Search results for: adopt a culture of continuous learning

Authors: Hediye Saglam

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This research has been carried out to bring in the 6 acquisitions in the 2014 Preschool Teaching Programme of the Turkish Ministry of Education through the method of analogy. This research is practiced based on the experimental pattern with pre-test and final test controlling groups. The working group of the study covers the group between 5-6 ages. The study takes 5 weeks including the 2 weeks spent for pre-test and the final test. It is conducted with the preschool teacher who gives the lesson along with the researcher in the in-class and out-of-class rainbow experiments of the students for 5 weeks. 'One Sample T Test' is used for the evaluation of the pre-test and final test. SPSS 17 programme is applied for the analysis of the data. Results: As an outcome of the study it is observed that analogy method affects children’s learning of the rainbow. For this very reason teachers should receive inservice training for different methods and techniques like analogy. This method should be included in preschool education programme and should be applied by teachers more often.

Keywords: acquisitions of preschool education programme, analogy method, pre-test/final test, rainbow experiments

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Authors: Venus Torabi

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ISIS, the terrorist organization, modified two videogames, ARMA III and Grand Theft Auto 5 (2013) as means of online recruitment and ideological propaganda. The urge to study the mechanism at work, whether it has been successful or not, derives (Digital) Humanities experts to explore how codes of terror, Islamic ideology and recruitment strategies are incorporated into the ludic mechanics of videogames. Another aspect of the significance lies in the fact that this is a latent problem that has not been fully addressed in an interdisciplinary framework prior to this study, to the best of the researcher’s knowledge. Therefore, due to the complexity of the subject, the present paper entangles with game studies, philosophical and religious poles to form the methodology of conducting the research. As a contextualized epistemology of such exploitation of videogames, the core argument is building on the notion of “Culture Industry” proposed by Theodore W. Adorno and Max Horkheimer in Dialectic of Enlightenment (2002). This article posits that the ideological underpinnings of ISIS’s cause corroborated by the action-bound mechanics of the videogames are in line with adhering to the Islamic Eschatology as a furnishing ground and an excuse in exercising terrorism. It is an account of ISIS’s modification of the videogames, a tool of technological progression to practice online radicalization. Dialectically, this practice is packed up in rhetoric for recognizing a religious myth (the advent of a savior), as a hallmark of regression. The study puts forth that ISIS’s wreaking havoc on the world, both in reality and within action videogames, is negotiating the process of self-assertion in the players of such videogames (by assuming one’s self a member of terrorists) that leads to self-annihilation. It tries to unfold how ludic Mod videogames are misused as tools of mass deception towards ethnic cleansing in reality and line with the distorted Eschatological myth. To conclude, this study posits videogames to be a new avenue of mass deception in the framework of the Culture Industry. Yet, this emerges as a two-edged sword of mass deception in ISIS’s modification of videogames. It shows that ISIS is not only trying to hijack the minds through online/ludic recruitment, it potentially deceives the Muslim communities or those prone to radicalization into believing that it's terrorist practices are preparing the world for the advent of a religious savior based on Islamic Eschatology. This is to claim that the harsh actions of the videogames are potentially breeding minds by seeds of terrorist propaganda and numbing them to violence. The real world becomes an extension of that harsh virtual environment in a ludic/actual continuum, the extension that is contributing to the mass deception mechanism of the terrorists, in a clandestine trend.

Keywords: culture industry, dialectic, ISIS, islamic eschatology, mass deception, video games

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Authors: Piedad Garrido, Fernando Repulles, Andy Bloor, Julio A. Sanguesa, Jesus Gallardo, Vicente Torres, Jesus Tramullas

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With the emergence and development of Information and Communications Technologies (ICTs), Higher Education is experiencing rapid changes, not only in its teaching strategies but also in student’s learning skills. However, we have noticed that students often have difficulty when seeking innovative, useful, and interesting learning resources for their work. This is due to the lack of supervision in the selection of good query tools. This paper presents AINA, an Information Retrieval (IR) computer system aimed at providing motivating and stimulating content to both students and teachers working on different areas and at different educational levels. In particular, our proposal consists of an open virtual resource environment oriented to the vast universe of Disney comics and cartoons. Our test suite includes Disney’s long and shorts films, and we have performed some activities based on the Just In Time Teaching (JiTT) methodology. More specifically, it has been tested by groups of university and secondary school students.

