Search results for: academic learning stress

Authors: Micheale Yifter Weldemichael, Hailay Mehari Gebremedhn, Teklehaimanot Hailesslasie

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The advancement of target-specific genome editing tools, including clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein9 (Cas9), mega-nucleases, base editing (BE), prime editing (PE), transcription activator-like endonucleases (TALENs), and zinc-finger nucleases (ZFNs), have paved the way for a modern era of gene editing. CRISPR/Cas9, as a versatile, simple, cost-effective and robust system for genome editing, has dominated the genome manipulation field over the last few years. The application of CRISPR/Cas9 in sorghum improvement is particularly vital in the context of ecological, environmental and agricultural challenges, as well as global climate change. In this context, gene editing using CRISPR/Cas9 can improve nutritional value, yield, resistance to pests and disease and tolerance to different abiotic stress. Moreover, CRISPR/Cas9 can potentially perform complex editing to reshape already available elite varieties and new genetic variations. However, existing research is targeted at improving even further the effectiveness of the CRISPR/Cas9 genome editing techniques to fruitfully edit endogenous sorghum genes. These findings suggest that genome editing is a feasible and successful venture in sorghum. Newer improvements and developments of CRISPR/Cas9 techniques have further qualified researchers to modify extra genes in sorghum with improved efficiency. The fruitful application and development of CRISPR techniques for genome editing in sorghum will not only help in gene discovery, creating new, improved traits in sorghum regulating gene expression sorghum functional genomics, but also in making site-specific integration events.

Keywords: CRISPR/Cas9, genome editing, quality, sorghum, stress, yield

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Authors: Jack R. McKenzie, Peter A. Appleby, Thomas House, Neil Walton

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Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items. To address this challenge, a contextual bandit algorithm – the Fast Approximate Bayesian Contextual Cold Start Learning algorithm (FAB-COST) – is proposed, which is designed to provide improved accuracy compared to the traditionally used Laplace approximation in the logistic contextual bandit, while controlling both algorithmic complexity and computational cost. To this end, FAB-COST uses a combination of two moment projection variational methods: Expectation Propagation (EP), which performs well at the cold start, but becomes slow as the amount of data increases; and Assumed Density Filtering (ADF), which has slower growth of computational cost with data size but requires more data to obtain an acceptable level of accuracy. By switching from EP to ADF when the dataset becomes large, it is able to exploit their complementary strengths. The empirical justification for FAB-COST is presented, and systematically compared to other approaches on simulated data. In a benchmark against the Laplace approximation on real data consisting of over 670, 000 impressions from autotrader.co.uk, FAB-COST demonstrates at one point increase of over 16% in user clicks. On the basis of these results, it is argued that FAB-COST is likely to be an attractive approach to cold-start recommendation systems in a variety of contexts.

Keywords: cold-start learning, expectation propagation, multi-armed bandits, Thompson Sampling, variational inference

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Authors: Kyung-Tae Lee, Li Li Wang, Kwang-Woo Jung, Yong-Sun Bahn

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Microtubules are involved in mechanical support, cytoplasmic organization as well as in a number of cellular processes by interacting with diverse microtubule-associated proteins (MAPs), such as plus-end tracking proteins, motor proteins, and tubulin-folding cofactors. A common feature of these proteins is the presence of a cytoskeleton-associated protein-glycine-rich (CAP-Gly) domain, which is evolutionarily conserved and generally considered to bind to α-tubulin to regulate functions of microtubules. However, there has been a dearth of research on CAP-Gly proteins in fungal pathogens, including Cryptococcus neoformans, which causes fatal meningoencephalitis globally. In this study, we identified five CAP-Gly proteins encoding genes in C. neoformans. Among these, Cgp1, encoded by CNAG_06352, has a unique domain structure that has not been reported before in other eukaryotes. Supporting the role of Cpg1 in microtubule-related functions, we demonstrate that deletion or overexpression of CGP1 alters cellular susceptibility to thiabendazole, a microtubule destabilizer, and Cgp1 is co-localized with cytoplasmic microtubules. Related to the cellular functions of microtubules, Cgp1 also governs maintenance of membrane stability and genotoxic stress responses. Furthermore, we demonstrate that Cgp1 uniquely regulates sexual differentiation of C. neoformans with distinct roles in the early and late stage of mating. Our domain analysis reveals that the CAP-Gly domain plays major roles in all the functions of Cgp1. Finally, the cgp1Δ mutant is attenuated in virulence. In conclusion, this novel CAP-Gly protein, Cgp1, has pleotropic roles in regulating growth, stress responses, differentiation and pathogenicity of C. neoformans.

