Search results for: shared/mental models

Authors: Eman Alhajjar, Albatool Jamjoom, Fatmah Bugshan

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This research aims to investigate the artificial intelligence (AI) application Jung GPT and how helpful it is, as a therapy AI, to users. Jung GPT has the potential to make mental health care more accessible and cheaper while also providing tailored support and advice. However, it is not intended to be a substitute for human therapists. Jung GPT is instructed to understand a wide range of concepts, including emojis, sensitive subjects, and various languages. Furthermore, participants were asked to fill out a survey based on their experience with Jung GPT. Additionally, analysis of the responses indicated that Jung GPT was helpful in identifying and exploring challenges, and the use of Jung GPT by participants in the future is highly possible. The results demonstrate that Jung GPT does help in recognizing challenges or problems within the users. On this basis, it is recommended that individuals use Jung GPT to explore their thoughts, feelings, and challenges. Moreover, further research is needed to better evaluate the effectiveness of Jung GPT.

Keywords: Jung GPT, artificial intelligence, therapy, mental health, AI application

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Authors: Louisa Browne Kirk

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In recent press interviews to promote Portrait de la jeune fille en feu (2019, translated to Portrait of a Lady on Fire in English), director and screenwriter Céline Sciamma and actors Adèle Haenel and Noémie Merlant repeatedly state that they understand the film as (if not uniquely, then unusually) produced via and supportive of ‘the female gaze’. Such a way of seeing stands in opposition to ‘the male gaze’, first theorised by Laura Mulvey as the way in which the female figure is a bearer, not maker, of meaning, a silent signifier through and against whom the male creator/viewer produces his fantasies and obsessions. What, then, is the female gaze? How does a woman produce meaning in and through film? Portrait de la jeune fille en feu and another very recent film, Little Women (2019, directed by Greta Gerwig), are unlikely companion films that understand the female gaze to be the act of one woman looking at another woman, a looking that is mediated through the production of art. In Sciamma’s film this looking is sexual and mediated through painting and in Gerwig’s film looking is familial and mediated through writing. In the schema of these films, art, love, looking and meaning are produced through collaboration. The painted and the painter, the written and the writer, are no longer rendered as subject and object but as dual creators, both always seeing and seen. The gaze of the cinematic woman, mediated through shared artistic practice, is ‘the desire-that-gives’.

Keywords: female gaze, Gerwig, Sciamma, shared artistic practice

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Authors: Ma. Gracia Corazon Cayanan, Kai Yuen Cheong, Li Sha

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Training a language model for a minority language has been a challenging task. The lack of available corpora to train and fine-tune state-of-the-art language models is still a challenge in the area of Natural Language Processing (NLP). Moreover, the need for high computational resources and bulk data limit the attainment of this task. In this paper, we presented the following contributions: (1) we introduce and used a translation pair set of Tagalog and English (TL-EN) in pre-training a language model to a minority language resource; (2) we fine-tuned and evaluated top-ranking and pre-trained semantic textual similarity binary task (STSB) models, to both TL-EN and STS dataset pairs. (3) then, we reduced the size of the model to offset the need for high computational resources. Based on our results, the models that were pre-trained to translation pairs and STS pairs can perform well for STSB task. Also, having it reduced to a smaller dimension has no negative effect on the performance but rather has a notable increase on the similarity scores. Moreover, models that were pre-trained to a similar dataset have a tremendous effect on the model’s performance scores.

Keywords: semantic matching, semantic textual similarity binary task, low resource minority language, fine-tuning, dimension reduction, transformer models

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Authors: Hamda Ajmal, Karen Young, Ruth Melia, John Bogue, Mary O'Sullivan, Jim Duggan, Hannah Wood