Keywords: information retrieval, animation, educational resources, JiTT

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Authors: Ioanna Taouki, Marie Lallier, David Soto

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Metacognition refers to the capacity to reflect upon our own cognitive processes. Although there is an ongoing discussion in the literature on the role of metacognition in learning and academic achievement, little is known about its neurodevelopmental trajectories in early childhood, when children begin to receive formal education in reading. Here, we evaluate the metacognitive ability, estimated under a recently developed Signal Detection Theory model, of a cohort of children aged between 6 and 7 (N=60), who performed three two-alternative-forced-choice tasks (two linguistic: lexical decision task, visual attention span task, and one non-linguistic: emotion recognition task) including trial-by-trial confidence judgements. Our study has three aims. First, we investigated how metacognitive ability (i.e., how confidence ratings track accuracy in the task) relates to performance in general standardized tasks related to students' reading and general cognitive abilities using Spearman's and Bayesian correlation analysis. Second, we assessed whether or not young children recruit common mechanisms supporting metacognition across the different task domains or whether there is evidence for domain-specific metacognition at this early stage of development. This was done by examining correlations in metacognitive measures across different task domains and evaluating cross-task covariance by applying a hierarchical Bayesian model. Third, using robust linear regression and Bayesian regression models, we assessed whether metacognitive ability in this early stage is related to the longitudinal learning of children in a linguistic and a non-linguistic task. Notably, we did not observe any association between students’ reading skills and metacognitive processing in this early stage of reading acquisition. Some evidence consistent with domain-general metacognition was found, with significant positive correlations between metacognitive efficiency between lexical and emotion recognition tasks and substantial covariance indicated by the Bayesian model. However, no reliable correlations were found between metacognitive performance in the visual attention span and the remaining tasks. Remarkably, metacognitive ability significantly predicted children's learning in linguistic and non-linguistic domains a year later. These results suggest that metacognitive skill may be dissociated to some extent from general (i.e., language and attention) abilities and further stress the importance of creating educational programs that foster students’ metacognitive ability as a tool for long term learning. More research is crucial to understand whether these programs can enhance metacognitive ability as a transferable skill across distinct domains or whether unique domains should be targeted separately.

Keywords: confidence ratings, development, metacognitive efficiency, reading acquisition

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Authors: Min-Tao Hsu

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This study aims to explore the lived experiences of women with breast cancer, including their life world of illness and their adaptation to breast cancer. Breast cancer is not only a potentially lethal disease, but also a disease that may lead to many irreversible changes for female patients. Especially, in a culture where the wholeness is pursuit as an essential value, the sickness and/or broken body bring great challenge of life. Based on holism and symbolic interactionism, this study used interpretive ethnography including in-depth interviews and participant observations to collect the narrative of women with breast cancer concerning their illness experience. In addition, this study used Agar’s hermeneutic cycle to analyze data. The average age of 35 participants was 54.2. A total of 15 patients were within 2 years of onset, 5 patients were within 2-5 years of the treatment observation period, and 15 patients suffered from breast cancer for more than 5 years. The average age of onset was 50.4. Result: The main storyline of the life world of illness is ‘breast cancer is a turning point of life.’ Loss of breast was in terms of ‘no more a woman’ in Taiwanese culture. Two young women, one in her newly wedded and another right before marry, were divorced and cancelled wedding right after being diagnosed. All of them addressed that they have a ‘broken body.’ Single women accounted that they won’t marry for not being humiliated and most of married women said they never show female body in front of her husband or partner even in intimacy encounter. Three common themes were discovered: 1) new self and new identity; 2) new social relationships and new me; 3) new body and new life. The intertwining bodies, illness, selves, suffering, and medical treatments of female patients were observed. More, the recovery, of cause, was happened when new self, relationship, and new body were generated. Their identity to be a woman and a wife is shattered and their life is urged into another facet. For helping them to recovery from such situation, building a new identity and new social fabric on the new body need to be included in nursing care plan.

Keywords: breast cancer, illness narrative, world of illness, self-healing, interpretive ethnography

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Authors: Shima Nikanjam, Badiossadat Hassanpour, Adi Irfan Che Ani

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The design process in architecture education is based upon the Learning-by-Doing method, which leads students to understand how to design by practicing rather than studying. First-year design studios, as starting educational stage, provide integrated knowledge and skills of design for newly jointed architecture students. Within the basic design studio environment, students are guided to transfer their abstract thoughts into visual concrete decisions under the supervision of design educators for the first time. Therefore, introductory design studios have predominant impacts on students’ operational thinking and designing. Architectural design thinking is quite different from students’ educational backgrounds and learning habits. This educational challenge at basic design studios creates a severe need to study the reality of design education at foundation year and define appropriate educational methods with convenient project types with the intention of enhancing architecture education quality. Material for this study has been gathered through long-term direct observation at a first year second semester design studio at the faculty of architecture at EMU (known as FARC 102), fall and spring academic semester 2014-15. Distribution of a questionnaire among case study students and interviews with third and fourth design studio students who passed through the same methods of education in the past 2 years and conducting interviews with instructors are other methodologies used in this research. The results of this study reveal a risk of a mismatch between the implemented teaching method, project type and scale in this particular level and students’ learning styles. Although the existence of such risk due to varieties in students’ profiles could be expected to some extent, recommendations can support educators to reach maximum compatibility.