Keywords: human fungal pathogen, CAP-Glycine protein, microtubule, meningoencephalitis

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Authors: Ágnes Győri, Éva Perpék, Zsófia Bauer, Zsuzsanna Elek

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The aim of the research (funded by Hungarian national grant, NFKI- FK 138315) is to examine the professional mobility, mental health and work environment of social workers with a complex approach. Previous international and Hungarian research has pointed out that those working in the helping professions are strongly exposed to the risk of emotional-mental-physical exhaustion due to stress. Mental and physical strain, as well as lack of coping (can) cause health problems, but its role in career change and high labor turnover has also been proven. Even though satisfaction with working conditions of those employed in the human service sector in the context of the stress burden has been researched extensively, there is a lack of large-sample international and Hungarian domestic studies exploring the effects of profession-specific conditions. Nor has it been examined how the specific features of the social profession and mental health affect the career mobility of the professionals concerned. In our research, these factors and their correlations are analyzed by means of mixed methodology, utilizing the benefits of netnographic big data analysis and a sector-specific quantitative survey. The netnographic analysis of open web content generated inside and outside the social profession offers a holistic overview of the influencing factors related to mental health and the work environment of social workers. On the one hand, the topics and topoi emerging in the external discourse concerning the sector are examined, and on the other hand, focus on mentions and streams of comments regarding the profession, burnout, stress, coping, as well as labor turnover and career changes among social professionals. The analysis focuses on new trends and changes in discourse that have emerged during and after the pandemic. In addition to the online conversation analysis, a survey of social professionals with a specific focus has been conducted. The questionnaire is based on input from the first two research phases. The applied approach underlines that the mobility paths of social professionals can only be understood if, apart from the general working conditions, the specific features of social work and the effects of certain aspects of mental health (emotional-mental-physical strain, resilience) are taken into account as well. In this paper, the preliminary results from this innovative methodological mix are presented, with the aim of highlighting new opportunities and dimensions in the research on social work. A gap in existing research is aimed to be filled both on a methodological and empirical level, and the Hungarian domestic findings can create a feasible and relevant framework for a further international investigation and cross-cultural comparative analysis. Said results can contribute to the foundation of organizational and policy-level interventions, targeted programs whereby the risk of burnout and the rate of career abandonment can be reduced. Exploring different aspects of resilience and mapping personality strengths can be a starting point for stress-management, motivation-building, and personality-development training for social professionals.

Keywords: burnout, mixed methods, netnography, professional mobility, social work

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Authors: Shri Krishna Mishra

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In an educational context, technology can prompt interactive plight only when it is used in conjunction with interactive plight methods. This study, therefore, examines the relationships among components that contribute to higher levels of interactive plight and execution, such as interactive Plight methods, technology, intrinsic motivation and deep learning. 526 students participated in this study. With structural equation modelling, the authors test the conceptual model and identify satisfactory model fit. The results indicate that interactive Plight methods, technology and intrinsic motivation have significant relationship with interactive Plight; deep learning mediates the relationships of the other variables with Execution.

Keywords: searching the relationship among components, contribute to interactive plight, educational execution, intrinsic motivation

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Authors: Shrikrishna Mishra, Badri Yadav

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In this complex society of today where experiences are innumerable and varied, it is not at all possible to present every situation in its original colors hence the opportunities for learning by actual experiences always are not at all possible. It is only through the use of proper audio visual aids that the life situation can be trough in the class room by an enlightened teacher in their simplest form and representing the original to the highest point of similarity which is totally absent in the verbal or lecture method. In the presence of audio aids, the attention is attracted interest roused and suitable atmosphere for proper understanding is automatically created, but in the existing traditional method greater efforts are to be made in order to achieve the aforesaid essential requisite. Inspire of the best and sincere efforts on the side of the teacher the net effect as regards understanding or learning in general is quite negligible.

