Search results for: prediction fatigue life

Authors: Varsha Pandurang Nagargoje, K. S. James

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Background: Social participation is considered as one of the central components of successful and healthy aging. This study aimed to examine the moderating role of marital status and living arrangement in the relationship between social participation and life satisfaction and other potential factors associated with life satisfaction of Indian older adults. Method: For analyses, the nationally representative study sample of 31,464 adults aged ≥60 years old was extracted from the Longitudinal Ageing Study in India (LASI) wave 1, 2017-18. Descriptive statistics and bivariate analysis have been performed to determine the proportion of life satisfaction. The first set of multivariable linear regression analyses examined Diener’s Satisfaction with Life Scale and its association with various predictor variables, including social participation, marital status, living arrangements, socio-demographic, economic, and health-related variables. Further, the second and third sets of regression investigated the moderating role of marital status and living arrangements respectively in the association of social participation and level of life satisfaction among Indian older adults. Results: Overall, the proportion of life satisfaction among older men was relatively higher than women counterparts in most background characteristics. Regression results stressed the importance of older adults’ involvement in social participation [β = 0.39, p < 0.05], being in marital union [β = 0.68, p < 0.001] and co-residential living arrangements either only with spouse [β = 1.73, p < 0.001] or with other family members [β = 2.18, p < 0.001] for the improvement of life satisfaction. Results also showed that some factors were significant for life satisfaction: in particular, increased age, having a higher level of educational status, MPCE quintile, and caste category. Higher risk of life dissatisfaction found among Indian older adults who were exposed to vulnerabilities like consuming tobacco, poor self-rated health, having difficulty in performing ADL and IADL were of major concern. The interaction effect of social participation with marital status or with living arrangements explained that currently married older individuals, and those older adults who were either co-residing with their spouse only or with other family members irrespective of their involvement in social participation remained an important modifiable factor for life satisfaction. Conclusion: It would be crucial for policymakers and practitioners to advocate social policy programs and service delivery oriented towards meaningful social connections, especially for those Indian older adults who were staying alone or currently not in the marital union to enhance their overall life satisfaction.

Keywords: Indian, older adults, social participation, life satisfaction, marital status, living arrangement

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Authors: Bojan Najdenov, Danco Davcev

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Reduction of traffic congestions and the effects of pollution and waste of resources that come with them has been a big challenge in the past decades. Having reliable systems to facilitate the process of modeling and prediction of traffic conditions would not only reduce the environmental pollution, but will also save people time and money. Social networks play big role of people’s lives nowadays providing them means of communicating and sharing thoughts and ideas, that way generating huge knowledge bases by crowdsourcing. In addition to that, crowdsourcing as a concept provides mechanisms for fast and relatively reliable data generation and also many services are being used on regular basis because they are mainly powered by the public as main content providers. In this paper we present the Social-NETS-Traffic-Control System (SNTCS) that should serve as a facilitator in the process of modeling and prediction of traffic congestions. The main contribution of our system is to integrate data from social networks as Twitter and also implements a custom created crowdsourcing subsystem with which users report traffic conditions using an android application. Our first experience of the usage of the system confirms that the integrated approach allows easy extension of the system with other social networks and represents a very useful tool for traffic control.

Keywords: traffic, congestion reduction, crowdsource, social networks, twitter, android

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Ravi Soni, Irfan Pathan, Manish Pande

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The current product development process needs simultaneous consideration of different physics. The performance of the product needs to be considered under both structural and fluid loads. Examples include ducts and valves where structural behavior affects fluid motion and vice versa. Simulation of fluid-structure interaction involves modeling interaction between moving components and the fluid flow. In these scenarios, it is difficult to calculate the damping provided by fluid flow because of dynamic motions of components and the transient nature of the flow. Abaqus Explicit offers general capabilities for modeling fluid-structure interaction with the Coupled Eulerian-Lagrangian (CEL) method. The Coupled Eulerian-Lagrangian technique has been used to simulate fluid spillage through fuel valves during dynamic closure events. The technique to simulate pressure drops across Eulerian domains has been developed using stagnation pressure. Also, the fluid flow is calculated considering material flow through elements at the outlet section of the valves. The methodology has been verified on Eaton products and shows a good correlation with the test results.