Keywords: architecture education, basic design studio, educational method, forms creation skill

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Authors: Tina Sleiman

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In the early 1970s, a graphics revolution - in quantity and quality - marked the youth publications sector in France. The increased interest in youth publications was supported with the emergence of youth libraries and major publishing houses. In parallel, the 'Agence de Cooperation Culturelle et Technique' (currently the International Organization of the Francophonie) was created, and several Arab countries had joined as members. In spite of political turmoil in the Middle East, French schools in Arab countries were still functioning and some even flourishing. This is a testament that French culture was, and still is, a major export to the region. This study focuses on the aesthetic value of the graphic styles that characterize French children’s books from the 1970s, and their personal value to Francophone people who have consumed these artifacts, in the Middle East. The first part of the study looks at the artifact itself: starting from the context of creation and consumption of these books, and continuing to the preservation and remaining collections. The aesthetic value is studied and compared to similar types of visuals of juxtaposed time periods. The second part examines the audience’s response to the visuals in terms of style recognition or identification, along with emotional significance or associations, and the personal value the artifacts might hold to their consumers. The methods of investigation consist of a literature review, a survey of book collections, and a visual questionnaire, supported by personal interviews. As an outcome, visual patterns will be identified: elements from 1970s children’s books reborn in contemporary youth-based publications. Results of the study shall inform us directly on the aesthetic and personal value of illustrated French children’s books in the Middle East, and indirectly on the capacity of youth-targeted design to create a long-term emotional response from its audience.

Keywords: children’s books, French visual culture, graphic style, publication design, revival

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Authors: Sebastian Del Corral Winder, Kiriana Sanchez, Mixalis Poulakis, Samantha Gray

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This paper seeks to explore White privilege and Whiteness. Being White in the U.S. is often perceived as the norm and it brings significant social, economic, educational, and health privileges that often are hidden in social interactions. One quality of Whiteness has been its invisibility given its intrinsic impact on the system, which becomes only visible when paying close attention to White identity and culture and during cross-cultural interactions. The cross-cultural interaction provides an emphasis on differences between the participants and people of color are often viewed as “the other.” These interactions may promote an increased opportunity for discrimination and negative stereotypes against a person of color. Given the recent increase of violence against culturally diverse groups, there has been an increased sense of otherness and division in the country. Furthermore, the accent prestige theory has found that individuals who speak English with a foreign accent are perceived as less educated, competent, friendly, and trustworthy by White individuals in the United States. Using the consensual qualitative research (CQR) methodology, this study explored the cross-cultural dyad from the White individual’s perspective focusing on the psychotherapeutic relationship. The participants were presented with an audio recording of a conversation between a psychotherapist with a Hispanic accent and a patient with an American English accent. Then, the participants completed an interview regarding their perceptions of race, culture, and cross-cultural interactions. The preliminary results suggested that the Hispanic accent alone was enough for the participants to assign stereotypical ethnic and cultural characteristics to the individual with the Hispanic accent. Given the quality of the responses, the authors completed a secondary analysis to explore Whiteness and White privilege in more depth. Participants were found to be on a continuum in their understanding and acknowledgment of systemic racism; while some participants listed examples of inequality, other participants noted: “all people are treated equally.” Most participants noted their feelings of discomfort in discussing topics of cultural diversity and systemic racism by fearing to “say the ‘wrong thing.” Most participants placed the responsibility of discussing cultural differences with the person of color, which has been observed to create further alienation and otherness for culturally diverse individuals. The results indicate the importance of examining racial and cultural biases from White individuals to promote an anti-racist stance. The results emphasize the need for greater systemic changes in education, policies, and individual awareness regarding cultural identity. The results suggest the importance for White individuals to take ownership of their own cultural biases in order to promote equity and engage in cultural humility in a multicultural world. Future research should continue exploring the role of White ethnic identity and education as they appear to moderate White individuals’ attitudes and beliefs regarding other races and cultures.

