until 2014 no woman had been prosecuted in Spain for terrorist activities of a jihadist nature. It is fundamental when we talk about the prevention of radicalization and counterterrorism that we do not underestimate the potential threat to the security of countries like Spain that women from the West can assume to the global jihadist movement. This work aims to deepen the radicalization processes of these women and their profiles influencing the female inmate population. It also wants to focus on the importance of creating de-radicalization programs for these inmates since women are a crucial element in radicalization processes. A special focus it is made on young radicalized female inmate population as this target group is the most recoverable and on which it would result more fruitful to intervene. De-radicalization programs must also be designed to fit their profiles and circumstances; a sensitive environment will be prisons and juvenile centers, areas that until now had been unrelated to this problem and which are already hosting the first convicted in judicial offices in Spanish territory. A qualitative research and an empirical and analytical method has been implemented in this work, focused on the cases that took place in Spain of young women and the imaginary that the Islamic State uses for the processes of radicalization for this target group and how it does not fit with their real role in the Jihad, as opposed to other movements in which women do have a real and active role in the armed conflict as YPJ do it as a part of the armed wing of the Democratic Union Party of Syria.

Keywords: caliphate, de-radicalization, foreign fighter, gender perspective, ISIS, jihadism, recruitment

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Isabelle Gobatto

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Based on an anthropological approach, this paper explores the medical profession and the construction of medical practices by considering the multiform articulations between structural poverty and the production of care from a low-resource francophone West African country, Burkina Faso. This country is considered in its exemplary dimension of culturally differentiated countries of the African continent that share the same situation of structural poverty. The objective is to expose the effects of structural poverty on the ways of constructing professional knowledge and thinking about the sense of the medical profession. If doctors are trained to have the same capacities in South and West countries, which are to treat and save lives whatever the cultural contexts of the practice of medicine, the ways of investing their role and of dealing with this context of action fracture the homogenization of the medical profession. In the line of anthropology of biomedicine, this paper outlines the complex effects of structural poverty on health care, care relations, and the moral economy of doctors. The materials analyzed are based on an ethnography including two temporalities located thirty years apart (1990-1994 and 2020-2021), based on long-term observations of care practices conducted in healthcare institutions, interviews coupled with the life histories of physicians. The findings reveal that disabilities faced by doctors to deliver care are interpreted as policy gaps, but they are also considered by physicians as constitutive of the social and cultural characteristics of patients, making their capacities and incapacities in terms of accompanying caregivers in the production of care. These perceptions have effects on know-how, structured around the need to act even when diagnoses are not made so as not to see patients desert health structures if the costs of care are too high for them. But these interpretations of highly individualizing dimensions of these difficulties place part of the blame on patients for the difficulties in using learned knowledge and delivering effective care. These situations challenge the ethics of caregivers but also of ethnologists. Firstly because the interpretations of disabilities prevent caregivers from considering vulnerabilities of care as constituting a common condition shared with their patients in these health systems, affecting them in an identical way although in different places in the production of care. Correlatively, these results underline that these professional conceptions prevent the emergence of a figure of victim, which could be shared between patients and caregivers who, together, undergo working and care conditions at the limit of the acceptable. This dimension directly involves politics. Secondly, structural poverty and its effects on care challenge the ethics of the anthropologist who observes caregivers producing, without intent to arm, experiences of care marked by an ordinary violence, by not giving them the care they need. It is worth asking how anthropologists could get doctors to think in this light in west-African societies.

Keywords: Africa, care, ethics, poverty

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Authors: Valentina Zhang

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While facial expression is a complex and individualized behavior, all facial emotion recognition (FER) systems known to us rely on a single facial representation and are trained on universal data. We conjecture that: (i) different facial representations can provide different, sometimes complementing views of emotions; (ii) when employed collectively in a discussion group setting, they enable more accurate emotion reading which is highly desirable in autism care and other applications context sensitive to errors. In this paper, we first study FER using pixel-based DL vs semantics-based DL in the context of deepfake videos. Our experiment indicates that while the semantics-trained model performs better with articulated facial feature changes, the pixel-trained model outperforms on subtle or rare facial expressions. Armed with these findings, we have constructed an adaptive FER system learning from both types of models for dyadic or small interacting groups and further leveraging the synthesized group emotions as the ground truth for individualized FER training. Using a collection of group conversation videos, we demonstrate that FER accuracy and personalization can benefit from such an approach.