Keywords: Audio-Visual Aids, the secondary school teaching, complex society, audio

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Authors: Irfan Ahmed Rind

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Science education has recently become the top priority of government of Pakistan. Number of schemes has been initiated for the improvement of science teaching and learning at primary and secondary levels of education, most importantly training in-service science teachers on inquiry based teaching and learning to empower students and encourage creativity, critical thinking, and innovation among them. Therefore, this approach has been promoted in the recent continuous professional development trainings for the in-service teachers. However, the follow ups on trained science teachers and educators suggest that these teachers fail to implement the inquiry based teaching and learning in their classes. In addition, these trainings also fail to bring any significant change in students’ science content knowledge and understanding as per the annual national level surveys conducted by government and independent agencies. Research suggests that science has been taught using scientific positivism, which supports objectivity based on experiments and mathematics. In contrary, the inquiry based teaching and learning are based on constructivism, which conflicts with the positivist epistemology of science teachers. It was, therefore, assumed that science teachers struggle to implement the inquiry based teaching approach as it conflicts with their basic epistemological beliefs. With this assumption, this research aimed to (i) understand how science teachers conceptualize the nature of science, and how this influence their understanding of learning, learners, their own roles as teachers and their teaching strategies, (ii) identify the conflict of science teachers’ epistemological beliefs with the inquiry based teaching approach, and (iii) find the ways in which science teachers epistemological beliefs may be developed from positivism to constructivism, so that they may effectively use the inquiry based teaching approach in teaching science. Using qualitative case study approach, thirty six secondary and higher secondary science teachers (21 male and 15 female) were selected. Data was collected using interviewed, participatory observations (sixty lessons were observed), and twenty interviews from students for verifications of teachers’ responses. The findings suggest that most of the science teacher were positivist in defining the nature of science. Most of them limit themselves to one fix answer that is provided in the books and that there is only one 'right' way to teach science. There is no room for students’ or teachers’ own opinion or bias when it comes to scientific concepts. Inquiry based teaching seems 'no right' to them. They find it difficult to allow students to think out of the box. However, some interesting exercises were found to be very effective in bringing the change in teachers’ epistemological beliefs. These will be discussed in detail in the paper. The findings have major implications for the teachers, educators, and policymakers.

Keywords: science teachers, epistemology, metacognition, inquiry based teaching

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Authors: Neda Jalaliyoon, Nooh Abu Bakar, Hamed Taherdoost

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The present research recommended balanced scorecard (BSC) framework to appraise the performance of the universities. As the original model of balanced scorecard has four perspectives in order to implement BSC in present research the same model with “financial perspective”, “customer”,” internal process” and “learning and growth” is used as well. With applying fuzzy Delphi method (FDM) and questionnaire sixteen measures of performance were identified. Moreover, with using the analytic network process (ANP) the weights of the selected indicators were determined. Results indicated that the most important BSC’s aspect were Internal Process (0.3149), Customer (0.2769), Learning and Growth (0.2049), and Financial (0.2033) respectively. The proposed BSC framework can help universities to enhance their efficiency in competitive environment.

Keywords: balanced scorecard, higher education, fuzzy delphi method, analytic network process (ANP)

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Authors: Geir Sigurðsson

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This paper compares ancient Daoist and Confucian approaches to the human body as a locus for learning, edification or personal cultivation. While pointing out some major differences between ancient Chinese and mainstream Western visions of the body, it seeks at the same time inspiration in some seminal Western phenomenological and post-structuralist writings, in particular from Maurice Merleau-Ponty and Pierre Bourdieu. By clarifying the somewhat dissimilar scopes of foci found in Daoist and Confucian philosophies with regard to the role of and attitude to the body, the conclusion is nevertheless that their approaches are comparable, and that both traditions take the physical body to play a vital role in the cultivation of excellence. Lastly, it will be argued that cosmological underpinnings prevent the Confucian li from being rigid and invariable and that it rather emerges as a flexible learning device to train through active embodiment a refined sensibility for one’s cultural environment.

Keywords: body, Confucianism, Daoism, li (ritual), phenomenology

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Authors: Gaye Senbag

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In English language teaching, feedback is considered as one of the main components of writing instruction. Teachers put a lot of time and effort in order to provide learners with written feedback for effective language learning. At Anadolu University School of Foreign Languages (AUSFL) students are given written feedback for their each piece of writing through online platforms such as Edmodo and Turnitin, and traditional methods. However, little is known regarding how learners value and respond to teacher-provided feedback. As the perceptions of the students remarkably affect their learning, this study examines how they perceive the effectiveness of feedback provided by the teacher. Aiming to analyse it, 30 intermediate level (B1+ CEFR level) students were given a questionnaire, which includes Likert scale questions. The results will be discussed in detail.

Keywords: feedback, perceptions, writing, English Language Teaching (ELT)

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Authors: Jiangping Wang

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This presentation describes the design and implementation of PaaS (platform as a service) cloud-based labs that are used in database-related courses to teach students practical skills. Traditionally, all labs are implemented in a desktop-based environment where students have to install heavy client software to access database servers. In order to release students from that burden, we have successfully deployed the cloud-based solution to support database-related courses, from which students and teachers can practice and learn database topics in various database courses via cloud access. With its development environment, execution runtime, web server, database server, and collaboration capability, it offers a shared pool of configurable computing resources and comprehensive environment that supports students’ needs without the complexity of maintaining the infrastructure.