Keywords: culture, qualitative research, whiteness, white privilege

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Authors: Zakaria Kharbouch, Mustapha Bouchaara, F. Elkouihen, A. Habbal, A. Ratnani, A. Faik

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Solid-state lithium-ion batteries have garnered increasing interest in modern energy research due to their potential for safer, more efficient, and sustainable energy storage systems. Among the critical components of these batteries, the electrolyte plays a pivotal role, with LLZO garnet-based electrolytes showing significant promise. Garnet materials offer intrinsic advantages such as high Li-ion conductivity, wide electrochemical stability, and excellent compatibility with lithium metal anodes. However, optimizing ionic conductivity in garnet structures poses a complex challenge, primarily due to the multitude of potential dopants that can be incorporated into the LLZO crystal lattice. The complexity of material design, influenced by numerous dopant options, requires a systematic method to find the most effective combinations. This study highlights the utility of machine learning (ML) techniques in the materials discovery process to navigate the complex range of factors in garnet-based electrolytes. Collaborators from the materials science and ML fields worked with a comprehensive dataset previously employed in a similar study and collected from various literature sources. This dataset served as the foundation for an extensive data refinement phase, where meticulous error identification, correction, outlier removal, and garnet-specific feature engineering were conducted. This rigorous process substantially improved the dataset's quality, ensuring it accurately captured the underlying physical and chemical principles governing garnet ionic conductivity. The data refinement effort resulted in a significant improvement in the predictive performance of the machine learning model. Originally starting at an accuracy of 0.32, the model underwent substantial refinement, ultimately achieving an accuracy of 0.88. This enhancement highlights the effectiveness of the interdisciplinary approach and underscores the substantial potential of machine learning techniques in materials science research.

Keywords: lithium batteries, all-solid-state batteries, machine learning, solid state electrolytes

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Authors: Norihiro Kato, Yuriko Takayama

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Some gram-negative bacteria enable the simultaneous activation of gene expression involved in N-acylhomoserine lactone (AHL) dependent cell-to-cell communication system. Such regulatory system for the bacterial group behavior is termed as quorum sensing (QS) because a diffusible AHL signal can accumulate around the cell during the increase of the cell density and trigger activation of the sequential QS process. By blocking the QS, the expression of diverse genes related to infection, antibiotic production, and biofilm formation is inhibited. Conditioning of QS by regulation of the DNA-receptor-AHL interaction is a potential target for enhancing host defenses against pathogenicity. We focused on engineered application of transcriptional regulator SpnR produced in opportunistic human pathogen Serratia marcescens. The SpnR can interact with AHL signals at an N-terminal domain and also with a promoter region of a QS target gene at a C-terminal domain. As the initial process of the QS activation, the SpnR forms a complex with the AHL to enhance the expression of pig cluster; the SpnR normally acts as an activator for the expression of the QS-dependent gene. In this research, we attempt to artificially control QS by changing the role of SpnR. The QS-dependent prodigiosin production is expected to inhibit by externally added SpnR in the culture broth of AS-1 strain because the AHL concentration was kept below the threshold by AHL-SpnR complex formation. Maltose-binding protein (MBP)-tagged SpnR (MBP-SpnR) was overexpressed in Escherichia coli and purified using an affinity chromatography equipped with an amylose resin column. The specific interaction between AHL and MBP-SpnR was demonstrated by quartz crystal microbalance (QCM) sensor. AHL with amino end-group was coupled with COOH-terminated self-assembled monolayer prepared on a gold electrode of 27-MHz quartz crystal sensor using water-soluble carbodiimide. After the injection of MBP-SpnR into a cup-type sensor cell filled with the buffer solution, time course of resonant frequency change (ΔFs) was determined. A decrease of ΔFs clearly showed the uptake of MBP-SpnR onto the AHL-immobilized electrode. Furthermore, no binding affinity was observed after the heat-inactivation of MBP-SpnR at 80ºC. These results suggest that MBP-SpnR possesses a specific affinity for AHL. MBP-SpnR was added to the culture medium as an AHL trap to study inhibitory effects on intracellularly accumulated prodigiosin. With approximately 2 µM MBP-SpnR, the amount of prodigiosin induced was half that of the control without any additives. In conclusion, the function of SpnR could be switched by adding it to the cell culture. Exogenously added MBP-SpnR possesses high affinity for AHL derived from cells and acts as an inhibitor of AHL-mediated QS.

Keywords: intracellular signaling, microbial biotechnology, quorum sensing, transcriptional regulator

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Authors: Tuaumelsan Shumye Gebre, Shimelis Admassu Emire, Simon Okomo Aloo, Ramachandran Chelliah, Deog-Hwan Oh