Keywords: neurodivergence care, facial emotion recognition, deep learning, ground truth for supervised learning

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Authors: Lutfi AuliaRahman, Hari Rizki Ananda

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The traditional food is a typical food of a certain region that has a taste of its own unique and typically consumed by a society in certain areas, one of which is Gudeg, a regional specialties traditional food of Yogyakarta and Central Java which is made of young jackfruit cooked in coconut milk, edible with rice and served with thick coconut milk (areh), chicken, eggs, tofu and sambal goreng krecek. However, lately, the image of traditional food has declined among people, so with gudeg, which today's society, especially among young people, tend to prefer modern types of food such as fast food and some other foods that are popular. Moreover, traditional food usually only preferred by consumers of local communities and lack of demand by consumers from different areas for different tastes. Thus, the traditional food producers increasingly marginalized and their consumers are on the wane. This study aimed to evaluate the management used by producers of traditional food with a case study of Gudeg Sagan which located in the city of Yogyakarta, with the ability of their management in creating core competencies, which includes the competence of cost, competence of flexibility, competence of quality, competence of time, and value-based competence. And then, in addition to surviving and continuing to exist with the existing external environment, Gudeg Sagan can increase the number of consumers and also reach a broader segment of teenagers and adults as well as consumers from different areas. And finally, in this paper will be found positive impact on the creation of the core competencies of the existence and progress of the traditional food business based on case study of Gudeg Sagan.

Keywords: Gudeg Sagan, traditional food, core competencies, existence

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Authors: Sarika Gupta, Harshika Khanna, Ajay K Verma, Surya Kant

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Background: Drug-resistant tuberculosis (DR-TB) is a growing health challenge to global TB control efforts. Pediatric DR-TB is one of the neglected infectious diseases. In our previously published report, we have notified an increased prevalence of DR-TB in the pediatric population at a tertiary health care centre in North India which was estimated as 17.4%, 15.1%, 18.4%, and 20.3% in (%) in the year 2018, 2019, 2020, and 2021. Limited evidence exists about a pattern of drug resistance, side effect profile and programmatic outcomes of Paediatric DR-TB treatment. Therefore, this study was done to find out the pattern of resistance, side effect profile and treatment outcome. Methodology: This was a prospective cohort study conducted at the nodal drug-resistant tuberculosis centre of a tertiary care hospital in North India from January 2021 to December 2022. Subjects included children aged between 0-18 years of age with a diagnosis of DR-TB, on the basis of GeneXpert (rifampicin [RIF] resistance detected), line probe assay and drug sensitivity testing (DST) of M. tuberculosis (MTB) grown on a culture of body fluids. Children were classified as monoresistant TB, polyresistant TB (resistance to more than 1 first-line anti-TB drug, other than both INH and RIF), MDR-TB, pre-XDR-TB and XDR-TB, as per the WHO classification. All the patients were prescribed DR TB treatment as per the standard guidelines, either shorter oral DR-TB regimen or a longer all-oral MDR/XDR-TB regimen (age below five years needed modification). All the patients were followed up for side effects of treatment once per month. The patient outcomes were categorized as good outcomes if they had completed treatment and cured or were improving during the course of treatment, while bad outcomes included death or not improving during the course of treatment. Results: Of the 50 pediatric patients included in the study, 34 were females (66.7%) and 16 were male (31.4%). Around 33 patients (64.7%) were suffering from pulmonary TB, while 17 (33.3%) were suffering from extrapulmonary TB. The proportions of monoresistant TB, polyresistant TB, MDR-TB, pre-XDR-TB and XDR-TB were 2.0%, 0%, 50.0%, 30.0% and 18.0%, respectively. Good outcome was reported in 40 patients (80.0%). The 10 bad outcomes were 7 deaths (14%) and 3 (6.0%) children who were not improving. Adverse events (single or multiple) were reported in all the patients, most of which were mild in nature. The most common adverse events were metallic taste 16(31.4%), rash and allergic reaction 15(29.4%), nausea and vomiting 13(26.0%), arthralgia 11 (21.6%) and alopecia 11 (21.6%). Serious adverse event of QTc prolongation was reported in 4 cases (7.8%), but neither arrhythmias nor symptomatic cardiac side effects occurred. Vestibular toxicity was reported in 2(3.9%), and psychotic symptoms in 4(7.8%). Hepatotoxicity, hypothyroidism, peripheral neuropathy, gynaecomastia, and amenorrhea were reported in 2 (4.0%), 4 (7.8%), 2 (3.9%), 1(2.0%), and 2 (3.9%) respectively. None of the drugs needed to be withdrawn due to uncontrolled adverse events. Conclusion: Paediatric DR TB treatment achieved favorable outcomes in a large proportion of children. DR TB treatment regimen drugs were overall well tolerated in this cohort.

