Search results for: machine learning approach for neurological disorder assessment

Authors: M. Radunovic

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Region of Southeastern Europe has a substantial energy resource potential and is witnessing an increasing rate of power and energy project investments. This comes as a result of countries harmonizing their legal framework and market regulations to conform the ones of European Union, enabling direct private investments. Funding in the power and energy market in this region originates from various resources and investment entities, including commercial and institutional ones. Risk anticipation and assessment is crucial to project success, especially given the long exploitation period of project in power and energy domain, as well as the wide range of stakeholders involved. This paper analyzes the possibility of combined application of tools used in total quality management and international financing institutions for project planning, execution and evaluation, with the goal of anticipating, assessing and minimizing the risks that might occur in the development and execution phase of a power and energy project in the market of southeastern Europe. History of successful project management and investments both in the industry and institutional sector provides sufficient experience, guidance and internationally adopted tools to provide proper project assessment for investments in power and energy. Business environment of southeastern Europe provides immense potential for developing power and engineering projects of various magnitudes, depending on stakeholders’ interest. Diversification on investment sources provides assurance that there is interest and commitment to invest in this market. Global economic and political developments will be intensifying the pace of investments in the upcoming period. The proposed approach accounts for key parameters that contribute to the sustainability and profitability of a project which include technological, educational, social and economic gaps between the southeastern European region and western Europe, market trends in equipment design and production on a global level, environment friendly approach to renewable energy sources as well as conventional power generation systems, and finally the effect of the One Belt One Road Initiative led by People’s Republic of China to the power and energy market of this region in the upcoming period on a long term scale. Analysis will outline the key benefits of the approach as well as the accompanying constraints. Parallel to this it will provide an overview of dominant threats and opportunities in present and future business environment and their influence to the proposed application. Through concrete examples, full potential of this approach will be presented along with necessary improvements that need to be implemented. Number of power and engineering projects being developed in southeastern Europe will be increasing in the upcoming period. Proper risk analysis will lead to minimizing project failures. The proposed successful combination of reliable project planning tools from different investment areas can prove to be beneficial in the future power and engineering investments, and guarantee their sustainability and profitability.

Keywords: capital investments, lean six sigma, logical framework approach, logical framework matrix, one belt one road initiative, project management tools, quality function deployment, Southeastern Europe, total quality management

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Authors: Brenda Ravenscroft, Ulemu Luhanga, Bev King

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A comprehensive evaluation of online learning needs to include a blend of educational design, technology use, and online instructional practices that integrate technology appropriately for developing and delivering quality online courses. Research shows that classroom-based evaluation tools do not adequately capture the dynamic relationships between content, pedagogy, and technology in online courses. Furthermore, studies suggest that using classroom evaluations for online courses yields lower than normal scores for instructors, and may affect faculty negatively in terms of administrative decisions. In 2014, the Faculty of Arts and Science at Queen’s University responded to this evidence by seeking an alternative to the university-mandated evaluation tool, which is designed for classroom learning. The Faculty is deeply engaged in e-learning, offering large variety of online courses and programs in the sciences, social sciences, humanities and arts. This paper describes the process by which a new student survey instrument for online courses was developed and piloted, the methods used to analyze the data, and the ways in which the instrument was subsequently adapted based on the results. It concludes with a critical reflection on the challenges of evaluating e-learning. The Student Evaluation of Online Teaching Effectiveness (SEOTE), developed by Arthur W. Bangert in 2004 to assess constructivist-compatible online teaching practices, provided the starting point. Modifications were made in order to allow the instrument to serve the two functions required by the university: student survey results provide the instructor with feedback to enhance their teaching, and also provide the institution with evidence of teaching quality in personnel processes. Changes were therefore made to the SEOTE to distinguish more clearly between evaluation of the instructor’s teaching and evaluation of the course design, since, in the online environment, the instructor is not necessarily the course designer. After the first pilot phase, involving 35 courses, the results were analyzed using Stobart's validity framework as a guide. This process included statistical analyses of the data to test for reliability and validity, student and instructor focus groups to ascertain the tool’s usefulness in terms of the feedback it provided, and an assessment of the utility of the results by the Faculty’s e-learning unit responsible for supporting online course design. A set of recommendations led to further modifications to the survey instrument prior to a second pilot phase involving 19 courses. Following the second pilot, statistical analyses were repeated, and more focus groups were used, this time involving deans and other decision makers to determine the usefulness of the survey results in personnel processes. As a result of this inclusive process and robust analysis, the modified SEOTE instrument is currently being considered for adoption as the standard evaluation tool for all online courses at the university. Audience members at this presentation will be stimulated to consider factors that differentiate effective evaluation of online courses from classroom-based teaching. They will gain insight into strategies for introducing a new evaluation tool in a unionized institutional environment, and methodologies for evaluating the tool itself.