Keywords: PaaS, database environment, e-learning, web server

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Authors: Annette Cosgrove

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The objectives of this research study is to examine the impact of professional development in Technology Enhanced Learning (TEL) and the digitisation of learning in teaching communities across multiple higher education sites in the ATU (Atlantic Technological University *) ( 2020-2025), including the proposal of an evidence based digital teaching model for use in a future pandemic. The research strategy undertaken for this PhD Study is a multi-site study using mixed methods. Qualitative & quantitative methods are being used in the study to collect data. A pilot study was carried out initially , feedback collected and the research instrument was edited to reflect this feedback, before being administered. The purpose of the staff questionnaire is to evaluate the impact of professional development in the area of TEL, and to capture the practitioners views on the perceived impact on their teaching practice in the higher education sector across ATU (West of Ireland – 5 Higher education locations ). The phenomenon being explored is ‘ the impact of professional development in the area of technology enhanced learning and on teaching practice in a higher education institution.’ The research methodology chosen for this study is an Action based Research Study. The researcher has chosen this approach as it is a prime strategy for developing educational theory and enhancing educational practice . This study includes quantitative and qualitative methods to elicit data which will quantify the impact that continuous professional development in the area of digital teaching practice and technologies has on the practitioner’s teaching practice in higher education. The research instruments / data collection tools for this study include a lecturer survey with a targeted TEL Practice group ( Pre and post covid experience) and semi-structured interviews with lecturers.. This research is currently being conducted across the ATU multisite campus and targeting Higher education lecturers that have completed formal CPD in the area of digital teaching. ATU, a west of Ireland university is the focus of the study , The research questionnaire has been deployed, with 75 respondents to date across the ATU - the primary questionnaire and semi- formal interviews are ongoing currently – the purpose being to evaluate the impact of formal professional development in the area of TEL and its perceived impact on the practitioners teaching practice in the area of digital teaching and learning . This paper will present initial findings, reflections and data from this ongoing research study.

Keywords: TEL, DTL, digital teaching, digital assessment

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Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

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The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Rachel Buchanan, Rebecca LaRiviere

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Context: The ongoing effects of colonialism continue to be experienced through paternalistic policies and funding processes that cause disjuncture between and across Indigenous early childhood programming on-reserve and in urban and Northern settings in Canada. While various educational organizations and social service providers have risen to address these challenges in the short, medium and long term, there continues to be a lack in nation-wide cohesive, culturally grounded, and meaningful early learning programming for Indigenous children in Canada. Indigenous-centered early learning programs tend to face one of two scaling dilemmas: their program goals are too prescriptive to enable the program to be meaningfully replicated in different cultural/ community settings, or their program goals are too broad to be meaningfully adapted to the unique cultural and contextual needs and desires of Indigenous communities (the “franchise approach”). There are over 600 First Nations communities in Canada representing more than 50 Nations and languages. Consequently, Indigenous early learning programming cannot be applied with a universal or “one size fits all” approach. Sustainable and comprehensive programming must be responsive to each community context, building upon existing strengths and assets to avoid program duplication and irrelevance. Thesis: Community-driven and culturally adapted early childhood programming is critical but cannot be achieved on a large scale within traditional program models that are constrained by prescriptive overarching program goals. Principles, rather than goals, are an effective way to navigate and evaluate complex and dynamic systems. Principles guide an intervention to be adaptable, flexible and scalable. The Martin Family Initiative (MFI) ’s Early Years program engages a principles-based approach to programming. As will be discussed in this paper, this approach enables the program to catalyze existing community-based strengths and organizational assets toward bridging gaps across and disjuncture between Indigenous early learning programs, as well as to scale programming in sustainable, context-responsive and dynamic ways. This paper argues that using a principles-driven and adaptive scaling approach, the Early Years model establishes important learnings for culturally adapted Indigenous early learning programming in Canada. Methodology: The Early Years has leveraged this approach to develop an array of programming with partner organizations and communities across the country. The Early Years began as a singular pilot project in one First Nation. In just three years, it has expanded to five different regions and community organizations. In each context, the program supports the partner organization through different means and to different ends, the extent to which is determined in partnership with each community-based organization: in some cases, this means supporting the organization to build home visiting programming from the ground-up; in others, it means offering organization-specific culturally adapted early learning resources to support the programming that already exists in communities. Principles underpin but do not define the practices of the program in each of these relationships. This paper will explore numerous examples of principles-based adaptability with the context of the Early Years, concluding that the program model offers theadaptability and dynamism necessary to respond to unique and ever-evolving community contexts and needs of Indigenous children today.

