Search results for: social mental models

Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Jung Lee

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This study identifies how the cultivation capabilities of social capital influence the overall attitudes of social media users and how these influences differ across user groups. First, the cultivation capabilities of social capital are identified from three aspects, namely, social capital accessibility, potentiality and sensitivity. These three types of social capital acquisition capabilities collectively represent how the social media users perceive the social media environment in terms of possibilities for social capital creation. These three capabilities are hypothesized to influence social media satisfaction and continuing use intention. Next, two essential activities in social media are identified, namely, posting and replying, to categorise social media users based on behavioral patterns. Various social media activities consist of the combinations of these two basic activities. Posting represents the broadcasting aspect of social media, whereas replying represents the communicative aspect of social media. We categorize users into four from communicators to observers by using these two behaviors to develop usage pattern matrix. By applying the usage pattern matrix to the capability model, we argue that posting behavior generally has a positive moderating effect on the attitudes of social media users, whereas replying behavior occasionally exhibits the negative moderating effect. These different moderating effects of posting and replying behavior are explained based on the different levels of social capital sensitivity and expectation of individuals. When a person is highly expecting social capital from social media, he or she would post actively. However, when one is highly sensitive to social capital, he or she would actively respond and reply to postings of other people because such an act would create a longer and more interactive relationship. A total of 512 social media users are invited to answer the survey. They were asked about their attitudes toward the social media and how they expect social capital through this practice. They were asked to check their general social media usage pattern for user categorization. Result confirmed that most of the hypotheses were supported. Three types of social capital cultivation capabilities are significant determinants of social media attitudes, and two social media activities (i.e., posting and replying) exhibited different moderating effects on attitudes. This study provides following discussions. First, three types of social capital cultivation capabilities were identified. Despite the numerous concerns about social media, such as whether it is a decent and real environment that produces social capital, this study confirms that people explicitly expect and experience social capital values from social media. Second, posting and replying activities are two building blocks of social media activities. These two activities are useful in explaining different the attitudes of social media users and predict future usage.

Keywords: social media, social capital, social media satisfaction, social media use intention

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Authors: Imene Skhakhfa, Lahbaci Ouerdachi

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To ensure the overall coherence of simulated results, it is necessary to develop a robust validation process. In many applications, it is no longer content to calibrate and validate the model only in relation to the hydro graph measured at the outlet, but we try to better simulate the functioning of the watershed in space. Therefore the timing also performs compared to other variables such as water level measurements in intermediate stations or groundwater levels. As part of this work, we limit ourselves to modeling flood of short duration for which the process of evapotranspiration is negligible. The main parameters to identify the models are related to the method of unit hydro graph (HU). Three different models were tested: SNYDER, CLARK and SCS. These models differ in their mathematical structure and parameters to be calibrated while hydrological data are the same, the initial water content and precipitation. The models are compared on the basis of their performance in terms six objective criteria, three global criteria and three criteria representing volume, peak flow, and the mean square error. The first type of criteria gives more weight to strong events whereas the second considers all events to be of equal weight. The results show that the calibrated parameter values are dependent and also highlight the problems associated with the simulation of low flow events and intermittent precipitation.

Keywords: model calibration, intensity, runoff, hydrograph

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Authors: Abdunaser Abduelmula, Maria Luisa M. Bastos, José A. Gonçalves

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The modeling of the earth's surface and evaluation of urban environment, with 3D models, is an important research topic. New stereo capabilities of high-resolution optical satellites images, such as the tri-stereo mode of Pleiades, combined with new image matching algorithms, are now available and can be applied in urban area analysis. In addition, photogrammetry software packages gained new, more efficient matching algorithms, such as SGM, as well as improved filters to deal with shadow areas, can achieve denser and more precise results. This paper describes a comparison between 3D data extracted from tri-stereo and dual stereo satellite images, combined with pixel based matching and Wallis filter. The aim was to improve the accuracy of 3D models especially in urban areas, in order to assess if satellite images are appropriate for a rapid evaluation of urban environments. The results showed that 3D models achieved by Pleiades tri-stereo outperformed, both in terms of accuracy and detail, the result obtained from a Geo-eye pair. The assessment was made with reference digital surface models derived from high-resolution aerial photography. This could mean that tri-stereo images can be successfully used for the proposed urban change analyses.