Keywords: evaluation, online courses, student survey, teaching effectiveness

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Authors: An-Jui. Li, Kelvin Lim, Chien-Kuo Chiu, Benson Hsiung

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This paper details the utilization of artificial intelligence (AI) in the field of slope stability whereby quick and convenient solutions can be obtained using the developed tool. The AI tool used in this study is the artificial neural network (ANN), while the slope stability analysis methods are the finite element limit analysis methods. The developed tool allows for the prompt prediction of the safety factors of fill slopes and their corresponding probability of failure (depending on the degree of variation of the soil parameters), which can give the practicing engineer a reasonable basis in their decision making. In fact, the successful use of the Extreme Learning Machine (ELM) algorithm shows that slope stability analysis is no longer confined to the conventional methods of modeling, which at times may be tedious and repetitive during the preliminary design stage where the focus is more on cost saving options rather than detailed design. Therefore, similar ANN-based tools can be further developed to assist engineers in this aspect.

Keywords: landslide, limit analysis, artificial neural network, soil properties

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Authors: Kathleen Seers, Rachel Hogg

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Diagnosis of Autism Spectrum Disorder (ASD) in women is increasing, prompting research into the presentation of female ASD and exploring why females are failing to meet the diagnostic threshold. One explanation is the use of masking behaviors, where traits of ASD are suppressed and gender-appropriate behaviors are mimicked to reduce the visibility and victimization of ASD girls. Current research explores ASD presentation and the lived experiences of ASD girls and adolescents; however, there is a paucity of literature in relation to the intra- and inter- psychic experiences of ASD women. Through a social constructionist framework, this qualitative study sought to understand how the construction of gender and the medicalisation of ASD influences women’s experiences of ASD. This study also explored the use and consequence of masking strategies and the impact this has on well-being. Eight women were interviewed, and three major themes were identified. The themes outline the influence of gender expectations and social norms on the women’s experiences, the significance of diagnosis to their identity, and the influence of the medicalization of ASD. Participants shared experiences of feeling different and internalizing blame for this difference. The feeling of difference was a major contributor to the women’s positive or negative mental well-being. The process of diagnosis allowed participants to create and confirm their identity. Diagnosis also led to improvements in well-being, however, the findings also explore the complexity of labeling individuals with a disorder and the difficulties that arise from the construct of ‘functionality’ for those with Autism. The study also explores the temporal nature of ASD and the changing experiences of women as they mature. It is hoped this study promotes discussion and provides clinicians and those connected to ASD women with insights into the support ASD women require to live authentic lives.

Keywords: female autism, gender, masking, social constructionism

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Authors: Tsirekidze M., Aprasidze T.

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Introduction: The problem of dissocial behavior is at the heart of the social sciences and psychiatry; however, it should be noted that its psychiatric aspect is little studied, and some issues of the problem are still controversial. This is complicated by the diversity of terminological concepts in defining “dissocial behavior”, “behavioral disorder”, “abnormal behavior”, “deviant behavior”, “delinquent behavior”, etc. In literature, there is no comprehensive definition of the essence of dissociative behavior. Numerous attempts to systematize dissociative disorders should also be considered unsatisfactory, which is primarily related to the lack of solid criteria for defining this group of disorders. According to the clinical classification, dissocial behavior is divided into psychotic and non-psychotic forms. Such differentiation is conditional in nature since it is not always possible to draw precise, clear distinctions between these forms, and in addition, there is a transition of a behavior disorder or so-called intermediate forms. One group of authors distinguishes two main forms of deviant behavior in terms of both theoretical and practical significance - non-pathological and pathological. In recent years, especially, the non-pathological form of behavior disorder has become topical. It refers to a large group of forms of deviant behavior, the emergence of which is associated with psychologically full-fledged reactions of children and adolescents to stressful situations and extreme conditions. According to the authors, its concept is understandable-it is difficult to draw a line between psychologically understandable reactions and psychogenically induced reactive states. In addition, the concept of "normal" child and adolescent is, to some extent, a vague concept, as in medicine, any definition of the norm. From a practical (more precisely, pragmatic) point of view, the term "abnormal behavioral disorder" undoubtedly makes sense, especially for the purpose of forensic psychiatric examination. Non-pathological deviation mainly includes transient situational reactions, microsocial-pedagogical backwardness, and character accentuation.Deviant behavior was predominantly manifested in a non-pathological form, which, in our opinion, is due to the difficult socio-economic situation of the country, moral-ethical deprivation, and expressed frustration. By itself, society is an indicator of deviation. Add to this situation complicated factors such as micro-social-pedagogical leave, unfavorable family environment, and parenting defects. Consideration is also given to the connection of acceptable deviation with the personal structural features of the adolescent. Aim: The topic of our discussion is the dissocial behavior of the non-psychotic register. Methods: We surveyed 120 adolescents with deviant behaviors. 61% of them were diagnosed with various neuropsychiatric disorders. Results: Abnormal forms of deviant behavior were observed in 13%, and non-pathological forms in -69%. A combination of non-pathological and pathological forms was present in 10% of cases. In the case of non-pathological deviation, microsocial-pedagogical acceptance was revealed in 62%, character accentuation in 22%; during the pathological forms, pathological reactions were observed in 21%, and abnormal formation of the person -21%. Conclusion: It should be emphasized that in case of any of the above defects, if the so-called family psychosis, and medical and pedagogical habilitation measures for the adolescent, it is quite possible to prevent the abnormal development of the child's personality, correct his character, regulate behavior and develop positive labor-social relations.

