Search results for: LimA predictor

Authors: Joel Podgorski, Manouchehr Amini, Annette Johnson, Michael Berg

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One-third of the world’s population relies on groundwater for its drinking water. Natural geogenic arsenic and fluoride contaminate ~10% of wells. Prolonged exposure to high levels of arsenic can result in various internal cancers, while high levels of fluoride are responsible for the development of dental and crippling skeletal fluorosis. In poor urban and rural settings, the provision of drinking water free of geogenic contamination can be a major challenge. In order to efficiently apply limited resources in the testing of wells, water resource managers need to know where geogenically contaminated groundwater is likely to occur. The Groundwater Assessment Platform (GAP) fulfills this need by providing state-of-the-art global arsenic and fluoride contamination hazard maps as well as enabling users to create their own groundwater quality models. The global risk models were produced by logistic regression of arsenic and fluoride measurements using predictor variables of various soil, geological and climate parameters. The maps display the probability of encountering concentrations of arsenic or fluoride exceeding the World Health Organization’s (WHO) stipulated concentration limits of 10 µg/L or 1.5 mg/L, respectively. In addition to a reconsideration of the relevant geochemical settings, these second-generation maps represent a great improvement over the previous risk maps due to a significant increase in data quantity and resolution. For example, there is a 10-fold increase in the number of measured data points, and the resolution of predictor variables is generally 60 times greater. These same predictor variable datasets are available on the GAP platform for visualization as well as for use with a modeling tool. The latter requires that users upload their own concentration measurements and select the predictor variables that they wish to incorporate in their models. In addition, users can upload additional predictor variable datasets either as features or coverages. Such models can represent an improvement over the global models already supplied, since (a) users may be able to use their own, more detailed datasets of measured concentrations and (b) the various processes leading to arsenic and fluoride groundwater contamination can be isolated more effectively on a smaller scale, thereby resulting in a more accurate model. All maps, including user-created risk models, can be downloaded as PDFs. There is also the option to share data in a secure environment as well as the possibility to collaborate in a secure environment through the creation of communities. In summary, GAP provides users with the means to reliably and efficiently produce models specific to their region of interest by making available the latest datasets of predictor variables along with the necessary modeling infrastructure.

Keywords: arsenic, fluoride, groundwater contamination, logistic regression

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Authors: A. A. James, A. O. Adesanya, M. R. Odekunle, D. G. Yakubu

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This paper examines the development of one step, five hybrid point method for the solution of first order initial value problems. We adopted the method of collocation and interpolation of power series approximate solution to generate a continuous linear multistep method. The continuous linear multistep method was evaluated at selected grid points to give the discrete linear multistep method. The method was implemented using a constant order predictor of order seven over an overlapping interval. The basic properties of the derived corrector was investigated and found to be zero stable, consistent and convergent. The region of absolute stability was also investigated. The method was tested on some numerical experiments and found to compete favorably with the existing methods.

Keywords: interpolation, approximate solution, collocation, differential system, half step, converges, block method, efficiency

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Authors: Bin Liu

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Prokaryotic promoter, consisted of two short DNA sequences located at in -35 and -10 positions, is responsible for controlling the initiation and expression of gene expression. Different types of promoters have different functions, and their consensus sequences are similar. In addition, their consensus sequences may be different for the same type of promoter, which poses difficulties for promoter identification. Unfortunately, all existing computational methods treat promoter identification as a binary classification task and can only identify whether a query sequence belongs to a specific promoter type. It is desired to develop computational methods for effectively identifying promoters and their types. Here, a two-layer predictor is proposed to try to deal with the problem. The first layer is designed to predict whether a given sequence is a promoter and the second layer predicts the type of promoter that is judged as a promoter. Meanwhile, we also analyze the importance of feature and sequence conversation in two aspects: promoter identification and promoter type identification. To the best knowledge of ours, it is the first computational predictor to detect promoters and their types.

Keywords: promoter, promoter type, random forest, sequence information

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Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang

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Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.