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This study investigates the fermentation of cereal substrates, based on the Ethiopian traditional beverage borde, using probiotic strains of Pediococcus acidilactici WS07 and Saccharomyces cerevisiae AM18 used singly and in co-culture. The pH and titratable acidity, microbial growth dynamics, fermentable sugars profile, volatile organic compounds, total flavonoid content, total phenolic content, antioxidant activity, pancreatic lipase, and α-glucosidase inhibition were analyzed. The viability of every tested strain remained higher than 7 log CFU/mL, satisfying the requirements suggested for probiotic food items. The formation of organic acids is what caused the pH to decrease from roughly 6.6 to 3.8, yet this had no effect on the viability of the microorganisms. The fermentation process, involving P. acidilactici WS07 and S. cerevisiae AM18, led to the utilization of initial carbohydrates, production of organic acids, and generation of volatile compounds that enhance flavor and aroma. Ethanol and glycerol concentrations increased during fermentation, particularly in co-culture assays, contributing to the sensory qualities and stability of the beverages. The primary organic acids generated during fermentation were lactic and acetic acids. A total of 22 volatile substances, such as acids, alcohols, aldehydes, esters, ketones, and other substances, were found. Furthermore, the study demonstrates that fermentation of maize and sorghum with P. acidilactici WS07 and S. cerevisiae AM18 enhances the antioxidant activity and inhibition of pancreatic lipase and α-glucosidase, suggesting potential benefits in managing obesity and diabetes. Therefore, co-cultivating S. cerevisiae AM18 and P. acidilactici WS07 in cereal fermentation led to the successful production of probiotic drinks.

Keywords: probiotic beverage, Pediococcus acidilactici, Saccharomyces cerevisiae, volatile compounds

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Authors: C. Zimmermann, E. Lackner, M. Ebner

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Massive Open Online Courses (MOOCs) are increasingly popular learning hubs that are boasting considerable participant numbers, innovative technical features, and a multitude of instructional resources. Still, there is a high level of evidence showing that almost all MOOCs suffer from a declining frequency of participant activity and fairly low completion rates. In this paper, we would like to share the lessons learned in implementing several design patterns that have been suggested in order to foster participant activity. Our conclusions are based on experiences with the ‘Dr. Internet’ MOOC, which was created as an xMOOC to raise awareness for a more critical approach to online health information: participants had to diagnose medical case studies. There is a growing body of recommendations (based on Learning Analytics results from earlier xMOOCs) as to how the decline in participant activity can be alleviated. One promising focus in this regard is instructional design patterns, since they have a tremendous influence on the learner’s motivation, which in turn is a crucial trigger of learning processes. Since Medieval Age storytelling, micro-learning units and specific comprehensible, narrative structures were chosen to animate the audience to follow narration. Hence, MOOC participants are not likely to abandon a course or information channel when their curiosity is kept at a continuously high level. Critical aspects that warrant consideration in this regard include shorter course duration, a narrative structure with suspense peaks (according to the ‘storytelling’ approach), and a course schedule that is diversified and stimulating, yet easy to follow. All of these criteria have been observed within the design of the Dr. Internet MOOC: 1) the standard eight week course duration was shortened down to six weeks, 2) all six case studies had a special quiz format and a corresponding resolution video which was made available in the subsequent week, 3) two out of six case studies were split up in serial video sequences to be presented over the span of two weeks, and 4) the videos were generally scheduled in a less predictable sequence. However, the statistical results from the first run of the MOOC do not indicate any strong influences on the retention rate, so we conclude with some suggestions as to why this might be and what aspects need further consideration.

Keywords: case study, Dr. internet, experience, MOOCs, design patterns

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

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Authors: P. Mounnarath, U. Schmitz, Ch. Zhang

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Fragility analysis is an effective tool for the seismic vulnerability assessment of civil structures in the last several years. The design of the expansion joints according to various bridge design codes is almost inconsistent, and only a few studies have focused on this problem so far. In this study, the influence of the expansion joint clearances between the girder ends and the abutment backwalls on the seismic fragility assessment of continuous integral bridge frames is investigated. The gaps (ranging from 60 mm, 150 mm, 250 mm and 350 mm) are designed by following two different bridge design code specifications, namely, Caltrans and Eurocode 8-2. Five bridge models are analyzed and compared. The first bridge model serves as a reference. This model uses three-dimensional reinforced concrete fiber beam-column elements with simplified supports at both ends of the girder. The other four models also employ reinforced concrete fiber beam-column elements but include the abutment backfill stiffness and four different gap values. The nonlinear time history analysis is performed. The artificial ground motion sets, which have the peak ground accelerations (PGAs) ranging from 0.1 g to 1.0 g with an increment of 0.05 g, are taken as input. The soil-structure interaction and the P-Δ effects are also included in the analysis. The component fragility curves in terms of the curvature ductility demand to the capacity ratio of the piers and the displacement demand to the capacity ratio of the abutment sliding bearings are established and compared. The system fragility curves are then obtained by combining the component fragility curves. Our results show that in the component fragility analysis, the reference bridge model exhibits a severe vulnerability compared to that of other sophisticated bridge models for all damage states. In the system fragility analysis, the reference curves illustrate a smaller damage probability in the earlier PGA ranges for the first three damage states, they then show a higher fragility compared to other curves in the larger PGA levels. In the fourth damage state, the reference curve has the smallest vulnerability. In both the component and the system fragility analysis, the same trend is found that the bridge models with smaller clearances exhibit a smaller fragility compared to that with larger openings. However, the bridge model with a maximum clearance still induces a minimum pounding force effect.