Keywords: dissocial personality, deviant behavior, dissocial, delinquent behavior

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Authors: Youn-Hwan Kim, Jae-Won Moon, Hae-Joong Kim

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This paper introduces the results of the study on the development of accelerated life test methods for the motor used in machine tools. In recent years, as well as efficiency for motors, there is a growing need for research on life expectancy of motors. It is considered impossible to calculate the acceleration coefficient by increasing the rotational load or temperature load as the acceleration stress in the motor system because the temperature of the copper exceeds the wire thermal class rating. This paper describes the equipment development procedure for the highly accelerated life test (HALT) of the 12kW three-phase squirrel-cage induction motors (SCIMs). After the test, the lifetime analysis was carried out, and it is compared with the life expectancy by finite element method (FEM) and bearing theory.

Keywords: acceleration coefficient, bearing, HALT, life expectancy, motor

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Ehsan Mehryaar

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The rate of penetration is (RoP) one of the vital factors in the cost and time of tunnel boring projects; therefore, predicting it can lead to a substantial increase in the efficiency of the project. RoP is heavily dependent geological properties of the project site and TBM properties. In this study, 151-point data from Queen’s water tunnel is collected, which includes unconfined compression strength, peak slope index, angle with weak planes, and distance between planes of weaknesses. Since the size of the data is small, it was observed that it is imbalanced. To solve that problem synthetic minority oversampling technique is utilized. The model based on the model tree is proposed, where each leaf consists of a support vector machine model. Proposed model performance is then compared to existing empirical equations in the literature.

Keywords: Model tree, SMOTE, rate of penetration, TBM(tunnel boring machine), SVM

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Authors: Goh Yung Hong, Mona Masood

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This paper explores the effects of gamification on lower secondary school students’ motivation and engagement in the classroom. Two-group posttest-only experimental design were employed to study the influence of gamification teaching method (GTM) when compared with conventional teaching method (CTM) on 60 lower secondary school students. The Student Engagement Instrument (SEI) and Intrinsic Motivation Inventory (IMI) were used to assess students’ intrinsic motivation and engagement level towards the respective teaching method. Finding indicates that students who completed the GTM lesson were significantly higher in intrinsic motivation to learn than those from the CTM. Although the result were insignificant and only marginal difference in the engagement mean, GTM still show better potential in raising student’s engagement in class when compared with CTM. This finding proves that the GTM is likely to solve the current issue of low motivation to learn and low engagement in class among lower secondary school students in Malaysia. On the other hand, despite being not significant, higher mean indicates that CTM positively contribute to higher peer support for learning and better teacher and student relationship when compared with GTM. As a conclusion, gamification approach is flexible and can be adapted into many learning content to enhance the intrinsic motivation to learn and to some extent, encourage better student engagement in class.

Keywords: conventional teaching method, gamification teaching method, motivation, engagement

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Authors: Anthony Townsend, Robyn Fasser

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While complete objectivity is a desirable scientific position from which to conduct a care and contact evaluation (CCE), it is precisely the recognition that we are inherently incapable of operating objectively that is the foundation of ethical practice and skilled assessment. Drawing upon recent research from Daniel Kahneman (2021) on the differences between noise and bias, as well as different inherent biases collectively termed “The Elephant in the Brain” by Kevin Simler and Robin Hanson (2019) from Oxford University, this presentation addresses both the various ways in which our judgments, perceptions and even procedures can be distorted and contaminated while conducting a CCE, but also considers the value of second order cybernetics and the psychodynamic concept of ‘ambivalence’ as a conceptual basis to inform our assessment methodologies to limit such errors or at least better identify them. Both a conceptual framework for ambivalence, our higher-order capacity to allow for the convergence and consideration of multiple emotional experiences and cognitive perceptions to inform our reasoning, and a practical methodology for assessment relying on data triangulation, Bayesian inference and hypothesis testing is presented as a means of promoting ethical practice for health care professionals conducting CCEs. An emphasis on widening awareness and perspective, limiting ‘splitting’, is demonstrated both in how this form of emotional processing plays out in alienating dynamics in families as well as the assessment thereof. In addressing this concept, this presentation aims to illuminate the value of ambivalence as foundational to ethical practice for assessors.

Keywords: ambivalence, forensic, psychology, noise, bias, ethics

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Authors: Richard Roberts, Anna Kravtcova