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The Covid-19 pandemic led to a range of public health mitigation strategies in order to suppress the SARS-CoV-2 virus. The drastic changes in everyday life due to lockdowns had the potential for a significant negative impact on public mental health, and a key public health goal is to now assess the evidence from available Irish datasets to provide useful insights on this issue. Text-50808 is an online text-based mental health support service, established in Ireland in 2020, and can provide a measure of revealed distress and mental health concerns across the population. The aim of this study is to explore statistical associations between public mental health in Ireland and the Covid-19 pandemic. Uniquely, this study combines two measures of emotional wellbeing in Ireland: (1) weekly text volume at Text-50808, and (2) emotional wellbeing indicators reported by respondents of the Amárach public opinion survey, carried out on behalf of the Department of Health, Ireland. For this analysis, a multivariate graphical exploratory data analysis (EDA) was performed on the Text-50808 dataset dated from 15th June 2020 to 30th June 2021. This was followed by time-series analysis of key mental health indicators including: (1) the percentage of daily/weekly texts at Text-50808 that mention Covid-19 related issues; (2) the weekly percentage of people experiencing anxiety, boredom, enjoyment, happiness, worry, fear and stress in Amárach survey; and Covid-19 related factors: (3) daily new Covid-19 case numbers; (4) daily stringency index capturing the effect of government non-pharmaceutical interventions (NPIs) in Ireland. The cross-correlation function was applied to measure the relationship between the different time series. EDA of the Text-50808 dataset reveals significant peaks in the volume of texts on days prior to level 3 lockdown and level 5 lockdown in October 2020, and full level 5 lockdown in December 2020. A significantly high positive correlation was observed between the percentage of texts at Text-50808 that reported Covid-19 related issues and the percentage of respondents experiencing anxiety, worry and boredom (at a lag of 1 week) in Amárach survey data. There is a significant negative correlation between percentage of texts with Covid-19 related issues and percentage of respondents experiencing happiness in Amárach survey. Daily percentage of texts at Text-50808 that reported Covid-19 related issues to have a weak positive correlation with daily new Covid-19 cases in Ireland at a lag of 10 days and with daily stringency index of NPIs in Ireland at a lag of 2 days. The sudden peaks in text volume at Text-50808 immediately prior to new restrictions in Ireland indicate an association between a rise in mental health concerns following the announcement of new restrictions. There is also a high correlation between emotional wellbeing variables in the Amárach dataset and the number of weekly texts at Text-50808, and this confirms that Text-50808 reflects overall public sentiment. This analysis confirms the benefits of the texting service as a community surveillance tool for mental health in the population. This initial EDA will be extended to use multivariate modeling to predict the effect of additional Covid-19 related factors on public mental health in Ireland.

Keywords: COVID-19 pandemic, data analysis, digital health, mental health, public health, digital health

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Authors: Diteboho Xaba, Kolentino Mpeta, Tlotliso Qejoe

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This paper assesses the in-sample forecasting of the South African exchange rates comparing a linear ARIMA model and a SETAR model. The study uses a monthly adjusted data of South African exchange rates with 420 observations. Akaike information criterion (AIC) and the Schwarz information criteria (SIC) are used for model selection. Mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) are error metrics used to evaluate forecast capability of the models. The Diebold –Mariano (DM) test is employed in the study to check forecast accuracy in order to distinguish the forecasting performance between the two models (ARIMA and SETAR). The results indicate that both models perform well when modelling and forecasting the exchange rates, but SETAR seemed to outperform ARIMA.

Keywords: ARIMA, error metrices, model selection, SETAR

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Authors: H. Taylan Selamlar, I. Yavuz, G. Yapar

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It is difficult to make predictions especially about the future and making accurate predictions is not always easy. However, better predictions remain the foundation of all science therefore the development of accurate, robust and reliable forecasting methods is very important. Numerous number of forecasting methods have been proposed and studied in the literature. There are still two dominant major forecasting methods: Box-Jenkins ARIMA and Exponential Smoothing (ES), and still new methods are derived or inspired from them. After more than 50 years of widespread use, exponential smoothing is still one of the most practically relevant forecasting methods available due to their simplicity, robustness and accuracy as automatic forecasting procedures especially in the famous M-Competitions. Despite its success and widespread use in many areas, ES models have some shortcomings that negatively affect the accuracy of forecasts. Therefore, a new forecasting method in this study will be proposed to cope with these shortcomings and it will be called ATA method. This new method is obtained from traditional ES models by modifying the smoothing parameters therefore both methods have similar structural forms and ATA can be easily adapted to all of the individual ES models however ATA has many advantages due to its innovative new weighting scheme. In this paper, the focus is on modeling the trend component and handling seasonality patterns by utilizing classical decomposition. Therefore, ATA method is expanded to higher order ES methods for additive, multiplicative, additive damped and multiplicative damped trend components. The proposed models are called ATA trended models and their predictive performances are compared to their counter ES models on the M3 competition data set since it is still the most recent and comprehensive time-series data collection available. It is shown that the models outperform their counters on almost all settings and when a model selection is carried out amongst these trended models ATA outperforms all of the competitors in the M3- competition for both short term and long term forecasting horizons when the models’ forecasting accuracies are compared based on popular error metrics.

