Search results for: psychological distress prediction

Authors: Aroona Hashmi

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An economic recession can lead people to feel more insecure about their financial situation. The series of events leading into a recession can be especially distressing for Educated Youth. One of the most salient factors linking economic recession to psychological distress is unemployment. It is proved that a large number of educated young people are facing higher unemployment rate in Pakistan. Young people are likely to get frustrated at the lack of opportunities made available to them. If the young population increases more rapidly than job opportunities, then number of unemployment is likely to increase. The aim of present study was to investigate the relationship between economic instability, growing rate of aggression and frustration among educated youth. The study aimed to find out the impact of increased economic instability on the learning abilities of the students. Data was gathered from six university students of Punjab, Pakistan. The sample of the study consisted of three hundred male and female university students. The data was analyzed by applying Chi -square test. The results of the research indicate that there is a significant relationship between low household income and growing rate of aggression among educated youth. The increasing trend of economic instability significantly influences the learning abilities of the students. The study concludes that feeling of deprivation produce frustration and could be expressed through aggression. Therefore, if factors that are responsible for youth unemployment in Pakistan are addressed, psychological effects will be reduced. The right way of tackling the youth bulge is to turn the youth into a productive workforce. There is a dire need to transform the education system to societal needs. At the same time creating demand for the young workforce is achieved through dynamic changes in the economic structure.

Keywords: psychological effects, economic recession, educated youth, environmental factors

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Authors: Agustyas Tjiptaningrum, Intanri Kurniati, Fadilah Fadilah, Linda Erlina, Tiwuk Susantiningsih

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Coronavirus disease-19 (COVID-19) is still one of the health problems. It can be a severe condition that is caused by a cytokine storm. In a cytokine storm, several proinflammatory cytokines are released massively. It destroys epithelial cells, and subsequently, it can cause death. The anti-inflammatory agent can be used to decrease the number of severe Covid-19 conditions. Melaleuca cajuputi is a plant that has antiviral, antibiotic, antioxidant, and anti-inflammatory activities. This study was carried out to analyze the prediction mechanism of the M. cajuputi extract from Lampung, Indonesia, as an anti-inflammatory agent for COVID-19. This study constructed a database of protein host target that was involved in the inflammation process of COVID-19 using data retrieval from GeneCards with the keyword “SARS-CoV2”, “inflammation,” “cytokine storm,” and “acute respiratory distress syndrome.” Subsequent protein-protein interaction was generated by using Cytoscape version 3.9.1. It can predict the significant target protein. Then the analysis of the Gene Ontology (GO) and KEGG pathways was conducted to generate the genes and components that play a role in COVID-19. The result of this study was 30 nodes representing significant proteins, namely NF-κβ, IL-6, IL-6R, IL-2RA, IL-2, IFN2, C3, TRAF6, IFNAR1, and DOX58. From the KEGG pathway, we obtained the result that NF-κβ has a role in the production of proinflammatory cytokines, which play a role in the COVID-19 cytokine storm. It is an important factor for macrophage transcription; therefore, it will induce inflammatory gene expression that encodes proinflammatory cytokines such as IL-6, TNF-α, and IL-1β. In conclusion, the blocking of NF-κβ is the prediction mechanism of the M. cajuputi extract as an anti-inflammation agent for COVID-19.

Keywords: antiinflammation, COVID-19, cytokine storm, NF-κβ, M. cajuputi

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Authors: Carl van Walraven, Meltem Tuna

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Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation.

Keywords: prediction model accuracy, scaled brier score, fixed effects methods, concurrent external validation

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Authors: Arvinder Kaur, Deepti Chopra

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Software Entropy Metrics for bug prediction have been validated on various software systems by different researchers. In our previous research, we have validated that Software Entropy Metrics calculated for Mozilla subsystem’s predict the future bugs reasonably well. In this study, the Software Entropy metrics are calculated for a subsystem of Android and it is noticed that these metrics are not suitable for bug prediction. The results are compared with a subsystem of Mozilla and a comparison is made between the two software systems to determine the reasons why Software Entropy metrics are not applicable for Android.