Keywords: culturally adapted programming, indigenous early learning, principles-based approach, program scaling

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Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni

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Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone

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Authors: Andrea Fiorista

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The learning of prepositions is a quite problematic aspect in foreign language instruction, and Italian is certainly not an exception. In their prototypical function, prepositions express schematic relations of two entities in a highly abstract, typically image-schematic way. In other terms, prepositions assume concepts such as directionality, collocation of objects in space and time and, in Cognitive Linguistics’ terms, the position of a trajector with respect to a landmark. Learners of different native languages may conceptualize them differently, implying that they are supposed to operate a recategorization (or create new categories) fitting with the target language. However, most current Italian Foreign/Second Language handbooks and didactic grammars do not facilitate learners in carrying out the task, as they tend to provide partial and idiosyncratic descriptions, with the consequent learner’s effort to memorize them, most of the time without success. In their prototypical meaning, prepositions are used to specify precise topographical positions in the physical environment which become less and less accurate as they radiate out from what might be termed a concrete prototype. According to that, the present study aims to elaborate a cognitive and conceptually well-grounded analysis of some extensive uses of the Italian preposition a, in order to propose effective pedagogical solutions in the Teaching/Learning process. Image schemas, cognitive metaphors and embodiment represent efficient cognitive tools in a task like this. Actually, while learning the merely spatial use of the preposition a (e.g. Sono a Roma = I am in Rome; vado a Roma = I am going to Rome,…) is quite straightforward, it is more complex when a appears in constructions such as verbs of motion +a + infinitive (e.g. Vado a studiare = I am going to study), inchoative periphrasis (e.g. Tra poco mi metto a leggere = In a moment I will read), causative construction (e.g. Lui mi ha mandato a lavorare = He sent me to work). The study reports data from a teaching intervention of Focus on Form, in which a basic cognitive schema is used to facilitate both teachers and students to respectively explain/understand the extensive uses of a. The educational material employed translates Cognitive Linguistics’ theoretical assumptions, such as image schemas and cognitive metaphors, into simple images or proto-scenes easily comprehensible for learners. Illustrative material, indeed, is supposed to make metalinguistic contents more accessible. Moreover, the concept of embodiment is pedagogically applied through activities including motion and learners’ bodily involvement. It is expected that replacing rote learning with a methodology that gives grammatical elements a proper meaning, makes learning process more effective both in the short and long term.

Keywords: cognitive approaches to language teaching, image schemas, embodiment, Italian as FL/SL

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Authors: Florin Leon, Silvia Curteanu

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Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms.

Keywords: batch bulk methyl methacrylate polymerization, adaptive sampling, machine learning, large margin nearest neighbor regression

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Authors: Shofiayuningtyas Luftiani

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Some private schools in Indonesia started integrating the online program Gizmos in the teaching-learning process. Gizmos was developed to supplement the existing curriculum by integrating it into the instructional programs. The program has some features using an inquiry-based simulation, in which students conduct exploration by using a worksheet while teachers use the teacher guidelines to direct and assess students’ performance In this study, the discussion about Gizmos highlights its features as the assessment media of mathematics learning for secondary school students. The discussion is based on the case study and literature review from the Indonesian context. The purpose of applying Gizmos as an assessment media refers to the diagnostic assessment. As a part of the diagnostic assessment, the teachers review the student exploration sheet, analyze particularly in the students’ difficulties and consider findings in planning future learning process. This assessment becomes important since the teacher needs the data about students’ persistent weaknesses. Additionally, this program also helps to build student’ understanding by its interactive simulation. Currently, the assessment over-emphasizes the students’ answers in the worksheet based on the provided answer keys while students perform their skill in translating the question, doing the simulation and answering the question. Whereas, the assessment should involve the multiple perspectives and sources of students’ performance since teacher should adjust the instructional programs with the complexity of students’ learning needs and styles. Consequently, the approach to improving the assessment components is selected to challenge the current assessment. The purpose of this challenge is to involve not only the cognitive diagnosis but also the analysis of skills and error. Concerning the selected setting for this diagnostic assessment that develops the combination of cognitive diagnosis, skills analysis and error analysis, the teachers should create an assessment rubric. The rubric plays the important role as the guide to provide a set of criteria for the assessment. Without the precise rubric, the teacher potentially ineffectively documents and follows up the data about students at risk of failure. Furthermore, the teachers who employ the program of Gizmos as the diagnostic assessment might encounter some obstacles. Based on the condition of assessment in the selected setting, the obstacles involve the time constrain, the reluctance of higher teaching burden and the students’ behavior. Consequently, the teacher who chooses the Gizmos with those approaches has to plan, implement and evaluate the assessment. The main point of this assessment is not in the result of students’ worksheet. However, the diagnostic assessment has the two-stage process; the process to prompt and effectively follow-up both individual weaknesses and those of the learning process. Ultimately, the discussion of Gizmos as the media of the diagnostic assessment refers to the effort to improve the mathematical learning process.

