Search results for: emotional and intelligence quotient

Authors: Oluwayemisi D. Ishola, Lekan Ajijola

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Nigeria faces a demographic challenge characterized by a burgeoning youth population and an escalating fertility rate. A notable decline in the use of modern contraceptives among adolescent girls and young women compounds the challenge. The youthful demographic stands at a critical juncture in the nation's pursuit to fulfill its pledge of achieving a 27% modern contraceptive rate by 2030, embodying the potential to translate this ambitious commitment into a tangible reality. This research undertook a multi-dimensional examination to scrutinize contraceptive ideational disparities among adolescents and young women in Nigeria, with a particular emphasis on ideational factors. The data underpinning this study were drawn from a cross-sectional household survey carried out in the Nigerian states of Edo, Ogun, Plateau, and Niger between October 2019 and January 2020. The survey encompassed 2,857 sexually active women aged 15-24 years. Employing an ideational framework focusing on behavior that accentuates psychosocial factors, the study dissected nine unique ideational variables into three principal domains: social, cognitive, and emotional. Multivariate logistics regression analyses were used to assess associations between ideational elements and contraceptive use within the total sample and specific age brackets (adolescents of 15-19 years and youth of 20-24 years). For this study, a p-value less than 0.05 was considered indicative of statistical significance. The study's results revealed significant associations between the ideational variables and contraceptive use in total sample and among adolescent and youth, ranging from p < .05 to p < .001. The influence of each domain's predictors on Family Planning (FP) manifested variations when assessed separately and across the different age groups. Notably, cognitive and emotional domains were found to be the strongest predictor of contraceptive use when compared with social domains in the general sample and among youth. This study’s findings highlight the complex interplay of social, cognitive, and emotional factors in contraceptive use among young individuals. Understanding these dynamics is crucial in developing effective strategies to overcome barriers and improve access to contraceptive services among young women in Nigeria.

Keywords: adolescents, contraception, ideation, youth

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Authors: Alexandra Serra, Nair Torrão, Rui G. Serôdio, José A. Lima

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The present study aimed to evaluate the incidence and the prevalence of domestic violence legitimatory beliefs, emotional regulation difficulties and, early maladaptive schemas regarding intimidate partner violence in a sample of 50 Portuguese inmates. As expected, results show high levels of legitimatory beliefs, significant difficulties of emotional regulation and a set of high levels of early maladaptive schemas that clearly compromise the inmates affective-cognitive functioning. The most prevalent set of maladaptive schemas are associated with depression, anxiety, hostility, reduced ability to empathize and, dependence on the approval of others, which, combined, may trigger aggressive responses towards the intimate’s partner. Being victimized in their childhood and having committing murder are not differentiating factors on the measures we analyzed, but alcohol consumption may be associated with an intensification of domestic violence legitimatory beliefs. In the discussion of our findings, we compare the pattern of the psychosocial measures we used with the equivalent results obtained with convicted individuals that attend a community compulsory program, specifically designed for domestic violence perpetrators. We also highlight the importance of implementing specialized interventions in prison settings focusing on an evidence-based-practice.

Keywords: domestic violence, intimate partner offenders, incidence and prevalence of legitimatory beliefs, Portuguese inmates

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Authors: Paul Lam, Kevin Wong, Chi Him Chan

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

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

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Authors: Eleanor F. Willard

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The attrition rate on distance learning courses can be high. This research examines how online students often react when faced with poor results. Using q methodology, it was found that the emotional response level and the type of social support sought by students were key influences on their attitude to failure. As educational and psychological researchers, we are adept at measuring learning and achievement, but examining attitudes towards barriers to learning are not so well researched. The distance learning student has differing needs from onsite learners and, as the attrition rate is notoriously high in the online student population, examining learners’ attitude towards adversity and barriers is important. Self-report measures such as questionnaires are useful in terms of ascertaining levels of constructs such as resilience and academic confidence. Interviewing, too, can gain in depth detail of the opinions of such a population, but only in individuals. The aim of this research was to ascertain what the feelings and attitudes of online students were when faced with a setback. This was achieved using q methodology due to its use of both quantitative and qualitative methodology and its suitability for exploratory research. The emphasis with this methodology is the attitudes, not the individuals. The work was focused upon a population of distance learning students who attended a school on site for one week as part of their studies. They were engaged in a psychology masters conversion course and, as such, were graduate students. The Q sort had 30 items taken from the Academic Resilience Scale (ARS-30). The scale items represent three constructs; perseverance, reflecting (including adaptive help-seeking) and negative affect. These are widely acknowledged as being relevant concepts underpinning psychological resilience. The q sort was conducted with 19 students in total. This is done by participants arranging statement cards regarding how similar to themselves they believe each statement to be. This was done after reading a vignette describing an experience of academic failure. Commonalities and differences between the sorts from all participants are then analyzed in terms of correlations and response patterns. Following data collection, the participants' responses were initially analyzed and the key perspectives (factors) to emerge were labelled ‘persevering individuals’ and ‘emotional networkers’. The differences between the two perspectives centre around the level of emotion felt when faced with barriers and the extent that students enlist the help of others inside and outside of the university. The dominant factor to emerge from the sorts of ‘persevering individuals’ demonstrated that many distance learners are tenacious. However, for other students, the level of emotional and social support is pivotal in helping them complete their studies when facing adversity. This was demonstrated by the ‘emotional networkers’ perspective. This research forms a starting point for further work on engaging and retaining online students at university and can potentially provide insight into how universities can lower attrition rates on distance learning courses.