Keywords: 3D models, environment, matching, pleiades

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Authors: Mouktafi Amine, Tahiri Asmaa

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A substantial number of traditional medical research has been put forward to managing and treating mental disorders. At the present time, to our best knowledge, it is believed that a fundamental understanding of the underlying causes of the majority of psychological disorders needs to be explored further to inform early diagnosis, managing symptoms and treatment. The emerging field of evolutionary psychology is a promising prospect to address the origin of mental disorders, potentially leading to more effective treatments. Schizophrenia as a topical mental disorder has been linked to the evolutionary adaptation of the human brain represented in the brain connectivity and asymmetry directly linked to humans' higher brain cognition in contrast to other primates being our direct living representation of the structure and connectivity of our earliest common African ancestors. As proposed in the evolutionary psychology scientific literature, the pathophysiology of schizophrenia is expressed and directly linked to altered connectivity between the Hippocampal Formation (HF) and Dorsolateral Prefrontal Cortex (DLPFC). This research paper presents the results of the use of tractography analysis using multiple open access Diffusion Weighted Imaging (DWI) datasets of healthy subjects, schizophrenia-affected subjects and primates to illustrate the relevance of the aforementioned brain regions' connectivity and the underlying evolutionary changes in the human brain. Deterministic fiber tracking and streamline analysis were used to generate connectivity matrices from the DWI datasets overlaid to compute distances and highlight disconnectivity patterns in conjunction with other fiber tracking metrics: Fractional Anisotropy (FA), Mean Diffusivity (MD) and Radial Diffusivity (RD).

Keywords: tractography, diffusion weighted imaging, schizophrenia, evolutionary psychology

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Authors: Amanda Kavner, Richard Lamb

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Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

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Authors: Katalin Gocze, Gabriella Kiss, Zsuzsanna Gurdan, Krisztian Kvell, Attila Trabert

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Introduction: On a worldwide scale running has become an increasingly popular leisure time activity during the past decade. Since the participation rate of women has risen significantly the aim of our study was to analyze the mental status, sleeping habits and the prevalence of depression among female runners. Methods: Cross-sectional analysis included the use of validated and globally used surveys for the comprehensive evaluation of insomnia (AIS), depression (BDI), exercise dependence (EDS) and exercise addiction (EAI). Recreational and amateur female runners participating at half-marathon events in Hungary were asked to take part in our pilot study. Results: Participants mean age was 42.03±9.03 years. The prevalence of imsomnia was 18.87%. 60.34% has worries regarding their weight and 43.1% think that they have an actual weight problem. 77.6% stated that their body weight has an influence on their mood. 2.7% displayed borderline clinical depression, the prevalence of mild mood disorders was 10.81%. 17.2% had previously problems with disordered eating. Participants had a mean total EDS score of 46.94±15.55 and a mean total of 13.49±3.80 on EAI. Component scores were the highest for tolerance (a need for increased amounts of exercise to achieve the desired effect or a diminished effect occurs with continued use of the same amount of exercise). Conclusion: Even tough running can help improve mental health, tackle depression and overcome adversity, athletes are at risk of experiencing psychological difficulties which have an impact on their physical perfomance as well. Further research can help initiate targeted educational and screening programs to ensure that female athletes find a path to emotional well-being.

Keywords: depression, eating disorder, exercise addiction, exercise dependence, insomnia, running

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Authors: Gonzalo García-Reyes

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Conformastat spherically symmetric exact solutions of Einstein's field equations representing matter distributions made of fluid both perfect and anisotropic from given solutions of Poisson's equation of Newtonian gravity are investigated. The approach is used in the construction of new relativistic models of thick spherical shells and three-component models of galaxies (bulge, disk, and dark matter halo), writing, in this case, the metric in cylindrical coordinates. In addition, the circular motion of test particles (rotation curves) along geodesics on the equatorial plane of matter configurations and the stability of the orbits against radial perturbations are studied. The models constructed satisfy all the energy conditions.