Keywords: expansion joint clearance, fiber beam-column element, fragility assessment, time history analysis

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Authors: Toby Li, Julian Zhu

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In this paper, a model based on a simplified geometry is introduced to give a very conservative collision probability prediction for the Starlink satellite in its most densely clustered region. Under the model in this paper, the probability of collision for Starlink satellite where it clustered most densely is found to be 8.484 ∗ 10^−4. It is found that the predicted collision probability increased nonlinearly with the increased safety distance set. This simple model provides evidence that the continuous development of maneuver avoidance systems is necessary for the future of the orbital safety of satellites under the harsher Lower Earth Orbit environment.

Keywords: Starlink, collision probability, debris, geometry model

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Authors: Claudia Martínez-Araneda, Mariella Gutiérrez, Pedro Gómez, Diego Maldonado, Alejandra Segura, Christian Vidal-Castro

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The purpose of this document is to showcase the preliminary work in developing an EduChatbot-type tool and measuring the effects of its use aimed at providing effective feedback to students in programming courses. This bot, hereinafter referred to as tutorBot+, was constructed based on chatGPT and is tasked with assisting and delivering timely positive feedback to students in the field of computer science at the Universidad Católica de Concepción. The proposed working method consists of four stages: (1) Immersion in the domain of Large Language Models (LLMs), (2) Development of the tutorBot+ prototype and integration, (3) Experiment design, and (4) Intervention. The first stage involves a literature review on the use of artificial intelligence in education and the evaluation of intelligent tutors, as well as research on types of feedback for learning and the domain of chatGPT. The second stage encompasses the development of tutorBot+, and the final stage involves a quasi-experimental study with students from the Programming and Database labs, where the learning outcome involves the development of computational thinking skills, enabling the use and measurement of the tool's effects. The preliminary results of this work are promising, as a functional chatBot prototype has been developed in both conversational and non-conversational versions integrated into an open-source online judge and programming contest platform system. There is also an exploration of the possibility of generating a custom model based on a pre-trained one tailored to the domain of programming. This includes the integration of the created tool and the design of the experiment to measure its utility.

Keywords: assessment, chatGPT, learning strategies, LLMs, timely feedback

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Clare Flood, Shareen Forbes

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Background: Type 1 diabetes mellitus (T1DM) is an autoimmune disorder affecting >400,000 people in the UK alone, with the global prevalence expected to double in the next decade. Islet transplant offers a minimally-invasive procedure with very low morbidity and almost no mortality, and is now as effective as whole pancreas transplant. The procedure was introduced to the UK in 2011 for patients with the most severe type 1 diabetes mellitus (T1DM) – those with unstable blood glucose, frequently occurring episodes of severe hypoglycemia and impaired awareness of hypoglycemia (IAH). Objectives: To evaluate the effectiveness of islet transplantation in improving glycemic control, reducing the burden of hypoglycemia and improving awareness of hypoglycemia through a single-centre cohort study at the Royal Infirmary of Edinburgh. Glycemic control and degree of hypoglycemic awareness will be determined and monitored pre- and post-transplantation to determine effectiveness of the procedure. Methods: A retrospective analysis of data collected over three years from the 16 patients who have undergone islet transplantation in Scotland. Glycated haemoglobin (HbA1c) was measured and continuous glucose monitoring systems (CGMS) were utilised to assess glycemic control, while Gold and Clarke score questionnaires tested IAH. Results: All patients had improved glycemic control following transplant, with optimal control seen visually at 3 months post-transplant. Glycemic control significantly improved, as illustrated by percentage time in hypoglycemia in the months following transplant (p=0.0211) and HbA1c (p=0.0426). Improved Clarke (p=0.0034) and Gold (p=0.0001) scores indicate improved glycemic awareness following transplant. Conclusion: While the small sample of islet transplant recipients at the Royal Infirmary of Edinburgh prevents definitive conclusions being drawn, it is indicated that through our retrospective, single-centre cohort study of 16 patients, islet transplant is capable of improving glycemic control, reducing the burden of hypoglycemia and IAH post-transplant. Data can be combined with similar trials at other centres to increase statistical power but from research in Edinburgh, it can be suggested that the minimally invasive procedure of islet transplantation offers selected patients with extremely unstable T1DM the incredible opportunity to regain control of their condition and improve their quality of life.

