Search results for: emotional intelligence

Authors: John Onyima, Ikechukwu Ezepue

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Virtualization and cloud computing are among the fast-growing computing innovations in recent times. Organisations all over the world are moving their computing services towards the cloud this is because of its rapid transformation of the organization’s infrastructure and improvement of efficient resource utilization and cost reduction. However, this technology brings new security threats and challenges about safety, reliability and data confidentiality. Evidently, no single security technique can guarantee security or protection against malicious attacks on a cloud computing network hence an integrated model of intrusion detection and prevention system has been proposed. Anomaly-based and signature-based detection techniques will be integrated to enable the network and its host defend themselves with some level of intelligence. The anomaly-base detection was implemented using the local deviation factor graph-based (LDFGB) algorithm while the signature-based detection was implemented using the snort algorithm. Results from this collaborative intrusion detection and prevention techniques show robust and efficient security architecture for cloud computing networks.

Keywords: anomaly-based detection, cloud computing, intrusion detection, intrusion prevention, signature-based detection

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Authors: Otuu O. Obasi, Jude C. Ajaraogu, Happiness C. Anthony-Ikpe

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Women in Igbo land generally experience culture-related mistreatment in the event of the death of their husbands. The mistreatment ranges from scraping of widows’ hair to denial of the right to see their husbands’ corpses. The objectives of the study were to determine the forms and prevalence of widowhood practice in the studied communities, the effects of the socio-cultural attributes of the people on the practice, and the perceived effect of the practice on the victims. Data for the study were collected from 64 randomly selected communities out of 640 communities in Imo State, Nigeria. 450 copies of the researcher-made-questionnaire were distributed across the three senatorial zones of the State. A total of 418 or 92.8% were completely filled and returned. The result of the study showed, among other things, that the majority of males and females recognized widowhood practice as dehumanizing, but opined that it cannot be stopped because it is rooted in culture. However, 30.2% of the female population did not agree that the practice is dehumanizing to women since it was their cultural practice. The study also revealed that scrapping of widows’ hair was the commonest practice while sleeping alone with the husband’s corpse was the least practice. Regarding the effect which this practice has on widows, emotional trauma topped the list; and was followed by economic hardship and health deterioration. Also shown by the study was that the level of education and religion did not have a notable effect on widowhood practice. With regard to possible stoppage measures, greater number of the respondents (38%) indicated that a synergy of efforts was needed to curb the social scourge.

Keywords: widowhood practice, socio-cultural attributes, violence, impact

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Authors: Igwenagu Chinelo Mercy

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This study is on the concept of Neurostatistics in relation to neuroscience. Neuroscience also known as neurobiology is the scientific study of the nervous system. In the study of neuroscience, it has been noted that brain function and its relations to the process of acquiring knowledge and behaviour can be better explained by the use of various interrelated methods. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. On the other hand, Neurostatistics based on this study is viewed as a statistical concept that uses similar techniques of neuron mechanisms to solve some problems especially in the field of life science. This study is imperative in this era of Artificial intelligence/Machine leaning in the sense that clear understanding of the technique and its proper application could assist in solving some medical disorder that are mainly associated with the nervous system. This will also help in layman’s understanding of the technique of the nervous system in order to overcome some of the health challenges associated with it. For this concept to be well understood, an illustrative example using a brain associated disorder was used for demonstration. Structural equation modelling was adopted in the analysis. The results clearly show the link between the techniques of statistical model and nervous system. Hence, based on this study, the appropriateness of Neurostatistics application in relation to neuroscience could be based on the understanding of the behavioural pattern of both concepts.

Keywords: brain, neurons, neuroscience, neurostatistics, structural equation modeling

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: Miron Wolnicki

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In the past 75 years the world was either influenced by, competing with or learning from U.S. corporations. This is no longer true. As the economic power shifts from the West to the East, U.S. corporations are lagging behind Asian competitors. Moreover, U.S. statecraft fails to address this decline. In a world dominated by interventionist and neo-mercantilist states, having an ineffective non-activist government becomes a costly neoclassic delusion which weakens the world’s largest economy. American conservative economists continue talking about the superiority of the free market system in generating new technologies. The reality is different. The U.S. is sliding further into an overregulated, over-taxed, anti-business state. This paper argues that in order to maintain its economic strength and technological leadership, the U.S. must reform federal institutions to increase support for artificial intelligence and other cutting-edge technologies. The author outlines a number of institutional reforms, under one umbrella, which he calls the American Entrepreneurial State (AES). The AES will improve productivity and bring about coherent business strategies for the next 10-15 years. The design and inspiration for the AES come from the experience of successful statecraft examples in Asia and also other parts the global economy.

