Search results for: community learning and development

Authors: Thomas Benson

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This paper leverages theoretical insights into two different justice approaches – environmental justice and ecological justice – to examine the effectiveness of sustainable development within smart cities and related smart city technology initiatives. Through theoretical development, the author seeks to establish an Eco Framework for smart cities and urban sustainable development. In turn, this paper aims to proffer the notion that there are ecologically sustainable ways in which smart cities can get smarter, and that such strategies can be compatible with ecological justice and environmental justice. Ultimately, a single conceptual framework is put forward to integrate the above approaches and concepts with normative prescriptions, which can serve researchers in the continued examination of smart cities and policymakers in their sustainable development of smart cities.

Keywords: ecological justice, environmental justice, normative framework, smart cities, sustainable development

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Authors: Mochammad Thalut

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This essay discusses how educational leadership response the Muslims pupils’ problems and their expectation about education in the UK. As we know, the Muslims community in the country is increasing. However, the debate about educational leadership is still limited to the separation between religion and academic by westerns approach. It is found that there are four major problems of Muslims pupils that need to solve by the educational leader to provide social justice in education. Leader-teacher as an Islamic concept of the educational leader is an alternative approach that can be used by the educational leader to overcome the problems. In the end, it is strongly recommended to bring this issue to the leadership development program in the UK to give all aspiring heads understanding about Muslims expectation about education.

Keywords: Muslim, education, leadership, identity

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Authors: Anshu Radha Aggarwal

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As per the Right to Education Act (RTE), 2009, every child in the age group of 6-14 years shall be admitted in a neighborhood school. All the Out of School Children identified have to be enrolled / mainstreamed in to age appropriate class and there-after be provided special training. This paper addresses issues emerging from provisions in the RTE Act that specifically refer to the enrolment of out-of school children into age appropriate classes and the requirement to provide special trainings that will enable this to take place. In the context of RTE Act, the Out-of-School Children are first enrolled in the formal school and then they are provided with Special Training through NRSTCs (Long Term / Short term basis). These centers are functioning in formal school campus itself. This paper specifies the role of special training centers (STC). It presents a re-envisioning of assessment that recognizes two principal functions of assessment, assessment for learning and assessment of learning, instead of the more familiar categories of formative, diagnostic, summative, and evaluative assessment. The use of these two functions of assessment highlights and emphasizes the role of special training centers (STC) to assess their level for giving them appropriate special training and to evaluate their improvement in learning level. Challenge of problem faced by teachers to do diagnostic assessment, including its place in the sequence of assessment procedures appropriate in identifying and addressing individual children’s learning difficulties are solved by special training centers (STC). It is important that assessment is used to identify children with learning difficulties at the earliest possible stage so that appropriate support and intervention can be put in place. So appropriate challenges with tools are presented here for their assessment at entry level and at completion level of primary children by special training centers (STC).

Keywords: right to education, assessment, challenges, out of school children

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Authors: Rim Messaoudi, Nogaye-Gueye Gning, François Azelart

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Automatic text classification applies mostly natural language processing (NLP) and other AI-guided techniques to automatically classify text in a faster and more accurate manner. This paper discusses the subject of using predictive maintenance to manage incident tickets inside the sociality. It focuses on proposing a tool that treats and analyses comments and notes written by administrators after resolving an incident ticket. The goal here is to increase the quality of these comments. Additionally, this tool is based on NLP and machine learning techniques to realize the textual analytics of the extracted data. This approach was tested using real data taken from the French National Railways (SNCF) company and was given a high-quality result.

Keywords: machine learning, text classification, NLP techniques, semantic representation

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Authors: Eid Nasr Saad Nasr

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Although human rights protection in the industrial sector has increased, human rights violations continue to occur. Although the government has passed human rights laws, labor laws, and an international treaty ratified by the United States, human rights crimes continue to occur and go undetected. The growing number of textile companies in Bekasi is also leading to an increase in human rights violations as the government has no obligation to protect them. The United States government and business leaders should respect, protect and defend the human rights of workers. The article discusses the human rights violations faced by garment factory workers in the context of the law, as well as ideas for improving the protection of workers' rights. The connection between development and human rights has long been the subject of academic debate. Therefore, to understand the dynamics between these two concepts, a number of principles have been adopted, ranging from the right to development to a human rights-based approach to development. Despite these attempts, the precise connection between development and human rights is not yet fully understood. However, the inherent interdependence between these two concepts and the idea that development efforts must respect human rights guarantees has gained momentum in recent years. It will then be examined whether the right to sustainable development is recognized.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