Keywords: conditional quantile, kernel, nonparametric, stationary

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Authors: Nuruddin Mohammod Zahangir, Syed Tanvir Ahmady, Firoz Ahmed, Mainul Kabir, Tamjid Mohammad Najmus Sakib Khan, Nazmul Hossain, Niaz Ahmed, Madhava Janardhan Naik

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The management of combined Coronary Artery Disease and Peripheral Vascular Disease is a challenge and brings with it numerous clinical dilemmas.The 56 year old gentleman presented to our department with significant triple vessel disease with occluded lower end of aorta just before bifurcation and bilateral superficial femoral arteries. Operation was done on 11.03.14. The The Left Internal Mammary Artery (LIMA) and the Right Internal Mammary Artery (RIMA) were harvested in skeletonized manner. The free RIMA was then anastomosed with LIMA to make LIMA-RIMA Y. Cardio Pulmonary Bypass was then established and coronary artery bypass grafts performed. LIMA was anastomosed to the Left Anterior Descending artery. RIMA was anastomosed to Posterior Descending Artery, 1st and 2nd Obtuse Marginal arteries in a sequential manner. Abdomen was opened by midline incision. The infrarenal aorta exposed and was found to be severely diseased. A Vascular Clamp was applied infrarenally, aortotomy done and limited endarterectomy performed. An end-to-side anastomosis was done with upper end of PTFE synthetic Y-graft (14/7 mm) to the infarenal Aorta and the Clamp released. Good flow noted in both limbs of the graft. Patient was then slowly weaned off from Cardio Pulmonary Bypass without difficulty. The distal two limbs of the Y graft were passed to the groin through retroperitoneal tunnels and anastomosed end-to-side with the common femoral arteries. Saphenous vein was interposed between common femoral and popliteal arteries bilaterally through subfascial tunnels in both thigh. On 12th postoperative day he was discharged from hospital in good general condition. Follow up after 3 months of operation the patient is doing good and free of chest pain and claudication pain.

Keywords: total arterial, coronary revascularization, aorto-bifemoral bypass, bifemoro-bipopliteal bypass

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Authors: Javaid Marium, Ahmad Iftikhar

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This study was aimed to identify factors that affect negative mood and body image dissatisfaction in women. positive and negative affect, self esteem, body image satisfaction and figure rating scale was administered to 97 female undergraduate students. This served as a base line data for correlation analysis in the first instance. One week later participants who volunteered to appear in the second phase of the study (N=47) were shown thin- ideal images as an intervention and soon after they completed positive and negative affect schedule and body image states scale again as a post test. Results indicated body mass as a strong negative predictor of body image dis/satisfaction, self esteem was a moderate predictor and mood was not a significant predictor. The participants whose actual body shape was markedly discrepant with the ideally desired body shape had significantly low level of body image satisfaction (p < .001) than those with low discrepancy. Similar results were found for self esteem (p < .004). Both self esteem and body mass predicted body satisfaction about equally and significantly. However, on viewing thin-ideal images, the participants of different body weight showed no change in their body image satisfaction than before. Only the overweight participants were significantly affected on negative mood as a short term reaction after viewing the thin ideal images. Comparing the three groups based on their body mass, one-way ANOVA revealed significant difference on negative mood as well as body image satisfaction. This reveals body mass as a potent and stable factor that consistently and strongly affected body satisfaction not the transient portrayal of thin ideal images.

Keywords: body image satisfaction, thin-ideal images, media, mood affects, self esteem

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Authors: Poonam Khurana, Mikelsh Prasad Yadav

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All human beings are virtue by God as they get controlling mind and an attitude towards something with the help of which they can experience spirituality. People have different psychological thoughts and beliefs relating to god. Some people feel the immense pleasure in performing religious activities while some believe in the thought “Ahem Brahmasmi” i.e I am part of the divine and they feel pleasure in activities which motivate them to achieve peace. Religion in psychology is considered as a specific fundamental set of beliefs, thoughts and practices generally agreed and controlled by a number of person. It is the psychology of a person which commands their mind in deciding at which path they can attain the eternal feelings of the universe and accordingly they will develop their behavior towards religion and spirituality. Keeping the same in mind, the present study is based much on literature and a brief analysis of 50 teachers in Delhi and NCR colleges and makes an attempt to study the relationship of spiritual quotient and effective teaching and suggest spiritual quotient as a considerable predictor to make the workplace accessible.