Keywords: accuracy, exponential smoothing, forecasting, initial value

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Authors: Sidique Fofanah

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This paper explores the intersection of traditional communication theories and modern digital technologies, analyzing how established models adapt to contemporary communication platforms. It examines the evolving nature of interpersonal, group, and mass communication within digital environments, emphasizing the role of social media, AI-driven communication tools, and virtual reality in reshaping communication paradigms. The paper also discusses the implications for future research and practice in communication studies, proposing an integrated framework that accommodates both classical and emerging theories.

Keywords: communication, traditional models, emerging platforms, digital media

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Authors: Ridha Fethi Mechlouch, Ahlem Ayadi, Ammar Ben Brahim

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Given the importance of the preservation of polyphenols and carotenoids during thermal processing, we attempted in this study to investigate the variation of these two parameters in faba beans during microwave treatment using different power densities (1; 2; and 3W/g), then to perform a mathematical modeling by using non-linear regression analysis to evaluate the models constants. The variation of the carotenoids and polyphenols ratio of faba beans and the models are tested to validate the experimental results. Exponential models were found to be suitable to describe the variation of caratenoid ratio (R²= 0.945, 0.927 and 0.946) for power densities (1; 2; and 3W/g) respectively, and polyphenol ratio (R²= 0.931, 0.989 and 0.982) for power densities (1; 2; and 3W/g) respectively. The effect of microwave power density Pd(W/g) on the coefficient k of models were also investigated. The coefficient is highly correlated (R² = 1) and can be expressed as a polynomial function.

Keywords: microwave treatment, power density, carotenoid, polyphenol, modeling

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Authors: Ruoyu Mao

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The fast-paced and high-stress lifestyle of modern cities is an important cause of mental health problems and chronic physical diseases, and at the same time, all kinds of health problems will react to physical and mental stress, further aggravating the health risks; therefore, stress adjustment should be considered as an important perspective of the spatial environment to intervene in the health of the population. The purpose of this paper is to analyse the structural and therapeutic characteristics of the built environment of urban parks, to analyse the path of its effect on the stress adjustment of the population, and to summarise the mechanism of the built environment of urban parks to intervene in the health of the population from the perspective of stress adjustment.

Keywords: stress adjustment, health interventions, urban parks, built environments

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Authors: Leema Jacob

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The concept of psychological well-being is complex and has familiar use not only in psychology but also in the area of lifespan development. Eudaimonic well-being is finding a purpose and meaning in life, and this depends on both the individual and society, especially during adolescence; the social-cognitive environment can be decisive. The social environment of adolescents, including family, school, and friends, is recognized as an essential context for successful human life. The development of mature social relationships is also undoubtedly important. Theory of Mind is an emerging domain in cognitive neuroscience that involves the ability to attribute mental states to oneself and others. ToM skills training constitutes a new aspect of the adolescent’s social development, including four domains: cognitive ToM, affective ToM, and an inter-intra-personal understanding of social norms. Still, little effort has been made to promote this training as a modality to foster their psychological well-being. This study aims to use the eudaimonic approach to evaluate psychological well-being with a quasi-experimental research design (pre-post-test). The major objective of the study was to identify the effect of ToM skills training on the eudaimonic well-being of adolescents with low social cognition. The data was analyzed to find their effect size from a sample of 74 adolescents from India between 17 and 19 years old. The result revealed that ToM skills training has a positive outcome on the well-being of adolescents post-training. The results are discussed based on the effect of ToM skills training on psychological well-being during adolescence, as well as on the importance of focusing on mental health as a developmental asset that can potentially influence mental well-being in the future.

