Search results for: non-formal learning contexts

Authors: Nashwa Moheyeldine

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Education for sustainable development (ESD) aims to preserve the rights of the present and future generations as well as preserving the globe, both humans and nature. ESD should aim not only to bring about consciousness of the current and future issues, but also to foster student agency to bring about change at schools, communities and nations. According to the Freirian concept of conscientização, (conscientization) — “learning to perceive social, political, and economic contradictions, and to take action against the oppressive elements of reality”, education aims to liberate people to understand and act upon their worlds. Social justice is greatly intertwined with a nation’s social, political and economic rights, and thus, should be targeted through ESD. “Literacy researchers have found that K-12 students who engage in social justice inquiries develop vital academic knowledge and skills, critical understandings about oppression in the world, and strong dispositions to continue working toward social justice beyond the initial inquiries they conduct”. Education for social justice greatly equips students with the critical thinking skills and sense of agency, that are required for responsible decision making that would ensure a sustainable world. In fact teaching for social justice is intersecting with many of the pedagogies such as multicultural education, cultural relevant pedagogy, education for sustainable development, critical theory pedagogy, (local and global) citizenship education, all of which aim to prepare students for awareness, responsibility and agency. Social justice pedagogy has three specific goals, including helping students develop 1) a sociopolitical consciousness - an awareness of the symbiotic relationship between the social and political factors that affect society, 2) a sense of agency, the freedom to act on one’s behalf and to feel empowered as a change agent, and 3) positive social and cultural identities. The keyword to social justice education is to expose the realities to the students, and challenge the students not only to question , but also to change. Social justice has been usually discussed through the subjects of history and social sciences, however, an interdisciplinary approach is essential to enhance the students’ understanding of their world. Teaching social justice through various subjects is also important, as it make students’ learning relevant to their lives. The main question that this paper seeks to answer is ‘How could social justice be taught through different subjects and tools, such as mathematics, literature through story-telling, geography, and service learning will be shown in this paper. Also challenges to education for social justice will be described. Education is not a neutral endeavor, but is either oriented toward the cause of liberation or in support of domination. In fact , classrooms can be “a microcosm of the emancipatory societies we seek to encourage”, education for the 21st century should be relevant to students' lives where it exposes life's realities to them. Education should also provide students with the basics of school subjects with the bigger goal of helping them make the world a better, more just place to live in.

Keywords: teaching for social justice, student agency, citizenship education, education

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Authors: A. V. Varfolomeeva, N. S. Tkachenko, A. G. Tishchenko

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The problem of violation of internal validity in studies of psychological structures is considered. The role of epistemological attitudes of researchers in the planning of research within the methodology of the system-evolutionary approach is assessed. Alternative programs of psychological research involving representatives of different biological species are presented. On the example of the results of two research series the variants of solving the problem are discussed.

Keywords: epistemological attitudes, experimental design, validity, psychological structure, learning

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Authors: Shane Theunissen, Rob Grandy

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Historically and we would fairly conventionally within our formal schooling systems, we have convergent testing where all the students are expected to converge on the same answer, and that answer has been determined by an external authority that is reproducing knowledge of the hegemon. Many youths may not embody the cultural capital that's rewarded in formal schooling contexts as they aren't able to converge on the required answer that's being determined by the classroom teacher or the administrators. In this paper, we explore divergent processes that promote creative problem-solving. We embody this divergent process in our measurement of fundamental growth needs. To this end, we utilize the Mosaic Approach as a method for implementing the Outcomes That Matter framework. Outcomes That Matter is the name of the measurement tool built around the Circle of Courage framework, which is a way of identifying fundamental growth needs for young people. The Circle of Courage was developed by Martin-Broken-Leg and colleagues as a way to connect indigenous child-rearing philosophies with contemporary resilience and positive psychology research. The Outcomes that Matter framework puts forward four categories of growth needs for young people. These are: Belonging, which on a macro scale is acceptance into the greater community of practice, Mastery which includes a constellation of concepts including confidence, motivation, self-actualization, and self-determination, Independence refers to a sense of personal power into autonomy within a context where creativity and problem solving, and a personal voice can begin to emerge, and finally Generosity which includes interpersonal things like conflict resolution and teamwork. Outcomes of Matter puts these four domains into a measurement tool that facilitates collaborative assessment between the youth, teachers, and recreation therapists that allows for youth-led narratives pertaining to their fundamental growth outcomes. This application of the Outcomes That Matter framework is unique as it may be the first application of this framework in an educational boatbuilding context.

Keywords: collaboration, empowerment, outcomes that matter, mosaic approach, boat building

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Authors: Hien Thi Dieu Truong, Mahmoud Al-Sarayreh, Pullanagari Reddy, Marlon M. Reis, Richard Archer