Keywords: effectiveness, quotient, teaching, spiritual

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Authors: Enes Ergün, Sedat Geli̇bolu

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The purpose of this study is to determine the relationship between self-efficacy and subjective well-being among university students. We are aiming to determine whether self efficacy of university students predicts their subjective well-being and if there is a statistically significant difference among boys and girls in this context. Sample of this study consists of 245 university students from Çanakkale, ages ranging between 17 and 24. 72% (n=171) of the participants were girls and 28% (n=69) boys. Three different scales were utilized as data collection tools that Life Satisfaction Scale, General Self-Efficacy Scale, and Positive Negative Experiences Scale. Pearson correlation coefficient, independent sample t test and simple linear regression were used for data analyses. Results showed that well-being is significantly correlated with self-efficacy and self-efficacy is a statistically significant predictor of well-being too. In terms of gender differences, there is no significant difference between self-efficacy scores of boys and girls which shows the same case with well being scores, as well. Fostering university students' academic, social and emotional self-efficacy will increase their well-being which is very important for young adults especially their freshman years.

Keywords: positive psychology, self-efficacy, subjective well being, university students

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Authors: Firdaus Amri, Ima Sri Rahmani

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Traffic jam becomes the main problem among the people in a metropolitan city, especially in Indonesia. Nebengers, as one of ridesharing community in Indonesia, offer solutions against this problem. Nebengers is a social media community that connect people who want to give the ride and who want to ask for the ride. But in reality, Nebengers still have quite problems, especially regarding the safety issue. Practically, in this issue, they (people in Nebengers) only depend on trust between the one who gives the ride and who asks for the ride. Trust is defined as an expectation that partners, including potential partners, have goodwill and benign intent in their dealing with one and another. This paper will examine the role of personality type as predictor of trust among Nebengers community. Hexaco Personality consists of six dimensions (honesty-humility, emotionality, agreeableness, conscientiousness, extraversion, and openness to experience) is used in this research. By using snowball sampling, we took 250 respondents from Nebengers community as participants for this research. They complete questionnaires which are provided in online form. The role of each personality type as a basic analysis to understand trust among Nebengers community will be discussed in this research.

Keywords: Nebengers, personality type, ridesharing, trust

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Authors: Vered Shenaar-Golan, Nava Wald, Uri Yatzkar

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Background: Parenting children with mental health problems poses multiple challenges, including coping with difficult behavior and negative child emotions. The impact on parents includes financial strain, negative social stigma, and negative feelings of guilt or blame, resulting in significant stress and lower levels of well-being. Given findings that self-compassion plays a significant role in reducing stress and improving well-being, the current study examined the role of self-compassion in the experience of parents raising a child with mental health problems. The study tested (1) whether child behavioral/emotional problem severity is associated with higher parental stress and lower parental well-being; (2) whether self-compassion is associated with lower parental stress and higher parental well-being; and (3) whether self-compassion is a stronger predictor of parental stress and well-being than child behavioral/emotional problem severity. Methods: Three hundred and six mothers and two hundred and fifty-six fathers of children attending a hospital child and adolescent psychiatric center were assessed at admission. Consenting parents completed four questionnaires: Child Strength and Difficulty – parent version, Self-compassion, Parent Feeling Inventory, and Well-Being. Results: Child behavioral/emotional problem severity was associated with higher parental stress and lower parental well-being, and self-compassion was a stronger predictor of parental stress and well-being levels than child behavioral/emotional problem severity. For children with internalizing but not externalizing behavioral/emotional problems, parental self-compassion was the only predictor of parental well-being beyond the severity of child behavioral/emotional problems. Conclusions: Cultivating self-compassion is important in reducing parental stress and increasing parental well-being, particularly with internalizing presentations, and should be considered when designing therapeutic interventions for parents.

Keywords: parenting children with mental health problems, self-compassion, parental stress, feelings, well-being

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Authors: Proud Arunrangsiwed, Sarinya Kongtieng

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Teachers and university lecturers face an unsolved problem, which is students’ multitasking behaviors during class time, such as texting or playing a game. It is important to examine the most powerful predictor that can result in students’ educational performances. Meta-analysis was used to analyze the research articles, which were published with the keywords, multitasking, class performance, and texting. We selected 14 research articles published during 2008-2013 from online databases, and four articles met the predetermined inclusion criteria. Effect size of each pair of variables was used as the dependent variable. The findings revealed that the students’ expectancy and value on SNSs usages is the best significant predictor of their educational performances, followed by their motivation and ability in using SNSs, prior educational performances, usage behaviors of SNSs in class, and their personal characteristics, respectively. Future study should conduct a longitudinal design to better understand the effect of multitasking in the classroom.