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Authors: Diana Santiago, Tania Lisboa, Sophie Lee, Alexander P. Demos, Monica C. S. Vasconcelos

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Memorization has long been recognized as an arduous and anxiety-evoking task for musicians, and yet, it is an essential aspect of performance. Research shows that musicians are often not taught how to memorize. While memorization and practice strategies of professionals have been studied, little research has been done to examine how student musicians learn to practice and memorize music in different cultural settings. We present the process of developing and testing a questionnaire of music memorization and musical practice for student musicians in the UK and Brazil. A survey was developed for a cross-cultural research project aiming at examining how young orchestral musicians (aged 7–18 years) in different learning environments and cultures engage in instrumental practice and memorization. The questionnaire development included members of a UK/US/Brazil research team of music educators and performance science researchers. A pool of items was developed for each aspect of practice and memorization identified, based on literature, personal experiences, and adapted from existing questionnaires. Item development took the varying levels of cognitive and social development of the target populations into consideration. It also considered the diverse target learning environments. Items were initially grouped in accordance with a single underlying construct/behavior. The questionnaire comprised three sections: a demographics section, a section on practice (containing 29 items), and a section on memorization (containing 40 items). Next, the response process was considered and a 5-point Likert scale ranging from ‘always’ to ‘never’ with a verbal label and an image assigned to each response option was selected, following effective questionnaire design for children and youths. Finally, a pilot study was conducted with young orchestral musicians from diverse learning environments in Brazil and the United Kingdom. Data collection took place in either one-to-one or group settings to facilitate the participants. Cognitive interviews were utilized to establish response process validity by confirming the readability and accurate comprehension of the questionnaire items or highlighting the need for item revision. Internal reliability was investigated by measuring the consistency of the item groups using the statistical test Cronbach’s alpha. The pilot study successfully relied on the questionnaire to generate data about the engagement of young musicians of different levels and instruments, across different learning and cultural environments, in instrumental practice and memorization. Interaction analysis of the cognitive interviews undertaken with these participants, however, exposed the fact that certain items, and the response scale, could be interpreted in multiple ways. The questionnaire text was, therefore, revised accordingly. The low Cronbach’s Alpha scores of many item groups indicated another issue with the original questionnaire: its low level of internal reliability. Several reasons for each poor reliability can be suggested, including the issues with item interpretation revealed through interaction analysis of the cognitive interviews, the small number of participants (34), and the elusive nature of the construct in question. The revised questionnaire measures 78 specific behaviors or opinions. It can be seen to provide an efficient means of gathering information about the engagement of young musicians in practice and memorization on a large scale.

Keywords: cross-cultural, memorization, practice, questionnaire, young musicians

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Authors: Pinkey Yaqub

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The aim of this study was to investigate secondary (grades IX and X) students’ attitudes towards learning the English language based on the medium of instruction of the school, the gender of the students and the grade level in which they studied. A further aim was to determine students’ proficiency in the English language according to their gender, the grade level and the medium of instruction of the school. A survey was used to investigate the attitudes of secondary students towards English language learning. Simple random sampling was employed to obtain a representative sample of the target population for the research study as a comprehensive list of established English medium schools, and newly established English medium schools were available. A questionnaire ‘Attitude towards English Language Learning’ (AtELL) was adapted from a research study on Libyan secondary school students’ attitudes towards learning English language. AtELL was reviewed by experts (n=6) and later piloted on a representative sample of secondary students (n= 160). Subsequently, the questionnaire was modified - based on the reviewers’ feedback and lessons learnt during the piloting phase - and directly administered to students of grades 9 and 10 to gather information regarding their attitudes towards learning the English language. Data collection spanned a month and a half. As the data were not normally distributed, the researcher used Mann-Whitney tests to test the hypotheses formulated to investigate students’ attitudes towards learning English as well as proficiency in the language across the medium of instruction of the school, the gender of the students and the grade level of the respondents. Statistical analyses of the data showed that the students of established English medium schools exhibited a positive outlook towards English language learning in terms of the behavioural, cognitive and emotional aspects of attitude. A significant difference was observed in the attitudes of male and female students towards learning English where females showed a more positive attitude in terms of behavioural, cognitive and emotional aspects as compared to their male counterparts. Moreover, grade 10 students had a more positive attitude towards learning English language in terms of behavioural, cognitive and emotional aspects as compared to grade 9 students. Nonetheless, students of newly established English medium schools were more proficient in English as gauged by their examination scores in this subject as compared to their counterparts studying in established English medium schools. Moreover, female students were more proficient in English while students studying in grade 9 were less proficient in English than their seniors studying in grade 10. The findings of this research provide empirical evidence to future researchers wishing to explore the relationship between attitudes towards learning language and variables such as the medium of instruction of the school, gender and the grade level of the students. Furthermore, policymakers might revisit the English curriculum to formulate specific guidelines that promote a positive and gender-balanced outlook towards learning English for male and female students.