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New Zealand mānuka honey is a honeybee product derived mainly from Leptospermum scoparium nectar. The potent antibacterial activity of mānuka honey derives principally from methylglyoxal (MGO), in addition to the hydrogen peroxide and other lesser activities present in all honey. MGO is formed from dihydroxyacetone (DHA) unique to L. scoparium nectar. Mānuka honey also has an idiosyncratic phenolic profile that is useful as a chemical maker. Authentic mānuka honey is highly valuable, but almost all honey is formed from natural mixtures of nectars harvested by a hive over a time period. Once diluted by other nectars, mānuka honey irrevocably loses value. We aimed to apply hyperspectral imaging to honey frames before bulk extraction to minimise the dilution of genuine mānuka by other honey and ensure authenticity at the source. This technology is non-destructive and suitable for an industrial setting. Chemometrics using linear Partial Least Squares (PLS) and Support Vector Machine (SVM) showed limited efficacy in interpreting chemical footprints due to large non-linear relationships between predictor and predictand in a large sample set, likely due to honey quality variability across geographic regions. Therefore, an advanced modelling approach, one-dimensional convolutional neural networks (1D-CNN), was investigated for analysing hyperspectral data for extraction of biochemical information from honey. The 1D-CNN model showed superior prediction of honey quality (R² = 0.73, RMSE = 2.346, RPD= 2.56) to PLS (R² = 0.66, RMSE = 2.607, RPD= 1.91) and SVM (R² = 0.67, RMSE = 2.559, RPD=1.98). Classification of mono-floral manuka honey from multi-floral and non-manuka honey exceeded 90% accuracy for all models tried. Overall, this study reveals the potential of HSI and deep learning modelling for automating the evaluation of honey quality in frames.

Keywords: mānuka honey, quality, purity, potency, deep learning, 1D-CNN, chemometrics

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Authors: María González Alriols, Itziar Egües, María A. Andrés, Mirari Antxustegi

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Several collaborative learning proposals were prepared to be applied in the laboratory sessions of chemistry in the first course of engineering studies. The aim was to engage the students from the beginning and to avoid absenteeism as well as to reach a more homogeneous level in the class. The students, divided into small groups of four or five mates, were asked to do an exercise before having the practical session in the lab. Precisely, each one of the groups was asked to study the theoretical fundamentals and the practical aspects of one lab session and to prepare a didactical video with this content, including the materials, equipment and reactants required, and the detailed experimental procedure. Furthermore, they should include the performance of the experiment step by step, indicating the faced difficulties and the obtained results and conclusions. After watching the video of this precise activity, the other groups of students would go to the lab to put into practice the session following the commands explained in the video. The evaluation of the video activity that is worth the 50% of the total mark of the laboratory sessions, is done depending on the success that the other groups of students had while doing the practical session that was explained in the video. This means that the successful transmission of knowledge to the rest of the mates in the class through the video was compulsory to pass the practical sessions and the subject. The other 50% of the mark depended on the understanding of the other students’ explanations and the success in the corresponding practical sessions. The experience was found to be very positive, as the engagement level was considerably higher, the absenteeism lower and the attitude in the laboratory much more responsible. The materials, reactants and equipment were used carefully, and no incidents were registered. Furthermore, the fact of having peer experts was useful to encourage critical thinking in a more relaxed way, with the teacher figure in a secondary position. Finally, the academic achievements were satisfactory as well, with a high percentage of students over the level required for passing the subject.

Keywords: collaborative learning, engineering instruction, chemistry, laboratory sessions

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Authors: Mohan Li

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Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions.

Keywords: big data, domain model, urban planning, urban quantitative analysis, machine learning, workflow design

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Authors: Michaels Aibangbee, Sowbhagya Micheal, Pranee Liamputtong, Elias Mpofu, Tinashe Dune

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Background: The challenges migrants and refugee youth (MRY) experience in maintaining their sexual and reproductive health and rights (SRHR) continues to be a global public health issue. Consequently, MRY is more likely to encounter adverse SRH experiences due to limited access to and knowledge of SRH services. Using a socio-ecological framework, this study examined the MRY’s SRHR micro-level experiences to macro-levels analyses of SRH-related social systems and constructions. Methods: Eighteen focus groups were conducted using participatory action research (PAR) methodology to understand the phenomena. The focus groups included MRY participants (ages 16-26) living in Greater Western Sydney and facilitated by youth project liaisons (YPL). The data was afterward synthesised and analysed using the thematic-synthesis method. Results: In total, 86 MRY (male n= 25, female n= 61) MRY (across 20 different cultural backgrounds) participated in the focus groups. The findings identified socio-ecological factors characterising MRY SRHR, highlighting facilitators such as social media and significant barriers such as lack of access to services and socio-cultural dissonance, and the under-implementation of SRHR support and services by MRY. Key themes from the data included traditional and institutional stigma, lack of SRH education, high reliance on social media for SRH information, anonymity, and privacy concerns. Conclusion: The data shows a limited extent to which MRY SRHR is considered and the intergenerational understanding and stigma affecting the rights of MRY. Therefore, these findings suggest a need for policies and practices to empower MRY’s agency through a collaborative SRHR strategy and policy design to maintain relevance in multicultural contexts.

Keywords: migrant and refugee youth, sexual health, reproductive health, sexual and reproductive health and rights, culture, agency

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Authors: Sedigheh Kalantari, Yaping Huang

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The historic area of cities has experienced different phases of transformation. The beginning of the twentieth century, modernism, and modern development changed the integrated pattern of change and the historic urban quarter were regarded as subject comprehensive redevelopment. In this respect, historic area of Iranian cities have not been safe from these changes and affected by widespread evolutions; in particular after Islamic Revolution eras (1978) cities have traveled through an evolution in conservation and development policies and practices. Moreover, moving toward a specific approach and specific attention paid to the regeneration of the historical urban centers in Iran has started since the 1990s. This reveals the great importance attached to the historical centers of cities. This paper is an approach to examine an experience on urban regeneration in Iran through a case study. The study relies on multiple source of evidence. The use of multiple sources of evidence can help substantially improve the validity and reliability of the research. The empirical core of this research, therefore, rests in the process of urban revitalization of the old square in Isfahan. Isfahan is one of the oldest city of Persia. The historic area of city encompasses a large number of valuable buildings and monuments. One of the cultural and historical region of Isfahan is Atiq Square (Old Square). It has been the backbone node of the city that in course of time has being ignored more and more and transformed negatively. The complex had suffered from insufficiencies especially with respect to social and spatial aspects. Therefore, reorganization of that complex as the main and most important urban center of Isfahan became an inevitable issue; So this paper except from reminding the value of such historic-cultural heritage and review of its transformation, focused on an experience of urban revitalization project in this heritage site. The outcome of this research shows that situated in different socio-economic political and historical contexts and in face of different urban regeneration issues, Iran have displayed significant differences in the way of urban regeneration.