Keywords: meta-regression analysis, social networking sites, academic Performances, multitasking, motivation

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Authors: Elnaz Bandeh

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This study aimed to predict marital burnout based on irrational beliefs and sexual dysfunction of couples. The research method was descriptive-correlational, and the statistical population included all couples who consulted to counseling clinics in the fall of 2016. The sample consisted of 200 people who were selected by convenience sampling and answered the Ahwaz Irrational Beliefs Questionnaire, Pines Couple Burnout, and Hudson Marital Satisfaction Questionnaire. The data were analyzed using regression coefficient. The results of regression analysis showed that there was a linear relationship between irrational beliefs and couple burnout and dimensions of helplessness toward change, expectation of approval from others, and emotional irresponsibility were positive and significant predictors of couple burnout. However, after avoiding the problem of power, it was not a significant predictor of marital dissatisfaction. There was also a linear relationship between sexual dysfunction and couple burnout, and sexual dysfunction was a positive and significant predictor of couple burnout. Based on the findings, it can be concluded that irrational beliefs and sexual dysfunction play a role in couple dysfunction.

Keywords: couple burnout, irrational beliefs, sexual dysfunction, marital relationship

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Authors: Abdreheman Seid Abdella

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This study examined instructors willingness, self-efficacy beliefs, attitudes and knowledge about provisions of instructional accommodations for students with disabilities in universities. Major concepts used in this study operationally defined and some models of disability were reviewed. Questionnaires were distributed to a total of 181 instructors from four universities and quantitative data was generated. Then to analyze the data, appropriate methods of data analysis were employed. The result indicated that on average instructors had positive willingness, strong self-efficacy beliefs and positive attitudes towards providing instructional accommodations. In addition, the result showed that the majority of participants had moderate level of knowledge about provision of instructional accommodations. Concerning the relationship between instructors background variables and dependent variables, the result revealed that location of university and awareness raising training about Inclusive Education showed statistically significant relationship with all dependent variables (willingness, self-efficacy beliefs, attitudes and knowledge). On the other hand, gender and college/faculty did not show a statistically significant relationship. In addition, it was found that among the inter-correlation of dependent variables, the correlation between attitudes and willingness to provide accommodations was the strongest. Furthermore, using multiple linear regression analysis, this study also indicated that predictor variables like self-efficacy beliefs, attitudes, knowledge and teaching methodology training made statistically significant contribution to predicting the criterion willingness. Predictor variables like willingness and attitudes made statistically significant contribution to predicting self-efficacy beliefs. Predictor variables like willingness, Special Needs Education course and self-efficacy beliefs made statistically significant contribution to predict attitudes. Predictor variables like Special Needs Education courses, the location of university and willingness made statistically significant contribution to predicting knowledge. Finally, using exploratory factor analysis, this study showed that there were four components or factors each that represent the underlying constructs of willingness and self-efficacy beliefs to provide instructional accommodations items, five components for attitudes towards providing accommodations items and three components represent the underlying constructs for knowledge about provisions of instructional accommodations items. Based on the findings, recommendations were made for improving the situation of instructional accommodations in Ethiopian universities.

Keywords: willingness, self-efficacy belief, attitude, knowledge

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Authors: Nai-Jiin Yang, Tyler Renshaw

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Prior research has suggested that youth internalizing and externalizing problems significantly correlate with student subjective wellbeing (SSW) and achievement problems (SAP). Yet, only a few studies have used data from mental health screener based on the dual-factor model to explore the empirical relationships among internalizing problems, externalizing problems, academic problems, and student wellbeing. This study was conducted through a secondary analysis of previously collected data in school-wide mental health screening activities across secondary schools within a suburban school district in the western United States. The data set included 1880 student responses from a total of two schools. Findings suggest that both internalizing and externalizing problems are substantial predictors of both student wellbeing and academic problems. However, compared to internalizing problems, externalizing problems were a much stronger predictor of academic problems. Moreover, this study did not support academic problems that moderate the relationship between SSW and youth internalizing problems (YIP) and between youth externalizing problems (YEP) and SSW. Lastly, SAP is the strongest predictor of SSW than YIP and YEP.