Keywords: post-neoliberal system, entrepreneurial state, government and economy, American entrepreneurial state

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Authors: Baylasan Al-Amoudi, Hala Abdulmajeed, Amjad Jilani

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This project explores the role of artificial intelligence (AI) in enhancing user engagement on TikTok by examining the app’s AI-driven features. Through a structured survey of 4 main questions and experimental analysis, we tried to examine how TikTok’s recommendations, algorithms, search engine, and filter tools influence user interactions and satisfaction. A diverse cohort of 20 participants, including casual users and content creators, were involved to provide a broad perspective on user experiences. The examination highlights the recommendation algorithm’s ability to deliver highly personalized content, creating a seamless and engaging experience. TikTok’s search engine is shown to simplify content discovery by enabling users to find specific topics or trends related to their preferences. Meanwhile, the filter tools are found to encourage creativity, particularly for content creators, by offering versatile options to enhance video quality and visual appeal. By evaluating the unique roles of these AI features, the project underscores their significance in maintaining TikTok’s appeal and driving consistent user engagement.

Keywords: TikTok, hashtags, filters, viral sounds, for you page

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

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Authors: Hamid Rostami Jaz, Kamran Ameri Siahooei

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Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach.

Keywords: exchange index, forecasting, perceptron neural network, Tehran stock exchange

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Authors: Chan Ka Lok Sobel

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The rapid evolution of digital media platforms has given rise to new forms of narrative engagement, particularly through multi-channel storytelling. This research focuses on exploring the teaching pedagogy and effectiveness of multi-channel storytelling for social media, cinema, games, and streaming platforms. The study employs case studies of the popular series "Squid Game" to investigate the diverse pedagogical approaches and strategies used in teaching multi-channel storytelling. Through qualitative research methods, including interviews, surveys, and content analysis, the research assesses the effectiveness of these approaches in terms of student engagement, knowledge acquisition, critical thinking skills, and the development of digital literacy. The findings contribute to understanding best practices for incorporating multi-channel storytelling into educational contexts and enhancing learning outcomes in the digital media landscape.

Keywords: digital literacy, game-based learning, artificial intelligence, animation production, educational technology

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Authors: Teh Kean Kheng, Low Soon Yee, Burra Venkata Durga Kumar

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In 2022, as the future world becomes increasingly computer-related, more individuals are attempting to study coding for themselves or in school. This is because they have discovered the value of learning code and the benefits it will provide them. But learning coding is difficult for most people. Even senior programmers that have experience for a decade year still need help from the online source while coding. The reason causing this is that coding is not like talking to other people; it has the specific syntax to make the computer understand what we want it to do, so coding will be hard for normal people if they don’t have contact in this field before. Coding is hard. If a user wants to learn bash code with bash prompt, it will be harder because if we look at the bash prompt, we will find that it is just an empty box and waiting for a user to tell the computer what we want to do, if we don’t refer to the internet, we will not know what we can do with the prompt. From here, we can conclude that the bash prompt is not user-friendly for new users who are learning bash code. Our goal in writing this paper is to give an idea to implement a user-friendly Bash prompt in Ubuntu OS using Artificial Intelligent (AI) to lower the threshold of learning in Bash code, to make the user use their own words and concept to write and learn Bash code.