Keywords: historic area, Iran, urban regeneration, revitalization

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Authors: Han Zheng

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This research is being conducted to explore women's leadership in China's National Governing Bodies ( in order to identify the barriers to women's leadership and provide feasible solutions. Extensive research has been undertaken internationally, which has identified and acknowledged the underrepresentation of women in leadership positions across multiple industries and global contexts. According to these studies, leadership specifically within the sports industry was both historically and is still currently male-dominated. Within China, the underrepresentation of women in leadership positions is also evident, which women only occupy 16% of the leadership in business enterprises and 5.6% in scientific and technological research institutions, yet there is limited research that has looked to examine why this is the case regarding women's leadership in China, especially within in sports industry. Therefore, this research gap drives the purpose, which aims to explore the current situation of women's leadership in sports National Governing Bodies (NGBs) in China. By using both questionnaires and interviews, data from NGBs in China will be collected. This research will achieve the following three goals: 1, determine the representation level of women's leadership in the target organizations. 2, identify barriers to women's leadership and their causes. 3, provide feasible solutions. Based on the multi-level framework, this study develops a "barrier matrix" framework: according to the analysis of the previous literature, it concludes that there are eight main barriers that hinder the development of women's leadership. The research combines qualitative and quantitative analysis, using questionnaires and interviews. Key findings according to the analysis of the primary data collected: 1. The average proportion of female occupational leadership in China's sports NGBs is less than 17.5%. 2. 50.8% of China's sports NGBs have no equal employment opportunity policy. 3. According to the preliminary qualitative analysis of the interviews, it is found that the core barriers affecting women's leadership development are mainly in the following areas: male-dominated culture and gender stereotyping (macro-level), biased organizational policies and procedures (meso-level), work-family conflicts and self-limiting behaviors (micro-level).

Keywords: women leadership, sport management, gender equality, sport leadership, sport NGBs

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Authors: Hasan Muslemani, Xi Liang, Kathi Kaesehage, Francisco Ascui, Jeffrey Wilson

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The creation of a market for lower-carbon steel products, here called ‘green steel’, has been identified as an important means to support the introduction of breakthrough emission reduction technologies into the steel sector. However, the definition of what ‘green’ entails in the context of steel production, the implications on the competitiveness of green steel products in local and international markets, and the necessary market mechanisms to support their successful market penetration remain poorly explored. This paper addresses this gap by holding semi-structured interviews with international sustainability experts and commercial managers from leading steel trade associations, research institutes and steelmakers. Our findings show that there is an urgent need to establish a set of standards to define what ‘greenness’ means in the steelmaking context; standards that avoid market disruptions, unintended consequences, and opportunities for greenwashing. We also highlight that the introduction of green steel products will have implications on product competitiveness on three different levels: 1) between primary and secondary steelmaking routes, 2) with traditional, lesser green steel, and 3) with other substitutable materials (e.g. cement and plastics). This paper emphasises the need for steelmakers to adopt a transitional approach in deploying different low-carbon technologies, based on their stage of technological maturity, applicability in certain country contexts, capacity to reduce emissions over time, and the ability of the investment community to support their deployment. We further identify market mechanisms to support green steel production, including carbon border adjustments and public procurement, highlighting a need for implementing a combination of complementary policies to ensure the products’ roll-out. The study further shows that the auto industry is a likely candidate for green steel consumption, where a market would be supported by price premiums paid by willing consumers, such as those of high-end luxury vehicles.

Keywords: green steel, decarbonisation, business model innovation, market analysis

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Authors: Mohammad Hassanzadeh

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As far as incidental vocabulary learning is concerned, the basic contention of the Involvement Load Hypothesis (ILH) is that retention of unfamiliar words is, generally, conditional upon the degree of involvement in processing them. This study examined input modality and incidental vocabulary uptake in a task-induced setting whereby three variously loaded task types (marginal glosses, fill-in-task, and sentence-writing) were alternately assigned to one group of students at Allameh Tabataba’i University (n=2l) during six classroom sessions. While one round of exposure was comprised of the audiovisual medium (TV talk shows), the second round consisted of textual materials with approximately similar subject matter (reading texts). In both conditions, however, the tasks were equivalent to one another. Taken together, the study pursued the dual objectives of establishing a litmus test for the ILH and its proposed values of ‘need’, ‘search’ and ‘evaluation’ in the first place. Secondly, it sought to bring to light the superiority issue of exposure to audiovisual input versus the written input as far as the incorporation of tasks is concerned. At the end of each treatment session, a vocabulary active recall test was administered to measure their incidental gains. Running a one-way analysis of variance revealed that the audiovisual intervention yielded higher gains than the written version even when differing tasks were included. Meanwhile, task 'three' (sentence-writing) turned out the most efficient in tapping learners' active recall of the target vocabulary items. In addition to shedding light on the superiority of audiovisual input over the written input when circumstances are relatively held constant, this study for the most part, did support the underlying tenets of ILH.