Keywords: attitude, behavioral aspect of attitude, cognitive aspect of attitude, emotional aspect of attitude

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Authors: Yi-Min Lan, Hung-Wen Cheng, Hsuan-Ling Chang, Jou-Ping Yu

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The study applies machine learning models to construct momentum strategies and utilizes the information coefficient as an indicator for selecting stocks with strong and weak momentum characteristics. Through this approach, the study has built investment portfolios capable of generating superior returns and conducted a thorough analysis. Compared to existing research on momentum strategies, machine learning is incorporated to capture non-linear interactions. This approach enhances the conventional stock selection process, which is often impeded by difficulties associated with timeliness, accuracy, and efficiency due to market risk factors. The study finds that implementing bidirectional momentum strategies outperforms unidirectional ones, and momentum factors with longer observation periods exhibit stronger correlations with returns. Optimizing the number of stocks in the portfolio while staying within a certain threshold leads to the highest level of excess returns. The study presents a novel framework for momentum strategies that enhances and improves the operational aspects of asset management. By introducing innovative financial technology applications to traditional investment strategies, this paper can demonstrate significant effectiveness.

Keywords: information coefficient, machine learning, momentum, portfolio, return prediction

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Authors: Rubina Jahan, Mohammad Zayeed Bin Alam, Razia Sultana, Md Faroque Miah

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Mental health remains a significant global public health challenge, profoundly affecting millions worldwide. In Bangladesh, the situation is dire, with the National Mental Health Survey 2018-19 indicating that 19% of adults suffer from any kind of mental disorders, including severe mental disorder of around 2%. Despite these high prevalence rates, there is a substantial treatment gap in low- and middle-income countries, including Bangladesh, where up to 92% of individuals with mental illnesses do not receive adequate care. This gap is exacerbated by social barriers such as stigma, discrimination, social exclusion, poverty, homelessness, and human rights violations. To address these challenges, the SAJIDA Foundation launched the Proshanti in November 2022. Proshanti is a community based supportive housing intervention designed to provide cost-effective, sustainable, long-term care for individuals with chronic mental illnesses. It aims to rehabilitate participants by improving their mental health, quality of life, and equipping them with skills necessary for independent living and social mobility. Currently, Proshanti operates seven houses in Manikganj and Habiganj districts of Bangladesh, accommodating up to 40 individuals. Over a two-year period, individuals have received personalized support from trained personal assistants and care coordinators, regular health checkups, and opportunities for vocational training and community engagement. In this presentation, we will present the outcome of such intervention on individual’s functionality, quality of life and psychological health generated from 24 months of journey. Additionally, a qualitative approach will be employed to understand the facilitators and barriers of program implementation. The Proshanti program represents a promising model for addressing the significant mental health treatment gap in Bangladesh at the community level. Our findings will provide crucial insights into the program's feasibility, effectiveness, and the factors influencing its implementation, potentially guiding future mental health interventions in similar contexts.

Keywords: mental health, community based supportive housing, treatment gap, bangladesh

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Authors: C. W. Kan, H. F. Cheung, Y. S. Lee

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This study examines the results of colour fading of cotton fabric by plasma-induced ozone treatment, with an aim to provide learning materials for fashion designers when designing colour fading effects in fashion products. Cotton knitted fabrics were dyed with red reactive dye with a colour depth of 1.5% and were subjected to ozone generated by a commercially available plasma machine for colour fading. The plasma-induced ozone treatment was conducted with different parameters: (i) air concentration = 10%, 30%, 50% and 70%; (ii) water content in fabric = 35% and 45%, and (iii) treatment time = 10 minutes, 20 minutes and 30 minutes. Finally, the colour properties of the plasma–induced ozone treated fabric were measured by spectrophotometer under illuminant D₆₅ to obtain the CIE L*, CIE a* and CIE b* values.