Keywords: user-friendly, bash code, artificial intelligence, threshold, semantic similarity, lexical similarity

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Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

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Authors: Jasim Azhar, Morten Gjerde

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A city’s space is comprehended as a phenomenon that emerges from the ongoing negotiation between the constructed environment, urban processes, and bodily experience. Many spaces do not represent a static notion but are continually challenged and reconstituted. The ability to recognize those leftover spaces in the urban context is an integral part of an urban redevelopment process, where structured and layered approaches become useful in understanding to transform these spaces into places. Contemporary urban leftover spaces exist as a result of several factors and are present in every major city that often disrupts the flow of districts by creating visually unappealing places. These spaces can be designed, transformed and integrated so as to achieve environmental gains and social preferences. The paper explores how those small changes in visual quality of an urban leftover spaces in Wellington city influence a person’s experience significantly and its potential usage. These spaces can be seen as a catalyst for a change through an ecological sustainability’s framework. A creative and flexible design would lead to psychologically healthy places by improving the image of a city from within. The qualitative research is undertaken through the visual preference studies which will inform the planning initiatives by knowing what people feel about those visual changes in these leftover spaces. Those visual preferences can guide behavior and the emotional responses of different users for the redesign of those spaces with the meaningful attributes. The research is driven by the hypothesis that if the attributes are made visible, the likelihood of stimulating the interest of users should increase.

Keywords: leftover spaces, visual preferences, tactical urbanism, ecological sustainability

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Authors: Ahmad Tijani Surajudeen, Muhammad Zahiri Awang Mat, Aliy Abdulwahid Adebisi

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With arrival of Islam in the South-Western Nigeria in the late fifteenth and early sixteenth centuries, various masājid established in different parts of the area played vital roles towards the betterment and unity of the Muslims. However, despite the fact that the masājid in the South-Western part of Nigeria contributed immensely to the spiritual and educational enhancement of the Muslims, it has not fully captured the holistic educational roles as a unique model used by the Prophet (S.A.W). Therefore, the primary objective of this paper is to investigate and broaden the roles of masjid towards its compartmentalized and holistic contributions among the Muslims in the south-western Nigeria. The findings from the paper have identified five holistic roles of masjid, namely, spiritual, intellectual, physical, social and emotional contributions which have been exemplified in the prophetic model of masjid. The paper has argued that the five factors must be unreservedly unified towards the betterment of the Muslims and enhancement of Islamic education and Arabic Language in the South-Western Nigeria. However, the challenges of masjid management in the South-Western Nigeria are the main hindrance in achieving the holistic roles of masjid. It is thereby suggested that, the management of masjid should take the identified prophetic model of masjid into account in order to positively improve the affairs of Muslims as well as promoting the teaching and learning of Islamic education and Arabic language among the Muslims in the South-Western Nigeria.

Keywords: worship, Islamic education, Arabic language, prophetic holistic model

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Authors: Roseline Chizoba Onuoha, Rose Ngozi Uzoka

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This study aimed at investigating perceived effect of physical exercise on healthy well-being of pregnant mothers in Imo state. The study was guided by three research questions and three null hypotheses tested at 0.05 level of significance. The study was a quasi-experimental non-equivalent control group design involving pre and post tests. A sample of 92 pregnant women drawn from a total population of 922 registered pregnant women in ten randomly selected health centers in Imo State through multistage sampling technique was used. A 41 item structured instrument titled Physical Exercise Pregnancy Test (PEPT) was used for the study. The PEPT was validated by three experts from measurement and evaluation, educational psychology and health education. Crombach Alpha method was used to determine the reliability of Physical Exercise Pregnancy Test (PEPT) and reliability index of 0.82 was obtained. Mean and standard deviation were used to answer the research questions; while Analysis of Covariance (ANCOVA) was used in analyzing the hypotheses. Findings of the study revealed that physical exercise affects physical, social and emotional wellbeing scores of pregnant women. The study also indicated that intervention using physical exercise significantly enhanced healthy well-being scores of pregnant mothers who were exposed to physical exercise than those who received conventional health talks; Location has no significant interaction effect on the mean well-being scores of pregnant women via PEPT. Among recommendations made were that pregnant women should participate in physical exercise.