Keywords: general relativity, exact solutions, spherical symmetry, galaxy, kinematics and dynamics, dark matter

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Authors: Subhan Ahmad, Pradeep Bhargava, Ajay Chourasia

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Shear failure in reinforced concrete beams without shear reinforcement leads to loss of property and life since a very little or no warning occurs before failure as in case of flexural failure. Shear strength of reinforced concrete beams decreases as its depth increases. This phenomenon is generally called as the size effect. In this paper, a comparative analysis is performed to estimate the performance of shear strength models in capturing the size effect of reinforced concrete beams made with conventional concrete, self-compacting concrete, and recycled aggregate concrete. Four shear strength models that account for the size effect in shear are selected from the literature and applied on the datasets of slender reinforced concrete beams. Beams prepared with conventional concrete, self-compacting concrete, and recycled aggregate concrete are considered for the analysis. Results showed that all the four models captured the size effect in shear effectively and produced conservative estimates of the shear strength for beams made with normal strength conventional concrete. These models yielded unconservative estimates for high strength conventional concrete beams with larger effective depths ( > 450 mm). Model of Bazant and Kim (1984) captured the size effect precisely and produced conservative estimates of shear strength of self-compacting concrete beams at all the effective depths. Also, shear strength models considered in this study produced unconservative estimates of shear strength for recycled aggregate concrete beams at all effective depths.

Keywords: reinforced concrete beams; shear strength; prediction models; size effect

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Authors: Islah Ud Din, Amna Bibi

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The current study was carried out to examine the perceived social support, resilience and relapse risk in recovered addicts. A purposive sampling technique was used to collect data from recovered addicts. A multidimensional scale of perceived social support by was used to measure the perceived social support. The brief Resilience Scale (BRS) was used to assess resilience. The Stimulant Relapse Risk Scale (SRRS) was used to examine the relapse risk. Resilience and Perceived social support have substantial positive correlations, whereas relapse risk and perceived social support have significant negative associations. Relapse risk and resilience have a strong inverse connection. Regression analysis was used to check the mediating effect of resilience between perceived social support and relapse risk. The findings revealed that perceived social support negatively predicted relapse risk. Results showed that Resilience plays a role as partial mediation between perceived social support and relapse risk. This Research will allow us to explore and understand the relapse risk factor and the role of perceived social support and resilience in recovered addicts. The study's findings have immediate consequences in the prevention of relapse. The study will play a significant part in drug rehabilitation centers, clinical settings and further research.

Keywords: perceived social support, resilience, relapse risk, recovered addicts, drugs addiction

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Authors: Supannee Chaiumporn

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This article is to introduce the meaning and form of social quality moving process as indicated by members of two suburb communities with different social and cultural contexts. The form of social quality moving process is very significant for the community and social development, because it will make the people living together with sustainable happiness. This is a qualitative study involving 30 key-informants from two suburb communities. Data were collected though key-informant interviews, and analyzed using logical content description and descriptive statistics. This research found that on the social quality component, the people in both communities stressed the procedure for social quality-making. This includes the generousness, sharing and assisting among people in the communities. These practices helped making people to live together with sustainable happiness. Living as a family or appear to be a family is the major social characteristic of these two communities. This research also found that form of social quality’s moving process of both communities stress relation of human and nature; “nature overpower humans” paradigm and influence of religious doctrine that emphasizes relations among humans. Both criteria make the form of social’s moving process simple, adaptive to nature and caring for opinion sharing and understanding among each other before action. This form of social quality’s moving process is composed of 4 steps; (1) awareness building, (2) motivation to change, (3) participation from every party concerned (4) self-reliance.

Keywords: social quality, form of social quality moving process, happiness, different social and cultural context

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Authors: Tewelde Gebresslase Haile, S. P. Singh

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To bridge the existing knowledge gap on public intervention implementations, this study estimates the impact of social protection intervention (SPI) on alleviating social vulnerability. Following a multi-stage sampling, primary information was gathered through a self-administered questionnaire, FGD, and interviews from the target households located at four systematically selected districts of Tigrai, Ethiopia. Factor analysis and Propensity Score Matching are applied to construct Social Vulnerability Index (SVI) and measuring the counterfactual impact of selected intervention. As a multidimensional challenge, social vulnerability is found as an important concept used to guide policy evaluation. Accessibility of basic services of Social Affairs, Agriculture, Health and Education sectors, and Food Security Program are commonly used as SPIs. Finally, this study discovers that the households who had access to SPI have scored 9.65% lower SVI than in the absence of the intervention. Finally, this study suggests the provision of integrated, proactive, productive, and evidence-based SPIs to alleviate social vulnerability.