Keywords: Keywords— Evaluation, incidental vocabulary learning, input mode, Involvement Load Hypothesis, need, search.

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Authors: Taiebe Shokri, Shahram Yazdani, Leila Afshar, Soleiman Ahmadi

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Introduction: The importance of professionalism in higher education not only determines the appropriate and inappropriate behaviors and guides faculty members in the implementation of professional responsibilities, but also guarantees faculty members' adherence to professional principles and values, ensures the quality of teaching and facilitator will be the teaching-learning process in universities and will increase the commitment to meet the needs of students as well as the development of an ethical culture based on ethics. Therefore, considering the important role of medical education teachers to prepare teachers and students in the future, the need to determine the concept of professional teacher and teacher, and the characteristics of teacher professionalism, we have explained the concept of professionalism in teachers in this study. Methods: The concept analysis method used in this study was Walker and Avant method which has eight steps. Walker and Avant state the purpose of concept analysis as follows: The process of distinguishing between the defining features of a concept and its unrelated features. The process of concept analysis includes selecting a concept, determining the purpose of the analysis, identifying the uses of the concept, determining the defining features of the concept, identifying a model, identifying boundary and adversarial items, identifying the precedents and consequences of the concept, and defining empirical references. is. Results: Professionalism in its general sense, requires deep knowledge, insight, creating a healthy and safe environment, honesty and trust, impartiality, commitment to the profession and continuous improvement, punctuality, criticism, professional competence, responsibility, and Individual accountability, especially in social interactions, is an effort for continuous improvement, the acquisition of these characteristics is not easily possible and requires education, especially continuous learning. Professionalism is a set of values, behaviors, and relationships that underpin public trust in teachers.

Keywords: concept analysis, medical education, professionalism, faculty members

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Authors: Ningfei Xiao, Simon Twose, Hannah Hopewell

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In urban settings worldwide, artists and researchers have drawn from shamanic healing, providing insightful responses to the environment. This project is a transdisciplinary creative research project where architecture and art practice draw from shamanic healing and provide the potential to expand knowledge of public space and inspire more aesthetic explorations of public spatial visions. The research started from the encounters with the Ewengki/Evenki shaman tribe in settlement areas of northern China in 2019 and extended through the partnerships with Maori artists in Poneke Aotearoa, New Zealand, in 2023. Based on the learnings and collaborations with female indigenous tradition practitioners and the healing that the researcher received from the land, a spatial installation exhibition was developed in this project. Indigenous practices are intricately woven with contemporary technology, merging visuals, soundscapes, and other non-visual aesthetics influenced by the researcher's personal experiences of embodied shamanic healing with brainwave generative technology. This synthesis seeks to ritualize and reimagine future public spaces, encompassing streetscapes and greenscapes from China to Aotearoa, and fostering connections between urbanized human body, mind, spirit, and land. In doing so, the project presents a feminist posthuman inquiry into how individuals perceive materiality within the context of a future city. Grounded in creative research and embodied methodologies, this paper focuses on the conceptual and autoethnographic aspects of visual-non-visual aesthetics and their creative representation. Through the exploration of aesthetics beyond the visual realm within urban and spatial contexts, this project showcases the spatial installation exhibition as an example of shamanic influence and related response to public space through embodied artistry and transdisciplinary creative inquiry.

Keywords: aesthetic, embodiment, visual and/or non-visual, spatial installation, shamanic healing, public space

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Authors: Kehinde Agbele, Longe Olumide, Daniel Ekong, Dele Seluwa, Akintoye Onamade

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One major problem of most existing information retrieval systems (IRS) is that they provide even access and retrieval results to individual users specially based on the query terms user issued to the system. When using IRS, users often present search queries made of ad-hoc keywords. It is then up to IRS to obtain a precise representation of user’s information need, and the context of the information. In effect, the volume and range of the Internet documents is growing exponentially and consequently causes difficulties for a user to obtain information that precisely matches the user interest. Diverse combination techniques are used to achieve the specific goal. This is due, firstly, to the fact that users often do not present queries to IRS that optimally represent the information they want, and secondly, the measure of a document's relevance is highly subjective between diverse users. In this paper, we address the problem by investigating the optimization of IRS to individual information needs in order of relevance. The paper addressed the development of algorithms that optimize the ranking of documents retrieved from IRS. This paper addresses this problem with a two-fold approach in order to retrieve domain-specific documents. Firstly, the design of context of information. The context of a query determines retrieved information relevance using personalization and context-awareness. Thus, executing the same query in diverse contexts often leads to diverse result rankings based on the user preferences. Secondly, the relevant context aspects should be incorporated in a way that supports the knowledge domain representing users’ interests. In this paper, the use of evolutionary algorithms is incorporated to improve the effectiveness of IRS. A context-based information retrieval system that learns individual needs from user-provided relevance feedback is developed whose retrieval effectiveness is evaluated using precision and recall metrics. The results demonstrate how to use attributes from user interaction behavior to improve the IR effectiveness.