Keywords: educational psychology, Imo state, Physical exercise, pregnant women

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Authors: K. O'Malley

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Background: The emergence and rapid evolution of large language models (LLMs) (i.e., models of generative artificial intelligence, or AI) has been unprecedented. ChatGPT is one of the most widely used LLM platforms. Using natural language processing technology, it generates customized responses to user prompts, enabling it to mimic human conversation. Responses are generated using predictive modeling of vast internet text and data swathes and are further refined and reinforced through user feedback. The popularity of LLMs is increasing, with a growing number of students utilizing these platforms for study and revision purposes. Notwithstanding its many novel applications, LLM technology is inherently susceptible to bias and error. This poses a significant challenge in the educational setting, where academic integrity may be undermined. This study aims to evaluate the performance of the latest iteration of ChatGPT (ChatGPT4.0) in standardized state medical licensing examinations. Methods: A considered search strategy was used to interrogate the PubMed electronic database. The keywords ‘ChatGPT’ AND ‘medical education’ OR ‘medical school’ OR ‘medical licensing exam’ were used to identify relevant literature. The search included all peer-reviewed literature published in the past five years. The search was limited to publications in the English language only. Eligibility was ascertained based on the study title and abstract and confirmed by consulting the full-text document. Data was extracted into a Microsoft Excel document for analysis. Results: The search yielded 345 publications that were screened. 225 original articles were identified, of which 11 met the pre-determined criteria for inclusion in a narrative synthesis. These studies included performance assessments in national medical licensing examinations from the United States, United Kingdom, Saudi Arabia, Poland, Taiwan, Japan and Germany. ChatGPT 4.0 achieved scores ranging from 67.1 to 88.6 percent. The mean score across all studies was 82.49 percent (SD= 5.95). In all studies, ChatGPT exceeded the threshold for a passing grade in the corresponding exam. Conclusion: The capabilities of ChatGPT in standardized academic assessment in medicine are robust. While this technology can potentially revolutionize higher education, it also presents several challenges with which educators have not had to contend before. The overall strong performance of ChatGPT, as outlined above, may lend itself to unfair use (such as the plagiarism of deliverable coursework) and pose unforeseen ethical challenges (arising from algorithmic bias). Conversely, it highlights potential pitfalls if users assume LLM-generated content to be entirely accurate. In the aforementioned studies, ChatGPT exhibits a margin of error between 11.4 and 32.9 percent, which resonates strongly with concerns regarding the quality and veracity of LLM-generated content. It is imperative to highlight these limitations, particularly to students in the early stages of their education who are less likely to possess the requisite insight or knowledge to recognize errors, inaccuracies or false information. Educators must inform themselves of these emerging challenges to effectively address them and mitigate potential disruption in academic fora.

Keywords: artificial intelligence, ChatGPT, generative ai, large language models, licensing exam, medical education, medicine, university

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Authors: Narmeen Ali, Muhammad Arshad

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Cohesion has an exact association with family functioning and enmeshment (togetherness) on one side and disengagement (separateness) on the other. Family cohesion can apprehend as a concerned association that family members have with each other and an affirmation of association inside the family. Family cohesion, assigned as the level of congruity or sympathetic or emotional attachment that relatives have toward each other, and it was seen to be associated with relational well-being and feeling of comfort in the young generation. The cross-sectional research design was used by the researcher to answer the research questions. A stratified sampling technique was used to collect the data from the participants. The data was collected equally from the males and females of different universities and different departments of Lahore, Pakistan. A self-report questionnaire was developed of given literature and which were found to be associated with family cohesion, interpersonal difficulties and mental health problems of university students. The demographic information included age, gender, university’s name, class, family system, parent’s education, parent’s profession, number of siblings and birth order. Correlation shows the negative relation between balanced cohesion and interpersonal difficulties, while interpersonal difficulties have a highly positive relationship with mental health problems. Mental health problems also have a negative correlation with the balanced family cohesion. Gender, family system, depression and anxiety are the significant predictors of interpersonal difficulties scale in university students. And gender showed a significant difference regarding family cohesion and interpersonal difficulty scale, as women reported more interpersonal difficulties than men.

Keywords: family cohesion, interpersonal difficulties, mental health problems, university students

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Authors: Rahul Mohite, Lahcen Ouarbya

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This paper focuses on the interplay between humans and computer systems and the ability of these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these systems is that it requires large training data sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on the combination of facial expression and speech, outperforms existing ones, which are based solely either on facial or verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper, the increasing significance and demand for facial recognition technology in emotion recognition are also discussed.