Keywords: ToM training, adolescents, eudaimonic well-being, social cognition

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Authors: Zahid Ahmad, Nosheen Imran, Nauman Ali, Farah Amir

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The objective of the study is to forecast the US Dollar and Pak Rupee exchange rate by using time series models. For this purpose, daily exchange rates of US and Pakistan for the period of January 01, 2007 - June 2, 2017, are employed. The data set is divided into in sample and out of sample data set where in-sample data are used to estimate as well as forecast the models, whereas out-of-sample data set is exercised to forecast the exchange rate. The ADF test and PP test are used to make the time series stationary. To forecast the exchange rate ARIMA model and GARCH model are applied. Among the different Autoregressive Integrated Moving Average (ARIMA) models best model is selected on the basis of selection criteria. Due to the volatility clustering and ARCH effect the GARCH (1, 1) is also applied. Results of analysis showed that ARIMA (0, 1, 1 ) and GARCH (1, 1) are the most suitable models to forecast the future exchange rate. Further the GARCH (1,1) model provided the volatility with non-constant conditional variance in the exchange rate with good forecasting performance. This study is very useful for researchers, policymakers, and businesses for making decisions through accurate and timely forecasting of the exchange rate and helps them in devising their policies.

Keywords: exchange rate, ARIMA, GARCH, PAK/USD

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Authors: Cheng-Hao Jiang, Mu-Xuan Tao

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The rigid diaphragm model may cause errors in the calculation of internal forces due to neglecting the in-plane deformation of the diaphragm. This paper thus studies the effects of different diaphragm in-plane models (including in-plane rigid model and in-plane flexible model) on the seismic performance of structures. Taking an actual industrial plant as an example, the seismic performance of the structure is predicted using different floor diaphragm models, and the analysis errors caused by different diaphragm in-plane models including deformation error and internal force error are calculated. Furthermore, the influence of the aspect ratio on the analysis errors is investigated. Finally, the code rationality is evaluated by assessing the analysis errors of the structure models whose floors were determined as rigid according to the code’s criterion. It is found that different floor models may cause great differences in the distribution of structural internal forces, and the current code may underestimate the influence of the floor in-plane effect.

Keywords: industrial plant, diaphragm, calculating error, code rationality

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Authors: Tereza Hruskova

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200 million people globally experience serious mental health problems, and only one third seek professional help, and help-seeking is described as a last resort. Adolescents and young adults have a high prevalence of mental illness. Mental stigma is a key element in the decision to seek help and is divided into (i) self-stigma (self-stigmatization), including internal beliefs, low self-esteem, and lower quality of life, and (ii) public stigma (social stigma) containing stereotypes, beliefs and society's disapproval of help-seeking having a negative effect on help-seeking and our attitudes. Previous research has mainly focused on examining the construct of help seeking, avoidance, and delaying separately and trying to find out why people do not seek help in time and what obstacles stand in the way. Barriers are not static and may change over time and the stage of help-seeking. Attitudes are closely related to self-stigma and social stigma and predict whether a person will seek help. Barriers (stigmatization, a sense of humiliation, insufficient recognition of the problem, preferences, solving it alone, and distrust of a professional) and facilitators (previous experience with mental problems, social support, and help from others) are factors influencing help-seeking. The current research on the Czech population of young adults responds to the gap between a person with mental health problems and actually seeking professional help. The aim of the study is to describe in detail the individual constructs and factors, to understand the person seeking help, and to define possible obstacles on this path of seeking help. A sample of approximately 250 participants (age 18-35) would take part in the online questionnaire, conducted in May-June 2023, and would be administered a demographic questionnaire and four scales measuring attitudes (Attitudes Toward Seeking Professional Psychological Help – Short form), barriers (Barrier to Help Seeking Scale), self-stigma (Self Stigma of Seeking Help) and stigmatization (Perceptions of Stigmatization by Others for seeking help). Firstly, all four scales would be translated into the Czech language. The aim is (I) to determine the validity and reliability of the Czech translation of the scales, (II) to examine the factors of the scales on the Czech population and compare them retrospectively with the results of reliability and validity from the original language of the scales and (III) to examine the connections between attitudes towards seeking, avoidance or delaying the search for professional psychological help due to the demographic and individual differences of the participants, barriers, self-stigmatization and social stigmatization. We expect to carry out the first study on the given topic in the Czech Republic, to identify and better understand the factors leading to the avoidance of seeking professional help and to reveal the relationships between stigmatization, attitudes and barriers leading to the avoidance or postponement of seeking professional help. The belief is to find out whether the Czech population of young adults differs from the data found on the foreign population in individual constructs, as cultural differences in individual countries were found.