Keywords: academic resilience, distance learning, online learning, q methodology

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Authors: T. S. Terence Ma, Irene T. Ho

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Conducting adequate assessment of the challenges students with ASD (Autism Spectrum Disorder) face and the support they need is imperative for promoting their school adjustment. Students with ASD often show deficits in communication, social interaction, emotional regulation, and self-management in learning. While targeting these areas in intervention is often helpful, we argue that not enough attention has been paid to weak self-regulation being a key factor underlying their manifest difficulty in all these areas. Self-regulation refers to one’s ability to moderate their behavioral or affective responses without assistance from others. Especially for students with high functioning autism, who often show problems not so much in acquiring the needed skills but rather in applying those skills appropriately in everyday problem-solving, self-regulation becomes a key to successful adjustment in daily life. Therefore, a greater understanding of the construct of self-regulation, its relationship with other daily skills, and its role in school functioning for students with ASD would generate insights on how students’ school adjustment could be promoted more effectively. There were two focuses in this study. Firstly, we examined the extent to which self-regulation is a distinct construct that is differentiable from other daily skills and the most salient indicators of this construct. Then we tested a model of relationships between self-regulation and other daily school skills as well as their relative and combined effects on school adjustment. A total of 1,345 Grade1 to Grade 6 students with ASD attending mainstream schools in Hong Kong participated in the research. In the first stage of the study, teachers filled out a questionnaire consisting of 136 items assessing a wide range of student skills in social, emotional and learning areas. Results from exploratory factor analysis (EFA) with 673 participants and subsequent confirmatory factor analysis (CFA) with another group of 672 participants showed that there were five distinct factors of school skills, namely (1) communication skills, (2) pro-social behavior, (3) emotional skills, (4) learning management, and (5) self-regulation. Five scales representing these skill dimensions were generated. In the second stage of the study, a model postulating the mediating role of self-regulation for the effects of the other four types of skills on school adjustment was tested with structural equation modeling (SEM). School adjustment was defined in terms of the extent to which the student is accepted well in school, with high engagement in school life and self-esteem as well as good interpersonal relationships. A 5-item scale was used to assess these aspects of school adjustment. Results showed that communication skills, pro-social behavior, emotional skills and learning management had significant effects on school adjustment only indirectly through self-regulation, and their total effects were found to be not high. The results indicate that support rendered to students with ASD focusing only on the training of well-defined skills is not adequate for promoting their inclusion in school. More attention should be paid to the training of self-management with an emphasis on the application of skills backed by self-regulation. Also, other non-skill factors are important in promoting inclusive education.

Keywords: autism, assessment, factor analysis, self-regulation, school adjustment

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Authors: Avinash Malladhi

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Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

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Authors: Ghazal Doostmohammadi, Susan Rahimzadeh

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Aim: This research aimed to investigate the relationship between empathy and interpersonal trust and recognize the mediating role of loneliness between them in both genders. Methods: With a correlational descriptive design, 192 university students (130 female and 62 male) responded to the questionnaires on “empathy quotient,” “loneliness,” and “interpersonal trust” tests. These tests were designed and validated by experts in the field. Data were analysed using Pearson correlation and path analysis, which is a statistical technique that uses standard linear regression equations to determine the degree of conformity of a theoretical causal model with reality. Results: The data analysis showed that there was no significant correlation between interpersonal trust, both with loneliness (t=0.169) and empathy (t=0.186), while there was a significant negative correlation (t=0.359) between empathy and loneliness. This means that there is an inverse correlation between empathy and loneliness. The path analysis confirmed the hypothesis of the research about the mediating role of loneliness between empathy and interpersonal trust. But gender did not play a role in this relationship. Conclusion: As an outcome, clinical professionals and education trainers should pay more attention to interpersonal trust as a basic need and try to recreate and shape it to prevent people's social breakdown, and on the other hand, self-disclosure training (especially in Men), expression of feelings and courage should be given double importance to prevent the consequences of loneliness.

Keywords: empathy, loneliness, interpersonal trust, gender

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Authors: Robi Lou Logarta, Meliz Ann Marilag, Kristyl Lee Nisnisan, Felipe Lula Jr.

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Nowadays, alcohol is consumed widely around the globe for plenty of reasons. College years are the time that the students really decide if whether they will or will not engage into alcohol, although alcohol drinking begins before students arrive at college. The reasons on why college students consume alcohol vary in many categories. The norms on alcohol drinking are addiction, emotional pain reliever, popularity purposes, socialization, and a medium of euphoria for most students; college students in particular are most likely to feel this need. After tons of requirements to be complied and courses to be reviewed, they felt a need for celebration and relaxation which ends up in drinking with college mates and a few old friends. A lot of reasons consist the consumption of alcohol and this research determined the reasons behind the students’ onset for alcohol consumption; the main reason for such action and the experiences they encountered after in-take, furthermore, the correlation of alcohol drinking to the average allowance of the involved participants; Mindanao State University-Iligan Institute of Technology Students whether it affects their spending towards alcohol or not. This study assumes that alcohol drinking for MSU-IIT students’ is done to relieve emotional pain caused by flunking in particular subjects as well as dealing with romance, as part of the student body, these acts are noticeable enough which made this hypothesis be formulated. Selected MSU-IIT students were asked about their opinions regarding reasons of alcohol consumption. There were 100 respondents consisting of first year to fifth-year students aging 17-23 years old. Choices were given to the students to mark their most favorable reason for drinking that is adult influence, curiosity, family/personal problems, peer pressure, stress. Using the bar and pie chart illustrations, the collected data was then analyzed and among the given choices, the result has invalidated the hypothesis. The outcome shows that curiosity is the topmost reason why students start to drink and not due to emotional pain. With this, another hypothesis is formulated stating that millennial is a curious generation; this generation has changed the norm of drinking. One of the characteristics of the Y generation is being adventurous which correlates to how they get curious about things and the same goes for alcohol consumption, compared to the latter, this generation can be considered early drinkers in this manner. Therefore, it is concluded that MSU-IIT students which are part of the generation Y are adventurous enough to try unfamiliar beverages to satisfy their curious minds.