Keywords: learning materials, colour fading, colour properties, fashion products

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Authors: Bilal Ahmed, Muhammad Rafiq, Choongjae Im

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The tech-savvy Gen Z's transfer towards interactive concept learning is hammering the demand for online collaborative learning environments, renovating conventional education approaches. The authors propose a novel approach to enhance learning outcomes to improve retention in 3D interactive education by connecting virtual reality (VR) and non-VR devices in the classroom and distance learning. The study evaluates students' experiences with VR interconnectivity devices in human anatomy lectures using real-time 3D interactive data visualization. Utilizing the renowned "Guo & Pooles Inventory" and the "Flow for Presence Questionnaires," it used an experimental research design with a control and experimental group to assess this novel connecting strategy's effectiveness and significant potential for in-person and online educational settings during the sessions. The experimental group's interactions, engagement levels, and usability experiences were assessed using the "Guo & Pooles Inventory" and "Flow for Presence Questionnaires," which measure their sense of presence, engagement, and immersion throughout the learning process using a 5-point Likert scale. At the end of the sessions, we used the "Perceived Usability Scale" to find our proposed system's overall efficiency, effectiveness, and satisfaction. By comparing both groups, the students in the experimental group used the integrated VR environment and VR to non-VR devices, and their sense of presence and attentiveness was significantly improved, allowing for increased engagement by giving students diverse technological access. Furthermore, learners' flow states demonstrated increased absorption and focus levels, improving information retention and Perceived Usability. The findings of this study can help educational institutions optimize their technology-enhanced teaching methods for traditional classroom settings as well as distance-based learning, where building a sense of connection among remote learners is critical. This study will give significant insights into educational technology and its ongoing progress by analyzing engagement, interactivity, usability, satisfaction, and presence.

Keywords: interactive learning environments, human-computer interaction, virtual reality, computer- supported collaborative learning

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Authors: Alica Höpken, Hergen Pargmann

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The resource-efficient planning of the complex production planning processes in the meat industry and the reduction of food waste is a permanent challenge. The complexity of the production planning process occurs in every part of the supply chain, from agriculture to the end consumer. It arises from long and uncertain planning phases. Uncertainties such as stochastic yields, fluctuations in demand, and resource variability are part of this process. In the meat industry, waste mainly relates to incorrect storage, technical causes in production, or overproduction. The high amount of food waste along the complex supply chain in the meat industry could not be reduced by simple solutions until now. Therefore, resource-efficient production planning by conventional methods is currently only partially feasible. The realization of intelligent, automated production planning is basically possible through the application of machine learning algorithms, such as those of reinforcement learning. By applying the adapted design thinking method, machine learning methods (especially reinforcement learning algorithms) are used for the complex production planning process in the meat industry. This method represents a concretization to the application area. A resource-efficient production planning process is made available by adapting the design thinking method. In addition, the complex processes can be planned efficiently by using this method, since this standardized approach offers new possibilities in order to challenge the complexity and the high time consumption. It represents a tool to support the efficient production planning in the meat industry. This paper shows an elegant adaption of the design thinking method to apply the reinforcement learning method for a resource-efficient production planning process in the meat industry. Following, the steps that are necessary to introduce machine learning algorithms into the production planning of the food industry are determined. This is achieved based on a case study which is part of the research project ”REIF - Resource Efficient, Economic and Intelligent Food Chain” supported by the German Federal Ministry for Economic Affairs and Climate Action of Germany and the German Aerospace Center. Through this structured approach, significantly better planning results are achieved, which would be too complex or very time consuming using conventional methods.

Keywords: change management, design thinking method, machine learning, meat industry, reinforcement learning, resource-efficient production planning

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Authors: R. Bawazir, P. Jones

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Social Stories are widely used to teach social and communication skills or concepts to individuals on the autistic spectrum. This paper presents a theoretical framework for using Social Stories in conjunction with the creative arts. The paper argues that Bandura’s social learning theory can be used to explain the mechanisms behind Social Stories and the way they influence changes in response, while Gardner’s multiple intelligences theory can be used simultaneously to demonstrate the role of the creative arts in learning. By using Social Stories with the creative arts for individuals on the autistic spectrum, the aim is to meet individual needs and help individuals with autism to develop in different areas of learning and communication.

Keywords: individuals on the autistic spectrum, social stories, the creative arts, theoretical framework

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Authors: Jansirani Natarajan, Mickael Antoinne Joseph