Keywords: mental health, stigma, problems, seeking psychological help

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Authors: Kumari Bandana Bhatt, Navin Bhatt

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Background: Orphans and vulnerable children (OVC) are high risk of physical, mental, sexual and emotional abuse and face social stigma and discrimination which significantly increase the risk of mental and behavioral disorders such as anxiety, depression or emotional problems even they stay in well run child care homes. The objective of this study was to estimate the prevalence of depression and determine the determinants among OVC in child care homes in Nepal. Methods: An institutional-based analytical cross-sectional study was conducted in twenty orphanages of five districts of Nepal. Six hundred two children were recruited into the study. After the informed consent form obtaining, the guardian and assent were interviewed by a semi-structured questionnaire and Beck Depression Inventory-II (BDI-II). Logistic regression was used for detecting the association between variables at the significant level of =0.05. Results: The study revealed that 33.20% of OVC had depression. Among them 66.80% of children experienced minimal depression, 17.40% had mild depression, 11.30% had moderate depression 4.50% had severe depression. Sex, alcohol drinking, congenital problem, social support and bully were the main variables associated with depression among OVC of the child care homes in Nepal. Conclusion: Prevalence of depression was high among the orphans and vulnerable children living in child care homes especially among the female children in Nepal. Therefore, early identification and instituting of preventive measures of depression are essential to reduce this problem in this special group of children living in child care homes.

Keywords: Mental health, Depression, Orphans and vulnerable children, child care homes

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Authors: Kathleen Seers, Rachel Hogg

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

Keywords: female autism, gender, masking, social constructionism

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Authors: Bowen Ding, Yihao Kuang

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In recent years, large-scale pre-trained language models have achieved state-of-the-art performance on a variety of natural language processing tasks. The word vectors produced by these language models can be viewed as dense encoded presentations of natural language that in text form. However, it is unknown how much linguistic information is encoded and how. In this paper, we construct several corresponding probing tasks for multiple linguistic information to clarify the encoding capabilities of different language models and performed a visual display. We firstly obtain word presentations in vector form from different language models, including BERT, ELMo, RoBERTa and GPT. Classifiers with a small scale of parameters and unsupervised tasks are then applied on these word vectors to discriminate their capability to encode corresponding linguistic information. The constructed probe tasks contain both semantic and syntactic aspects. The semantic aspect includes the ability of the model to understand semantic entities such as numbers, time, and characters, and the grammatical aspect includes the ability of the language model to understand grammatical structures such as dependency relationships and reference relationships. We also compare encoding capabilities of different layers in the same language model to infer how linguistic information is encoded in the model.

Keywords: language models, probing task, text presentation, linguistic information

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Authors: Idrissa Kayijuka

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The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model.

Keywords: logistic regression model, Cox regression model, survival analysis, hazard ratio

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Authors: Saba Rahman, Arvind K. Jain, S. D. Bharti, T. K. Datta

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The present study compares the semi-empirical wake-oscillator models that are used to predict vortex-induced vibration of structures. These models include those proposed by Facchinetti, Farshidian, and Dolatabadi, and Skop and Griffin. These models combine a wake oscillator model resembling the Van der Pol oscillator model and a single degree of freedom oscillation model. In order to use these models for estimating the top displacement of chimneys, the first mode vibration of the chimneys is only considered. The modal equation of the chimney constitutes the single degree of freedom model (SDOF). The equations of the wake oscillator model and the SDOF are simultaneously solved using an iterative procedure. The empirical parameters used in the wake-oscillator models are estimated using a newly developed approach, and response is compared with experimental data, which appeared comparable. For carrying out the iterative solution, the ode solver of MATLAB is used. To carry out the comparative study, a tall concrete chimney of height 210m has been chosen with the base diameter as 28m, top diameter as 20m, and thickness as 0.3m. The responses of the chimney are also determined using the linear model proposed by E. Simiu and the deterministic model given in Eurocode. It is observed from the comparative study that the responses predicted by the Facchinetti model and the model proposed by Skop and Griffin are nearly the same, while the model proposed by Fashidian and Dolatabadi predicts a higher response. The linear model without considering the aero-elastic phenomenon provides a less response as compared to the non-linear models. Further, for large damping, the prediction of the response by the Euro code is relatively well compared to those of non-linear models.