Keywords: adult influence, curiosity, family/personal problems, peer pressure, stress

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Authors: Digambar J. Darekar, Sharvari D. Darekar

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Childlessness is a universal problem. It particularly affects the mental health of childless couple. It leads to anxiety, frustration, nervousness, depression, loneliness, helplessness, hopelessness, etc. After reviewing the literature, it is found that mental health of married couples is negatively related to childlessness. To understand emotional and psychological problems of a childless couple, researcher surveyed and interviewed 50 childless couples with the help of medical practitioner and gynecologist. Personal adjustment and mental health inventory and marital adjustment inventory along with semi-structured interview questionnaire was used. On the basis of responses from the subject, distinction is made between the problems of male subjects and female subjects and common problem separately. The researcher found that childlessness leads to the conflict between in-laws, harassment, hopelessness, feeling of emptiness and vacuumed, frustration, lack of hope and desire for life, restlessness, loss of sleep, ideas of committing suicide, increased emotional distance and disturbed marital life. The childlessness leads to sorrow for women and anger for men. Men turns towards addiction and women tend to avoid social contact and face problems of social adjustments. Childless couples are sufferers of personal and marital adjustment problems which in turn affect their mental health adversely.

Keywords: childlessness, marital adjustments, mental health, social adjustment

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Authors: Noel Busch-Armendariz, Caitlin Sulley

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Introduction: This study investigated the development of college experiences, COVID-19 pandemic experiences, alcohol use, and sexual violence. The longitudinal study includes 656 college students living in the same dormitory. Students' alcohol use and social network structure were investigated to better understand the relationship with sexual violence risk. Basic Methodologies: Over two years, students repeated five web-based surveys, including a pre-college survey and surveys during four consecutive semesters. Questions were added in the fourth wave to assess students’ experiences of the COVID-19 pandemic, administered from November-January 2021, including mental and behavioral health. Analyses include the impact of COVID on living arrangements, drinking behaviors, and daily life; experiences of COVID symptoms, testing, and diagnosis, responses to COVID such as social distancing, quarantining, not working, increased health care needs; experience of fear, worry, stigma, emotional well-being, loneliness, and mental health; experiences of financial loss, lack of basic supplies, receiving emotional and financial support, and comparison with academic disengagement. Concluding Statement: Findings and discussion will include strategies to strengthen mental and behavioral health programs and policies.

Keywords: COVID, mental health, substance abuse, college students, sexual misconducts

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Authors: Haïfa Ben Mna, Ayed Added

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To evaluate the trace metals contamination status in the Gulf of Tunis, forty one sediment samples were analyzed using different approaches. According to certain contamination and ecological risk indices (Contamination Factor, Geoaccumulation index and Ecological risk index), Hg has the highest contamination level while pollution by Ni, Pb, Cd and Cr was absent. The highest concentrations of trace metals were found in sediments collected from the offshore and coastal areas located opposite the main exchange points with the gulf particularly, the Mejerda and Meliane Rivers, the Khalij Channel, Ghar El Melh and El Malah lagoons, Tunis Lake and Sebkhat Ariana. However, further ecological indices (Potential ecological risk index, Toxic unit and Mean effect-range median quotient) and comparison with sediment quality guidelines suggest that in addition to Mercury, Cr, Pb and Ni concentrations are detrimental to biota in both the offshore and areas near to the exchange points with the gulf. Moreover, in these areas the results from sequential extraction and individual contamination factor calculation pointed to the mobility and bioavailability of Cr, Pb and Ni.

Keywords: sediment, trace metals, contamination assessment, ecological risk, Tunis gulf

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Authors: Anat Yaron Antar, Tomer Einat

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This study explores the effects of the imprisonment of women together with females with mental disorders on the well-being of the former both during imprisonment and after their release from prison. Based on in-depth interviews with 22 women ex-prisoners who had been imprisoned for a period of at least two years in the single Israeli female correctional facility, Neve Tirza Prison, and released one to three months before the initiation of the study to a community-based agency managed by the Israeli Prisoner Rehabilitation Authority, and based on a qualitative, constructive strategy. We found that: (i) mentally ill prisoners’ conduct creates severe feelings of stress and discomfort among many of the prisoners without a mental disorder prisoners; (ii) The intimate and often long-term encounters with prisoners with a mental illness lead to increased feelings of distress, helplessness, fear, and frustration among many of the women prisoners; (iii) the damaging encounters between women prisoners and mentally-ill prisoners harmed the reintegration of the formers into society after release, and (iv) The women ex-prisoners lacked the basic mental, cognitive, and social tools necessary for dealing with female inmates with a mental illness and had received no psychological or emotional support from the prison personnel. Consequently, they suffered – and still suffer – from traumatic and upsetting memories Our findings led us to conclude that women prisoners should be imprisoned separately from female prisoners with mental disorders or be offered a wide range of psychological and emotional coping tools as well as various rehabilitative treatment programs.

Keywords: women, prisoners, mentally ill, health

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Authors: Ndidi Ofole

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Ongoing Boko Haram crisis and instability in North-Eastern Nigeria has placed additional stress on academic resilience of female undergraduates who are already challenged by gender discrimination in educational opportunities. Students without resilience lack stress hardiness to cope with academic challenges. There is a limited study on academic resilience targeting this disadvantaged population in Nigeria. Consequently, survey research design was employed to investigate the contextual and personal factors that could predict academic resilience among female undergraduates in Boko Haram Neighbourhood in North-Eastern, Nigeria. Five hundred and thirty female students with age range of 18 to 24 years ( = 19.2; SD=6.9) were randomly drawn from 3 Universities in North-Eastern Nigeria. They responded to five instruments, namely; Academic Resilience scale (r=0.72); Social Support questionnaire (r=0. 64); Social Connectedness questionnaire (r=0.75); Self-Efficacy scale (r=0. 68) and Emotional Regulation questionnaire (r=78). Results showed that there was significant positive relationship between the four independent variables and academic resilience. The variables jointly contributed 5.9% variance in the prediction of academic resilience. In terms of magnitude, social support was most potent while self-efficacy was the least. It concluded that the factors considered in this study are academic resilience facilitators. The outcomes of the study have both theoretical and practical implications.