Keywords: diabetes, islet, transplant, CGMS

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Authors: Yaxin Bi

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Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Tracy Smith-Carrier

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Burgeoning research extols the benefits of innovative experiential learning activities to increase participants’ engagement, enhance their individual learning, and bridge the gap between theory and practice. This presentation discusses findings from a multiple case study on poverty simulation events conducted with two samples: undergraduate students and community participants. After exploring the nascent research on the benefits and limitations of poverty simulation activities, the study explores whether participating in a poverty simulation resulted in changes to participants’ beliefs about the causes and effects of poverty, as well as shifts in their attitudes and actions toward people experiencing poverty. For the purposes of triangulation, quantitative and qualitative data from a variety of sources were analyzed: participant feedback surveys, qualitative responses, and pre, post, and follow-up questionnaires. Findings show statistically significant results (p<.05) from both samples on cumulative scores of the modified Attitudes Toward Poverty Scale, indicating an improvement in participants’ attitudes toward poverty. Although generally positive about their experiences, participating in the simulation did not appear to have prompted participants to take specific actions to reduce poverty. Conclusions drawn from the research study suggest that poverty simulation planners should be wary of adopting scenarios that emphasize, or fail to adequately contextualize, behaviours or responses that might perpetuate individual explanations of poverty. Moreover, organizers must carefully consider how to ensure participants in their audience currently experiencing low-income do not become emotionally distressed, triggered or further marginalized in the process. While overall participants were positive about their experiences in the simulation, the events did not appear to have prompted them to action. Moving beyond the goal of increasing participants’ understandings of poverty, interventions that foster greater engagement in poverty issues over the long-term are necessary.

Keywords: empathy, experiential learning, poverty awareness, poverty simulation

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Authors: Václav Maněna, Roman Dostál, Štěpán Hubálovský

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Social networks play an important role in the lives of children and young people. Along with the high penetration of mobile technologies such as smartphones and tablets among the younger generation, there is an increasing use of social networks already in elementary school. The paper presents the results of research, which was realized at schools in the Hradec Králové region. In this research, the authors focused on issues related to communications on social networks for children, teenagers and young people in the Czech Republic. This research was conducted at selected elementary, secondary and high schools using anonymous questionnaires. The results are evaluated and compared with the results of the research, which has been realized in 2008. The authors focused on the possibilities of using social networks in education. The paper presents the possibility of using the most popular social networks in education, with emphasis on increasing motivation for learning. The paper presents comparative analysis of social networks, with regard to the possibility of using in education as well.

Keywords: social networks, motivation, e-learning, mobile technology

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Authors: Ani Hambardzumyan

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The influence of Greek culture and Greek mythology on the fashion industry is enormous. The first reason behind this is that Greek culture is one of the core elements to form the clothing tradition in Europe. French fashion houses have always been considered one of the leading cloth representatives in the world. As we could perceive in the first chapter, they are among the first ones to get inspired from Greek cultural heritage and apply it while creating their garments. The French fashion industry has kept traditional classical elements in clothes for decades. However, from the second half of the 20th century, this idea started to alter step by step. Society was transforming its vision with the influence of avant-garde movements. Hence, the fashion industry needed to transform its conception as well. However, it should be mentioned that fashion brands never stopped looking at the past when creating a new perspective or vision. Paradoxically, Greek mythology and clothing tradition continued to be applied even in the search of new ideas or new interpretations. In 1997 Alexander McQueen presents his first Haute Couture collection for French fashion house Givenchy, inspired by Greek mythology and titled ‘‘Search for The Golden Fleece.’’ Perhaps, this was one of the most controversial Haute Couture shows that French audience could expect to see and French media could capture and write about. The paper discuss Spring/Summer 1997 collection ‘‘The Search of Golden Fleece’’ by Alexander McQueen. It should be mentioned that there has not been yet conducted researches to analyze the mythological and archetypal nature of the collection, as well as general observations that go beyond traditional historical reviews are few in number. Here we will observe designer’s transformative new approach regarding Greek heritage and the media’s perception of it while collection was presented. On top of that, we will observe Alexander McQueen life in the parallel line with the fashion show since the collection is nothing else but the sublimation of his personal journey and drama.

Keywords: mythology, mcqueen, the argonaut, french fashion, golden fleece, givenchy

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Authors: Kirubasankar, Sanjeevkumar, Aravindh Nagappan

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This paper addresses a system for monitoring patients using biomedical sensors and displaying it in a remote place. The main challenges in present health monitoring devices are lack of remote monitoring and logging for future evaluation. Typical instruments used for health parameter measurement provide basic information regarding health status. This paper identifies a set of design principles to address these challenges. This system includes continuous measurement of health parameters such as Heart rate, electrocardiogram, SpO2 level and Body temperature. The accumulated sensor data is relayed to a processing device using a transceiver and viewed by the implementation of cloud services.