Keywords: android, bug prediction, mining software repositories, software entropy

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Authors: Chang Su Woo, Hyun Sung Park

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Useful lifetime evaluation of chevron rubber spring was very important in design procedure to assure the safety and reliability. It is, therefore, necessary to establish a suitable criterion for the replacement period of chevron rubber spring. In this study, we performed characteristic analysis and useful lifetime prediction of chevron rubber spring. Rubber material coefficient was obtained by curve fittings of uni-axial tension, equi bi-axial tension and pure shear test. Computer simulation was executed to predict and evaluate the load capacity and stiffness for chevron rubber spring. In order to useful lifetime prediction of rubber material, we carried out the compression set with heat aging test in an oven at the temperature ranging from 50°C to 100°C during a period 180 days. By using the Arrhenius plot, several useful lifetime prediction equations for rubber material was proposed.

Keywords: chevron rubber spring, material coefficient, finite element analysis, useful lifetime prediction

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Authors: Amit R. Bhende, G. K. Awari

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Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Bearing degradation data at three different conditions from run to failure is used. A RUL prediction model is separately built in each condition. Feed forward back propagation neural network models are developed for prediction modeling.

Keywords: bearing degradation data, remaining useful life (RUL), back propagation, prognosis

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Authors: Qiang Zhang, Chun Yuan

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Since the PU (Prediction Unit) decision process is the most time consuming part of the emerging HEVC (High Efficient Video Coding) standardin intra and inter frame coding, this paper proposes the fast PU decision algorithm and speed up the algorithm using CUDA (Compute Unified Device Architecture). In intra frame coding, the fast PU decision algorithm uses the texture features to skip intra-frame prediction or terminal the intra-frame prediction for smaller PU size. In inter frame coding of HEVC, the fast PU decision algorithm takes use of the similarity of its own two Nx2N size PU's motion vectors and the hierarchical structure of CU (Coding Unit) partition to skip some modes of PU partition, so as to reduce the motion estimation times. The accelerate algorithm using CUDA is based on the fast PU decision algorithm which uses the GPU to make the motion search and the gradient computation could be parallel computed. The proposed algorithm achieves up to 57% time saving compared to the HM 10.0 with little rate-distortion losses (0.043dB drop and 1.82% bitrate increase on average).

Keywords: HEVC, PU decision, inter prediction, intra prediction, CUDA, parallel

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Authors: J. K. Alhassan, C. S. Actsu

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This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781.

Keywords: academic performance, artificial neural network, prediction, students

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Authors: Oyvind Nordvik, Peder O. L. Heggdal, Jonas Brannstrom, Flemming Vassbotn, Anne Kari Aarstad, Hans Jorgen Aarstad

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Background: Hearing Loss (HL) is a condition that may affect people in all stages of life, but the prevalence increases with age, mostly because of age-related HL, generally referred to as presbyacusis. As human speech is related to relatively high frequencies, even a limited hearing loss at high frequencies may cause impaired speech intelligibility. Being diagnosed with, treated for and living with a chronic condition such as HL, must for many be a disabling and stressful condition that put ones coping resources to test. Stress is a natural part of life and most people will experience stressful events or periods. Chronic diseases, such as HL, are risk factor for distress in individuals, causing anxiety and lowered mood. How an individual cope with HL may be closely connected to the level of distress he or she is experiencing and to personality, which can be defined as those characteristics of a person that account for consistent patterns of feelings, thinking, and behavior. Thus, as to distress in life, such as illness or disease, available coping strategies may be more important than the challenge itself. The same line of arguments applies to level of experienced health-related quality of life (HRQoL). Aim: The aim of this study was to investigate the relationship between distress, HRQoL, reported hearing loss, personality and coping in patients with HL. Method: 158 adult (aged 18-78 years) patients with HL, referred for hearing aid (HA) fitting at Haukeland University Hospital in western Norway, participated in the study. Both first-time users, as well as patients referred for HA renewals were included. First-time users had been pre-examined by an ENT-specialist. The questionnaires were answered before the actual HA fitting procedure. The pure-tone average (PTA; frequencies 0.5, 1, 2, and 4 kHz) was determined for each ear. The Eysenck personality inventory, neuroticism and lie scales, the Theoretically Originated Measure of the Cognitive Activation Theory of Stress (TOMCATS) measuring active coping, hopelessness and helplessness, as well as distress (General Health Questionnaire (GHQ) - 12 items) and the EORTC Quality of Life Questionnaire general part were answered. In addition, we used a revised and shortened version of the Abbreviated Profile of Hearing Aid Benefit (APHAB) as a measure of patient-reported hearing loss. Results: Significant correlations were determined between APHAB (weak), HRQoL scores (strong), distress scores (strong) on the one side and personality and choice of coping scores on the other side. As measured by stepwise regression analyses, the distress and HRQoL scores were scored secondary to the obtained personality and coping scores. The APHAB scores were as determined by regression analyses scored secondary to PTA (best ear), level of neuroticism and lie score. Conclusion: We found that reported employed coping style, distress/HRQoL and personality are closely connected to each other in this patient group. Patient-reported HL was associated to hearing level and personality. There is need for further investigations on these questions, and how these associations may influence the clinical context.