Keywords: context, document relevance, information retrieval, personalization, user search behaviors

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Authors: Paul Lam, Kevin Wong, Chi Him Chan

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Constant practicing and accurate, immediate feedback are the keys to improving students’ speaking skills. However, traditional oral examination often fails to provide such opportunities to students. The traditional, face-to-face oral assessment is often time consuming – attending the oral needs of one student often leads to the negligence of others. Hence, teachers can only provide limited opportunities and feedback to students. Moreover, students’ incentive to practice is also reduced by their anxiety and shyness in speaking the new language. A mobile app was developed to use artificial intelligence (AI) to provide immediate feedback to students’ speaking performance as an attempt to solve the above-mentioned problems. Firstly, it was thought that online exercises would greatly increase the learning opportunities of students as they can now practice more without the needs of teachers’ presence. Secondly, the automatic feedback provided by the AI would enhance students’ motivation to practice as there is an instant evaluation of their performance. Lastly, students should feel less anxious and shy compared to directly practicing oral in front of teachers. Technically, the program made use of speech-to-text functions to generate feedback to students. To be specific, the software analyzes students’ oral input through certain speech-to-text AI engine and then cleans up the results further to the point that can be compared with the targeted text. The mobile app has invited English teachers for the pilot use and asked for their feedback. Preliminary trials indicated that the approach has limitations. Many of the users’ pronunciation were automatically corrected by the speech recognition function as wise guessing is already integrated into many of such systems. Nevertheless, teachers have confidence that the app can be further improved for accuracy. It has the potential to significantly improve oral drilling by giving students more chances to practice. Moreover, they believe that the success of this mobile app confirms the potential to extend the AI-assisted assessment to other language skills, such as writing, reading, and listening.

Keywords: artificial Intelligence, mobile learning, oral assessment, oral practice, speech-to-text function

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Authors: Boubacar Niang, Denis Raulin

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This paper presents the design of a dedicated board for learning and programming with ATMEL AVR new generation micro controller’s family. This board designed as a "shield" for the Arduino Uno allows us to focus on the design and programming of basic micro controller functionalities in high level language with a considerable time saving because of dealing with additional components is not required.

Keywords: Arduino, microcontroller, programming, language

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: Shenlun Chen, Leonard Wee

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Tumor grading is an essential reference for colorectal cancer (CRC) staging and survival prognostication. The widely used World Health Organization (WHO) grading system defines histological grade of CRC adenocarcinoma based on the density of glandular formation on whole slide images (WSI). Tumors are classified as well-, moderately-, poorly- or un-differentiated depending on the percentage of the tumor that is gland forming; >95%, 50-95%, 5-50% and <5%, respectively. However, manually grading WSIs is a time-consuming process and can cause observer error due to subjective judgment and unnoticed regions. Furthermore, pathologists’ grading is usually coarse while a finer and continuous differentiation grade may help to stratifying CRC patients better. In this study, a deep learning based automatic differentiation grading algorithm was developed and evaluated by survival analysis. Firstly, a gland segmentation model was developed for segmenting gland structures. Gland regions of WSIs were delineated and used for differentiation annotating. Tumor regions were annotated by experienced pathologists into high-, medium-, low-differentiation and normal tissue, which correspond to tumor with clear-, unclear-, no-gland structure and non-tumor, respectively. Then a differentiation prediction model was developed on these human annotations. Finally, all enrolled WSIs were processed by gland segmentation model and differentiation prediction model. The differentiation grade can be calculated by deep learning models’ prediction of tumor regions and tumor differentiation status according to WHO’s defines. If multiple WSIs were possessed by a patient, the highest differentiation grade was chosen. Additionally, the differentiation grade was normalized into scale between 0 to 1. The Cancer Genome Atlas, project COAD (TCGA-COAD) project was enrolled into this study. For the gland segmentation model, receiver operating characteristic (ROC) reached 0.981 and accuracy reached 0.932 in validation set. For the differentiation prediction model, ROC reached 0.983, 0.963, 0.963, 0.981 and accuracy reached 0.880, 0.923, 0.668, 0.881 for groups of low-, medium-, high-differentiation and normal tissue in validation set. Four hundred and one patients were selected after removing WSIs without gland regions and patients without follow up data. The concordance index reached to 0.609. Optimized cut off point of 51% was found by “Maxstat” method which was almost the same as WHO system’s cut off point of 50%. Both WHO system’s cut off point and optimized cut off point performed impressively in Kaplan-Meier curves and both p value of logrank test were below 0.005. In this study, gland structure of WSIs and differentiation status of tumor regions were proven to be predictable through deep leaning method. A finer and continuous differentiation grade can also be automatically calculated through above models. The differentiation grade was proven to stratify CAC patients well in survival analysis, whose optimized cut off point was almost the same as WHO tumor grading system. The tool of automatically calculating differentiation grade may show potential in field of therapy decision making and personalized treatment.