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The COVID-19 pandemic has interrupted face-to-face education and forced universities into an emergency remote teaching curriculum over a short duration. This abrupt transition in the Spring 2020 semester left both faculty and students without proper preparation for continuing higher education in an online environment. Online learning took place in different formats, including fully synchronous, fully asynchronous, and blended in our university through the e-learning platform MOODLE. Studies have shown that students’ engagement, is a critical factor for optimal online teaching. Very few studies have assessed online engagement with ERT during the COVID-19 pandemic. Purpose: Therefore, this study, sought to understand how the sudden transition to emergency remote teaching impacted nursing students’ engagement with online courses in a Middle Eastern public university. Method: A cross-sectional descriptive research design was adopted in this study. Data were collected through a self-reported online survey using Dixon’s online students’ engagement questionnaire from a sample of 177 nursing students after the ERT learning semester. Results The maximum possible engagement score was 95, and the maximum scores in the domains of skills engagement, emotional engagement, participation engagement, and performance engagement were 30, 25, 30, and 10 respectively. Dixson (2010) noted that a mean item score of ≥3.5 (total score of ≥66.5) represents a highly engaged student. The majority of the participants were females (71.8%) and 84.2% were regular BSN students. Most of them (32.2%) were second-year students and 52% had a CGPA between 2 and 3. Most participants (56.5%) had low engagement scores with ERT learning during the COVID lockdown. Among the four engagement domains, 78% had low engagement scores for the participation domain. There was no significant association found between the engagement and the demographic characteristics of the participants. Conclusion The findings supported the importance of engaging students in all four categories skill, emotional, performance, and participation. Based on the results, training sessions were organized for faculty on various strategies for engaging nursing students in all domains by using the facilities available in the MOODLE (online e-learning platform). It added value as a dashboard of information regarding ERT for the administrators and nurse educators to introduce numerous active learning strategies to improve the quality of teaching and learning of nursing students in the University.

Keywords: engagement, perception, emergency remote learning, COVID-19

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Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

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Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

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Authors: Venkata Narayana Chejarla, Nafisa Parvez Shaik, Gopi Vara Prasad Marabathula, Deva Kumar Bejjam

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Yoga is an advanced, well-recognized method with roots in Indian philosophy. Yoga benefits both the body and the psyche. Yoga is a regular exercise that helps people relax and sleep better while also enhancing their balance, endurance, and concentration. Yoga can be learned in a variety of settings, including at home with the aid of books and the internet as well as in yoga studios with the guidance of an instructor. Self-learning does not teach the proper yoga poses, and doing them without the right instruction could result in significant injuries. We developed "Online Yoga Asana Trainer using Deep Learning" so that people could practice yoga without a teacher. Our project is developed using Tensorflow, Movenet, and Keras models. The system makes use of data from Kaggle that includes 25 different yoga poses. The first part of the process involves applying the movement model for extracting the 17 key points of the body from the dataset, and the next part involves preprocessing, which includes building a pose classification model using neural networks. The system scores a 98.3% accuracy rate. The system is developed to work with live videos.

Keywords: yoga, deep learning, movenet, tensorflow, keras, CNN

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Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

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The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

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Authors: Y. Kim, H. Lee, M. Park, B. Lee

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Planning is understood as the determination of what has to be performed, how, in which sequence, when, what resources are needed, and their cost within the organization before execution. In most construction project, it is evident that the inherent nature of planning is dynamic, and initial planning is subject to be changed due to various uncertain conditions of construction project. Planners take a continuous revision process during the course of a project and until the very end of project. However, current practice lacks reliable, systematic tool for setting variance thresholds to determine when and what corrective actions to be taken. Rather it is heavily dependent on the level of experience and knowledge of the planner. Thus, this paper introduces a machine learning approach to evaluate project control threshold reliability incorporating project-specific data and presents a method to automate the process. The results have shown that the model improves the efficiency and accuracy of the monitoring process as an early warning.

Keywords: machine learning, project control, project progress monitoring, schedule

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Authors: Alice V. Brown, Patrick Egan, Dorothy Bagshaw, Jackie Oakley, Emma Vieira, Louise Southalan, Duc Dau, Lucy Spanswick, Lindey Andrews, Mandy Wilson, Jocelyn Jones

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Western Australia has recently adopted a 10-year plan to end homelessness across its State, with sections of the plan focused particularly on the Aboriginal and Torres Strait Islander population. In 2022, we engaged with 70-90 Aboriginal people experiencing homelessness in Perth, Western Australia, through qualitative interviews and creative methods, listening to their experiences of homelessness and their views on how services, State plans, and policies could better support them. This research was driven by the Aboriginal community through a Community Ownership Group of 16 Aboriginal Elders, elected by Elders’ groups, from across the Perth metropolitan area. The Community Ownership Group met every six weeks across the 15-month project timeline to guide the research team, endorse methods chosen, and provide richer context to research findings to ensure they adequately represent the experiences of Aboriginal people. These meetings were audio-recorded when possible and documented through meeting notes, verbal and visual minutes, and film, providing insights into homelessness from the perspective of Aboriginal Elders. In this paper, we compare the views of those experiencing homelessness with the views of the Aboriginal Elders -many of whom have experienced homelessness firsthand- and literature regarding how those experiencing homelessness can be better supported. We detail the ‘survival-directed thinking’ of those we engaged with who was in the throes of homelessness, leading them to focus more on immediate solutions such as food and housing. We then compare these narratives to Elders’ views that have been more regularly focused on connection to culture and long-term plans for healing homelessness, alongside immediate outreach -views also reflected in the literature. Through these comparisons, we highlight the importance of engaging both with those currently experiencing homelessness as well as with Aboriginal Elders as important cultural caretakers and authorities. We demonstrate how these varied voices uncover both long and short-term perspectives on how homelessness can be better managed in policy and service provision. We also highlight the potential role Aboriginal Elders can play in supporting the Aboriginal homeless community and their transition into housing.