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Introduction: The realisation that personality can change over the course of a lifetime has led to a new companion model to the Big Five, the behavioural, emotional, and social skills approach (BESSA). BESSA hypothesizes that this set of skills represents how the individual is thinking, feeling, and behaving when the situation calls for it, as opposed to traits, which represent how someone tends to think, feel, and behave averaged across situations. The five major skill domains share parallels with the Big Five Factor (BFF) model creativity and innovation (openness), self-management (conscientiousness), social engagement (extraversion), cooperation (agreeableness), and emotional resilience (emotional stability) skills. We point to noteworthy limitations in the current operationalisation of BESSA skills (i.e., via Likert-type items) and offer up a different measurement approach: forced choice. Method: In this forced-choice paradigm, individuals were given three skill items (e.g., managing my time) and asked to select one response they believed they were “worst at” and “best at”. The Thurstonian IRT models allow these to be placed on a normative scale. Two multivariate studies (N = 1178) were conducted with a 22-item forced-choice version of the BESSA, a published measure of the BFF, and various criteria. Findings: Confirmatory factor analysis of the forced-choice assessment showed acceptable model fit (RMSEA<0.06), while reliability estimates were reasonable (around 0.70 for each construct). Convergent validity evidence was as predicted (correlations between 0.40 and 0.60 for corresponding BFF and BESSA constructs). Notable was the extent the forced-choice BESSA assessment improved upon test-criterion relationships over and above the BFF. For example, typical regression models find BFF personality accounting for 25% of the variance in life satisfaction scores; both studies showed incremental gains over the BFF exceeding 6% (i.e., BFF and BESSA together accounted for over 31% of the variance in both studies). Discussion: Forced-choice measurement models offer up the promise of creating equated test forms that may unequivocally measure skill gains and are less prone to fakability and reference bias effects. Implications for practitioners are discussed, especially those interested in selection, succession planning, and training and development. We also discuss how the forced choice method can be applied to other constructs like emotional immunity, cross-cultural competence, and self-estimates of cognitive ability.

Keywords: Big Five, forced-choice method, BFF, methods of measurements

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Authors: Danielle Page

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Foregoing studies, statistics, and medical evaluations have established a relationship between Posttraumatic stress disorder (PTSD) and criminal justice involvement in Vietnam veterans. War is highly trauma inducing and can leave combat veterans with mental disorders ranging from psychopathic thoughts to suicidal ideation. The majority of those suffering are unaware that they have PTSD, and as a coping mechanism, they often turn to self isolation. Beyond isolation, many veterans with symptomatic PTSD turn to aggression and substance abuse to cope with their internal agony. The most common crimes committed by veterans with PTSD fall into the assault and drug/alcohol abuse categories. Thus, a relationship is established between veteran populations and the criminal justice system. This research aims to define the relationship between PTSD and criminal justice involvement in veterans, explore the mediating factors in this relationship, and analyze numerous court cases in this subject area. Further, it will examine the ways in which crime rates can be reduced for veterans with symptoms of PTSD. This ranges from the improvement of healthcare systems to the implementation of special courts to handle veteran cases. The contribution of this work to the field of forensic psychology will be significant, as it will analyze preexisting case studies and experimental data in an effort to improve the ways in which veteran cases are handled in the criminal justice system. Military personnel involved in the criminal justice system are a vulnerable population in need of healthcare and legislative attention, and this work will bring us one step closer to providing them with just that.

Keywords: forensic psychology, psychotraumatology, PTSD, veterans

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Authors: Jon-Chao Hong, Tsai-Ru Fan, Shih-Min Hsu

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Virtual reality (VR) can generate mirror neurons that facilitate learners to transfer virtual skills to a real environment in skill training, and most studies approved the positive effect of applying in many domains. However, rare studies have focused on the experiential values of participants from a gender perspective. To address this issue, the present study used a VR program named kitchen assistant training, focusing on cutting vegetables and invited 400 students to practice for 20 minutes. Useful data from 367 were subjected to statistical analysis. The results indicated that male participants. From the comparison of average, it seems that females perceived higher than males in learning effectiveness. Expectedly, the VR-Cutting vegetables can be used for pre-training of real vegetable cutting.

Keywords: exponential value, facilitate learning, gender difference, virtual reality

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Authors: Louise Penzenstadler, Tiphaine Robet, Radu Iuga, Daniele Zullino

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Context: The outpatient clinic of the psychiatric addiction service of Geneva University Hospital has been providing support to individuals affected by various narcotics for 30 years. However, the increasing consumption of crack cocaine in Geneva has presented a new challenge for the healthcare system. Research Aim: The aim of this research is to evaluate the impact of an outreach intervention on crack cocaine addiction in users with co-occurring opioid use disorder. Methodology: The research utilizes a combination of quantitative and qualitative retrospective data analysis to evaluate the effectiveness of the outreach intervention. Findings: The data collected from October 2023 to December 2023 show that the outreach program successfully made 1,071 contacts with drug users and led to 15 new requests for care and enrollment in treatment. Patients expressed high satisfaction with the intervention, citing easy and rapid access to treatment and social support. Theoretical Importance: This research contributes to the understanding of the challenges and specific needs of a complex group of drug users who face severe health problems. It highlights the importance of outreach interventions in establishing trust, connecting users with care, and facilitating medication-assisted treatment for opioid addiction. Data Collection: Data was collected through the outreach program's interactions with drug users, including street outreach interventions and presence at locations frequented by users. Patient satisfaction surveys were also utilized. Analysis Procedures: The collected data was analyzed using both quantitative and qualitative methods. The quantitative analysis involved examining the number of contacts made, new requests for care, and treatment enrollment. The qualitative analysis focused on patient satisfaction and their perceptions of the intervention. Questions Addressed: The research addresses the following questions: What is the impact of an outreach intervention on crack cocaine addiction in users with co-occurring opioid use disorder? How effective is the outreach program in connecting drug users with care and initiating medication-assisted treatment? Conclusion: The outreach program has proven to be an effective intervention in establishing trust with crack users, connecting them with care, and initiating medication-assisted treatment for opioid addiction. It has also highlighted the importance of addressing the specific challenges faced by this group of drug users.