Keywords: colorectal cancer, differentiation, survival analysis, tumor grading

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Authors: Sara T. K. Li, Belinda H. J. Chung, Jeffery C. N. Yip, Janet H. Hsiao

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Music notation and text reading both involve statistical learning of music or linguistic structures. However, it remains unclear how music reading expertise influences text reading behavior. The present study examined this issue through an eye-tracking study. Chinese-English bilingual musicians and non-musicians read English sentences, Chinese sentences, musical phrases, and sentences in Tibetan, a language novel to the participants, with their eye movement recorded. Each set of stimuli consisted of two conditions in terms of structural regularity: syntactically correct and syntactically incorrect musical phrases/sentences. They then completed a sentence comprehension (for syntactically correct sentences) or a musical segment/word recognition task afterwards to test their comprehension/recognition abilities. The results showed that in reading musical phrases, as compared with non-musicians, musicians had a higher accuracy in the recognition task, and had shorter reading time, fewer fixations, and shorter fixation duration when reading syntactically correct (i.e., in diatonic key) than incorrect (i.e., in non-diatonic key/atonal) musical phrases. This result reflects their expertise in music reading. Interestingly, in reading Tibetan sentences, which was novel to both participant groups, while non-musicians did not show any behavior differences between reading syntactically correct or incorrect Tibetan sentences, musicians showed a shorter reading time and had marginally fewer fixations when reading syntactically correct sentences than syntactically incorrect ones. However, none of the musicians reported discovering any structural regularities in the Tibetan stimuli after the experiment when being asked explicitly, suggesting that they may have implicitly acquired the structural regularities in Tibetan sentences. This group difference was not observed when they read English or Chinese sentences. This result suggests that music reading expertise facilities reading texts in a novel language (i.e., Tibetan), but not in languages that the readers are already familiar with (i.e., English and Chinese). This phenomenon may be due to the similarities between reading music notations and reading texts in a novel language, as in both cases the stimuli follow particular statistical structures but do not involve semantic or lexical processing. Thus, musicians may transfer their statistical learning skills stemmed from music notation reading experience to implicitly discover structures of sentences in a novel language. This speculation is consistent with a recent finding showing that music reading expertise modulates the processing of English nonwords (i.e., words that do not follow morphological or orthographic rules) but not pseudo- or real words. These results suggest that the modulation of music reading expertise on language processing depends on the similarities in the cognitive processes involved. It also has important implications for the benefits of music education on language and cognitive development.

Keywords: eye movement behavior, eye-tracking, music reading expertise, sentence reading, structural regularity, visual processing

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Authors: Obasa Joshua Ifeoluwa

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Phonetics is the foundation and most important part of language learning. This article, through an acoustic experiment as well as using Praat software, uses Yoruba students’ Chinese consonants, vowels, and tones pronunciation to carry out a visual comparison with that of native Chinese speakers. This article is aimed at Yoruba native speakers learning Chinese phonetics; therefore, Yoruba students are selected. The students surveyed are required to be at an elementary level and have learned Chinese for less than six months. The students selected are all undergraduates majoring in Chinese Studies at the University of Lagos. These students have already learned Chinese Pinyin and are all familiar with the pinyin used in the provided questionnaire. The Chinese students selected are those that have passed the level two Mandarin proficiency examination, which serves as an assurance that their pronunciation is standard. It is discovered in this work that in terms of Mandarin’s consonants pronunciation, Yoruba students cannot distinguish between the voiced and voiceless as well as the aspirated and non-aspirated phonetics features. For instance, while pronouncing [ph] it is clearly shown in the spectrogram that the Voice Onset Time (VOT) of a Chinese speaker is higher than that of a Yoruba native speaker, which means that the Yoruba speaker is pronouncing the unaspirated counterpart [p]. Another difficulty is to pronounce some affricates like [tʂ]、[tʂʰ]、[ʂ]、[ʐ]、 [tɕ]、[tɕʰ]、[ɕ]. This is because these sounds are not in the phonetic system of the Yoruba language. In terms of vowels, some students find it difficult to pronounce some allophonic high vowels such as [ɿ] and [ʅ], therefore pronouncing them as their phoneme [i]; another pronunciation error is pronouncing [y] as [u], also as shown in the spectrogram, a student pronounced [y] as [iu]. In terms of tone, it is most difficult for students to differentiate between the second (rising) and third (falling and rising) tones because these tones’ emphasis is on the rising pitch. This work concludes that the major error made by Yoruba students while pronouncing Chinese sounds is caused by the interference of their first language (LI) and sometimes by their lingua franca.

Keywords: Chinese, Yoruba, error analysis, experimental phonetics, consonant, vowel, tone

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Authors: Folasade R. Sulaiman

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Pupils’ health and nutrition are basically important to their schooling. The preponderance of avoidable deaths among children in Africa (WHO, 2000) may not be unconnected with the nutritional and health education status of families and communities that have their children as school clients. This study adopted a descriptive survey design focusing on the assessment of the level of nutritional and health education of families and community members in the rural riverine areas of Ogun State. Two research questions were raised. The Nutritional and Health Education of Families and Communities Inventory (NHEFCI) was used to collect data from 250 rural child-bearing aged women, and 0.73 test-retest reliability coefficient was established to determine the strength of the instrument. Data collected were analysed using descriptive statistics of frequency counts, percentages and mean in accordance with research questions raised in the study. The findings revealed amongst others: that 65% of the respondents had low level of nutritional and health education among the families and community members; while 72% had low level of awareness of the possible influence of nutritional and health education on the learning outcomes of the children. Based on the findings, it was recommended among others that government should intensify efforts on sensitization, mass literacy campaign etc.; also improve upon the already existing School Feeding Programme in Nigerian primary schools to provide at least one balanced diet for children while in school; community health workers, social workers, Non-Governmental Organizations (NGO) should collaborate with international Organizations like UNICEF, UNESCO, WHO etc. to organize sensitization programmes for members of the rural riverine communities on the importance of meeting the health and nutritional needs of their children in order to attain their educational potentials.