Keywords: Aboriginal and Torres strait islander peoples, aboriginal elders, homelessness, community-led research

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Authors: Prasansha Kumari

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Agritourism is a growing industry in many parts of the world. At present, agritourism is promoted by most of the countries in the world aiming at sustainable rural development. This study intends to identify and analyze the potential for agritourism development in Sri Lanka with special reference to five farming areas in Kegalle district. SWOT analysis used to identify the possibility of agritourism in this areas. The study rival that there are several opportunities to the development of agritourism while identified the main threat and weakness for developing agritourism in the study areas. The opportunities related to a number of tourist attraction places and increase the demand for agritourism. The main problems related to infrastructure facilities, large farming lands, knowledge and skill of farmers, government support, credits and financial assistance, attitude of young generation and environmental impact.

Keywords: agritourism, sustainable rural development, farming, tourism

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Authors: Oloruntegbe Kunle Oke, Alake Ese Monica, Odutuyi Olubu Musili

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This research explores the differing effectiveness of cascade and cluster models in professional development programs for educators in Ondo State, Nigeria. The cascade model emphasizes a top-down approach, where training is cascaded from expert trainers to lower levels of teachers. In contrast, the cluster model, a bottom-up approach, fosters collaborative learning among teachers within specific clusters. Through a review of the literature and empirical studies of the implementations of the former in two academic sessions followed by the cluster model in another two, the study examined their effectiveness on teacher development, productivity and students’ achievements. The study also drew a comparative analysis of the strengths and weaknesses associated with each model, considering factors such as scalability, cost-effectiveness, adaptability in various contexts, and sustainability. 2500 teachers from Ondo State Primary Schools participated in the cascade with intensive training in five zones for a week each in two academic sessions. On the other hand, 1,980 and 1,663 teachers in 52 and 34 clusters, respectively, were in the first and the following session. The programs were designed for one week of rigorous training of teachers by facilitators in the former while the latter was made up of four components: sit-in-observation, need-based assessment workshop, pre-cluster and the actual cluster meetings in addition to sensitization, and took place one day a week for ten weeks. Validated Cluster Impact Survey Instruments, CISI and Teacher’s Assessment Questionnaire (TAQ) were administered to ascertain the effectiveness of the models during and after implementation. The findings from the literature detailed specific effectiveness, strengths and limitations of each approach, especially the potential for inconsistencies and resistance to change. Findings from the data collected revealed the superiority of the cluster model. Response to TAQ equally showed content knowledge and skill update in both but were more sustained in the cluster model. Overall, the study contributes to the ongoing discourse on effective strategies for improving teacher training and enhancing student outcomes, offering practical recommendations for the development and implementation of future professional development projects.

Keywords: cascade model, cluster model, teachers’ development, productivity, students’ achievement

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Authors: Temidayo O. Omotehinwa, David O. Oyewola, Friday J. Agbo

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This study aims to gather a proper perspective of the development landscape of Computer Science research in Nigeria. Therefore, a bibliometric analysis of 4,333 bibliographic records of Computer Science research in Nigeria in the last 31 years (1991-2021) was carried out. The bibliographic data were extracted from the Scopus database and analyzed using VOSviewer and the bibliometrix R package through the biblioshiny web interface. The findings of this study revealed that Computer Science research in Nigeria has a growth rate of 24.19%. The most developed and well-studied research areas in the Computer Science field in Nigeria are machine learning, data mining, and deep learning. The social structure analysis result revealed that there is a need for improved international collaborations. Sparsely established collaborations are largely influenced by geographic proximity. The funding analysis result showed that Computer Science research in Nigeria is under-funded. The findings of this study will be useful for researchers conducting Computer Science related research. Experts can gain insights into how to develop a strategic framework that will advance the field in a more impactful manner. Government agencies and policymakers can also utilize the outcome of this research to develop strategies for improved funding for Computer Science research.