Keywords: coping, distress, hearing loss, personality

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Authors: Mohammad Ghavami, Reza S. Dilmaghani

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This paper presents an adaptive framework for modelling financial markets using equity risk premiums, risk free rates and volatilities. The recorded economic factors are initially used to train four adaptive filters for a certain limited period of time in the past. Once the systems are trained, the adjusted coefficients are used for modelling and prediction of an important financial market index. Two different approaches based on least mean squares (LMS) and recursive least squares (RLS) algorithms are investigated. Performance analysis of each method in terms of the mean squared error (MSE) is presented and the results are discussed. Computer simulations carried out using recorded data show MSEs of 4% and 3.4% for the next month prediction using LMS and RLS adaptive algorithms, respectively. In terms of twelve months prediction, RLS method shows a better tendency estimation compared to the LMS algorithm.

Keywords: adaptive methods, LSE, MSE, prediction of financial Markets

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

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Authors: Iqbal Abd. Muhbir Hadi Anam

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The purpose of the study is to discuss the relationship between organizational culture, teacher psychological empowerment, and organizational citizenship behavior at the University of Bangkalan District. The data was obtained using a survey of 100 respondents tested for validity and reliability. The analytical technique used is a hierarchical regression test. The results showed that the organizational culture of the university had a strong influence on the psychological empowerment of teachers and the psychological empowerment of teachers and that the organizational culture and psychological empowerment of teachers provided effective predictions of the psychological empowerment of the university. In addition, organizational culture directly or indirectly influences teachers' organizational citizenship behavior through psychological empowerment. Given these results, universities need to build an organizational culture that reflects the nature of the university.

Keywords: organizational behavior, teacher psychological empowerment, organizational citizenship behavior, universities

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Authors: Zoe Lynch, Kirsty Zieschank

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There are several factors that compromise the utility and wide-spread use of existing emotional and behavioural distress screening instruments. Some of these factors include lengthy administration times, high costs, feasibility issues, and a lack of self-report options for children under 12 years of age. This animation-based resource was developed to overcome as many of these factors as possible. Developed for educators and medical and mental health professionals, this resource offers children a self-guided mechanism for reporting any current emotional and behavioural distress. An avatar assistant, selected by the child, accompanies them through each stage of the screening process, offering further instruction if prompted. Children enter their age and gender before viewing comparative animations conveying common childhood emotional and behavioural difficulties. The child then selects the most relatable animations, along with the frequency with which they experience the depicted emotions. From a perspective of intellectual development, an engaging, animated format means that outcomes will not be constrained by children’s reading, writing, cognitive, or verbal expression abilities. Having been user-tested with children aged 6 to 12, this resource shows promising results as a self-guided screening instrument.

Keywords: animation-based screening instrument, mental health, primary-aged children, self-guided

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Authors: Mekdelawit Teferi Alamirew

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As a practical tool, the Geographical information system (GIS) was used for data integration, collection, management, analysis, and output presentation in pavement mangement systems . There are many GIS techniques to improve the maintenance activities like Dynamic segmentation and weighted overlay analysis which considers Multi Criteria Decision Making process. The results indicated that the developed MPI model works sufficiently and yields adequate output for providing accurate decisions. Hence considering multi criteria to prioritize the pavement sections for maintenance, as a result of the fact that GIS maps can express position, extent, and severity of pavement distress features more effectively than manual approaches, lastly the paper also offers digitized distress maps that can help agencies in their decision-making processes.

Keywords: pavement, flexible, maintenance, index

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Authors: Jiun Jeon, Byung-Ju Yi

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This paper investigates modeling and shape prediction of elastic kinematic chains such as colonoscopy. 2D and 3D models of elastic kinematic chains are suggested and their behaviors are demonstrated through simulation. To corroborate the effectiveness of those models, experimental work is performed using a magnetic sensor system.