Keywords: academic problems, externalizing problems, internalizing problems, school mental health, student wellbeing, universal mental health screening

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Authors: Shujie Liu

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This study aimed to investigate the relationship between teachers’ perceived transformational leadership behaviors and their job satisfaction in China after controlling for teacher self-efficacy. Hierarchical regression analysis (HRA) technique was employed to examine factors’ contributions to teacher job satisfaction with a sample of Chinese high school teachers. The finding of this study provided evidence that teachers’ perceived transformational leadership behaviors accounted for a large percentage (44.9%) of the variance in Chinese teachers’ job satisfaction. Uniquely, school principals’ sense of power was a negative significant predictor of teacher job satisfaction, meaning that the more teachers perceived their principals’ sense of power, the lower of their job satisfaction. Furthermore, this study provided evidence that teacher self-efficacy significantly contributes to teacher job satisfaction. Specifically, teachers’ self-efficacy on student engagement was found to be a significant predictor of teacher job satisfaction. The conclusions were discussed in terms of Chinese cultures. The authors pointed out that how to make teachers involved in school policy making is a challenge for China and that more shared leadership is needed in Chinese schools.

Keywords: Chinese teachers, teacher job satisfaction, teacher self-efficacy, transformational leadership

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Authors: Umberto Platini

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This paper analyses the role of government fragmentation as predictor of the use of emergency decrees in parliamentary democracies. In particular, it focuses on the relationship between ideological divisiveness within cabinets and the choice by executives to issue emergency decrees rather initiating ordinary legislative procedures. A Bayesian multilevel analysis conducted on the population of government-initiated legislation in Italy between 1996 and 2018 finds significant evidence that those legislative proposals which are further away from the ideological centre of gravity of the executive are around three times more likely to be issued as emergency decrees. Likewise, legislative projects regulating more contentious policy areas are significantly more likely to be issued by decree. However, for more contentious issues the importance of ideological distance as a predictor diminishes. This evidence suggests that cabinets prefer decrees to ordinary legislative procedures when they expect that the bargaining environment in Parliament is more hostile. These results persist regardless of the fluctuations of the political-economic cycle. Their robustness is also tested against a battery of controls and against fixed effects both at the government level and at the legislature level.

Keywords: Bayesian multilevel logit models, executive action, executive decrees, ideology, legislative studies, polarization

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Authors: Abayomi Olatoke Adejobi

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The study investigated maternal deprivation as predictor of academic performance and psychosocial adjustment of primary school pupils in Abeokuta metropolis. Three null hypotheses were formulated to guide the study. Two hundred public primary school pupils were randomly selected as subjects for the study. The instruments used for data collection were Index of Family Relations (IFR) by Hudson, modified version of Psychosocial Adjustment Scale (PAS) by O’ bailey and Academic records of the pupils from Cumulative Records Folder (CRF). The data collected were statistically treated and the three hypotheses were tested using t-test and Pearson Product Moment Correlation Confident statistical methods at 0.05 alpha level. The results of the analysis showed that there is a significant difference in the academic performance of children who suffered maternal deprivation and those who did not (t – 5.61; df = 198; P < 0.05). Also, there was a significant relationship between psychosocial adjustment of children and maternal deprivation (r = 0.37, df = 10; P < 0.05), while there was no significant difference in academic performance of boys and girls who suffered maternal deprivation (t = 0.32; df = 109; P > 0.05). Based on the results some recommendations were made.

Keywords: maternal deprivation, psychosocial adjustment, academic performance, primary school pupils

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Authors: Anand Kishore Kola, G. Uday Bhaskar Babu, Kotturi Ajay Kumar

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The modern dynamic systems used in industries are complex in nature and hence the fractional order controllers have been contemplated as a fresh approach to control system design that takes the complexity into account. Traditional integer order controllers use integer derivatives and integrals to control systems, whereas fractional order controllers use fractional derivatives and integrals to regulate memory and non-local behavior. This study provides a method based on the maximumsensitivity (Ms) methodology to discover all resilient fractional filter Internal Model Control - proportional integral derivative (IMC-PID) controllers that stabilize the closed-loop system and deliver the highest performance for a time delay system with a Smith predictor configuration. Additionally, it helps to enhance the range of PID controllers that are used to stabilize the system. This study also evaluates the effectiveness of the suggested controller approach for minimum phase system in comparison to those currently in use which are based on Integral of Absolute Error (IAE) and Total Variation (TV).