Keywords: bibliometric analysis, biblioshiny, computer science, Nigeria, science mapping

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Authors: Eu Tteum Ha, Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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The smart home environment backed up by IoT (internet of things) technologies enables intelligent services based on the awareness of the situation a user is currently in. One of the convenient sensors for recognizing the situations within a home is the smart meter that can monitor the status of each electrical appliance in real time. This paper aims at learning a Bayesian network that models the causal relationship between the user situations and the status of the electrical appliances. Using such a network, we can infer the current situation based on the observed status of the appliances. However, learning the conditional probability tables (CPTs) of the network requires many training examples that cannot be obtained unless the user situations are closely monitored by any means. This paper proposes a method for learning the CPT entries of the network relying only on the user feedbacks generated occasionally. In our case study with a robot vacuum cleaner, the feedback comes in whenever the user gives an order to the robot adversely from its preprogrammed setting. Given a network with randomly initialized CPT entries, our proposed method uses this feedback information to adjust relevant CPT entries in the direction of increasing the probability of recognizing the desired situations. Simulation experiments show that our method can rapidly improve the recognition performance of the Bayesian network using a relatively small number of feedbacks.

Keywords: Bayesian network, IoT, learning, situation -awareness, smart home

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Authors: Li-hsing Shih, Wei-Jen Hsu

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Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

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Authors: Salman Imran, Chris Healey

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Traditionally, medical care of children in England and Wales starts from primary care with a referral to secondary care paediatricians who may not investigate further. Many primary care doctors do not undergo a paediatric rotation/exposure in training. As a result, there are many who have not acquired the necessary skills to manage children hence increasing hospital referral. With the current demand on hospitals in the National Health Service managing more problems in the community is needed. One way of handling this is to set up clinics, meetings and huddles in GP surgeries where professionals involved (general practitioner, paediatrician, health visitor, community nurse, dietician, school nurse) come together and share information which can help improve communication and care. The increased awareness and education that paediatricians can impart in this way will help boost confidence for primary care professionals to be able to be more self-sufficient. This has been tried successfully in other regions e.g., St. Mary’s Hospital in London but is crucial for a more rural setting like ours. The primary aim of this project would be to educate specifically GP’s and generally all other health professionals involved. Additional benefits would be providing care nearer home, increasing patient’s confidence in their local surgery, improving communication and reducing unnecessary patient flow to already stretched hospital resources. Methods: This was done as a plan do study act cycle (PDSA). Three clinics were delivered in different practices over six months where feedback from staff and patients was collected. Designated time for teaching/discussion was used which involved some cases from the actual clinics. Both new and follow up patients were included. Two clinics were conducted by a paediatrician and nurse whilst the 3rd involved paediatrician and local doctor. The distance from hospital to clinics varied from two miles to 22 miles approximately. All equipment used was provided by primary care. Results: A total of 30 patients were seen. All patients found the location convenient as it was nearer than the hospital. 70-90% clearly understood the reason for a change in venue. 95% agreed to the importance of their local doctor being involved in their care. 20% needed to be seen in the hospital for further investigations. Patients felt this to be a more personalised, in-depth, friendly and polite experience. Local physicians felt this to be a more relaxed, familiar and local experience for their patients and they managed to get immediate feedback regarding their own clinical management. 90% felt they gained important learning from the discussion time and the paediatrician also learned about their understanding and gaps in knowledge/focus areas. 80% felt this time was valuable for targeted learning. Equipment, information technology, and office space could be improved for the smooth running of any future clinics. Conclusion: The acute paediatric outpatient clinic can be successfully established in primary care facilities. Careful patient selection and adequate facilities are important. We have demonstrated a further step in the reduction of patient flow to hospitals and upskilling primary care health professionals. This service is expected to become more efficient with experience.

Keywords: clinics, education, paediatricians, primary care

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Authors: Wafa Labib

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Teaching methods include lectures, workshops and tutorials for the presentation and discussion of ideas have become out of date; were developed outside the discipline of architecture from the college of engineering and do not satisfy the architectural students’ needs and causes them many difficulties in integrating structure into their design. In an attempt to improve structure teaching methods, this paper focused upon proposing a supportive teaching/learning tool using multi-media applications which seeks to better meet the architecture student’s needs and capabilities and improve the understanding and application of basic and intermediate structural engineering and technology principles. Before introducing the use of multi-media as a supportive teaching tool, a questionnaire was distributed to third year students of a structural design course who were selected as a sample to be surveyed forming a sample of 90 cases. The primary aim of the questionnaire was to identify the students’ learning style and to investigate whether the selected method of teaching could make the teaching and learning process more efficient. Students’ reaction on the use of this method was measured using three key elements indicating that this method is an appropriate teaching method for the nature of the students and the course as well.