Keywords: facial reputation, expression reputation, deep gaining knowledge of, photo reputation, facial technology, sign processing, photo type

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Authors: Priyanka Jain, Sushila Pareek

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Skin disease is often considered to be ‘only cosmetic’ by many medical professionals and lay-people alike but unlike most internal illnesses, skin disease is often immediately visible to others and therefore people suffering from dermatological conditions may suffer serious social and emotional consequences. The purpose of this research was to compare patients suffering from acne, alopecia areata (AA) and melanosis on perceived stress, social appearance anxiety and coping. The study included 120 patients (acne = 40 AA = 40 melanosis = 40) ages ranged from 15 to 25 years. Perceived Stress Scale (PSS), Social Appearance Anxiety Scale (SAAS), and The Brief COPE were administered to the patients. Analytical evaluation was done by Kruskal Wallis and ANOVA-tests. The results of the present study clearly revealed that perceived stress and social appearance anxiety were highest in patients with AA followed by acne patients and found least in patients with melanosis. With regard to coping, self-distraction as a coping technique was found highest in patients suffering from AA followed by acne and then melanosis. Denial was highest in acne patients followed by AA and experienced least by patients with melanosis. Behavioural disengagement was almost equal in patients with melanosis and acne and a little less in patients suffering from AA Acceptance was highest in patients with melanosis, followed by AA and least in acne patients. Self-blame was found highest in patients with acne, followed by AA patients further followed by patients suffering from melanosis. This study is an attempt to stimulate professionals working in the field of dermatology and mental health to explore their supportive communication and increase awareness regarding the difficulties that patients with skin disease can face.

Keywords: coping, dermatology, perceived stress, psychosocial impact, social appearance anxiety

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Authors: Anano Tenieshvili, Teona Lodia

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Social anxiety disorder (SAD) is one of the most common mental health problems not only in adults but also in adolescents. Individuals with SAD exhibit difficulties in interpersonal relationships, understanding emotions, and regulating them as well. For social and emotional adaptation, it is crucial to identify, understand, accept and manage emotions correctly. Researchers actively learn those factors that contribute to the development and maintenance of this condition. Therefore, the main purpose of this study is to acquire knowledge about the association between social anxiety and individual characteristics, such as theory of mind (ToM), verbal irony comprehension, and personal traits. 112 adolescents aged from 12 to 18 were selected for this research. 15 of them are diagnosed with Social anxiety disorder. Statistical analysis was performed on the entire sample, and furthermore, two groups, adolescents with and without social anxiety disorder, were compared separately. Social anxiety and personal traits were assessed by questionnaires. Theory of mind and comprehension of verbal irony were measured using tests. Statistical analysis indicated a positive relationship between social anxiety and comprehension of ironic criticism. Moreover, social anxiety was significantly positively correlated with neuroticism and isolation tendency, whereas it was negatively related to extraversion and frustration tolerance. On top of that, statistical analysis revealed a positive relationship between ToM and verbal irony comprehension. However, the relationship between social anxiety and ToM was not statistically significant. In conclusion, the current research expands knowledge about social anxiety and supports the results of some previous studies.

Keywords: personal traits, social anxiety, theory of mind, verbal irony comprehension

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Authors: Anano Tenieshvili, Teona Lodia

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Social anxiety disorder (SAD) is one of the most common mental health problems not only in adults but also in adolescents. Individuals with SAD exhibit difficulties in interpersonal relationships, understanding emotions and regulating them as well. For social and emotional adaptation, it is crucial to identify, understand, accept and manage emotions correctly. Researchers actively learn those factors that contribute to the development and maintenance of this condition. Therefore, the main purpose of this study is to acquire knowledge about the association between social anxiety and individual characteristics, such as the theory of mind (ToM), verbal irony comprehension and personal traits. 112 adolescents aged from 12 to 18 were selected for this research. 15 of them are diagnosed with Social anxiety disorder. Statistical analysis was performed on the entire sample and furthermore, two groups, adolescents with and without a social anxiety disorder, were compared separately. Social anxiety and personal traits were assessed by questionnaires. Theory of mind and comprehension of verbal irony was measured using tests. Statistical analysis indicated a positive relationship between social anxiety and comprehension of ironic criticism. Moreover, social anxiety was significantly positively correlated with neuroticism and isolation tendency, whereas it was negatively related to extraversion and frustration tolerance. On top of that, statistical analysis revealed a positive relationship between ToM and verbal irony comprehension. However, the relationship between social anxiety and ToM was not statistically significant. In conclusion, the current research expands knowledge about social anxiety and supports the results of some previous studies.