Keywords: social protection, livelihood assets, social vulnerability, public policy SVI

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Authors: Saba Aziz

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Social enterprises are playing a growing role in transforming the lives of individuals and communities around the world, providing innovative solutions to critical social and environmental issues such as education, job creation, and health care. Women are increasingly utilising services of these enterprises to overcome socio-economic constraints and increase their access to business and market. This article systematically reviews the available literature on the role of social enterprises on women's empowerment in South Asia. Twelve key terms were specified and researched on five databases. Some of the literature was excluded based on the lack of evidence on the involvement of social enterprises. Remaining literature was rated according to the quality; due to methodological inconsistency, the findings are presented in a descriptive form. The relevant studies review the impact of social enterprises on women’s economic, social, relational, health, personal and political aspects of empowerment. In discussion, we outline areas for further research on social enterprises activity that impacts women’s overall empowerment specifically in South Asia.

Keywords: social enterprise, women empowerment, systematic review, well-being, social impact, micro finance, South Asia, Pakistan

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Authors: Benjamin Cramer, Josephine Chau, Helen Little, Erica Randle

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Play Streets provide a safe and open space for children to play and adults to socialize by closing residential streets to through traffic. While research on Play Streets has typically focused on physical activity outcomes in children, there is limited research on the psychosocial health externalities for the wider community. Charles Sturt, a local government area in Adelaide, South Australia, has been hosting Play Streets for several years. The current study is a mixed-methods evaluation of the Charles Sturt Play Streets, concerned with the perceived psychological and social impacts that Play Streets impact on the community. A combination of semi-structured interviews of Play Street organizers and participants will be conducted and analyzed using inductive thematic analysis. Pre-existing survey data will also be analyzed quantitatively and qualitatively to triangulate the findings of the qualitative interviews. The implications of this research are far-reaching, from informing local councils of any additional health benefits of Play Streets, expanding the growing literature on Play Streets beyond childhood physical activity, informing the development of city infrastructure, and advancing the Sustainability Development Goals of Good Health and Wellbeing, Reduced Inequalities, and Sustainable Cities and Communities.

Keywords: play streets, mental health, social health, community health

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Authors: Jeremy Bekker, Guy Salazar

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Introduction: Nature-exposure interventions on university campuses may serve as an effective addition to overburdened counseling and student support centers. Nature-exposure interventions can work as a preventative well-being enhancement measure on campuses, which can be used adjacently with existing health resources. Specifically, this paper analyzes how spending time in nature impacts psychological well-being, cognitive functioning, and physical health. The poster covers the core findings and recommendations of this paper, which has been previously published in the BYU undergraduate psychology journal Intuition. Research Goals and Method: The goal of this paper was to outline the potential benefits of nature exposure for students’ physical health, mental well-being, and academic success. Another objective of this paper was to outline potential research-based interventions that use campus green spaces to improve student outcomes. Given that the core objective of this paper was to identify and establish research-based nature exposure interventions that could be used on college campuses, a broad literature review focused on these areas. Specifically, the databases Scopus and PsycINFO were used to screen for research focused on psychological well-being, physical health, cognitive functioning, and nature exposure interventions. Outcomes: Nature exposure has been shown to help increase positive affect, life satisfaction, happiness, coping ability and subjective well-being. Further, nature exposure has been shown to decrease negative affect, lower mental distress, reduce cognitive load, and decrease negative psychological symptoms. Finally, nature exposure has been shown to lead to better physical health. Findings and Recommendations: Potential interventions include adding green space to university buildings and grounds, dedicating already natural environments as nature restoration areas, and providing means for outdoor excursions. Potential limitations and suggested areas for future research are also addressed. Many campuses already contain green spaces, defined as any part of an environment that is predominately made of natural elements, and these green spaces comprise an untapped resource that is relatively cheap and simple.

Keywords: nature exposure, preventative care, undergraduate mental health, well-being intervention

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Authors: Igor A. Podgorny

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TurboTax AnswerXchange is a social Q/A system supporting users working on federal and state tax returns. Content quality and popularity in the AnswerXchange can be predicted with propensity models using attributes of the question and answer. Using uplift modeling, we identify features of questions and answers that can be modified during the question-asking and question-answering experience in order to optimize the AnswerXchange content quality. We demonstrate that adding details to the questions always results in increased question popularity that can be used to promote good quality content. Responding to close-ended questions assertively improve content quality in the AnswerXchange in 90% of cases. Answering knowledge questions with web links increases the likelihood of receiving a negative vote from 60% of the askers. Our findings provide a rationale for employing the uplift modeling approach for AnswerXchange operations.