Keywords: crack addiction, outreach treatment, peer intervention, polydrug use

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Authors: Keiko Sakai

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In Japan, all stages of public education aim to foster a zest for life. ‘Zest’ implies solving problems by oneself, using acquired knowledge and skills. It is related to the self-regulation of metacognition. To enhance this, establishing good learning habits is important. Tardiness in undergraduate students should be examined based on self-regulation. Accordingly, we focussed on self-monitoring and self-planning strategies among self-regulated learning factors to examine the causes of tardiness. This study examines the impact of self-monitoring and self-planning learning skills on the degree and reason for tardiness in undergraduate students. A questionnaire survey was conducted, targeted to undergraduate students in University X in the autumn semester of 2018. Participants were 247 (average age 19.7, SD 1.9; 144 males, 101 females, 2 no answers). The survey contained the following items and measures: school year, the number of classes in the semester, degree of tardiness in the semester (subjective degree and objective times), active participation in and action toward schoolwork, self-planning and self-monitoring learning skills, and reason for tardiness (open-ended question). First, the relation between strategies and tardiness was examined by multiple regressions. A statistically significant relationship between a self-monitoring learning strategy and the degree of subjective and objective tardiness was revealed, after statistically controlling the school year and the number of classes. There was no significant relationship between a self-planning learning strategy and the degree of tardiness. These results suggest that self-monitoring skills reduce tardiness. Secondly, the relation between a self-monitoring learning strategy and the reason of tardiness was analysed, after classifying the reason for tardiness into one of seven categories: ‘overslept’, ‘illness’, ‘poor time management’, ‘traffic delays’, ‘carelessness’, ‘low motivation’, and ‘stuff to do’. Chi-square tests and Fisher’s exact tests showed a statistically significant relationship between a self-monitoring learning strategy and the frequency of ‘traffic delays’. This result implies that self-monitoring skills prevent tardiness because of traffic delays. Furthermore, there was a weak relationship between a self-monitoring learning strategy score and the reason-for-tardiness categories. When self-monitoring skill is higher, a decrease in ‘overslept’ and ‘illness’, and an increase in ‘poor time management’, ‘carelessness’, and ‘low motivation’ are indicated. It is suggested that a self-monitoring learning strategy is related to an internal causal attribution of failure and self-management for how to prevent tardiness. From these findings, the effectiveness of a self-monitoring learning skill strategy for reducing tardiness in undergraduate students is indicated.

Keywords: higher-education, self-monitoring, self-regulation, tardiness

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Authors: Sohni Siddiqui, Anja Schultze-Krumbholz

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Our lives now revolve more around online-related tasks, as the internet has become a necessity. One of the disturbance concerns with high internet usage is the multiplication of cyber-associated risky behaviors such as cyber aggression and/or cyberbullying. Cyber Bullying is an emerging issue that needs immediate attention from many stakeholders such as parents, doctors, school administrators, policymakers, researchers, and others, especially in the COVID-19 pandemic when online learning has been adopted as an instructional strategy, and there is a continuous rise in cyberbullying cases. The aim of the article is to review existing successful and emerging interventions designed to control bullying and cyberbullying by engaging individuals through teachers’ professional development and adopting a whole-school approach. The study identified the strengths and limitations of the programs and suggested improvements to existing interventions. Preparing interventions with a strong theoretical framework, integrating applications of emerging theories in interventions, promoting proactive and reactive strategies in combination, beginning with the baseline needs assessment surveys, reducing digital time and digital divide among parents and children, promoting the concept of lead trainer, peer trainer, and hot spots, focusing on physical activities, use of landmarks are some of the recommendations proposed by authors. In addition to face-to-face intervention, the researchers recommend updating and improving previous intervention programs with games and apps. Especially in the time of pandemic crises, when face-to-face interactions are limited and cyberbullying is triggered, the use of apps, web-based interventions, and games can be an effective way to control electronic perpetration and victimization.

Keywords: anti bullying programs, cyber bullying, individualized trainings, teachers’ professional development, whole school interventions

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Authors: Christiane Dumont

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This paper proposes to revisit foreign-language learning as a tool to increase motivation through advocacy and develop effective natural communication skills, which are critical leadership qualities. To this end, collaborative initiatives undertaken by advanced university students of French with local and international community partners will be reviewed. Close attention will be paid to the acquisition of intercultural skills, the reflective process, as well as the challenges and outcomes. Two international development projects conducted in Haiti will be highlighted, i.e., collaboration with a network of providers in the Haitian cultural heritage preservation and tourism sector (2014-15) and development of investigation and teacher training tools for a primary/secondary school in the Port-au-Prince area (current). The choice of community-service learning as a framework to teach French-as-a-second-language stemmed from the need to raise awareness against stereotypes and prejudice, which hinder the development of effective intercultural skills. This type of experiential education also proved very effective in identifying and preventing miscommunication caused by the lack of face-to-face interaction in our increasingly technology-mediated world. Learners experienced first-hand, the challenges and advantages of face-to-face communication, which, in turn, enhanced their motivation for developing effective intercultural skills. Vygotsky's and Kolb's theories, current research on service learning (Dwight, Eyler), action/project-based pedagogy (Beckett), and reflective learning (TSC Farrell), will provide useful background to analyze the benefits and challenges of community-service learning. The ultimate goal of this paper is to find out what makes experiential learning truly unique and transformative for both the learners and the community they wish to serve. It will demonstrate how enhanced motivation, community engagement, and clear, concise, and respectful communication impact and empower learners. The underlying hope is to help students in high-profile, and leading-edge industries become effective global leaders.