Keywords: nutritional and health education, learning capacities, school-age children, universal basic education, rural riverine areas

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Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdelatif, Rabah Siham

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Camels play a vital role as multipurpose animals, providing milk meat and serving as a means of transportation. They serve as a financial reserve for pastoralists and hold significant cultural and social value. Camel milk, known for its exceptional nutritional properties, is considered a valuable substitute for human milk. However, udder infections, particularly mastitis, pose significant challenges to camel farming. Clinical and subclinical mastitis can lead to substantial economic losses. Mastitis, especially the subclinical form, is a persistent and prevalent condition affecting milk hygiene and quality in dairy camels. This review offers insights into the prevalence and risk factors associated with subclinical mastitis in camels. The prevalence of subclinical mastitis in dairy camels was found to range from 9.28% to 87.78%. Major pathogens responsible for camel mastitis include Staphylococcus aureus, Coagulase-negative Staphylococcus, Streptococcus agalactiae, Streptococcus dysgalactiae, Escherichia coli, Micrococcus spp, Pasteurella haemolytica and Corynebacterium spp. The study outlines key risk factors contributing to camel mastitis, emphasizing factors such as severe tick infestation, age, stage of lactation, parity, body condition score, skin lesion on the teats or udders, anti-suckling devices, previous history of the udder, conformation of the udder, breed, unhygienic milking practices, production system, amongst others have been reported to be important in the prevalence of subclinical mastitis. This comprehensive overview provides valuable insights into the multifaceted aspects of camel mastitis, encompassing prevalent bacterial pathogens and diverse risk factors. The findings underscore the importance of holistic management practices, emphasizing hygiene, health monitoring, and targeted interventions to ensure the well-being and productivity of camels in various agro-pastoral contexts.

Keywords: bacterial pathogens, camel, mastitis, risk factors

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Geraldine Landon, Enora Clero, Jean-Rene Jourdain

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In 2005, the Institut de Radioprotection et de Sûreté Nucléaire (IRSN, France) launched a research program named EPICE (acronym for 'Evaluation of Pathologies potentially Induced by CaEsium') to collect scientific information on non-cancer effects possibly induced by chronic exposures to low doses of ionizing radiation with the view of addressing a question raised by several French NGOs related to health consequences of the Chernobyl nuclear accident in children. The implementation of the program was preceded by a pilot phase to ensure that the project would be feasible and determine the conditions for implementing an epidemiological study on a population of several thousand children. The EPICE program focused on childhood cardiac arrhythmias started in May 2009 for 4 years, in partnership with the Russian Bryansk Diagnostic Center. The purpose of this cross-sectional study was to determine the prevalence of cardiac arrhythmias in the Bryansk oblast (depending on the contamination of the territory and the caesium-137 whole-body burden) and to assess whether caesium-137 was or not a factor associated with the onset of cardiac arrhythmias. To address these questions, a study bringing together 18 152 children aged 2 to 18 years was initiated; each child received three medical examinations (ECG, echocardiography, and caesium-137 whole-body activity measurement) and some of them were given with a 24-hour Holter monitoring and blood tests. The findings of the study, currently submitted to an international journal justifying that no results can be given at this step, allow us to answer clearly to the issue of radiation-induced childhood arrhythmia, a subject that has been debated for many years. Our results will be certainly helpful for health professionals responsible for the monitoring of population exposed to the releases from the Fukushima Dai-ichi nuclear power plant and also useful for future comparative study in children exposed to ionizing radiation in other contexts, such as cancer radiation therapies.

Keywords: Caesium-137, cardiac arrhythmia, Chernobyl, children

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Authors: Talar Agopian

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Teaching English as a Second Language (ESL) is a common practice in many Lebanese schools. However, ESL teaching is done in traditional ways. Methods such as constructivism are seldom used, especially in villages. Here lies the significance of this research which joins constructivism and Piaget’s theory of cognitive development in ESL classes in Lebanese villages. The purpose of the present study is to explore the effects of applying constructivist student-centered strategies in teaching grammar, reading comprehension, and poetry on students in elementary ESL classes in two villages in Lebanon, Zefta in South Lebanon and Boqaata in Mount Lebanon. 20 English teachers participated in a training titled “Teaching English with Fun”, which focused on strategies that create a student-centered class where active learning takes place and there is increased learner engagement and autonomy. The training covered three main areas in teaching English: grammar, reading comprehension, and poetry. After participating in the training, the teachers applied the new strategies and methods in their ESL classes. The methodology comprised two phases: in phase one, practice-based research was conducted as the teachers attended the training and applied the constructivist strategies in their respective ESL classes. Phase two included the reflections of the teachers on the effects of the application of constructivist strategies. The results revealed the educational benefits of constructivist student-centered strategies; the students of teachers who applied these strategies showed improved engagement, positive attitudes towards poetry, increased motivation, and a better sense of autonomy. Future research is required in applying constructivist methods in the areas of writing, spelling, and vocabulary in ESL classrooms of Lebanese villages.