Keywords: customer relationship management, human-machine interaction, text mining, uplift modeling

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Estifanos H. Zeru

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Introduction: Epidemiological research is important to draw need-based rational public health policy. However, research on child and adolescent mental health in low and middle income countries, where socioeconomic, political, cultural, biological and other mental health hazards are in abundance, is almost nonexistent. To the author's knowledge, there is no published research in this field in Eritrea, whose child and adolescent population constitutes 53% of its total population. Study Aims and Objectives: The objective of this study was to determine the prevalence and patterns of DSM-IV psychiatric disorders and identify their socio-demographic correlates among children and adolescents in Mendefera, Eritrea. The study aims to provide local information to public health policymakers to guide policy in service development. Methodology: In a cross-sectional two stage procedure, both the Parent and Child versions of the SDQ were used to screen 314 children and adolescents aged 4-17 years, recruited by a multi-stage random sampling method. All parents/adult guardians also completed a socio-demographic questionnaire. All children and adolescents who screened positive for any of the SDQ abnormality sub-classes were selected for the second stage interview, which was conducted using the K-SADS-PL 2009 Working Draft version to generate specific DSM-IV diagnoses. All data gathered was entered into CSPro version 6.2 and was then transported in to and analyzed using SPSS version 20 for windows. Results: Prevalence of DSM-IV psychiatric disorders was found to be 13.1%. Adolescents 11-17 years old and males had higher prevalence than children 4-10 years old and females, respectively. Behavioral disorders were the commonest disorders (9.9%), followed by affective disorders (3.2%) and anxiety disorders (2.5). Chronic medical illness in the child, poor academic performance, difficulties with teachers in school, psychopathology in a family member and parental conflict were found to be independently associated with these disorders. Conclusion: Prevalence of child and adolescent psychiatric disorders in Eritrea is high. Promotion, prevention, treatment, and rehabilitation for child and adolescent mental health services need to be made widely available in the country. The socio-demographic correlates identified by this study can be targeted for intervention. The need for further research is emphasized.

Keywords: adolescents, children, correlates, DSM-IV psychiatric disorders, Eritrea, K-SAD-PL 2009, prevalence and correlates, SDQ

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Authors: Awol Seid Ebrie

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HIV results as an incurable disease, AIDS. After a person is infected with virus, the virus gradually destroys all the infection fighting cells called CD4 cells and makes the individual susceptible to opportunistic infections which cause severe or fatal health problems. Several studies show that the CD4 cells count is the most determinant indicator of the effectiveness of the treatment or progression of the disease. The objective of this paper is to investigate the progression of the disease over time among patient under HAART treatment. Two main approaches of the generalized multilevel ordinal models; namely the proportional odds model and the nonproportional odds model have been applied to the HAART data. Also, the multilevel part of both models includes random intercepts and random coefficients. In general, four models are explored in the analysis and then the models are compared using the deviance information criteria. Of these models, the random coefficients nonproportional odds model is selected as the best model for the HAART data used as it has the smallest DIC value. The selected model shows that the progression of the disease increases as the time under the treatment increases. In addition, it reveals that gender, baseline clinical stage and functional status of the patient have a significant association with the progression of the disease.

Keywords: nonproportional odds model, proportional odds model, random coefficients model, random intercepts model

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Authors: Ekaterina Donii

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Understanding interpersonal competence, social interaction and peer relationships of gifted children is a concern for specialists in the field of gifted education. To gain more in-depth knowledge concerning the social functioning of gifted children among peers, we decided to study the social abilities of gifted children in a heterogeneous academic environment. Eight gifted children (5 of age 7, 1 of age 8.5, 1 of age 9.5 and 1 of age 10), their classmates (10 of age 7-8, 12 of age 8.5-9, 16 of age 9.5-10) and teachers participated in the study. The sociometric questionnaire analysis was based on the method of Rodríguez and Morera to check the social status of the gifted children among classmates. The Instrument Observational Protocol for Interactions within the Classroom (OPINTEC-v.5) was used to assess the social interactions between the gifted students, their classmates, and the teacher within the educational context. While doing a task together, the gifted children interacted more with popular and neither popular nor gifted classmates than with rejected classmates. While spending time together, the gifted children interacted more with neither popular nor rejected classmates than with popular or rejected classmates. All gifted children chose other gifted and non-gifted classmates for interaction, established close relations and demonstrated good social abilities interacting with their classmates. The aim of this study was to examine the social interactions, social status, and social network of the gifted students in a regular classroom. The majority of the gifted children were popular among their classmates and had good social skills. We should be alert, though, for those gifted children who do have social problems, in order to help them functioning in a regular classroom.