Keywords: academic resilience, emotional regulation, school connectedness, self-efficacy , social support

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Authors: Nattapong Techarattanased

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The objectives of this research were to study the influence of market orientation and supply chain management on competitive capability in case of the automotive parts industry in Thailand. This study employed by survey research and questionnaire was used to collect the data from 400 entrepreneurs in the automotive parts industry in Thailand. The descriptive statistics and multiple regression analysis were used to analyze data. The results revealed that the overall dimensions of marketing orientation, namely, responsiveness, intelligence generation, and intelligence dissemination were rated at the high level. As well, the overall dimensions of supply chain management, namely, collaboration, communication, trust, and commitment were also rated at the high level. Furthermore, the hypothesis testing results showed that supply chain management and market orientation affected competitive capability of the automotive parts industry in Thailand which these two variables could be combined to predict competitive capability of the automotive parts industry in Thailand by 31.5 percent.

Keywords: automotive parts industry, competitive capability, market orientation, supply chain management

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Authors: Swarna Pundir, Prabuddha Hans

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As AI has already stepped into the daily life of human society, one cannot be ignorant about the data it collects and used it to provide a quality of services depending up on the individuals’ choices. It also helps in giving option for making decision Vs choice selection with a calculation based on the history of our search criteria. Over the past decade or so, the way Artificial Intelligence (AI) has impacted society is undoubtedly large.AI has changed the way we shop, the way we entertain and challenge ourselves, the way information is handled, and has automated some sections of our life. We have answered as to what AI is, but not why one may see it as useful. AI is useful because it is capable of learning and predicting outcomes, using Machine Learning (ML) and Deep Learning (DL) with the help of Artificial Neural Networks (ANN). AI can also be a system that can act like humans. One of the major impacts be Joblessness through automation via AI which is seen mostly in manufacturing sectors, especially in the routine manual and blue-collar occupations and those without a college degree. It raises some serious concerns about AI in regards of less employment, ethics in making moral decisions, Individuals privacy, human judgement’s, natural emotions, biased decisions, discrimination. So, the question is if an error occurs who will be responsible, or it will be just waved off as a “Machine Error”, with no one taking the responsibility of any wrongdoing, it is essential to form some rules for using the AI where both machines and humans are involved.

Keywords: AI, ML, DL, ANN

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Authors: Ioanna Koumi

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The aim of the intervention presented is to show the positive effects a mindfulness programme can have on the behaviour of preschoolers (years 4-6). The programme was implemented as part of the psychologist's work in 5 preschool units on the Greek island of Chios. Classroom-based activities of mindfulness were shown and practiced in 5 sessions, in collaboration with teachers, in order to make preschoolers aware of how their brain affects their behaviour, as well as of how they can have more positive behaviours, especially in instances of negative feelings. The outcomes of the intervention were assessed via questionnaire completion before and after the sessions by the teachers, as well as focus groups procedures with students, teachers, and parents. Implications of how mindfulness programmes can also be implemented at home are further discussed. School year in which the programme is being implemented: 2022-23 Intervention method: based on basic mindfulness theory and practice, the 220 students (age 4-6) in 11 classes of the 5 preschools that participated were given lessons of how to become aware of their states of focusing, regulation, attention, emotional situation, as well as body and social situations. Furthermore, the preschoolers were encouraged to make more mindful choices when it came to negative situations and emotions. Assessment method: The school as a caring community Profile II – Questionnaire completed by 20 preschool teachers prior to and after the intervention, Focus group sessions with teachers, students, parents at the end of the intervention Results: the assessment will be completed in May 2023.

Keywords: preschool, mindfulness training, self-awareness, social-emotional development

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Authors: Khushboo Shah

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Categorical bans on the death penalty and life-without-parole sentences for juvenile offenders in a growing number of countries have established a new era in juvenile jurisprudence. This has been brought about by integration of the growing knowledge in cognitive neuroscience and appreciation of the inherent differences between adults and adolescents over the last ten years. This evolving understanding of being a child in the criminal system can be aptly reflected through policies that incorporate the mitigating traits of youth. First, the presentation will delineate the structures in cognitive neuroscience and in particular, focus on the prefrontal cortex, the amygdala, and the basal ganglia. These key anatomical structures in the brain are linked to three mitigating adolescent traits—an underdeveloped sense of responsibility, an increased vulnerability to negative influences, and transitory personality traits—that establish why juveniles have a lessened culpability. The discussion will delve into the details depicting how an underdeveloped prefrontal cortex results in the heightened emotional angst, high-energy and risky behavior characteristic of the adolescent time period or how the amygdala, the emotional center of the brain, governs different emotional expression resulting in why teens are susceptible to negative influences. Based on this greater understanding, it is incumbent that policies adequately reflect the adolescent physiology and psychology in the criminal system. However, it is important to ensure that these views are appropriately weighted while considering the jurisprudence for the treatment of children in the law. To ensure this balance is appropriately stricken, policies must incorporate the distinctive traits of youth in sentencing and legal considerations and yet refrain from the potential fallacies of absolving a juvenile offender of guilt and culpability. Accordingly, three policies will demonstrate how these results can be achieved: (1) eliminate housing of juvenile offenders in the adult prison system, (2) mandate fitness hearings for all transfers of juveniles to adult criminal court, and (3) use the post-disposition review as a type of rehabilitation method for juvenile offenders. Ultimately, this interdisciplinary approach of science and law allows for a better understanding of adolescent psychological and social functioning and can effectuate better legal outcomes for juveniles tried as adults.