Keywords: chimney, deterministic model, van der pol, vortex-induced vibration

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Authors: Ágnes Győri, Éva Perpék, Zsófia Bauer, Zsuzsanna Elek

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The aim of the research (funded by Hungarian national grant, NFKI- FK 138315) is to examine the professional mobility, mental health and work environment of social workers with a complex approach. Previous international and Hungarian research has pointed out that those working in the helping professions are strongly exposed to the risk of emotional-mental-physical exhaustion due to stress. Mental and physical strain, as well as lack of coping (can) cause health problems, but its role in career change and high labor turnover has also been proven. Even though satisfaction with working conditions of those employed in the human service sector in the context of the stress burden has been researched extensively, there is a lack of large-sample international and Hungarian domestic studies exploring the effects of profession-specific conditions. Nor has it been examined how the specific features of the social profession and mental health affect the career mobility of the professionals concerned. In our research, these factors and their correlations are analyzed by means of mixed methodology, utilizing the benefits of netnographic big data analysis and a sector-specific quantitative survey. The netnographic analysis of open web content generated inside and outside the social profession offers a holistic overview of the influencing factors related to mental health and the work environment of social workers. On the one hand, the topics and topoi emerging in the external discourse concerning the sector are examined, and on the other hand, focus on mentions and streams of comments regarding the profession, burnout, stress, coping, as well as labor turnover and career changes among social professionals. The analysis focuses on new trends and changes in discourse that have emerged during and after the pandemic. In addition to the online conversation analysis, a survey of social professionals with a specific focus has been conducted. The questionnaire is based on input from the first two research phases. The applied approach underlines that the mobility paths of social professionals can only be understood if, apart from the general working conditions, the specific features of social work and the effects of certain aspects of mental health (emotional-mental-physical strain, resilience) are taken into account as well. In this paper, the preliminary results from this innovative methodological mix are presented, with the aim of highlighting new opportunities and dimensions in the research on social work. A gap in existing research is aimed to be filled both on a methodological and empirical level, and the Hungarian domestic findings can create a feasible and relevant framework for a further international investigation and cross-cultural comparative analysis. Said results can contribute to the foundation of organizational and policy-level interventions, targeted programs whereby the risk of burnout and the rate of career abandonment can be reduced. Exploring different aspects of resilience and mapping personality strengths can be a starting point for stress-management, motivation-building, and personality-development training for social professionals.

Keywords: burnout, mixed methods, netnography, professional mobility, social work

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Authors: Susmita Panda, Arnab Banerjee, Ajinkya Baxy, Bappaditya Manna

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The design of short to medium-span high-speed bridges in critical locations is an essential aspect of vehicle-bridge interaction. Due to dynamic interaction between moving load and bridge, mathematical models or finite element modeling computations become time-consuming. Thus, to reduce the computational effort, a universal approximator using an artificial neural network (ANN) has been used to evaluate the dynamic response of the bridge. The data set generation and training of surrogate models have been conducted over the results obtained from mathematical modeling. Further, the robustness of the surrogate model has been investigated, which showed an error percentage of less than 10% with conventional methods. Additionally, the dependency of the dynamic response of the bridge on various load and bridge parameters has been highlighted through a parametric study.

Keywords: artificial neural network, mode superposition method, moving load analysis, surrogate models

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Authors: Michele M. Mahr

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The purpose of this study is to educate, inform, and facilitate scholarly conversation and discussion regarding the implementation of mindfulness techniques when working with individuals with substance use disorder (SUD) or addictive behaviors in mental health. Mindfulness can be recognized as the present moment, non-judgmental awareness, initiated by concentrated attention that is non-reactive and as openheartedly as possible. Individuals with SUD or addiction typically are challenged with triggers, environmental situations, cravings, or social pressures which may deter them from remaining abstinent from their drug of choice or addictive behavior. Also, mindfulness is recognized as one of the cognitive and behavioral treatment approaches and is both a physical and mental practice that encompasses individuals to become aware of internal situations and experiences with undivided attention. That said, mindfulness may be an effective strategy for individuals to employ during these experiences. This study will reveal how mental health practitioners and addiction counselors may find mindfulness to be an essential component of increasing wellness when working with individuals seeking mental health treatment. To this end, mindfulness is simply the ability individuals have to know what is actually happening as it is occurring and what they are experiencing at the moment. In the context of substance abuse and addiction, individuals may employ breathing techniques, meditation, and cognitive restructuring of the mind to become aware of present moment experiences. Furthermore, the notion of mindfulness has been directly connected to the development of neuropathways. The creation of the neural pathways then leads to creating thoughts which leads to developing new coping strategies and adaptive behaviors. Mindfulness strategies can assist individuals in connecting the mind with the body, allowing the individual to remain centered and focused. All of these mentioned above are vital components to recovery during substance abuse and addiction treatment. There are a variety of therapeutic modalities applying the key components of mindfulness, such as Mindfulness-Based Stress Reduction (MBSR) and Mindfulness-Based Cognitive Therapy for depression (MBCT). This study will provide an overview of both MBSR and MBCT in relation to treating individuals with substance abuse and addiction. The author will also provide strategies for readers to employ when working with clients. Lastly, the author will create and foster a safe space for discussion and engaging conversation among participants to ask questions, share perspectives, and be educated on the numerous benefits of mindfulness within wellness.