Keywords: teaching method, architecture, learning style, multi-media

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Authors: Kien Tsong Chau, Zarina Samsudin, Wan Ahmad Jaafar Wan Yahaya

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The paper examined the capability of a prototype of a tangible multimedia system that was augmented with tangible objects in motivating young preschoolers in learning. Preschoolers’ learning behaviour is highly captivated and motivated by external physical stimuli. Hence, conventional multimedia which solely dependent on digital visual and auditory formats for knowledge delivery could potentially place them in inappropriate state of circumstances that are frustrating, boring, or worse, impede overall learning motivations. This paper begins by discussion with the objectives of the research, followed by research questions, hypotheses, ARCS model of motivation adopted in the process of macro-design, and the research instrumentation, Persuasive Multimedia Motivational Scale was deployed for measuring the level of motivation of subjects towards the experimental tangible multimedia. At the close, a succinct description of the findings of a relevant research is provided. In the research, a total of 248 preschoolers recruited from seven Malaysian kindergartens were examined. Analyses revealed that the tangible multimedia system improved preschoolers’ learning motivation significantly more than conventional multimedia. Overall, the findings led to the conclusion that the tangible multimedia system is a motivation conducive multimedia for preschoolers.

Keywords: tangible multimedia, preschoolers, multimedia, tangible objects

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Authors: Chen Guo, Heng Tang, Ben Niu

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Clustering splits objects into different groups based on similarity, making the objects have higher similarity in the same group and lower similarity in different groups. Thus, clustering can be treated as an optimization problem to maximize the intra-cluster similarity or inter-cluster dissimilarity. In real-world applications, the datasets often have some complex characteristics: sparse, overlap, high dimensionality, etc. When facing these datasets, simultaneously optimizing two or more objectives can obtain better clustering results than optimizing one objective. However, except for the objectives weighting methods, traditional clustering approaches have difficulty in solving multi-objective data clustering problems. Due to this, evolutionary multi-objective optimization algorithms are investigated by researchers to optimize multiple clustering objectives. In this paper, the Data Clustering algorithm based on Multi-objective Periodic Bacterial Foraging Optimization with two Learning Archives (DC-MPBFOLA) is proposed. Specifically, first, to reduce the high computing complexity of the original BFO, periodic BFO is employed as the basic algorithmic framework. Then transfer the periodic BFO into a multi-objective type. Second, two learning strategies are proposed based on the two learning archives to guide the bacterial swarm to move in a better direction. On the one hand, the global best is selected from the global learning archive according to the convergence index and diversity index. On the other hand, the personal best is selected from the personal learning archive according to the sum of weighted objectives. According to the aforementioned learning strategies, a chemotaxis operation is designed. Third, an elite learning strategy is designed to provide fresh power to the objects in two learning archives. When the objects in these two archives do not change for two consecutive times, randomly initializing one dimension of objects can prevent the proposed algorithm from falling into local optima. Fourth, to validate the performance of the proposed algorithm, DC-MPBFOLA is compared with four state-of-art evolutionary multi-objective optimization algorithms and one classical clustering algorithm on evaluation indexes of datasets. To further verify the effectiveness and feasibility of designed strategies in DC-MPBFOLA, variants of DC-MPBFOLA are also proposed. Experimental results demonstrate that DC-MPBFOLA outperforms its competitors regarding all evaluation indexes and clustering partitions. These results also indicate that the designed strategies positively influence the performance improvement of the original BFO.

Keywords: data clustering, multi-objective optimization, bacterial foraging optimization, learning archives

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Authors: Karam Aziz Hamdy Fahmy

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Although human rights protection in the industrial sector has increased, human rights violations continue to occur. Although the government has passed human rights laws, labor laws, and an international treaty ratified by the United States, human rights crimes continue to occur and go undetected. The growing number of textile companies in Bekasi is also leading to an increase in human rights violations as the government has no obligation to protect them. The United States government and business leaders should respect, protect and defend the human rights of workers. The article discusses the human rights violations faced by garment factory workers in the context of the law, as well as ideas for improving the protection of workers' rights. The connection between development and human rights has long been the subject of academic debate. Therefore, to understand the dynamics between these two concepts, a number of principles have been adopted, ranging from the right to development to a human rights-based approach to development. Despite these attempts, the precise connection between development and human rights is not yet fully understood. However, the inherent interdependence between these two concepts and the idea that development efforts must respect human rights guarantees has gained momentum in recent years. It will then be examined whether the right to sustainable development is recognized.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

Procedia PDF Downloads 89