Keywords: criminal law, Juvenile Justice, interdisciplinary, neuroscience

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Authors: Fateme Aysin Anka, Farzad Kiani

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Forced migration is a situation in which people are forced to leave their homes against their will due to political conflicts, wars and conflicts, natural disasters, climate change, economic crises, or other emergencies. This type of migration takes place under conditions where people cannot lead a sustainable life due to reasons such as security, shelter and meeting their basic needs. This type of migration may occur in connection with different factors that affect people's living conditions. In addition to these general and widespread reasons, water security and resources will be one that is starting now and will be encountered more and more in the future. Forced migration may occur due to insufficient or depleted water resources in the areas where people live. In this case, people's living conditions become unsustainable, and they may have to go elsewhere, as they cannot obtain their basic needs, such as drinking water, water used for agriculture and industry. To cope with these situations, it is important to minimize the causes, as international organizations and societies must provide assistance (for example, humanitarian aid, shelter, medical support and education) and protection to address (or mitigate) this problem. From the international perspective, plans such as the Green New Deal (GND) and the European Green Deal (EGD) draw attention to the need for people to live equally in a cleaner and greener world. Especially recently, with the advancement of technology, science and methods have become more efficient. In this regard, in this article, a multidisciplinary case model is presented by reinforcing the water problem with an engineering approach within the framework of the social dimension. It is worth emphasizing that this problem is largely linked to climate change and the lack of a sustainable water management perspective. As a matter of fact, the United Nations Development Agency (UNDA) draws attention to this problem in its universally accepted sustainable development goals. Therefore, an artificial intelligence-based approach has been applied to solve this problem by focusing on the water management problem. The most general but also important aspect in the management of water resources is its correct consumption. In this context, the artificial intelligence-based system undertakes tasks such as water demand forecasting and distribution management, emergency and crisis management, water pollution detection and prevention, and maintenance and repair control and forecasting.

Keywords: water resource management, forced migration, multidisciplinary studies, artificial intelligence

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Authors: Ray Bryant

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3Strands Global Foundation (3SGF), a non-profit with a mission to mobilize communities to combat human trafficking through prevention education and reintegration programs, launched a groundbreaking study that calls out the usage and benefits of artificial intelligence in the war against human trafficking. Having gathered more than 30,000 stories from counselors and school staff who have gone through its PROTECT Prevention Education program, 3SGF sought to develop a methodology to measure the effectiveness of the training, which helps educators and school staff identify physical signs and behaviors indicating a student is being victimized. The program further illustrates how to recognize and respond to trauma and teaches the steps to take to report human trafficking, as well as how to connect victims with the proper professionals. 3SGF partnered with Levity, a leader in no-code Artificial Intelligence (AI) automation, to create the research study utilizing natural language processing, a branch of artificial intelligence, to measure the effectiveness of their prevention education program. By applying the logic created for the study, the platform analyzed and categorized each story. If the story, directly from the educator, demonstrated one or more of the desired outcomes; Increased Awareness, Increased Knowledge, or Intended Behavior Change, a label was applied. The system then added a confidence level for each identified label. The study results were generated with a 99% confidence level. Preliminary results show that of the 30,000 stories gathered, it became overwhelmingly clear that a significant majority of the participants now have increased awareness of the issue, demonstrated better knowledge of how to help prevent the crime, and expressed an intention to change how they approach what they do daily. In addition, it was observed that approximately 30% of the stories involved comments by educators expressing they wish they’d had this knowledge sooner as they can think of many students they would have been able to help. Objectives Of Research: To solve the problem of needing to analyze and accurately categorize more than 30,000 data points of participant feedback in order to evaluate the success of a human trafficking prevention program by using AI and Natural Language Processing. Methodologies Used: In conjunction with our strategic partner, Levity, we have created our own NLP analysis engine specific to our problem. Contributions To Research: The intersection of AI and human rights and how to utilize technology to combat human trafficking.

Keywords: AI, technology, human trafficking, prevention

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Authors: Oluwatosin Olofintoye, Josiah Adeyemo, Gbemileke Shomade

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The relationship between rainfall and runoff is an important issue in surface water hydrology therefore the understanding and development of accurate rainfall-runoff models and their applications in water resources planning, management and operation are of paramount importance in hydrological studies. This paper reviews some of the previous works on the rainfall-runoff process modeling. The hydrologic applications of conceptual models and artificial neural networks (ANNs) for the precipitation-runoff process modeling were studied. Gradient training methods such as error back-propagation (BP) and evolutionary algorithms (EAs) are discussed in relation to the training of artificial neural networks and it is shown that application of EAs to artificial neural networks training could be an alternative to other training methods. Therefore, further research interest to exploit the abundant expert knowledge in the area of artificial intelligence for the solution of hydrologic and water resources planning and management problems is needed.