Keywords: diagnostic assessment, error analysis, Gizmos online program, skills analysis

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Authors: Elisabeth Unterfrauner, Christian Voigt

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Since the foundation of the first Fab Lab by the Massachusetts Institute of Technology about 17 years ago, the Maker movement has spread globally with the foundation of maker spaces and Fab Labs worldwide. In these workshops, citizens have access to digital fabrication technologies such as 3D printers and laser cutters to develop and test their own ideas and prototypes, which makes it an attractive place for start-up companies. Know-How is shared not only in the physical space but also online in diverse communities. According to the Horizon report, the Maker movement, however, will also have an impact on educational settings in the following years. The European project ‘DOIT - Entrepreneurial skills for young social innovators in an open digital world’ has incorporated key elements of making to develop an early entrepreneurial education program for children between the age of six and 16. The Maker pedagogy builds on constructive learning approaches, learning by doing principles, learning in collaborative and interdisciplinary teams and learning through trial and error where mistakes are acknowledged as learning opportunities. The DOIT program consists of seven consecutive elements. It starts with a motivation phase where students get motivated by envisioning the scope of their possibilities. The second step is about Co-design: Students are asked to collect and select potential ideas for innovations. In the Co-creation phase students gather in teams and develop first prototypes of their ideas. In the iteration phase, the prototype is continuously improved and in the next step, in the reflection phase, feedback on the prototypes is exchanged between the teams. In the last two steps, scaling and reaching out, the robustness of the prototype is tested with a bigger group of users outside of the educational setting and finally students will share their projects with a wider public. The DOIT program involves 1,000 children in two pilot phases at 11 pilot sites in ten different European countries. The comprehensive evaluation design is based on a mixed method approach with a theoretical backbone on Lackeus’ model of entrepreneurship education, which distinguishes between entrepreneurial attitudes, entrepreneurial skills and entrepreneurial knowledge. A pre-post-test with quantitative measures as well as qualitative data from interviews with facilitators, students and workshop protocols will reveal the effectiveness of the program. The evaluation results will be presented at the conference.

Keywords: early entrepreneurial education, Fab Lab, maker education, Maker movement

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Authors: Shveta Puri, Shiv Jyoti Pandey, G. M. Bhat, Neha Raina

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TheJammu and Kashmir region of the Northwest Himalaya had witnessed many devastating earthquakes in the recent past and has remained unexplored for any kind of seismic investigations except scanty records of the earthquakes that occurred in this region in the past. In this study, we have used local seismic data of year 2013 that was recorded by the network of Broadband Seismographs in J&K. During this period, our seismic stations recorded about 207 earthquakes including two moderate events of Mw 5.7 on 1st May, 2013 and Mw 5.1 of 2nd August, 2013.We analyzed the events of Mw 3-4.6 and the main events only (for minimizing the error) for source parameters, b value and sense of movement through waveform modeling for understanding seismotectonic and seismic hazard of the region. It has been observed that most of the events are bounded between 32.9° N – 33.3° N latitude and 75.4° E – 76.1° E longitudes, Moment Magnitude (Mw) ranges from Mw 3 to 5.7, Source radius (r), from 0.21 to 3.5 km, stress drop, from 1.90 bars to 71.1 bars and Corner frequency, from 0.39 – 6.06 Hz. The b-value for this region was found to be 0.83±0 from these events which are lower than the normal value (b=1), indicating the area is under high stress. The travel time inversion and waveform inversion method suggest focal depth up to 10 km probably above the detachment depth of the Himalayan region. Moment tensor solution of the (Mw 5.1, 02:32:47 UTC) main event of 2ndAugust suggested that the source fault is striking at 295° with dip of 33° and rake value of 85°. It was found that these events form intense clustering of small to moderate events within a narrow zone between Panjal Thrust and Kishtwar Window. Moment tensor solution of the main events and their aftershocks indicating thrust type of movement is occurring in this region.