Keywords: personal traits, social anxiety, theory of mind, verbal irony comprehension

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Authors: Weerakarj Dokchan

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The purposes of this research are to find out the factors for developing trainers in the auto part manufacturing factories (AMF) in Amata Nakon Industrial Estate Cholburi. Population in this study included 148 operators to complete the questionnaires and 10 trainers to provide the information on the interview. The research statistics consisted of percentage, mean, standard deviation and step-wise multiple linear regression analysis.The analysis of the training model revealed that: The research result showed that the development factors of trainers in AMF consisted of 3 main factors and 8 sub-factors: 1) knowledge competency consisting of 4 sub-factors; arrangement of critical thinking, organizational loyalty, working experience of the trainers, analysis of behavior, and work and organization loyalty which could predict the success of the trainers at 55.60%. 2) Skill competency consisted of 4 sub-factors, arrangement of critical thinking, organizational loyalty and analysis of behavior and work and the development of emotional quotient. These 4 sub-factors could predict the success of the trainers in skill aspect 55.90%. 3) The attitude competency consisted of 4 sub-factors, arrangement of critical thinking, intention of trainee computer competency and teaching psychology. In conclusion, these 4 sub-factors could predict the success of the trainers in attitude aspect 58.50%.

Keywords: the development factors, trainers development, trainer competencies, auto part manufacturing factory (AMF), AmataNakon Industrial Estate Cholburi

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Authors: Devon Brown, Liu Chunmei

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This paper will present an analysis as part of the researchers dissertation topic focusing on the intersection of affective and analytical directed acyclic graphs (DAGs) in the context of Decision Support Systems (DSS). The researcher’s work involves analyzing decision theory models like Affective and Bayesian Decision theory models and how they could be implemented under an Affective Computing Framework using Information Fusion and Human-Centered Design. Additionally, the researcher is beginning research on an Affective-Analytic Decision Framework (AADF) model for their dissertation research and are looking to merge logic and analytic models with empathetic insights into affective DAGs. Data-collection efforts begin Fall 2024 and in preparation for the efforts this paper looks to analyze previous research in this area and introduce the AADF framework and propose conceptual models for consideration. For this paper, the research emphasis is placed on analyzing Bayesian networks and Markov models which offer probabilistic techniques during uncertainty in decision-making. Ideally, including affect into analytic models will ensure algorithms can increase user trust with algorithms by including emotional states and the user’s experience with the goal of developing emotionally intelligent A.I. systems that can start to navigate the complex fabric of human emotion during decision-making.

Keywords: decision support systems, explainable AI, HCAI techniques, affective-analytical decision framework

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Authors: Amanda Madden

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The purpose of this research was to identify students understanding of well-being and perceptions of the effective components of school-based well-being programs they have participated in during their time in secondary school. With one in four adolescents suffering from some form of mental health disorder, which has the potential to directly impact their academic ability, schools have moved towards a more holistic approach to education, resulting in the growth of school-based well-being programs. There is limited research on the effectiveness of school-based well-being programs, with fewer studies examining students’ perspectives on their well-being. A mixed-method design was utilized, framed by a social constructivist methodology. Quantitative data was collected through a researcher-developed self-report survey, and qualitative data were collected through one-on-one interviews and a semi-structured focus group undertaken with Year 12 students from three independent co-educational schools in Western Australia. Preliminary findings indicate that participants have experienced a minimal impact, either positively or negatively, on their well-being from school-based well-being programs. The data detailed that adolescents consider happiness, positive attitude, good physical health, balance, emotional fulfillment and confidence components of well-being. The findings also highlighted sports, positive family relationships, positive friendships, and pets positively enhanced well-being. This research suggests that researchers and educational leaders should consider students’ understanding of well-being in the development of school-based well-being assessments and interventions. Students are the recipients of school-based well-being programs and are best placed to inform what they will and will not respond to in the determination of appropriate well-being content.