Keywords: bio-medical sensors, monitoring, logging, cloud service

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Gianni Jacucci

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Epistemology traditionally finds its place within human research philosophies and methodologies. Artificial intelligence methods pose challenges, particularly given the unresolved relationship between AI and pivotal concepts in social arenas such as hermeneutics and accountability. We begin by examining the essential criteria governing scientific rigor in the human sciences. We revisit the three foundational philosophies underpinning qualitative research methods: empiricism, hermeneutics, and phenomenology. We elucidate the distinct attributes, merits, and vulnerabilities inherent in the methodologies they inspire. The integration of AI, e.g., deep learning algorithms, sparks an interest in evaluating these criteria against the diverse forms of AI architectures. For instance, Interpreted AI could be viewed as a hermeneutic approach, relying on a priori interpretations, while straight AI may be perceived as a descriptive phenomenological approach, processing original and uncontaminated data. This paper serves as groundwork for such explorations, offering preliminary reflections to lay the foundation and outline the initial landscape.

Keywords: artificial intelligence, deep learning, epistemology, qualitative research, methodology, hermeneutics, accountability

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Authors: Sami I. Jafar, Sami A. Ajeel, Zaman A. Abdulwahab

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The turbine blades used in electric power plants are made of low alloy steel type 52. These blades will be subjected to fatigue and also at other times to fatigue corrosion with aging time. Due to their continuous exposure to cyclic rotational stresses in corrosive steam environments, The current research aims to deal with this problem using the laser cladding method for low alloy steel type 52, which works to re-compose the metallurgical structure and improve the mechanical properties by strengthening the resulting structure, which leads to an increase in fatigue and wears resistance, therefore, an increase in the life of these blades is observed.

Keywords: fatigue, fatigue corrosion, turbine blades, laser cladding

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Authors: I. O. Oladipo, Olusegun Adewale Ajayi, Omoniyi Oladipupo Adigun

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Information Communication Technology (ICT) have the potential to contribute to different facets of educational development and effective learning; expanding access, promoting efficiency, improve the quality of learning, enhancing the quality of teaching and provide important mechanism for the economic crisis. Considering the prevalence of unemployment among the higher institution graduates in this nation, in which much seems not to have been achieved in this direction. In view of this, the purpose of this study is to create an awareness and enlightenment of ICT for empowerment opportunities after school. A self-developed modified 4-likert scale questionnaire was used for data collection among Colleges of Education, Physical and Health Education students in Oyo State. Inferential statistical analysis of chi-square set at 0.05 alpha levels was used to analyze the stated hypotheses. The study concludes that awareness and enlightenment of ICT significantly influence empowerment opportunities and recommended that college of education students should be encouraged on the application of ICT for job opportunity after school.

Keywords: employment, empowerment, information communication technology, physical education

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Authors: Nazeef, Suleiman, Umar, Danladi Muhammad, Ja'afar Ali, Zaliha Adamu Umar

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The population dynamics of Auchenoglanis occidentalis from the Dadin-Kowa reservoir were studied. Population dynamic parameters such as growth, mortality and recruitment patterns were analyzed using length frequency data over a 12-month period employing FiSAT II software. Findings revealed that LWR (b - constant) = 2.88, K = 0.72 -yr., L∞ = 40.91 cm and Tmax = 3.57 years and Ɵ’ = 3.14. Mortality indices revealed that natural mortality (M = 1.39), fishing mortality (F = 0.22) and exploitation ratio (E = 0.14), Lc/L∞ = 0.48, Emax = 0.64, while Lopt = 26.4 cm. Uni-modal recruitment peak observed with Lm = 27.3 cm. A restocking program is suitable to ensure its continuous existence as it seems to have a low population.

Keywords: fish population dynamics, auchenoglanis occidentalis, FISAT II, natural mortality

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Authors: Naila Naseer

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Emotional Intelligence (EI) concept is not new yet unique and interesting. EI is a person’s ability to be aware of his/her own emotions and to manage, handle and communicate emotions with others effectively. The present study was conducted to assess the relationship between emotional intelligence and age of graduate level students at Allama Iqbal Open University (AIOU). Population consisted of Allama Iqbal Open University students (B.Ed 3rd Semester, Autumn 2007) from Rawalpindi and Islamabad regions. Total number of sample consisted of 469 participants was randomly drawn out by using table of random numbers. Bar-On EQ-i was administered on the participants through personal contact. The instrument was also validated through pilot study on a random sample of 50 participants (B.Ed students Spring 2006), who had completed their B.Ed degree successfully. Data was analyzed and tabulated in percentages, frequencies, mean, standard deviation, correlation, and scatter gram in SPSS (version 16.0 for windows). The results revealed that students with higher age group had scored low on the scale (Bar-On EQ-i). Moreover, the students in low age groups exhibited higher levels of EI as compared with old age students.

Keywords: emotional intelligence, age level, learning, emotion-related feelings

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Authors: Syed Asif Hassan, Syed Atif Hassan

Abstract:

Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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