Keywords: b-value, moment tensor, seismotectonics, source parameters

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Authors: Kourosh Modarresi

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The abundance of media channels and devices has given users a variety of options to extract, discover, and explore information in the digital world. Since, often, there is a long and complicated path that a typical user may venture before taking any (significant) action (such as purchasing goods and services), it is critical to know how each node (media channel) in the path of user has contributed to the final action. In this work, the significance of each media channel is computed using statistical analysis and machine learning techniques. More specifically, “Regularized Singular Value Decomposition”, and “Sparse Principal Component” has been used to compute the significance of each channel toward the final action. The results of this work are a considerable improvement compared to the present approaches.

Keywords: multimedia attribution, sparse principal component, regularization, singular value decomposition, feature significance, machine learning, linear systems, variable shrinkage

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Authors: Mohammed Alhammad

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The movement towards the inclusion of students with special educational needs (SEN) in mainstream schools has become widely accepted practice in many countries. However in Saudi Arabia, this is not happening. Instead the practice for students with learning difficulties (LD) is to study in special classrooms in mainstream schools and they are not included with their peers, except at break times and morning assembly, and on school trips. There are a number of barriers that face implementing inclusion for students with LD in mainstream classrooms: one such barrier is the training of teachers. The training, either pre- or in-service, that teachers receive is seen as playing an important role in leading to the successful implementation of inclusion. The aim of this presentation is to explore how pre-service training and in-service training are acting as barriers for implementing inclusion of students with LD in mainstream primary schools in Saudi Arabia from the perspective of teachers. The qualitative research approach was used to explore this barrier. Twenty-four teachers (general education teachers, special education teachers) were interviewed using semi-structured interview and a number of documents were used as method of data collection. The result showed teachers felt that not much attention was paid to inclusion in pre-services training for general education teachers and special education teachers in Saudi Arabia. In addition, pre-service training for general education teachers does not normally including modules on special education. Regarding the in-service training, no courses at all about inclusion are provided for teachers. Furthermore, training courses in special education are few. As result, the knowledge and skills required to implemented inclusion successfully.

Keywords: inclusion, learning difficulties, Saudi Arabia, training

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Authors: B. Khatri, K. Lappe, M. Habedank, T. Müller, C. Megnin, T. Hanemann

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We present a process flow for the development of ceramic-filled polymer composite filaments compatible with the fused deposition modeling (FDM) 3D printing process. Thermoplastic-ceramic composites were developed using acrylonitrile butadiene styrene (ABS) and 10- and 20 vol.% barium titanate (BaTiO3) powder (corresponding to 39.47- and 58.23 wt.% respectively) and characterized for their flow properties. To make them compatible with the existing FDM process, the composites were extruded into filaments. These composite filaments were subsequently structured into tensile stress specimens using a commercially available FDM 3D printer and characterized for their mechanical properties. Rheometric characterization of the material composites revealed non-Newtonian behavior with the viscosity logarithmically decreasing over increasing shear rates, as well as higher viscosities for samples with higher BaTiO3 filler content for a given shear rate (with the ABS+20vol.% BaTiO3 composite being over 50% more viscous compared to pure ABS at a shear rate of 1x〖10〗^3 s^(-1)). Mechanical characterization of the tensile stress specimens exhibited increasingly brittle behavior as well as a linearly decreasing ultimate tensile strength of the material composites with increasing volumetric ratio of BaTiO3 (from σ_max=32.4MPa for pure ABS to σ_max=21.3MPa for ABS+20vol.% BaTiO3). Further studies being undertaken include the development of composites with higher filler concentrations, sintering of the printed composites to yield pure dielectric structures and the determination of the dielectric characteristics of the composites.

Keywords: ceramic composites, fused deposition modeling, material characterization, rapid prototyping

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Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

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Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

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Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

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Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Nuzhat Sharmin