Keywords: mindfulness, wellness, substance abuse, mental health

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Authors: Arthur C. Tsai, Chii-Shyang Kuo, Vincent S. C. Chien, Michelle Liou, Philip E. Cheng

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Meditation enhances mental abilities and it is an antidote to anxiety. However, very little is known about brain mechanisms and cortico-subcortical interactions underlying meditation-induced anxiety relief. In this study, the changes of phase-amplitude coupling (PAC) in which the amplitude of the beta frequency band were modulated in phase with delta rhythm were investigated after eight-week of meditation training. The study hypothesized that through a concentrate but relaxed mental training the delta-beta coupling in the frontal regions is attenuated. The delta-beta coupling analysis was applied to within and between maximally-independent component sources returned from the extended infomax independent components analysis (ICA) algorithm on the continuous EEG data during mediation. A unique meditative concentration task through relaxing body and mind was used with a constant level of moderate mental effort, so as to approach an ‘emptiness’ meditative state. A pre-test/post-test control group design was used in this study. To evaluate cross-frequency phase-amplitude coupling of component sources, the modulation index (MI) with statistics to calculate circular phase statistics were estimated. Our findings reveal that a significant delta-beta decoupling was observed in a set of frontal regions bilaterally. In addition, beta frequency band of prefrontal component were amplitude modulated in phase with the delta rhythm of medial frontal component.

Keywords: phase-amplitude coupling, ICA, meditation, EEG

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Authors: Animish Jain

Abstract:

This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

Abstract:

In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević, Lidija R. Jevrić, Stela Jokić

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The subject of this paper is to correlate antibacterial behavior of benzimidazole derivatives with their molecular characteristics using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on the inhibitory activity of benzimidazole derivatives against Staphylococcus aureus. The data were processed by linear least squares (LLS) and artificial neural network (ANN) procedures. The LLS mathematical models have been developed as a calibration models for prediction of the inhibitory activity. The quality of the models was validated by leave one out (LOO) technique and by using external data set. High agreement between experimental and predicted inhibitory acivities indicated the good quality of the derived models. These results are part of the CMST COST Action No. CM1306 "Understanding Movement and Mechanism in Molecular Machines".

Keywords: Antibacterial, benzimidazoles, chemometric, QSAR.

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Authors: Ahmed F. Elaksher, Islam Omar

Abstract:

In this project, we used MOLA-based DEMs to orthorectify HiRISE optical images. The MOLA data was interpolated using the kriging interpolation technique. Corresponding tie points were then digitized from both datasets. These points were employed in co-registering both datasets using GIS analysis tools. Different transformation models, including the affine and projective transformation models, were used with different sets and distributions of tie points. Additionally, we evaluated the use of the MOLA elevations in co-registering the MOLA and HiRISE datasets. The planimetric RMSEs achieved for each model are reported. Results suggested the use of 3D-2D transformation models.

Keywords: photogrammetry, Mars, MOLA, HiRISE

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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The present study deals with the selection of the most suitable quantitative structure-retention relationship (QSRR) models which should be used in prediction of the retention behavior of basic, neutral, acidic and phenolic pesticides which belong to different classes: fungicides, herbicides, metabolites, insecticides and plant growth regulators. Sum of ranking differences (SRD) approach can give a different point of view on selection of the most consistent QSRR model. SRD approach can be applied not only for ranking of the QSRR models, but also for detection of similarity or dissimilarity among them. Applying the SRD analysis, the most similar models can be found easily. In this study, selection of the best model was carried out on the basis of the reference ranking (“golden standard”) which was defined as the row average values of logarithm of retention time (logtr) defined by high performance liquid chromatography (HPLC). Also, SRD analysis based on experimental logtr values as reference ranking revealed similar grouping of the established QSRR models already obtained by hierarchical cluster analysis (HCA).