Keywords: active learning, constructivism, learner engagement, student-centered strategies

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Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

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Authors: M. D. Antoine

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Despite the widespread use of complementary and alternative medicine (CAM) for autistic children, little is known about the communication flow between the different parties involved in autism care (e.g., parents/caregivers, conventional providers, alternative practitioners). This study aimed to describe how communication occurs through the first year following an autism spectrum disorder (ASD) diagnosis to identify challenges and potential barriers to communication within the healthcare system in Ottawa, Canada. From an ecological perspective, we collected qualitative data through 12 semi-structured interviews with six parents/caregivers, three conventional providers (e.g., family doctor, neurodevelopmental pediatrician, psychologist), and three alternative practitioners (e.g., naturopath, occupational therapist, speech and language pathologist) operating in Ottawa. We interpreted the data using thematic analysis. Findings revealed communication challenges between the parents/caregivers and conventional providers while they experience better communication flow with fewer challenges in alternative care settings. However, parents/caregivers are the only links between the health professionals of both streams. From the five contexts examined: organizational, interpersonal, media, cultural, and political-legal, we found four themes (provider knowledge, care integration, flexible care, and time constraints) underlining specific barriers to communication flow between the parties involved in the care of autistic children. The increasing interest in alternative medicine is forcing changes in the healthcare system. Communications occur outside the norms making openings for better communication and information-sharing increasingly essential. Within the identified themes in the current study, the necessity for better communication between all parties involved in the care of autistic children is evident. More ASD and CAM-related training for providers would support effective parent/caregiver-provider communication. The findings of the current study contribute to a better understanding of the role of communication in the care management of autism, which has implications for effective autism care.

Keywords: alternative medicine, autism care management, autism spectrum disorder, conventional medicine, parent-provider communication

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Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

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Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

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Authors: Karolina Wieczorek, Zofia Przypaśniak

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Climate change is a rapidly growing threat to global health, and part of the responsibility to combat it lies within the healthcare sector itself, including adequate education of future medical professionals. To mitigate the consequences, the General Medical Council (GMC) has equipped medical schools with a list of outcomes regarding sustainability teaching. Students are expected to analyze the impact of the healthcare sector’s emissions on climate change. The delivery of the related teaching content is, however, often inadequate and insufficient time is devoted for exploration of the topics. Teaching curricula lack in-depth exploration of the learning objectives. This study aims to assess the extent and characteristics of climate change and sustainability subjects teaching in the curriculum of a chosen UK medical school (Barts and The London School of Medicine and Dentistry). It compares the data to the national average scores from the Climate Change and Sustainability Teaching (C.A.S.T.) in Medical Education Audit to draw conclusions about teaching on a regional level. This is a single-center audit of the timetabled sessions of teaching in the medical course. The study looked at the academic year 2020/2021 which included a review of all non-elective, core curriculum teaching materials including tutorials, lectures, written resources, and assignments in all five years of the undergraduate and graduate degrees, focusing only on mandatory teaching attended by all students (excluding elective modules). The topics covered were crosschecked with GMC Outcomes for graduates: “Educating for Sustainable Healthcare – Priority Learning Outcomes” as gold standard to look for coverage of the outcomes and gaps in teaching. Quantitative data was collected in form of time allocated for teaching as proxy of time spent per individual outcomes. The data was collected independently by two students (KW and ZP) who have received prior training and assessed two separate data sets to increase interrater reliability. In terms of coverage of learning outcomes, 12 out of 13 were taught (with the national average being 9.7). The school ranked sixth in the UK for time spent per topic and second in terms of overall coverage, meaning the school has a broad range of topics taught with some being explored in more detail than others. For the first outcome 4 out of 4 objectives covered (average 3.5) with 47 minutes spent per outcome (average 84 min), for the second objective 5 out of 5 covered (average 3.5) with 46 minutes spent (average 20), for the third 3 out of 4 (average 2.5) with 10 mins pent (average 19 min). A disproportionately large amount of time is spent delivering teaching regarding air pollution (respiratory illnesses), which resulted in the topic of sustainability in other specialties being excluded from teaching (musculoskeletal, ophthalmology, pediatrics, renal). Conclusions: Currently, there is no coherent strategy on national teaching of climate change topics and as a result an unstandardized amount of time spent on teaching and coverage of objectives can be observed.

Keywords: audit, climate change, sustainability, education

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Authors: Su-Lin Yu

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In recent years, we have witnessed the growing scholarship on transnational theorizing and activism within disability. In particular, the universalizing discourses of disability formulated in the Global North seem inadequate in engaging the vast diversity of discourses of disability that emerge in global and local policies as well as the everyday experiences of disabled people in the Global South. This study will further consider the future possibilities of how Taiwanese and global disability studies might interchange disability knowledge. First, this study will determine how a local literature of disability can be formed in Taiwan by examining life writings written by Taiwanese disabled women. Both the texts and the personal experiences are treated as social products which can, through their discourses, offer insight into the socio-cultural practices and norms of disability and womanhood in Taiwan. This paper argues that more than by the impairment in itself, the experiences of disabled women are shaped by the social and cultural discourses and practices that define disability and womanhood as well as the normative roles, places, and contexts associated with them. Simultaneous analysis of disability and womanhood exemplifies the way in which disability operates in a complex interaction with the socio-cultural discourses and practices of womanhood, thus producing gender-differentiated disabling obstacles for disabled women. Another purpose of this study is to gain an understanding of the transformative experience of women with disabilities and their perceptions of the self. Designed to provide positive, realistic pictures of the lives of women with disabilities and the social, economic, and political issues they face, their life writings demonstrate how they as disabled women simultaneously struggle with writing a new identity and creating an ethical narrative. These strong and articulate women construct narratives that attempt to recount the remarkable journey that transformed them from dependent women to community activists and writers who speak forcefully about the needs of people with disabilities. More than a story of one woman's struggle for independence, their writing, then, is a testimony to the importance of community building and organizing to enable local people with disabilities to live fulfilling lives.

Keywords: gender, disability, representation, agency

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