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Human being is the most integral part of the nature and responsible for maintaining ecological balance both in rural and urban areas. But unfortunately, we are not doing our job with a holistic approach. The rapid growth of urbanization is making human life more isolated from greenery. Nowadays modern urban living involves sensory deprivation and overloaded stress. In many cities and towns of the world are expanding unabated in the name of urbanization and industrialization and in fact becoming jungles of concrete. Dhaka is one of the examples of such cities where open and green spaces are decreasing because of accommodating the overflow of population. This review paper has been prepared based on interviewing 30 teenagers, both male and female in Dhaka city. There were 12 open-ended questions in the questionnaire. For the literature review information had been gathered from scholarly papers published in various peer-reviewed journals. Some information was collected from the newspapers and some from fellow colleagues working around the world. Ideally about 25% of an urban area should be kept open or with parks, fields and/or plants and vegetation. But currently Dhaka has only about 10-12% open space and these also are being filled up rapidly. Old Dhaka has only about 5% open space while the new Dhaka has about 12%. Dhaka is now one of the most populated cities in the world. Accommodating this huge influx of people Dhaka is continuously losing its open space. As a result, children and teenagers are losing their interest in playing games and making friends, rather they are mostly occupied by television, gadgets and social media. It has been known from the interview that only 28% of teenagers regularly play. But the majority of them have to play on the street and rooftop for the lack of open space. On an average they are occupied with electronic devices for 8.3 hours/day. 64% of them has chronic diseases and often visit doctors. Most shockingly 35% of them claimed for not having any friends. Green space offers relief from stress. Areas of natural environment in towns and cities are theoretically seen providing setting for recovery and recuperation from anxiety and strains of the urban environment. Good quality green spaces encourage people to walk, run, cycle and play. Green spaces improve air quality and reduce noise, while trees and shrubbery help to filter out dust and pollutants. Relaxation, contemplation and passive recreation are essential to stress management. All city governments that are losing its open spaces should immediately pay attention to this aesthetic issue for the benefit of urban people. All kinds of development must be sustainable both for human being and nature.

Keywords: greenery, health, human, urban

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Authors: Cristina Costa-Lobo, Sandra Fernandes, Miguel Magalhães, José Dinis-Carvalho, Alfredo Regueiro, Ana Carvalho

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This paper is a report on the findings of the construction and the validation of a questionnaire monetized in a portuguese higher education context with undergraduate students. The Questionnaire for the Evaluation of Entrepreneurship Project Psychopedagogical Practices consists of six scales: Critical appraisal of the project, Developed Learning and Skills, Teamwork, Teacher and Tutor Roles, Evaluation of Student Performance, and Project Effectiveness as a Teaching-Learning Methodology. The proceedings of its construction are analyzed, and the validity and internal consistency analysis are described. Findings indicate good indicators of validity, good fidelity and an interpretable factorial structure.

Keywords: entrepreneurship project, higher education, psychopedagogical practices, teacher and tutor roles

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Authors: Michael J. Haslip

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Character strength application improves performance and well-being in adults across industries, but the potential impact of character strength training among early childhood educators is mostly unknown. To explore how character strengths are applied by early childhood educators at work, a qualitative study was completed alongside professional development provided to a group of in-service teachers of children ages 0-5 in Philadelphia, Pennsylvania, United States. Study participants (n=17) were all female. The majority of participants were non-white, in full-time lead or assistant teacher roles, had at least ten years of experience and a bachelor’s degree. Teachers were attending professional development weekly for 2 hours over a 10-week period on the topic of social and emotional learning and child guidance. Related to this training were modules and sessions on identifying a teacher’s character strength profile using the Values in Action classification of 24 strengths (e.g., humility, perseverance) that have a scientific basis. Teachers were then asked to apply their character strengths to help resolve current workplace challenges. This study identifies which character strengths the teachers reported using most frequently and the nature of the workplace challenges being resolved in this context. The study also reports how difficult these challenges were to the teachers and their success rate at resolving workplace challenges using a character strength application plan. The study also documents how teachers’ own use of character strengths relates to their modeling of these same traits (e.g., kindness, teamwork) for children, especially when the nature of the workplace challenge directly involves the children, such as when addressing issues of classroom management and behavior. Data were collected on action plans (reflective templates) which teachers wrote to explain the work challenge they were facing, the character strengths they used to address the challenge, their plan for applying strengths to the challenge, and subsequent results. Content analysis and thematic analysis were used to investigate the research questions using approaches that included classifying, connecting, describing, and interpreting data reported by educators. Findings reveal that teachers most frequently use kindness, leadership, fairness, hope, and love to address a range of workplace challenges, ranging from low to high difficulty, involving children, coworkers, parents, and for self-management. Teachers reported a 71% success rate at fully or mostly resolving workplace challenges using the action plan method introduced during professional development. Teachers matched character strengths to challenges in different ways, with certain strengths being used mostly when the challenge involved children (love, forgiveness), others mostly with adults (bravery, teamwork), and others universally (leadership, kindness). Furthermore, teacher’s application of character strengths at work involved directly modeling character for children in 31% of reported cases. The application of character strengths among early childhood educators may play a significant role in improving teacher well-being, reducing job stress, and improving efforts to model character for young children.

Keywords: character strengths, positive psychology, professional development, social-emotional learning

Procedia PDF Downloads 80