Keywords: experiential learning, intercultural communication, reflective learning, effective leadership, learner motivation

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Authors: Abdul Razak Rahmat, Azizah Ahmad, Azman Ta’aa

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The deployment of Business Intelligence (BI) for organization at the beginning phase is very crucial. Results from the previous studies found that more than half of the BI project fails to meet the objective even though a lot money are spent. Based on that problem, the readiness level of BI for the organization is important to identify in order to reduce the risk before the actual BI project is implemented. In this paper, rigorous literature review on the aspect success factors such as Critical Success Factors (CSFs), Readiness Factors (RFs), Success Factors (SFs), are discussed by different authors. The paper also adopted a few models from previous study as a guide for the assessment of BI readiness. The expected finding from this research is the Business Intelligent Readiness Model (BiRM) as a guild before implement the BI system.

Keywords: business intelligence readiness model, business intelligence for higher learning, BI readiness factors, BI critical success factors(CSF)

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Authors: Gae Hee Song

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This paper aims to address the evolution of public-private partnerships for mainstreaming an evaluation approach in the community-based education project. It examines the distinctive features of Save the Children’s School Me project in terms of empowerment evaluation principles introduced by David M. Fetterman, especially community ownership, capacity building, and organizational learning. School Me is a Save the Children Korea funded-project, having been implemented in Cote d’Ivoire and Sierra Leone since 2016. The objective of this project is to reduce gender-based disparities in school completion and learning outcomes by creating an empowering learning environment for girls and boys. Both quasi-experimental and experimental methods for impact evaluation have been used to explore changes in learning outcomes, gender attitudes, and learning environments. To locate School Me in the public-private partnership framework for community empowerment and sustainability, the data have been collected from School Me progress/final reports, baseline, and endline reports, fieldwork observations, inter-rater reliability of baseline and endline data collected from a total of 75 schools in Cote d’Ivoire and Sierra Leone. The findings of this study show that School Me project has a significant evaluation component, including qualitative exploratory research, participatory monitoring, and impact evaluation. It strongly encourages key actors, girls, boys, parents, teachers, community leaders, and local education authorities, to participate in the collection and interpretation of data. For example, 45 community volunteers collected baseline data in Cote d’Ivoire; on the other hand, three local government officers and fourteen enumerators participated in the follow-up data collection of Sierra Leone. Not only does this public-private partnership improve local government and community members’ knowledge and skills of monitoring and evaluation, but the evaluative findings also help them find their own problems and solutions with a strong sense of community ownership. Such community empowerment enables Save the Children country offices and member offices to gain invaluable experiences and lessons learned. As a result, empowerment evaluation leads to community-oriented governance and the sustainability of the School Me project.

Keywords: community empowerment, Cote d’Ivoire, empowerment evaluation, public-private partnership, save the children, school me, Sierra Leone, sustainability

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Authors: Yinheng Li

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The remarkable advancements in large language models (LLMs) have brought about significant improvements in natural language processing tasks. This paper presents a comprehensive review of in-context learning techniques, focusing on different types of prompts, including discrete, continuous, few-shot, and zero-shot, and their impact on LLM performance. We explore various approaches to prompt design, such as manual design, optimization algorithms, and evaluation methods, to optimize LLM performance across diverse tasks. Our review covers key research studies in prompt engineering, discussing their methodologies and contributions to the field. We also delve into the challenges faced in evaluating prompt performance, given the absence of a single ”best” prompt and the importance of considering multiple metrics. In conclusion, the paper highlights the critical role of prompt design in harnessing the full potential of LLMs and provides insights into the combination of manual design, optimization techniques, and rigorous evaluation for more effective and efficient use of LLMs in various Natural Language Processing (NLP) tasks.

Keywords: in-context learning, prompt engineering, zero-shot learning, large language models

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Authors: Su-Hyun Cho, Chang-U Chae

Abstract:

Environmental impact assessment techniques have been developed as a result of the worldwide efforts to reduce the environmental impact of global warming. By using the quantification method in the construction industry, it is now possible to manage the greenhouse gas is to systematically evaluate the impact on the environment over the entire construction process. In particular, the proportion of greenhouse gas emissions at the production stage of construction material occupied is high, and efforts are needed in particular in the construction field. In this study, intended for concrete products for the construction materials, by using the LCA evaluation method, we compared the results of environmental impact assessment and carbon emissions of developing products that have been applied low-carbon technologies compared to existing products. As a results, by introducing a raw material of industrial waste, showed carbon reduction. Through a comparison of the carbon emission reduction effect of low-carbon technologies, it is intended to provide academic data for the evaluation of greenhouse gases in the construction sector and the development of low-carbon technologies of the future.