Keywords: modern dynamic systems, fractional order controllers, maximum-sensitivity, IMC-PID controllers, Smith predictor, IAE and TV

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: Ahmed Masrai

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Listening comprehension constitutes a considerable challenge for the second language (L2) learners, but a little is known about the explanatory power of different variables in explaining variance in listening comprehension. Since research in this area, to the researcher's knowledge, is relatively small in comparison to that focusing on the relationship between reading comprehension and factors such as vocabulary and working memory, there is a need for studies that are seeking to fill the gap in our knowledge about the specific contribution of working memory capacity (WMC), aural vocabulary knowledge and written vocabulary knowledge to explaining listening comprehension. Among 130 English as foreign language learners, the present study examines what proportion of the variance in listening comprehension is explained by aural vocabulary knowledge, written vocabulary knowledge, and WMC. Four measures were used to collect the required data for the study: (1) A-Lex, a measure of aural vocabulary knowledge; (2) XK-Lex, a measure of written vocabulary knowledge; (3) Listening Span Task, a measure of WMC and; (4) IELTS Listening Test, a measure of listening comprehension. The results show that aural vocabulary knowledge is the strongest predictor of listening comprehension, followed by WMC, while written vocabulary knowledge is the weakest predictor. The study discusses implications for the explanatory power of aural vocabulary knowledge and WMC to listening comprehension and pedagogical practice in L2 classrooms.

Keywords: listening comprehension, second language, vocabulary knowledge, working memory

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Authors: Qazi Mohammed Ahmed, Osman Sadiq Paracha, Iftikhar Hussain

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The study aims to explore the unaddressed relationship between social capital and consumers’ underlying behavioral intentions. The study postulates that this association is mediated by the role of attitudes and peer influence. The research attains evidence from a usable sample of 673 responses. The majority consists of the young and energetic social media users of Pakistan that utilize virtual communities as a way of life. A variance based structural equation modeling has been applied through SmartPLS 3. The results reveal that social capital exerts a statistically supportive association with both attitudes and peer influence. Contrastingly, this predictor variable shows an insignificant linkage with behavioral intentions but this relationship is fully mediated by consumer attitudes and peer influence. The paper enhances marketing literature with respect to an unexplored society of Pakistan. It also provides a lens for the contemporary advertisers, in terms of supporting their social media campaigns with affiliative and cohesive elements. The study also identifies a series of predictor variables that could further be tested with attitudes, subjective norms and behavioral responses.

Keywords: social capital, consumer attitudes, peer influence, behavioral intentions

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Authors: Muhammad Waqas, Shahan Waheed, Mohsin Qadeer, Ehsan Bari, Salman Ahmed, Iqra Patoli

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Objective: To determine the value of aPTT, platelets and INR as the predictor of unfavorable outcomes in patients with blunt isolated traumatic brain injury. Methods: This was an observational cohort study conducted in a tertiary care facility from 1st January 2008 to 31st December 2012. All the patients with isolated traumatic brain injury presenting within 24 hours of injury were included in the study. Coagulation parameters at presentation were recorded and Glasgow Outcome Scale calculated on last follow up. Outcomes were dichotomized into favorable and unfavorable outcomes. Relationship of coagulopathy with GOS and unfavorable outcomes was calculated using Spearman`s correlation and area under curve ROC analysis. Results: 121 patients were included in the study. The incidence of coagulopathy was found to be 6 %. aPTT was found to a significantly associated with unfavorable outcomes with an AUC = 0.702 (95%CI = 0.602-0.802). Predictive value of platelets and INR was not found to be significant. Conclusion: Incidence of coagulopathy was found to be low in current population compared to data from the West. aPTT was found to be a good predictor of unfavorable outcomes compared with other parameters of coagulation.

Keywords: aPTT, coagulopathy, unfavorable outcomes, parameters

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Authors: Hueiwang Anna Jeng, Norou Diawara, Nancy Welch, Cynthia Jackson, Rekha Singh, Kyle Curtis, Raul Gonzalez, David Jurgens, Sasanka Adikari

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Modeling effort is needed to predict the COVID-19 trends for developing management strategies and adaptation measures. The objective of this study was to assess whether SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalization cases, and death cases using copula-based time series modeling. SARS-CoV-2 RNA load in raw wastewater in Chesapeake VA was measured using the RT-qPCR method. Gaussian copula time series marginal regression model, incorporating an autoregressive moving average model and the copula function, served as a forecasting model. COVID-19 cases were correlated with wastewater viral load, hospitalization cases, and death cases. The forecasted trend of COVID-19 cases closely paralleled one of the reported cases, with over 90% of the forecasted COVID-19 cases falling within the 99% confidence interval of the reported cases. Wastewater SARS-CoV-2 viral load could serve as a predictor for COVID-19 cases and hospitalization cases.