Keywords: elastic kinematic chain, shape prediction, colonoscopy, modeling

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Authors: Marit D. Murry, Amy K. Marks

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The transition to college is a critical period that affords abundant opportunities for growth in conjunction with novel challenges for emerging adults. During this time, emerging adults are garnering experiences and acquiring hosts of new information that they are required to synthesize and use to inform life-shaping decisions. This stage is characterized by instability and exploration, which necessitates a diverse set of coping skills to successfully navigate and positively adapt to their evolving environment. However, important sociocultural factors result in differences that occur developmentally for minority emerging adults (i.e., emerging adults with an identity that has been or is marginalized). While the transition to college holds vast potential, not all are afforded the same chances, and many individuals enter into this stage at varying degrees of readiness. Understanding the nuance and diversity of student preparedness for college and contextualizing these factors will better equip systems to support incoming students. Emerging adulthood for ethnic, racial minority students presents itself as an opportunity for growth and resiliency in the face of systemic adversity. Ethnic, racial identity (ERI) is defined as an identity that develops as a function of one’s ethnic-racial group membership. Research continues to demonstrate ERI as a resilience factor that promotes positive adjustment in young adulthood. Adaptive coping responses (e.g., engaging in help-seeking behavior, drawing on personal and community resources) have been identified as possible mechanisms through which ERI buffers youth against stressful life events, including discrimination. Additionally, trait mindfulness has been identified as a significant predictor of general psychological health, and mindfulness practice has been shown to be a self-regulatory strategy that promotes healthy stress responses and adaptive coping strategy selection. The current study employed a person-centered approach to explore emerging patterns across ethnic identity development and psychological well-being criterion variables among college freshmen. Data from 283 incoming college freshmen at Northeastern University were analyzed. The Brief COPE Acceptance and Emotional Support scales, the Five Factor Mindfulness Questionnaire, and MIEM Exploration and Affirmation measures were used to inform the cluster profiles. The TwoStep auto-clustering algorithm revealed an optimal three-cluster solution (BIC = 848.49), which classified 92.6% (n = 262) of participants in the sample into one of the three clusters. The clusters were characterized as ‘Mixed Adjustment’, ‘Lowest Adjustment’, and ‘Moderate Adjustment.’ Cluster composition varied significantly by ethnicity X² (2, N = 262) = 7.74 (p = .021) and gender X² (2, N = 259) = 10.40 (p = .034). The ‘Lowest Adjustment’ cluster contained the highest proportion of students of color, 41% (n = 32), and male-identifying students, 44.2% (n = 34). Follow-up analyses showed higher ERI exploration in ‘Moderate Adjustment’ cluster members, also reported higher levels of psychological distress, with significantly elevated depression scores (p = .011), psychological diagnoses of depression (p = .013), anxiety (p = .005) and psychiatric disorders (p = .025). Supporting prior research, students engaging with identity exploration processes often endure more psychological distress. These results indicate that students undergoing identity development may require more socialization and different services beyond normal strategies.

Keywords: adjustment, coping, college, emerging adulthood, ethnic-racial identity, psychological well-being, resilience

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Authors: Tong Mei Yan

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Introduction: The experience of infertility could be excruciating but has not received much attention in Hong Kong. The strong Confucian culture pressures couples to continue their family lineage resulting in women facing more stress than men in the social-cultural milieu. In Vitro Fertilization (IVF) treatment is one of the common ways to deal with the problem. Abundant literature exists the psychological trajectories of people receiving IVF treatment in Europe, the USA and other east Asian societies but not in Hong Kong. Aim: This study aims to highlight the circumstances and needs of the women before, during and after IVF treatment through examining their lived experiences. It is hoped that the public, once informed of regarding their tribulations and needs , would support the adequate provision of the required psychological support . Methods: Qualitative analysis was adopted in this study. In-depth interviews were conducted with six women who have undergone at least one complete cycle of IVF treatment within the past five years. Data was analyzed through thematic analysis and narrative analysis. Results: 4 broad themes were found, including (i) emotional responses; (ii) experiences in medical consultation; (iii) impacts of the treatment; and (iv) their coping strategies. Additionally, specific events in three cases were chosen for narrative analysis to further examine their unresolved emotional distress and the ethical issues. Conclusion: IVF treatment distressed the interviewees immensely, both physically and psychologically, with the negative emotions outweighing their physical strains, a result unexpected by all of the interviewees. The pressure for lineage continuation, the demanding treatment process and the dearth of support from health professionals all contribute to their emotional pain which could linger for both successful and unsuccessful cases. Although a number of coping strategies were attempted, most of them completely failed to ease their psychological tension. The findings of this study therefore evidence the need for psychological support for this population. A service model to cater their needs of IVF treatment before, during and after treatment is therefore proposed.