Keywords: wellbeing, school based wellbeing, adolescents, wellbeing interventions

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Authors: Adel Atta Youssef Rezkalla

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Russia's invasion of Ukraine tested the international community and prompted not only states but also non-state actors to take deterrent measures in response. In fact, international sports federations, notably FIFA and UEFA, have managed to shift the power dynamic quite effectively by imposing a blanket ban on Russian national teams and clubs. The purpose of this article is to examine the human rights consequences of such actions by international sports organizations. First, the article moves away from assessing the legal status of FIFA and UEFA under international law and examines the question of how a legal connection can be established with their human rights obligations. Secondly, the human rights aspects of the controversial FIFA and UEFA measures against Russian athletes are examined and these are analyzed in more detail using the proportionality test than the principle of non-discrimination under international human rights law. Finally, the main avenues for redress for possible human rights violations related to the actions taken by these organizations are identified and the challenges of arbitration and litigation in Switzerland are highlighted.

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

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Authors: Claire Griffin

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Recent years have witnessed a surge in the use of innovative teaching approaches to support student engagement and higher-order learning within higher education. This paper seeks to explore the use of collaborative, interactive teaching and learning strategies to support student engagement in a final year undergraduate Developmental Psychology module. In particular, the use of the jigsaw method, in-class presentations and online discussion fora were adopted in a ‘lectorial’ style teaching approach, aimed at scaffolding learning, fostering social interdependence and supporting various levels of student engagement in higher education. Using the ‘Student Course Engagement Questionnaire’, the impact of such teaching strategies on students’ college classroom experience was measured, with additional qualitative student feedback gathered. Results illustrate the positive impact of the teaching methodologies on students’ levels of engagement, with positive implications emerging across the four engagement factors: skills engagement, emotional engagement, participation/interaction engagement and performance engagement. Thematic analysis on students’ qualitative comments also provided greater insight into the positive impact of the ‘lectorial’ teaching approach on students’ classroom experience within higher level education. Implications of the findings are presented in terms of informing effective teaching practices within higher education. Additional avenues for future research and strategy usage will also be discussed, in light of evolving practice and cutting edge literature within the field.

Keywords: learning, higher education, scaffolding, student engagement

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Authors: Lolwa Alansari, Abdelhamid Azhaghdani, Sufia Athar, Hanen Mrabet, Annaliza Cruz, Tamara Alshadafat, Almunzer Zakaria

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Introduction: Improving the patient experience is a fundamental pillar of healthcare's quadruple aims. Recognizing the importance of patient experiences and perceptions in healthcare interactions is pivotal for driving quality improvement. This abstract centers around the Patient Experience Program, an endeavor crafted with the purpose of comprehending and elevating the experiences of patients in the Obstetrics & Gynecology Emergency Department (OB/GYN ED). Methodology: This comprehensive endeavor unfolded through a structured sequence of phases following Plan-Do-Study-Act (PDSA) model, spanning over 12 months, focused on enhancing patient experiences in the Obstetrics & Gynecology Emergency Department (OB/GYN ED). The study meticulously examined the journeys of patients with acute obstetrics and gynecological conditions, collecting data from over 100 participants monthly. The inclusive approach covered patients of different priority levels (1-5) admitted for acute conditions, with no exclusions. Historical data from March and April 2022 serves as a benchmark for comparison, strengthening causality claims by providing a baseline understanding of OB/GYN ED performance before interventions. Additionally, the methodology includes the incorporation of staff engagement surveys to comprehensively understand the experiences of healthcare professionals with the implemented improvements. Data extraction involved administering open-ended questions and comment sections to gather rich qualitative insights. The survey covered various aspects of the patient journey, including communication, emotional support, timely access to care, care coordination, and patient-centered decision-making. The project's data analysis utilized a mixed-methods approach, combining qualitative techniques to identify recurring themes and extract actionable insights and quantitative methods to assess patient satisfaction scores and relevant metrics over time, facilitating the measurement of intervention impact and longitudinal tracking of changes. From the themes we discovered in both the online and in-person patient experience surveys, several key findings emerged that guided us in initiating improvements, including effective communication and information sharing, providing emotional support and empathy, ensuring timely access to care, fostering care coordination and continuity, and promoting patient-centered decision-making. Results: The project yielded substantial positive outcomes, significantly improving patient experiences in the OB/GYN ED. Patient satisfaction levels rose from 62% to a consistent 98%, with notable improvements in satisfaction with care plan information and physician care. Waiting time satisfaction increased from 68% to a steady 97%. The project positively impacted nurses' and midwives' job satisfaction, increasing from 64% to an impressive 94%. Operational metrics displayed positive trends, including a decrease in the "left without being seen" rate from 3% to 1%, the discharge against medical advice rate dropping from 8% to 1%, and the absconded rate reducing from 3% to 0%. These outcomes underscore the project's effectiveness in enhancing both patient and staff experiences in the healthcare setting. Conclusion: The use of a patient experience questionnaire has been substantiated by evidence-based research as an effective tool for improving the patient experience, guiding interventions, and enhancing overall healthcare quality in the OB/GYN ED. The project's interventions have resulted in a more efficient allocation of resources, reduced hospital stays, and minimized unnecessary resource utilization. This, in turn, contributes to cost savings for the healthcare facility.