Keywords: COVID-19, modeling, time series, copula function

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Authors: Joy Cao, Min Zhou

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Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

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Authors: Monica Alzate, Marcos Dono, Jose Manuel Sabucedo

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As long as collective action and its predictors constitute a big body of work in the field of political psychology, context-dependent studies and moral variables are a relatively new issue. The main goal of this presentation is to examine the differences in the predictors of collective action when taking into account two different types of protest, and also focus on the role of moral obligation as a predictor of collective action. To do so, we sampled both protesters and non-protesters from two mobilizations (N=376; N=563) of different nature (catalan Independence, and an 'indignados' march) and performed a logistic regression and a 2x2 MANOVA analysis. Results showed that the predictive variables that were more discriminative between protesters and non-protesters were identity, injustice, efficacy and moral obligation for the catalan Diada and injustice and moral obligation for the 'indignados'. Also while the catalans scored higher in the identification and efficacy variables, the indignados did so in injustice and moral obligation. Differences are evidenced between two types of collective action that coexist within the same protest cycle. The frames of injustice and moral obligation gain strength in the post-2010 mobilizations, a fact probably associated with the combination of materialist and post-materialist values that distinguish the movement. All of this emphasizes the need of studying protest from a contextual point of view. Besides, moral obligation emerges as key predictor of collective action engagement.

Keywords: collective action, identity, moral obligation, protest

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Authors: Ali Kassem, Esam Nada, Amro Abdelhamed, Shigeo Horie

Abstract:

Objective: Diabetes mellitus (DM) is associated with macrovascular complications, including coronary artery disease (CAD), and microvascular complications that contribute to the pathogenesis of erectile dysfunction (ED). On the other hand, silent myocardial ischemia (SMI) is more common in diabetic patients and is a strong predictor of cardiac events and mortality in diabetic and non-diabetic patients. Recently, Multidetector computed tomographic coronary angiography (MDCT-CA) has become a reliable non-invasive imaging modality for screening diabetic patients for SMI. We aim to evaluate the presence of SMI using (MDCT-CA) in patients with type 2DM having ED. Methods: This study evaluated 20 patients (mean age 61.45 ± 10.7 years), with DM and ED without any history of angina or angina equivalent. ED was tested with the Sexual Health Inventory for Men score, erection hardness score (EHS), and maximal penile circumferential change by an erect meter. Results: Of twenty studied patients, coronary artery stenosis was detected in 13 (65%) patients in the form of one-vessel disease (n = 6, 30%), two-vessel disease (n = 2, 10%), and three-vessel disease (n = 5, 25%). Maximum coronary artery stenosis was positively correlated with age (P < 0.016,) and negatively correlated with EHS (P <04). Multivariate regression analysis using age and EHS showed that age was the only independent predictor of SMI (P <04). Conclusion: MDCT-CA is a useful tool to identify SMI in patients with diabetes mellitus and ED. One should consider the possibility of SMI especially in elderly patients with DM who have ED.

Keywords: diabetes mellitus, erectile dysfunction, microvascular, silent ischemia

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Authors: Daniel Fundi Murithi

Abstract:

Missing data is a real bane in many surveys. To overcome the problems caused by missing data, partial deletion, and single imputation methods, among others, have been proposed. However, problems such as discarding usable data and inaccuracy in reproducing known population parameters and standard errors are associated with them. For regression and stochastic imputation, it is assumed that there is a variable with complete cases to be used as a predictor in estimating missing values in the other variable, and the relationship between the two variables is linear, which might not be realistic in practice. In this project, we estimate population total in presence of missing values in two-phase sampling. Instead of regression or stochastic models, non-parametric model based regression model is used in imputing missing values. Empirical study showed that nonparametric model-based regression imputation is better in reproducing variance of population total estimate obtained when there were no missing values compared to mean, median, regression, and stochastic imputation methods. Although regression and stochastic imputation were better than nonparametric model-based imputation in reproducing population total estimates obtained when there were no missing values in one of the sample sizes considered, nonparametric model-based imputation may be used when the relationship between outcome and predictor variables is not linear.