Keywords: coping strategies, emotional experiences, impacts of IVF, infertility, IVF treatment, medical experiences

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Sadia Saleem

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Infertility is a global health concern that may have a long-lasting effect on the psychosocial functioning of an individual. Rich research evidence has shown that women with infertility are at greater risk of adverse psychological experiences than men. The culture plays a risk factor when it comes to infertility. Family is considered as a central focus of a collectivistic culture like Pakistan and having children is the key factor that determines the quality of a marital relationship, individual well-being and overall standing in the society. In this collectivistic cultural context, women usually get the blame and experience more psychological distress and social isolation. A total sample of 121 (M 28.17, SD 4.73) women with primary infertility selected through purposive sampling were tested using Emotional Disclosure Questionnaire, Couple Satisfaction Index and Depression Anxiety Stress Scale. The results indicate that negative emotional disclosure positively mediates the relationship between marital satisfaction and mental health problems (p < .001) in women with primary infertility. The results are discussed in terms of psychosocial counseling and family psychoeducation in Pakistani collectivistic cultural context.

Keywords: infertility, couple satisfaction, emotional disclosure, mental health

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Authors: C. Lalitha, R. Sayee, D. Apoorva

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Reproductive failure or infertility may be due to several factors that are not limited to one sex. It remains a common problem causing significant psychological distress to those affected individuals and who are increasingly seeking medical advice. Male infertility means inability to induce conception in normal woman within a year. The etiological factors associated with male infertility are anatomical, developmental, seminal, hormonal, immunological and environmental factors. The paper was aimed to highlight the environmental factors and its association to male infertility and seasonality of birth and its influence. The data was collected from the 75 male patients referred with infertility for karyotyping and counseling. Their age ranged from 21 to 45 years. It is opined that certain occupations are preferentially associated with male infertility.

Keywords: environmental, occupational, seasonal, male infertility

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Authors: Ben Clapperton, Daniel Stahl, Kimberley Goldsmith, Trudie Chalder

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Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently can't be explained by any underlying medical condition. Although clinical trials support the effectiveness of cognitive behaviour therapy (CBT), the success rate for individual patients is modest. Patients vary in their response and little is known which factors predict or moderate treatment outcomes. The aim of the project is to develop a prediction model from baseline characteristics of patients, such as demographics, clinical and psychological variables, which may predict likely treatment outcome and provide guidance for clinical decision making and help clinicians to recommend the best treatment. The project is aimed at identifying subgroups of patients with similar baseline characteristics that are predictive of treatment effects using modern cluster analyses and data mining machine learning algorithms. The characteristics of these groups will then be used to inform the types of individuals who benefit from a specific treatment. In addition, results will provide a better understanding of for whom the treatment works. The suitability of different clustering methods to identify subgroups and their response to different treatments of CFS patients is compared.

Keywords: chronic fatigue syndrome, latent class analysis, prediction modelling, self-organizing maps

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Authors: Somayyeh Naeemi, Ezhar Tamam

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In most of the countries, Facebook allocated high rank of usage among other social network sites. Several studies have examined the effect of Facebook intensity on individuals’ psychological well-being. However, few studies have investigated its effect on eudaimonic well-being. The current study explored how emotional dependency to Facebook relates to psychological well-being in terms of eudaimonic well-being. The number of 402 adolescents 13-16 years old who studied in upper secondary school in Malaysia participated in this study. It was expected to find out a negative association between emotional dependency to Facebook and time spent on Facebook and psychological well-being. It also was examined the moderation effects of self-efficacy on psychological well-being. The results by Structural Equation Modeling revealed that emotional dependency to Facebook has a negative effect on adolescents’ psychological well-being. Surprisingly self-efficacy did not have moderation effect on the relationship between emotional dependency to Facebook and psychological well-being. Lastly, the emotional dependency to Facebook and not the time spent on Facebook lessen adolescents’ psychological well-being, suggesting the value of investigating Facebook usage among college students in future studies.