Keywords: CO₂ emissions, CO₂ reduction, ready-mixed concrete, environmental impact assessment

Procedia PDF Downloads 463

Authors: Hamad Almohamadi, Abdulrahman AlKassem, Majed Alamoudi

Abstract:

Dumping Aluminum (Al) waste into landfills causes several health and environmental problems. The pyrolysis process could treat Al waste to produce AlCl₃ and H₂. Using the Aspen Plus software, a techno-economic and feasibility assessment has been performed for Al waste pyrolysis. The Aspen Plus simulation was employed to estimate the plant's mass and energy balance, which was assumed to process 100 dry metric tons of Al waste per day. This study looked at two cases of Al waste treatment. The first case produces 355 tons of AlCl₃ per day and 9 tons of H₂ per day without recycling. The conversion rate must be greater than 50% in case 1 to make a profit. In this case, the MSP for AlCl₃ is $768/ton. The plant would generate $25 million annually if the AlCl₃ were sold at $1000 per ton. In case 2 with recycling, the conversion has less impact on the plant's profitability than in case 1. Moreover, compared to case 1, the MSP of AlCl₃ has no significant influence on process profitability. In this scenario, if AlCl₃ were sold at $1000/ton, the process profit would be $58 million annually. Case 2 is better than case 1 because recycling Al generates a higher yield than converting it to AlCl₃ and H₂.

Keywords: aluminum waste, aspen plus, process modelling, fast pyrolysis, techno-economic assessment

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

Abstract:

Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

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Authors: Rakhyun Kim, Sungho Tae

Abstract:

Global environmental issues such as abnormal weathers due to global warming, resource depletion, and ecosystem distortions have been escalating due to rapid increase of population growth, and expansion of industrial and economic development. Accordingly, initiatives have been implemented by many countries to protect the environment through indirect regulation methods such as Environmental Product Declaration (EPD), in addition to direct regulations such as various emission standards. Following this trend, life cycle assessment (LCA) techniques that provide quantitative environmental information, such as Human Toxicity Potential (HTP), for buildings are being developed in the construction industry. However, at present, the studies on the environmental database of building materials are not sufficient to provide this support adequately. The purpose of this study is to analysis human toxicity potential of major building material production stage using life cycle assessment. For this purpose, the theoretical consideration of the life cycle assessment and environmental impact category was performed and the direction of the study was set up. That is, the major material in the global warming potential view was drawn against the building and life cycle inventory database was selected. The classification was performed about 17 kinds of substance and impact index, such as human toxicity potential, that it specifies in CML2001. The environmental impact of analysis human toxicity potential for the building material production stage was calculated through the characterization. Meanwhile, the environmental impact of building material in the same category was analyze based on the characterization impact which was calculated in this study. In this study, establishment of environmental impact coefficients of major building material by complying with ISO 14040. Through this, it is believed to effectively support the decisions of stakeholders to improve the environmental performance of buildings and provide a basis for voluntary participation of architects in environment consideration activities.

Keywords: human toxicity potential, major building material, life cycle assessment, production stage

Procedia PDF Downloads 119

Authors: Leila Goosen, Dalize van Heerden

Abstract:

The performance outcomes of first year Computer Science and Information Technology students across the world are of great concern, whether they are being taught in a face-to-face environment or via distance education. In the face-to-face environment, it is, however, somewhat easier to teach and support students than it is in a distance education environment. The face-to-face academic can more easily gauge the level of understanding and participation of students and implement interventions to address issues, which may arise. With the inroads that Web 2.0 and Web 3.0 technologies are making, the world of online teaching and learning are rapidly expanding, bringing about technologies, which allows for similar interactions between online academics and their students as available to their face-to-face counter parts. At the University of South Africa (UNISA), the Learning Management System (LMS) is called myUNISA and it is deployed on a SAKAI platform. In this paper, we will take a look at some of the myUNISA technologies implemented in the teaching of a first year programming course, how they are implemented and, in some cases, we will indicate how this affects the performance outcomes of students.

Keywords: computer science, Distance Education Technologies, Learning Management System, face-to-face environment

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Authors: Navah Z. Ratzon, Shlomit Cohen, Sigal Portnoy

Abstract:

This work focuses on posture and risk factors for the musculoskeletal disorder in guitarists, which constitutes the largest group of musicians today. The source of the problems experienced by these musicians is linked to physical, psychosocial and personal risk factors. These muscular problems are referred to as Playing Related Musculoskeletal Disorder (PRMD). There is not enough research that specifically studies guitar players, and to the extent of our knowledge, there is almost no reference to the characteristics of their movement patterns while they play. This is in spite of the high prevalence of PRMD in this population. Kinematic research may provide a basis for the development of a prevention plan for this population and their unique characteristics of playing patterns. The aim of the study was to investigate the correlation between risk factors for PRMD among guitar players and self-reporting of pain in the skeletal muscles, and specifically to test whether there are differences in the kinematics of the upper body while playing in a sitting or standing posture. Twenty-five guitarists, aged 18-35, participated in the study. The methods included a motion analysis using a motion capture system, anthropometric measurements and questionnaires relating to risk factors. The questionnaires used were the Standardized Nordic Questionnaire for the Analysis of Musculoskeletal Symptoms and the Demand Control Support Questionnaire, as well as a questionnaire of personal details. All of the study participants complained of musculoskeletal pain in the past year; the most frequent complaints being in the left wrist. Statistically significant correlations were found between biodemographic indices and reports of pain in the past year and the previous week. No significant correlations were found between the physical posture while playing and reports of pain among professional guitarists. However, a difference was found in several kinematic parameters between seated and standing playing postures. In a majority of the joints, the joint angles while playing in a seated position were more extreme than those during standing. This finding may suggest a higher risk for musculoskeletal disorder while playing in a seated position. In conclusion, the results of the present research highlight the prevalence of musculoskeletal problems in guitar players and its correlation with various risk factors. The finding supports the need for intervention in the form of prevention through identifying the risk factors and addressing them. Relating to the person, to their occupation and environment, which are the basis of proper occupational therapy, can help meet this need.

Keywords: body posture, motion tracking, PRMD, guitarists

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Authors: Norbert Szulc, Jakub Wilk, Franciszek Górski

Abstract:

This paper presents an approach for simulating and testing robotic systems based on PX4, using a local Kubernetes cluster. The approach leverages modern cloud-native tools and runs on single-board computers. Additionally, this solution enables the creation of datasets for computer vision and the evaluation of control system algorithms in an end-to-end manner. This paper compares this approach to method commonly used Docker based approach. This approach was used to develop simulation environment for an unmanned surface vehicle (USV) for RoboBoat 2023 by running a containerized configuration of the PX4 Open-source Autopilot connected to ROS and the Gazebo simulation environment.

Keywords: cloud computing, Kubernetes, single board computers, simulation, ROS

Procedia PDF Downloads 61

Authors: Kunyu Yan

Abstract:

Early childhood music education plays a significant role in an individual’s growth. Music can help children understand themselves and relate to others, and make connections between family, school, and society. In recent years, with the development of early childhood education in China, an increasing number of kindergartens have been established, and many of them pay more attention to music education. This research has two main aims. One is to discover how and why music is used in both public and private kindergartens. The second aim is to make recommendations for widening the use of music in kindergartens. In order to achieve these aims, the research uses two main methods. Firstly, it considers the historical background and cultural context of early childhood education in China; and secondly, it uses an approach that compares public and private kindergartens. In this research, six kindergartens were chosen from Qingdao city in Shandong Province as case studies, including 3 public kindergartens and 3 private kindergartens. This research was based on using three types of data collection methods: observation, semi-structured interviews with teachers, and questionnaires with parents. Participant and non-participant observational methods were used and included in daily routines at the kindergartens in order to experience the situation of music education first-hand. Interviews were associated with teachers’ views of teaching and learning music, the perceptions of the music context, and their strategies of using music. Lastly, the questionnaire was designed to obtain the views of current music education from the children’s parents in the respective kindergartens. The results are shown with three main themes: (1) distinct characteristics of public kindergartens (e.g., similar equipment, low tuition fee, qualified teachers, etc); (2) distinct characteristics of private kindergartens (e.g., various tuition fees, own teaching system, trained teachers, etc); and (3) differences between public and private kindergartens (e.g., funding, requirements for teachers, parents’ demands, etc). According to the results, we can see that the main purpose of using music in China is to develop the musical ability of children, and teachers focus on musical learning, such as singing in tune and playing instruments. However, as revealed in this research, there are many other uses and functions of music in these educational settings, including music used for non-musical learning (e.g., counting, learning language, etc.) or in supporting social routines.

Keywords: differences between private and public school, early childhood education, music education, uses and functions of music

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

Abstract:

Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

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Authors: Kolade Olawande Owoeye, George Weir

Abstract:

Terrorism and extremism activities on the internet are becoming the most significant threats to national security because of their potential dangers. In response to this challenge, law enforcement and security authorities are actively implementing comprehensive measures by countering the use of the internet for terrorism. To achieve the measures, there is need for intelligence gathering via the internet. This includes real-time monitoring of potential websites that are used for recruitment and information dissemination among other operations by extremist groups. However, with billions of active webpages, real-time monitoring of all webpages become almost impossible. To narrow down the search domain, there is a need for efficient webpage classification techniques. This research proposed a new approach tagged: SentiPosit-based method. SentiPosit-based method combines features of the Posit-based method and the Sentistrenght-based method for classification of terrorism and extremism webpages. The experiment was carried out on 7500 webpages obtained through TENE-webcrawler by International Cyber Crime Research Centre (ICCRC). The webpages were manually grouped into three classes which include the ‘pro-extremist’, ‘anti-extremist’ and ‘neutral’ with 2500 webpages in each category. A supervised learning algorithm is then applied on the classified dataset in order to build the model. Results obtained was compared with existing classification method using the prediction accuracy and runtime. It was observed that our proposed hybrid approach produced a better classification accuracy compared to existing approaches within a reasonable runtime.

Keywords: sentiposit, classification, extremism, terrorism

Procedia PDF Downloads 260