Keywords: artificial intelligence, artificial neural networks, evolutionary algorithms, gradient training method, rainfall-runoff model

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Authors: Roberta Martino, Viviana Ventre

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Every day we face choices whose consequences are deferred in time. These types of choices are the intertemporal choices and play an important role in the social, economic, and financial world. The Discounted Utility Model is the mathematical model of reference to calculate the utility of intertemporal prospects. The discount rate is the main element of the model as it describes how the individual perceives the indeterminacy of subsequent periods. Empirical evidence has shown a discrepancy between the behavior expected from the predictions of the model and the effective choices made from the decision makers. In particular, the term temporal inconsistency indicates those choices that do not remain optimal with the passage of time. This phenomenon has been described with hyperbolic models of the discount rate which, unlike the linear or exponential nature assumed by the discounted utility model, is not constant over time. This paper explores the problem of inconsistency by tracing the decision-making process through the concept of impatience. The degree of impatience and the degree of decrease of impatience are two parameters that allow to quantify the weight of emotional factors and cognitive limitations during the evaluation and selection of alternatives. In fact, although the theory assumes perfectly rational decision makers, behavioral finance and cognitive psychology have made it possible to understand that distortions in the decision-making process and emotional influence have an inevitable impact on the decision-making process. The degree to which impatience is diminished is the focus of the first part of the study. By comparing consistent and inconsistent preferences over time, it was possible to verify that some anomalies in the discounted utility model are a result of the combination of cognitive bias and emotional factors. In particular: the delay effect and the interval effect are compared through the concept of misperception of time; starting from psychological considerations, a criterion is proposed to identify the causes of the magnitude effect that considers the differences in outcomes rather than their ratio; the sign effect is analyzed by integrating in the evaluation of prospects with negative outcomes the psychological aspects of loss aversion provided by Prospect Theory. An experiment implemented confirms three findings: the greatest variation in the degree of decrease in impatience corresponds to shorter intervals close to the present; the greatest variation in the degree of impatience occurs for outcomes of lower magnitude; the variation in the degree of impatience is greatest for negative outcomes. The experimental phase was implemented with the construction of the hyperbolic factor through the administration of questionnaires constructed for each anomaly. This work formalizes the underlying causes of the discrepancy between the discounted utility model and the empirical evidence of preference reversal.

Keywords: decreasing impatience, discount utility model, hyperbolic discount, hyperbolic factor, impatience

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Authors: Yen Ling Liu, Shu-Fen Wu, Chen-Fuh Lam, I-Ling Tsai, Chia-Yu Chen

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Purpose: Nurse anesthetists are confronting extraordinarily high job stress in their daily practice, deriving from the fast-track anesthesia care, risk of perioperative complications, routine rotating shifts, teaching programs and interactions with the surgical team in the operating room. This study investigated the influence of work stress on the burnout and turnover intention of nurse anesthetists in a regional general hospital in Southern Taiwan. Methods: This was a descriptive correlational study carried out in 66 full-time nurse anesthetists. Data was collected from March 2017 to June 2017 by in-person interview, and a self-administered structured questionnaire was completed by the interviewee. Outcome measurements included the Practice Environment Scale of the Nursing Work Index (PES-NWI), Maslach Burnout Inventory (MBI) and nursing staff turnover intention. Numerical data were analyzed by descriptive statistics, independent t test, or one-way ANOVA. Categorical data were compared using the chi-square test (x²). Datasets were computed with Pearson product-moment correlation and linear regression. Data were analyzed by using SPSS 20.0 software. Results: The average score for job burnout was 68.7916.67 (out of 100). The three major components of burnout, including emotional depletion (mean score of 26.32), depersonalization (mean score of 13.65), and personal(mean score of 24.48). These average scores suggested that these nurse anesthetists were at high risk of burnout and inversely correlated with turnover intention (t = -4.048, P < 0.05). Using linear regression model, emotional exhaustion and depersonalization were the two independent factors that predicted turnover intention in the nurse anesthetists (19.1% in total variance). Conclusion/Implications for Practice: The study identifies that the high risk of job burnout in the nurse anesthetists is not simply derived from physical overload, but most likely resulted from the additional emotional and psychological stress. The occurrence of job burnout may affect the quality of nursing work, and also influence family harmony, in turn, may increase the turnover rate. Multimodal approach is warranted to reduce work stress and job burnout in nurse anesthetists to enhance their willingness to contribute in anesthesia care.

Keywords: anesthesia nurses, burnout, job, turnover intention

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Authors: Janice Cheong, Tan Seying

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The growing years in adolescence are often a tumultuous time for both the individual and family; this is especially so for individuals with Autism Spectrum Disorder (ASD) and Social Communication Disorder (SCD). Tea Club, which is adapted from the PEERS® for Young Adults, seeks to address some of the social challenges faced by Singaporean adolescents with ASD/SCD while navigating social situations. Tea club (hybrid) consists of face-to-face sessions and virtual sessions. These sessions work with both the adolescent and their parents to tackle the individual's difficulties with social skills, empathy, and loneliness. Prior to the group intervention, both participants and their parents scored on the Test of Adolescent Social Skills Knowledge (TASSK) and Autism Spectrum Quotient (AQ), respectively. The session was spread across four months. At the end of the group based intervention, participants’ and parents’ scores were collected again and compared. Inputs on the programme and participant’s confidence in socialization were also gathered from both participants and their parents and looked at thematically. The findings highlight some of the challenges faced by teens with ASD in Singapore and the benefits of the intervention. Parental sentiments are also examined and discussed.

Keywords: adolescence autism, group intervention, social communication disorder, social skills

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Authors: John E. Boberg, Steven J. Bourgeois