Keywords: emotional dependency to facebook, psychological well-being, eudaimonic well-being, self-efficacy, adolescent

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Authors: Farzana Parveen, Shahina Maqbool

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Nowadays most of the researchers focused on adolescents that were the most crucial and critical stage of their life’s. In this study, we want to know to what extent does spirituality and psychological well-being plays a vital role in adolescent’s life, and to what extent both were correlated. The first objective of the present study is to examine difference between the mean scores of male and female students on spirituality and Psychological well-being. The second objective of this study is to find out the relationship between Spirituality and Psychological Well-being. The participants of the present study comprised N=100 students who are further divided n=50 male and n=50 female and they were from senior secondary schools and recruited from Metropolitan cities, their age ranged from 13 to 18 years. Data were collected through Spirituality questionnaire and Psychological well-being questionnaire. Results showed that there was insignificant difference between male and female students on spirituality and significant difference between male and female students on psychological well-being. Relationship showed that there was positive correlation between spirituality and psychological well-being. Result was analyzed by SPSS version 20.0 software. Insignificant difference was found between males and females students on Spirituality and significant difference showed between males and females students on Psychological well-being. There was positive correlation between Spirituality and psychological well-being.

Keywords: spirituality, psychological well-being, male and female, students

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Authors: Maha Salah, Hanaa Hashem, Mahmoud M. Alsagheir, Mohammed Salah

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Acute respiratory distress syndrome (ARDS) represents a complex clinical syndrome and carries a high risk for mortality. The severity of the clinical course, the uncertainty of the outcome, and the reliance on the full spectrum of critical care resources for treatment mean that the entire health care team is challenged. Researchers and clinicians have investigated the nature of the pathological process and explored treatment options with the goal of improving outcome. Through this application of research to practice, we know that some previous strategies have been ineffective, and innovations in mechanical ventilation, sedation, nutrition, and pharmacological intervention remain important research initiatives. Developed Clinical pathway is multidisciplinary plans of best clinical practice for this specified groups of patients that aid in the coordination and delivery of high quality care. They are a documented sequence of clinical interventions that help a patient to move, progressively through a clinical experience to a desired outcome. Although there is a lot of heterogeneity in patients with ARDS, this suggested developed clinical pathway with alternatives was built depended on a lot of researches and evidence based medicine and nursing practices which may be helping these patients to improve outcomes, quality of life and decrease mortality.

Keywords: acute respiratory distress syndrome (ARDS), clinical pathway, clinical syndrome

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Chang Li-Yu

Abstract:

Aims: This study investigates the impact of psychological capital on the mental health of economically disadvantaged youth and examines whether family cohesion acts as a mediating variable between psychological capital and mental health. Methods: The sample for the study was drawn from the "Taiwan Poverty Children's Database: Survey on the Living Trends of Disadvantaged Children and Youth." The data analysis methods included descriptive statistics, confirmatory factor analysis, and structural equation modeling. Results: The results indicated that both psychological capital and family cohesion can significantly negatively predict mental health, with psychological capital significantly positively predicting family cohesion. The mediation effect analysis revealed that family cohesion fully mediates the relationship between psychological capital and mental health, meaning that psychological capital influences mental health through family cohesion. Recommendations: Based on these findings, the study concretely discusses the significance of psychological capital and family cohesion for the mental health of economically disadvantaged youth and offers suggestions for psychological counseling, therapy, and future research.

Keywords: psychological capital, mental health, economically disadvantaged youth, family cohesion

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Authors: Milena Gojny-Zbierowska

Abstract:

In the last decades psychological tests have been gaining in popularity as a method used for evaluating personnel, and they bring consulting companies solid profits rising by up to 10% each year. The market is offering a growing range of tools for the assessment of personality. Tests are used in organizations mainly in the recruitment and selection of staff. This paper is an attempt to initially diagnose the state of the use of psychological tests in Polish companies on the basis of empirical research.

Keywords: psychological tests, personality, content analysis, NEO FFI, big five personality model

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Authors: Haozhe Xiang

Abstract:

With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

Procedia PDF Downloads 30