Keywords: pediatric, drug-resistant, tuberculosis, adverse events, treatment

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Authors: Durri Andriani, Irsan Tahar, Lilian Sarah Hiariey

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Every institution has to develop, implement, and control its policies to ensure the effectiveness of the institution. In doing so, all related stakeholders have to be involved to maximize the benefit of the policies and minimize the potential constraints and resistances. Open and distance education (ODE) institution is no different. As an education institution, ODE institution has to focus their attention to fulfilling academic needs of their students through open and distance measures. One of them is quality learning support system. Significant stakeholders in learning support system are tutors since they are the ones who directly communicate with students. Tutors are commonly seen as objects whose main responsibility is limited to implementing policies decided by management in ODE institutions. Nonetheless, tutors’ perceptions of tutorials are believed to influence tutors’ performances in facilitating learning support. It is therefore important to analyze tutors’ perception on various aspects of learning support. This paper presents analysis of tutors’ perceptions on policies of tutoriala in ODE institution using Policy Analysis Framework (PAF) modified by King, Nugent, Russell, and Lacy. Focus of this paper is on on-line tutors, those who provide tutorials via Internet. On-line tutors were chosen to stress the increasingly important used of Internet in ODE system. The research was conducted in Universitas Terbuka (UT), Indonesia. UT is purposely selected because of its large number (1,234) of courses offered and large area coverage (6000 inhabited islands). These posed UT in a unique position where learning support system has, to some extent, to be standardized while at the same time it has to be able to cater the needs of different courses in different places for students with different backgrounds. All 598 listed on-line tutors were sent the research questionnaires. Around 20% of the email addresses could not be reached. Tutors were asked to fill out open-ended questionnaires on their perceptions on definition of on-line tutorial, roles of tutors and students in on-line tutorials, requirement for on-line tutors, learning materials, and student evaluation in on-line tutorial. Data analyzed was gathered from 40 on-line tutors who sent back filled-out questionnaires. Data were analyzed qualitatively using content analysis from all 40 tutors. The results showed that using PAF as entry point in choosing learning support services as area of policy with delivery learning materials as the issue at UT has been able to provide new insights of aspects need to be consider in formulating policies in online tutorial and in learning support services. Involving tutors as source of information could be proven to be productive. In general, tutors had clear understanding about definition of online tutorial, roles of tutors and roles of students, and requirement of tutor. Tutors just need to be more involved in the policy formulation since they could provide data on students and problem faced in online tutorial. However, tutors need an adjustment in student evaluation which according tutors too focus on administrative aspects and subjective.

Keywords: distance education, on-line tutorial, tutorial policy, tutors’ perspectives

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Authors: Fathimath Warda, Mariyam Nihaadh

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Students with Special Education Needs (SEN) are increasing in the Maldives, like anywhere else in the world, due to the changes in lifestyle of the people and ease of being diagnosed with advancements in medical health. With the growth in the population of these students, the demand for professionals in various fields is unmet. Thus, with the introduction of the Inclusive Education Policy in 2013, all students are educated in the same classroom by the regular teacher. This poses problems as the teachers are not well trained and qualified to meet the varying needs of the students, given the limited time and the large number of students in the classroom. This is a major concern for all stakeholders in the education sector and research has been conducted by various local scholars in this area. However, studies on the role of parents of such students is an area that remains yet to be explored in the Maldives, which makes a study of this nature crucial. The main aim of this study is to determine the ways in which the education provided to Special Needs Students can be maximized for a better outcome. Therefore, the study intends to understand the involvement of parents in providing education to special needs students from the teachers' perspectives. The basis for this study is the Parent Development Theory developed by Mowder, which was initially known as Parent Role Development Theory. A qualitative research has thus been utilised for the purpose of the study as it requires to find the beliefs and attitudes of teachers, along with relevant justifications regarding the role of parents in educating students with special needs. Data was gathered using one-to-one interviews, as it is one of the most reliable ways of getting meaningful and in-depth data. The study employs a total of 8 participants who are teachers teaching in inclusive classes where students with special needs are included. Emphasis was paid to select teachers who have the experience of teaching students with different disorders commonly found in the Maldives, namely in the four areas, Autism Spectrum Disorder, Down Syndrome, Attention Deficit Hyperactive Disorder and speech impairment. Hence, purposive sampling will be used to select the participants. Data analysis has been done using thematic coding. The findings revealed that teachers highlighted that parents' involvement was a key factor in ensuring success of education in children with special needs. Thus, the study concludes that the role of parents as a necessary input for the proper development of children and in educating children with special needs, suggesting that extra measures have to be taken develop a positive relationship between teachers and parents in order to strengthen this aspect.

Keywords: involvement, parents' role, special education needs, teachers' voice

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