Keywords: finite population total, missing data, model-based imputation, two-phase sampling

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Authors: Rosemarie Basile, Maria Loizos, John Pallarino, Karen Gibbs

Abstract:

Given that complications can be significant following bariatric surgery and with rates of long-term success measured in excess weight loss varying as low as 33% after five years, an understanding of the psychological factors that may mitigate findings and increase success and result in better screening and supports prior to surgery are critical. An internally oriented locus of control (LOC) has been identified as a predictor for success in obesity therapy, but has not been investigated within the context of bariatric surgery. It is hypothesized that making behavioral changes prior to surgery which mirror those that are required post-surgery may ultimately predict long-term success. 122 subjects participated in a clinical interview and completed self-report measures including the Multidimensional Health Locus of Control Scale, Overeating Questionnaire (OQ), and Lifestyle Questionnaire (LQ). Pearson correlations were computed between locus of control orientation and likelihood to make behavior changes prior to surgery. Pearson correlations revealed a positive correlation between locus of control and likelihood to make behavior changes r = 0.23, p < .05. As hypothesized, there was a significant correlation between internal locus of control and likelihood to make behavior changes. Participants with a higher LOC believe that they are able to make decisions about their own health. Future research will focus on whether this positive correlation is a predictor for future bariatric surgery success.

Keywords: bariatric surgery, behavior modification, health locus of control, overeating questionnaire

Procedia PDF Downloads 277

Authors: James Adewale, Joshua Sunday

Abstract:

In this paper, we developed a linear multistep method, which is implemented in predictor corrector-method. The corrector is developed by method of collocation and interpretation of power series approximate solutions at some selected grid points, to give a continuous linear multistep method, which is evaluated at some selected grid points to give a discrete linear multistep method. The predictors were also developed by method of collocation and interpolation of power series approximate solution, to give a continuous linear multistep method. The continuous linear multistep method is then solved for the independent solution to give a continuous block formula, which is evaluated at some selected grid point to give discrete block method. Basic properties of the corrector were investigated and found to be zero stable, consistent and convergent. The efficiency of the method was tested on some linear, non-learn, oscillatory and stiff problems of first order, initial value problems of ordinary differential equations. The results were found to be better in terms of computer time and error bound when compared with the existing methods.

Keywords: predictor, corrector, collocation, interpolation, approximate solution, independent solution, zero stable, consistent, convergent

Procedia PDF Downloads 470

Authors: Sohyoung Won, Heebal Kim, Dajeong Lim

Abstract:

Genomic prediction is an effective way to measure the abilities of livestock for breeding based on genomic estimated breeding values, statistically predicted values from genotype data using best linear unbiased prediction (BLUP). Using haplotypes, clusters of linked single nucleotide polymorphisms (SNPs), as markers instead of individual SNPs can improve the reliability of genomic prediction since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with markers is higher. To efficiently use haplotypes in genomic prediction, finding optimal ways to define haplotypes is needed. In this study, 770K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 2506 cattle. Haplotypes were first defined in three different ways using 770K SNP chip data: haplotypes were defined based on 1) length of haplotypes (bp), 2) the number of SNPs, and 3) k-medoids clustering by LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; in each method, haplotypes defined to have an average number of 5, 10, 20 or 50 SNPs were tested respectively. A modified GBLUP method using haplotype alleles as predictor variables was implemented for testing the prediction reliability of each haplotype set. Also, conventional genomic BLUP (GBLUP) method, which uses individual SNPs were tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight was used as the phenotype for testing. As a result, using haplotypes defined by all three methods showed increased reliability compared to conventional GBLUP. There were not many differences in the reliability between different haplotype defining methods. The reliability of genomic prediction was highest when the average number of SNPs per haplotype was 20 in all three methods, implying that haplotypes including around 20 SNPs can be optimal to use as markers for genomic prediction. When the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles. Using haplotype alleles for genomic prediction showed better performance, suggesting improved accuracy in genomic selection. The number of predictor variables was decreased when the LD-based method was used while all three haplotype defining methods showed similar performances. This suggests that defining haplotypes based on LD can reduce computational costs and allows efficient prediction. Finding optimal ways to define haplotypes and using the haplotype alleles as markers can provide improved performance and efficiency in genomic prediction.

Keywords: best linear unbiased predictor, genomic prediction, haplotype, linkage disequilibrium

Procedia PDF Downloads 105