Keywords: chemometrics, chromatography, pesticides, sum of ranking differences

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Authors: Shan Li, Qiuwen Ma

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Integrated project delivery (IPD) is a project delivery method distinguished by a shared risk/rewards mechanism and multiparty agreement. IPD has drawn increasing attention from construction industry due to its reliability to deliver high-performing buildings. However, unavailable IPD specific insurance concerns the industry participants who are interested in IPD implementation. Even though the risk management capability can be enhanced using shared risk mechanism, some risks may occur when the partners do not commit themselves into the integrated practices in a desired manner. This is because the intense collaboration and close integration can not only create added value but bring new opportunistic behaviors and disputes. The study is aimed to investigate the risks of implementing IPD using Bayesian theory. IPD risk taxonomy is presented to identify all potential risks of implementing IPD and a risk network map is developed to capture the interdependencies between IPD risks. The conditional relations between risk occurrences and the impacts of IPD risks on project performances are evaluated and simulated based on Bayesian theory. The probability of project outcomes is predicted by simulation. In addition, it is found that some risks caused by integration are most possible occurred risks. This study can help the IPD project participants identify critical risks of adopting IPD to improve project performances. In addition, it is helpful to develop IPD specific insurance when the pertinent risks can be identified.

Keywords: Bayesian theory, integrated project delivery, project risks, project performances

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Authors: S. Gordon

Abstract:

Dual language immersion is growing fast in language teaching today. This study provides an overview and evaluation of the different models of Dual language immersion programs in US K-12 schools. First, the paper provides a brief current literature review on the theory of Dual Language Immersion (DLI) in Second Language Acquisition (SLA) studies. Second, examples of several types of DLI language teaching models in US K-12 public schools are presented (including 50/50 models, 90/10 models, etc.). Third, we focus on the unique example of DLI education in the state of Utah, a successful, growing program in K-12 schools that includes: French, Chinese, Spanish, and Portuguese. The project investigates the theory and practice particularly of the case of public elementary and secondary school children that study half their school day in the L1 and the other half in the chosen L2, from kindergarten (age 5-6) through high school (age 17-18). Finally, the project takes the observations of Utah French DLI elementary through secondary programs as a case study. To conclude, we look at the principal challenges, pedagogical objectives and outcomes, and important implications for other US states and other countries (such as France currently) that are in the process of developing similar language learning programs.

Keywords: dual language immersion, second language acquisition, language teaching, pedagogy, teaching, French

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Authors: Dragos Dragomir-Stanciu, Leon Marsh

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Background As a response to the gaps identified in recent research in the provision of residential care to address co-occurring health needs, including mental health problems and complexities Gamble Aware has facilitated the possibility to provide a new service which would extend the NGTS provision of residential rehabilitation for gambling disorder with complex and co-morbid presentation. Gordon Moody, together with Adferiad have been successful in securing the tender for this service and this presentation aims to introduce FOLD, the resulting model of treatment developed for the delivery of the service. Setting As a partnership, we have come together to coproduce a model which allows us to share our clinical and industry knowledge and build on our reputations as trusted treatment providers. The presentation will outline our expertise share in development of a unified approach to recovery-oriented models of care, clinical governance, risk assessment and management and aftercare and continuous recovery. We will also introduce our innovative specialist referral portal which will offer referring partners the ability to include the service user in planning their own recovery journey. Outcomes Our collaboration has resulted in the development of the FOLD model which includes three agile and flexible treatment packages aimed at offering the most enhanced and comprehensive treatment in UK, to date, for those most affected by gambling harm. The paper will offer insight into each treatment package and all recovery model stages involved, as well as into the partnership work with NGST providers, local mental health and social care providers and lived experience organisation that will enable us to offer support to more 100 people a year who would otherwise get “lost in the system”. Conclusion FOLD offers a great opportunity to develop, implement and evaluate a new, much needed, whole-person and whole-system approach to counter gambling related harms.

Keywords: gambling treatment, partnership working, integrated care pathways, NGTS, complex needs

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