Keywords: gifted, heterogeneous environment, sociometric status, social interactions

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Authors: Yan Yan, Erhong Sun, Lin Peng, Xuchun Ye

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Aims: The present study aimed to explore Intensive Care Unit(ICU) nurses’ mental workload (MWL) and associated factors with it in performing human-machine tasks. Background: A wide range of emerging technologies have penetrated widely in the field of health care, and ICU nurses are facing a dramatic increase in nursing human-machine tasks. However, there is still a paucity of literature reporting on the general MWL of ICU nurses performing human-machine tasks and the associated influencing factors. Methods: A cross-sectional survey was employed. The data was collected from January to February 2021 from 9 tertiary hospitals in 6 provinces (Shanghai, Gansu, Guangdong, Liaoning, Shandong, and Hubei). Two-stage sampling was used to recruit eligible ICU nurses (n=427). The data were collected with an electronic questionnaire comprising sociodemographic characteristics and the measures of MWL, self-efficacy, system usability, and task difficulty. The univariate analysis, two-way analysis of variance(ANOVA), and a linear mixed model were used for data analysis. Results: Overall, the mental workload of ICU nurses in performing human-machine tasks was medium (score 52.04 on a 0-100 scale). Among the typical nursing human-machine tasks selected, the MWL of ICU nurses in completing first aid and life support tasks (‘Using a defibrillator to defibrillate’ and ‘Use of ventilator’) was significantly higher than others (p < .001). And ICU nurses’ MWL in performing human-machine tasks was also associated with age (p = .001), professional title (p = .002), years of working in ICU (p < .001), willingness to study emerging technology actively (p = .006), task difficulty (p < .001), and system usability (p < .001). Conclusion: The MWL of ICU nurses is at a moderate level in the context of a rapid increase in nursing human-machine tasks. However, there are significant differences in MWL when performing different types of human-machine tasks, and MWL can be influenced by a combination of factors. Nursing managers need to develop intervention strategies in multiple ways. Implications for practice: Multidimensional approaches are required to perform human-machine tasks better, including enhancing nurses' willingness to learn emerging technologies actively, developing training strategies that vary with tasks, and identifying obstacles in the process of human-machine system interaction.

Keywords: mental workload(MWL), nurse, ICU, human-machine, tasks, cross-sectional study, linear mixed model, China

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Authors: C. Arslan, K. Yakar

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Social media is one of the most important and effective means of social interaction among people in which they create, share and exchange their ideas via photos, videos or voice messages. Although there are lots of communication tools. Social media sites are the most prominent ones that allows the users articulate themselves in a matter of seconds all around the world with almost any expenses and thus, they became very popular and widespread after its emergence. As the usage of social media increases, it becomes an effective instrument in social matters. While it is possible to use social media to emphasize basic human rights and protest some failures of any government as in “Arab Spring”, it is also possible to spread propaganda and misinformation just to cause long lasting insurgency, upheaval, turmoil or disorder as an instrument of intervention to internal affairs and state sovereignty by some hostile groups or countries. It is certain that social media has positive effects on participation in democracies allowing people express themselves freely and limitlessly, but obviously, the misuse of it is very common and it is quite possible that even a five-minute-long video record can topple down a government or give a solid reason to a government to review its policies on some certain areas. As one of the most important and effective means of participation, social media presents some opportunities as well as risks. In this study, the place of social media for participation in democracies will be demonstrated under the light of opportunities and risks.

Keywords: social media, democracy, participation, risks, opportunities

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Authors: Saba Latif

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The use of social media among university students is a topic of ongoing debate, with conflicting views on its impact on academic achievement. This study aimed to explore the relationship between social media use and academic achievement among university students and to identify factors that may contribute to positive or negative effects. The study used a mixed-methods design, including a survey of 500 university students and qualitative interviews with a subset of participants. The survey results showed that social media use was prevalent among students, with Facebook and Instagram are the most commonly used platforms. The findings also indicated a positive relationship between social media use and academic achievement, with students who reported higher levels of social media use also reporting higher GPAs. However, the qualitative interviews revealed that excessive use of social media could be a distraction that hinders academic performance, especially when students use it to procrastinate or to stay up late at night. Overall, the findings suggest that social media use can have both positive and negative effects on academic achievement among university students. Responsible and balanced use of social media, such as setting limits on usage and avoiding procrastination, may help students maximize the benefits while minimizing the risks.