Keywords: patient experience, patient survey, person centered care, quality initiatives

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Authors: Mutshinye Manguvhewa, Maria Florence, Mansoo Yu, Elize Koch, Kamal Kamaloodien

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Substance use among pregnant women is a perennial problem in the Western Cape Province of South Africa. There are many influential factors are associated with substance use among women of childbearing age. The study explored factors associated with substance use among pregnant women using a qualitative research design and the bio-ecological theoretical framework to explore and guide the researcher throughout the study. Participants were selected using purposive sampling. Only participants accessed from the Department of Social Development meeting the inclusion criteria of the study were interviewed using semi structured interviews. Immediate referral for psychological intervention during the interview was available for participants who needed it. Braun and Clarke's six phases of thematic analysis were utilised to analyse the data. The study adheres to ethical guidelines for the participants' protection. Participants were informed about the study before the initiation of the interviews and the details of their voluntary participation were explained. The key findings from this study illustrate that socio-cultural factors, personal factors, emotional response and intimate relationships are the major contributing factors to substance use among pregnant women in this sample. The results outline the preventative measures that pregnant women implement. Lastly, the study reveals the positive and negative perceptions of substance use programmes that participants share. Some of the study findings are similar to the existing literature and some of the findings differed. Recommendations emanating from the study include that the stakeholders, rehabilitation centres, Department of Health and future researchers should act proactively against substance use during pregnancy.

Keywords: substance addiction, antenatal care, pregnancy, substance use

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

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Authors: Mikaela E. Flood, Elaine Greidanus

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This study will examine how caregivers construct and interpret their experience in applying attachment-based interventions, guided by the research question: How do caregivers construct and interpret their experiences when attempting to apply attachment-based interventions in their relationships? Using a constructivist paradigm, this qualitative study aims to explore caregivers' experiences of attachment-based interventions through semi-structured interviews with five individuals. The research aims to uncover how caregivers perceive, implement, and reflect upon attachment-based interventions, focusing on challenges and successes encountered. Thematic analysis of interview data seeks to reveal recurring patterns and themes, offering insights into the practical implications of attachment theory and interventions within caregiver contexts. The findings may impact the field by integrating theoretical insights with practical applications. They may also inform therapeutic approaches and support services, as well as how attachment-based interventions are implemented, thereby enhancing caregivers' capacity to foster secure attachments. Moreover, this research may inform existing bodies of knowledge by providing empirical support and a deeper understanding of attachment theory's application in real-world caregiving scenarios. In terms of future research, this study may identify potential avenues for further exploration of the application and implementation of attachment-based interventions. Ultimately, this research aims to advance both theoretical understanding and practical applications of attachment-based interventions for enhancing relationship dynamics and emotional well-being in caregiving settings.

Keywords: children, attachment, intervention, caregiver

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