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The decline in student engagement and motivation through the middle years is well documented and clearly associated with a decline in mathematics achievement that persists through high school. To combat this trend and, very often, to meet high-stakes accountability standards, a growing number of parents, teachers, and schools have implemented various methods to incentivize learning. However, according to Self-Determination Theory, forms of incentivized learning such as public praise, tangible rewards, or threats of punishment tend to undermine intrinsic motivation and learning. By focusing on external forms of motivation that thwart autonomy in children, adults also potentially threaten relatedness measures such as trust and emotional engagement. Furthermore, these controlling motivational techniques tend to promote shallow forms of cognitive engagement at the expense of more effective deep processing strategies. Therefore, any short-term gains in apparent engagement or test scores are overshadowed by long-term diminished motivation, resulting in inauthentic approaches to learning and lower achievement. The current study focuses on the relationships between student trust, engagement, and motivation during these crucial years as students transition from elementary to middle school. In order to test the effects of controlled motivational techniques on achievement in mathematics, this quantitative study was conducted on a convenience sample of 22 elementary and middle schools from a single public charter school district in the south-central United States. The study employed multi-source data from students (N = 1,054), parents (N = 7,166), and teachers (N = 356), along with student achievement data and contextual campus variables. Cross-sectional questionnaires were used to measure the students’ self-regulated learning, emotional and cognitive engagement, and trust in teachers. Parents responded to a single item on incentivizing the academic performance of their child, and teachers responded to a series of questions about their acceptance of various incentive strategies. Structural equation modeling (SEM) was used to evaluate model fit and analyze the direct and indirect effects of the predictor variables on achievement. Although a student’s trust in teacher positively predicted both emotional and cognitive engagement, none of these three predictors accounted for any variance in achievement in mathematics. The parents’ use of incentives, on the other hand, predicted a student’s perception of his or her controlled motivation, and these two variables had significant negative effects on achievement. While controlled motivation had the greatest effects on achievement, parental incentives demonstrated both direct and indirect effects on achievement through the students’ self-reported controlled motivation. Comparing upper elementary student data with middle-school student data revealed that controlling forms of motivation may be taking their toll on student trust and engagement over time. While parental incentives positively predicted both cognitive and emotional engagement in the younger sub-group, such forms of controlling motivation negatively predicted both trust in teachers and emotional engagement in the middle-school sub-group. These findings support the claims, posited by Self-Determination Theory, about the dangers of incentivizing learning. Short-term gains belie the underlying damage to motivational processes that lead to decreased intrinsic motivation and achievement. Such practices also appear to thwart basic human needs such as relatedness.

Keywords: controlled motivation, student engagement, incentivized learning, mathematics achievement, self-determination theory, student trust

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Authors: Hadi Farahani, Mahsa Tahzibi, Laleh Golamrej Eliasi, Mohammad Torkashvand

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Iranian deafs are an under-researched population. The lack of research comes from the fact that if none, there are very few researchers capable of speaking sign language. The exclusion of this minority group from mainstream society often distorts the general understanding of prevalent issues of the deaf in Iran. The topic of this research was co-created through preliminary discussions with the Iranian deaf. Domestic violence then was picked up as an infrastructural issue impacting other dimensions of deaf lives such as work, education, and outside family relationships. For this purpose, we systematically searched the literature seeking a comprehensive questionnaire. We came across a 46-item standardized questionnaire measuring domestic violence in Iran. To adapt this questionnaire, we followed standard procedures reflected in another article. The inclusion criteria of the current research were married (had experienced living with a partner before) and +18-year-old deaf. Sampling was random and recruitment of the participants was through governmental or voluntary organizations for the deaf. 390 questionnaires then were analyzed through SPSS version 27. Analysis showed that the prevalence rate of domestic violence was 26% in general that emotional violence with 29% was the most prevalent type. Findings suggested that the more educated, and economically independent were the participants, the lower the probability of encountering domestic violence. Domestic violence within families where all members were deaf proved to be less usual than in families in which only the participant was deaf. Further interventional research is needed to assess how to empower the Iranian deaf regarding domestic violence.

Keywords: deaf, domestic violence, economic violence, emotional violence, physical violence, sexual violence

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Authors: Akhila Potluru

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Worldwide, more than 250 bodies of water are transboundary, meaning they cross the political boundaries of multiple countries. This creates a system of hydrological, economic, and social interdependence between communities reliant on these water sources. Transboundary water conflicts can occur as a result of this intense interdependence. Many factors contribute to the sparking of transboundary water conflicts, ranging from natural hydrological factors to hydro-political interactions. Previous attempts to predict transboundary water conflicts by analysing changes or trends in the contributing factors have typically failed because patterns in the data are hard to identify. However, there is potential for artificial intelligence and machine learning to fill this gap and identify future ‘hotspots’ up to a year in advance by identifying patterns in data where humans can’t. This research determines the extent to which AI can be used to predict and prevent transboundary water conflicts. This is done via a critical literature review of previous case studies and datasets where AI was deployed to predict water conflict. This research not only delivered a more nuanced understanding of previously undervalued factors that contribute toward transboundary water conflicts (in particular, culture and disinformation) but also by detecting conflict early, governance bodies can engage in processes to de-escalate conflict by providing pre-emptive solutions. Looking forward, this gives rise to significant policy implications and water-sharing agreements, which may be able to prevent water conflicts from developing into wide-scale disasters. Additionally, AI can be used to gain a fuller picture of water-based conflicts in areas where security concerns mean it is not possible to have staff on the ground. Therefore, AI enhances not only the depth of our knowledge about transboundary water conflicts but also the breadth of our knowledge. With demand for water constantly growing, competition between countries over shared water will increasingly lead to water conflict. There has never been a more significant time for us to be able to accurately predict and take precautions to prevent global water conflicts.