Keywords: social media, university, achievement, effective, learning

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Authors: Suha Alawadhi, Malak Alrasheed

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In the recent past, public participation in governments has been declined to a great extent, as citizens have been isolated from community life and their ability to articulate demands for good government has been noticeably decreased. However, the Internet has introduced new forms of interaction that could enhance different types of relationships, including government-public relationship. In fact, technology-enabled government has become a catalyst for enabling social inclusion. This exploratory study seeks to investigate public perceptions in Kuwait regarding the use of social media networks in government where social capital is built to achieve social inclusion. Social capital has been defined as social networks and connections amongst individuals, that are based on shared trust, ideas and norms, enable participants of a network to act effectively to pursue a shared objective. The quantitative method was used to generate empirical evidence. A questionnaire was designed to address the research objective and reflect the identified constructs: social capital dimensions (bridging, bonding and maintaining social capital), social inclusion, and social equality. In this pilot study, data was collected from a random sample of 61 subjects. The results indicate that all participants have a positive attitude towards the dimensions of social capital (bridging, bonding and maintaining), social inclusion and social equality constructs. Tests of identified constructs against demographic characteristics indicate that there are significant differences between male and female as they perceived bonding and maintaining social capital, social inclusion and social equality whereas no difference was identified in their perceptions of bridging social capital. Also, those who are aged 26-30 perceived bonding and maintaining social capital, social inclusion and social equality negatively compared to those aged 20-25, 31-35, and 40-above whose perceptions were positive. With regard to education, the results also show that those holding high school, university degree and diploma perceived maintaining social capital positively higher than with those who hold graduate degrees. Moreover, a regression model is proposed to study the effect of bridging, bonding, and maintaining social capital on social inclusion via social equality as a mediator. This exploratory study is necessary for testing the validity and reliability of the questionnaire which will be used in the main study that aims to investigate the perceptions of individuals towards building social capital to achieve social inclusion.

Keywords: government, social capital, social inclusion, social networks

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Authors: Eko Budi Santoso, Kazia Laturette, Stanislaus Adnanto Mastan

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This study aims to examine the association between disclosure of social responsibility and tax aggressiveness in developing countries, namely Thailand. This is due to the increasing trend of disclosure of social responsibility in developing countries, even though this disclosure of information is still voluntary. On the other hand, developing countries have low taxation rate and investor protection infrastructures that allow the disclosure of social responsibility to be used opportunistically as a tool to fool the attainment of interests. This study also examines the role of assurance on the association between corporate social responsibility disclosure and tax aggressiveness. The assurance aims to provide confidence that the disclosure of social responsibility by the company is valid. This research builds an index to measure the disclosure of social responsibility based on the rules issued by the innovative Global Reporting. The results of the study are based on a sample of publicly traded companies in Thailand, which showed a positive association between disclosure of corporate social responsibility and tax aggressiveness, but it was further discovered that these results were mitigated by the existence of assurance against disclosure of corporate social responsibility. The results of this study indicate that the disclosure of corporate social responsibility can show that the company cares about the issue of social responsibility but does not automatically make the company as one that holds ethical values ​​in its business practices.

Keywords: corporate social responsibility disclosure, tax aggressiveness, sustainability assurance, business ethics

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Authors: Abhishek Saha, Serene Tay, Gerard Pijcke

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The availability of high-resolution topography and rainfall information in urban areas has made it necessary to revise modeling approaches used for simulating flood risk assessments. Lidar derived elevation models that have 1m or lower resolutions are becoming widely accessible. The classical approaches of 1D-2D flow models where channel flow is simulated and coupled with a coarse resolution 2D overland flow models may not fully utilize the information provided by high-resolution data. In this context, a study was undertaken to compare three different modeling approaches to simulate flooding in an urban area. The first model used is the base model used is Sobek, which uses 1D model formulation together with hydrologic boundary conditions and couples with an overland flow model in 2D. The second model uses a full 2D model for the entire area with shallow water equations at the resolution of the digital elevation model (DEM). These models are compared against another shallow water equation solver in 2D, which uses a subgrid method for grid refinement. These models are simulated for different horizontal resolutions of DEM varying between 1m to 5m. The results show a significant difference in inundation extents and water levels for different DEMs. They are also sensitive to the different numerical models with the same physical parameters, such as friction. The study shows the importance of having reliable field observations of inundation extents and levels before a choice of model and data can be made for spatial flood risk assessments.