Keywords: artificial intelligence, machine learning, transboundary water conflict, water management

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Authors: Magdalena Igras, Joanna Grzybowska, Mariusz Ziółko

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The study of emotions expressed in emergency phone call is presented, covering both statistical analysis of emotions configurations and an attempt to automatically classify emotions. An emergency call is a situation usually accompanied by intense, authentic emotions. They influence (and may inhibit) the communication between caller and responder. In order to support responders in their responsible and psychically exhaustive work, we studied when and in which combinations emotions appeared in calls. A corpus of 45 hours of conversations (about 3300 calls) from emergency call center was collected. Each recording was manually tagged with labels of emotions valence (positive, negative or neutral), type (sadness, tiredness, anxiety, surprise, stress, anger, fury, calm, relief, compassion, satisfaction, amusement, joy) and arousal (weak, typical, varying, high) on the basis of perceptual judgment of two annotators. As we concluded, basic emotions tend to appear in specific configurations depending on the overall situational context and attitude of speaker. After performing statistical analysis we distinguished four main types of emotional behavior of callers: worry/helplessness (sadness, tiredness, compassion), alarm (anxiety, intense stress), mistake or neutral request for information (calm, surprise, sometimes with amusement) and pretension/insisting (anger, fury). The frequency of profiles was respectively: 51%, 21%, 18% and 8% of recordings. A model of presenting the complex emotional profiles on the two-dimensional (tension-insecurity) plane was introduced. In the stage of acoustic analysis, a set of prosodic parameters, as well as Mel-Frequency Cepstral Coefficients (MFCC) were used. Using these parameters, complex emotional states were modeled with machine learning techniques including Gaussian mixture models, decision trees and discriminant analysis. Results of classification with several methods will be presented and compared with the state of the art results obtained for classification of basic emotions. Future work will include optimization of the algorithm to perform in real time in order to track changes of emotions during a conversation.

Keywords: acoustic analysis, complex emotions, emotion recognition, machine learning

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Authors: Bilha Paryente, Rivi Frei Landau

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Autoethnography - a combination of autobiography and ethnography - focuses on the intersection of personal experiences and the culture in which they take place and is considered a viable method for exploring human experiences. The Russo-Ukrainian war has resulted in millions of forcibly displaced asylum-seeking refugees facing ambiguous loss. Whereas much is known about refugees' support needs, little is known about the needs and experiences of the humanitarian delegation members (HDM) who assist them. Through a debrief-based co-autoethnographic account of a female HDM who supported Ukrainian refugee mothers and children on the Polish borders, we explored the lived experiences involved in such a mission. Specifically, we conducted a transnational dyadic autoethnography debrief-based co-autoethnography which included both verbal and photo-based debriefing (8 two-hour sessions) alongside a reflexive (10-day) field diary analysis. Content analysis revealed cognitive dilemmas, emotional struggles, and practical adaptations occurring within the HDM's three identity-related domains: personal, professional (psychologist), and ethnic. The methodology presented and demonstrated in this paper enhances our theoretical understanding of the challenges faced by HDMs and may contribute to better future design of HDM training. Practically, the findings of the current study suggest the need for a three-stage accompaniment for HDMs relating to their personal, professional, and ethnic identities and considering their cognitive, emotional, and adaptive aspects. First, before leaving, HDMs should be briefed on personal and professional aspects of their experiences and ways of coping with them, as well as ethnic and religious affiliation issues. Second, while volunteering every evening their dilemmas, emotional struggles, and ways of adapting should be addressed for the three layers of identities. And finally, shortly after their return, there should be a final meeting to discuss all aspects of their identities and layers of personality. In this way, HDMs can become more effective in the important mission they fulfill. We hope that future HDMs and the bodies that send them on humanitarian missions of paramount importance will adopt these recommendations and generate proactive insights for members of future delegations.

Keywords: autoethnography, refugees, humanitarian delegation, ambiguous loss, Russo-Ukraine War, parenting

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Authors: George Cudjoe Agbemabiese, John Paul Kosiba, Michael Boadi Nyamekye, Vanessa Narkie Tetteh, Caleb Nunoo, Mohammed Muniru Husseini

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The smartphone industry continues to experience massive growth, evidenced by expanding markets and an increasing number of brands, models and manufacturers. As technology advances rapidly, manufacturers of smartphones are consistently introducing new innovations to keep up with the latest evolving industry trends and customer demand for more modern devices. This study aimed to assess the influence of artificial intelligence (AI) voice assistant (VA) on improving customer experience, resulting in the continuous use of mobile brands. Specifically, this article assesses the role of hedonic, utilitarian, and social benefits provided by AIVA on customer experience and the continuance intention to use mobile phone brands. Using a primary data collection instrument, the quantitative approach was adopted to examine the study's variables. Data from 348 valid responses were used for the analysis based on structural equation modeling (SEM) with AMOS version 23. Three main factors were identified to influence customer experience, which results in continuous usage of mobile phone brands. These factors are social benefits, hedonic benefits, and utilitarian benefits. In conclusion, a significant and positive relationship exists between the factors influencing customer experience for continuous usage of mobile phone brands. The study concludes that mobile brands that invest in delivering positive user experiences are in a better position to improve usage and increase preference for their brands. The study recommends that mobile brands consider and research their prospects' and customers' social, hedonic, and utilitarian needs to provide them with desired products and experiences.

Keywords: artificial intelligence, continuance usage, customer experience, smartphone industry

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Authors: Falek Kamel

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Various approaches are usually used in the dynamic analysis of beams vibrating transversally. For this, numerical methods allowing the solving of the general eigenvalue problem are utilized. The equilibrium equations describe the movement resulting from the solution of a fourth-order differential equation. Our investigation is based on the finite element method. The findings of these investigations are the vibration frequencies obtained by the Jacobi method. Two types of the elementary mass matrix are considered, representing a uniform distribution of the mass along with the element and concentrated ones located at fixed points whose number is increased progressively separated by equal distances at each evaluation stage. The studied beams have different boundary constraints representing several classical situations. Comparisons are made for beams where the distributed mass is replaced by n concentrated masses. As expected, the first calculus stage is to obtain the lowest number of beam parts that gives a frequency comparable to that issued from the Rayleigh formula. The obtained values are then compared to theoretical results based on the assumptions of the Bernoulli-Euler theory. These steps are used for the second type of mass representation in the same manner.

Keywords: structural elements, beams vibrating, dynamic analysis, finite element method, Jacobi method

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