Keywords: flooding, DEM, shallow water equations, subgrid

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Authors: LaShawnda N. Fields, Valandra

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There remains much to learn about the experiences of Black women within social work education. Higher education, in general, has a strained relationship with this demographic and while social work has espoused a code of ethics and core values, Black women report inequitable experiences similar to those in other disciplines. Research-intensive (R-1) Carnegie-designated institutions typically have lower representation of those with historically marginalized identities; this study focuses on Black women in these schools of social work. This study presents qualitative findings from 9 in-depth interviews with Black women faculty members as well as interviews with 11 Black women doctoral students at R-1 universities. Many of the poor professional outcomes for Black women in academia are a result of their experiences with imposter syndrome and feeling as though they cannot present their authentic selves. The finding of this study highlighted the many ways imposter syndrome manifests within these study participants, from an inability to be productive to overproducing in an effort to win the respect and support of colleagues. Being scrutinized and seen as unprofessional when being authentic has led to some Black women isolating themselves and struggling to remain in academia. Other Black women have decided that regardless of the backlash they may receive, they will proudly present their authentic selves and allow their work to speak for itself rather than conform to the dominant White culture. These semi-structured, in-depth interviews shined a spotlight on the ways Black women doctoral students were denied inclusion throughout their programs. These students often believed both faculty members and peers seemed to actively work to ensure discomfort in these women. In response to these negative experiences and a lack of support, many of these Black women doctoral students created their own networks of support. These networks of support often included faculty members within social work but also beyond their discipline and outside of the academy at large. The faculty members who offered support to this demographic typically shared their race and gender identities. Both Black women faculty members and doctoral students historically have been forced to prioritize surviving, not thriving as a result of toxic environments within their schools of social work. This has negatively impacted their mental health and their levels of productivity. It is necessary for these institutions to build trust with these women by respecting their diverse backgrounds, supporting their race-related research interests, and honoring the rigor in a range of methodologies if substantial, sustainable change is to be achieved.

Keywords: education, equity, inclusion, intersectionality

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Ilona Bontenbal

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In this research, the potential for societal change is researched through the idea of migrants as change agents. The viewpoint is on the potential that migrants have for affecting societal change in their country of origin through transmitting transnational peer-to-peer information. The focus is on the information that Russian migrants living in Finland transmit about their experiences and attitudes regarding the Nordic welfare state, its democratic foundation and the social rights embedded in it, to their family and friends in their country of origin. The welfare provision and level of democracy are very different in the two neighbouring countries of Finland and Russia. Finland is a Nordic welfare state with strong democratic institutions and a comprehensive actualizing of civil and social rights. In Russia, the state of democracy has on the other hand been declining, and the social and civil rights of its citizens are constantly undermined. Due to improvements in communications and travel technology, migrants can easily and relatively cheaply stay in contact with their family and friends in their country of origin. This is why it is possible for migrants to act as change agents. By telling about their experiences and attitudes about living in a democratic welfare state, migrants can affect what people in the country or origin know and think about welfare, democracy, and social rights. This phenomenon is approached through the concept of social remittances. Social remittances broadly stand for the ideas, know-how, world views, attitudes, norms of behavior, and social capital that flows through transnational networks from receiving- to sending- country communities and the other way around. The viewpoint is that historically and culturally formed democratic welfare models cannot be copied entirely nor that each country should achieve identical development paths, but rather that migrants themselves choose which aspects they see as important to remit to their acquaintances in their country of origin. This way the potential for social change and the agency of the migrants is accentuated. The empirical research material of this study is based on 30 qualitative interviews with Russian migrants living in Finland. Russians are the largest migrant group in Finland and Finland is a popular migration destination especially for individuals living in North-West Russia including the St. Petersburg region. The interviews are carried out in 2018-2019. The preliminary results indicate that Russian migrants discuss social rights and welfare a lot with their family members and acquaintances living in Russia. In general, the migrants feel that they have had an effect on the way that their friends and family think about Finland, the West, social rights and welfare provision. Democracy, on the other hand, is seen as a more difficult and less discussed topic. The transformative potential that the transmitted information and attitudes could have outside of the immediate circle of acquaintances on larger societal change is seen as ambiguous although not negligible.

Keywords: migrants as change agents, Russian migrants, social remittances, welfare and democracy

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Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

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Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

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