Keywords: Coupled Eulerian-Lagrangian Technique, fluid structure interaction, spillage prediction, stagnation pressure

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Authors: Kamran Yazdanbakhsh, Somayeh Mahmoudi

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Multiple sclerosis is the most common chronic disease of the central nervous system associated with demyelination of neurons and several demyelinated parts of the disease encompasses throughout the white matter and affects the sensory and motor function. This study compared the effectiveness of two methods of cognitive therapy and supportive-expressive therapy on the efficacy and quality of life in MS patients. This is an experimental project which has used developed group pretest - posttest and follow-up with 3 groups. The study included all patients with multiple sclerosis in 2013 that were members of the MS Society of Iran in Tehran. The sample included 45 patients with MS that were selected volunteerily of members of the MS society of Iran and randomly divided into three groups and pretest, posttest, and follow-up (three months) for the three groups had been done.The dimensions of quality of life in patients with multiple sclerosis scale, and general self-efficiency scale of Schwarzer and Jerusalem was used for collecting data. The results showed that there was a significant difference between the mean of quality of life scores at pretest, posttest, and follow-up of the experimental groups. There was no significant difference between the mean of quality of life of the experimental groups which means that both groups were effective and had the same effect. There was no significant difference between the mean of self-efficiency scores in control and experimental group in pretest, posttest and follow-up. Thus, by using cognitive and supportive-expressive group therapy we can improve quality of life in MS patients and make great strides in their mental health.

Keywords: cognitive group therapy, life style, MS, self-efficiency, supportive-expressive group therapy

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Authors: Arne Fagerstrom, Gary Cunningham, Fredrik Hartwig

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In this paper, a theory of sustainable enterprises, sustainable enterprise theory (SET), is developed. The sustainable enterprise theory can only be a valid theory if knowledge about life and nature is complete. Knowledge limitations should not stop enterprises from doing business with a goal of better long-term life on earth. Life demands stewardship of the resources used during one’s lifetime. This paper develops a model influenced by (the classical) enterprise theory and resource theory that includes more than money in the business activities of an enterprise. The sustainable enterprise theory is then used in an analysis of accountability and in discussions about sustainable businesses.

Keywords: sustainable business, sustainability reporting, sustainable values, theory of the firm

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Authors: Maria Janet, Anita David, P. Vijayasamundeeswarimaria

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Introduction: The main purpose of this study is to compare the effectiveness of web-based and blended learning on Paediatric Basic Life Support on competency among undergraduate nursing students in selected nursing colleges in Chennai. Materials and methods: A descriptive pre-test and post-test study design were used for this study. Samples of 100 Fourth year B.Sc., nursing students at Sri Ramachandra Faculty of Nursing SRIHER, Chennai, 100 Fourth year B.Sc., nursing students at Apollo College of Nursing, Chennai, were selected by purposive sampling technique. The instrument used for data collection was Knowledge Questionnaire on Paediatric Basic Life Support (PBLS). It consists of 29 questions on the general expansion of Basic Life Support and Cardiopulmonary Resuscitation, Prerequisites of Basic Life Support, and Knowledge on Paediatric Basic Life Support in which each question has four multiple choices answers, each right answer carrying one mark and no negative scoring. This questionnaire was formed with reference to AHA 2020 (American Heart Association) revised guidelines. Results: After the post-test, in the web-based learning group, 58.8% of the students had an inadequate level of objective performance score, while 41.1% of them had an adequate level of objective performance score. In the blended learning group, 26.5% of the students had an inadequate level of an objective performance score, and 73.4% of the students had an adequate level of an objective performance score. There was an association between the post-test level of knowledge and the demographic variables of undergraduate nursing students undergoing blended learning. The age was significant at a p-value of 0.01, and the performance of BLS before was significant at a p-value of 0.05. The results show that there was a significant positive correlation between knowledge and objective performance score of undergraduate nursing students undergoing web-based learning on paediatric basic life support.

Keywords: basic life support, paediatric basic life support, web-based learning, blended learning

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Authors: Jing (Claire) LI

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The topic of corporate social responsibility (CSR) is significant for pharmaceutical companies in China at this current stage. This is because, as a rapid growth industry in China in recent years, the pharmaceutical industry in China has been undergone continuous and terrible incidents relating to CSR. However, there is limited research and practice of CSR in Chinese pharmaceutical companies. Also, there is an urgent call for more research in an international context to understand the implications of corporate life cycle on CSR performance. To respond to the research need and research call, this study examines the relationship between corporate life cycle and CSR performance of Chinese listed companies in pharmaceutical industry. This research studies Chinese listed companies in pharmaceutical industry for the period of 2010-2017, where the data is available in database. Following the literature, this study divides CSR performance with regards to CSR dimensions, including shareholders, creditors, employees, customers, suppliers, the government, and the society. This study uses CSR scores of HEXUN database and financial measures of these CSR dimensions to measure the CSR performance. This study performed regression analysis to examine the relationship between corporate life cycle stages and CSR performance with regards to CSR dimensions for pharmaceutical listed companies in China. Using cash flow pattern as proxy of corporate life cycle to classify corporate life cycle stages, this study found that most (least) pharmaceutical companies in China are in maturity (decline) stage. This study found that CSR performance for most dimensions are highest (lowest) in maturity (decline) stage as well. Among these CSR dimensions, performing responsibilities for shareholder is the most important among all CSR responsibilities for pharmaceutical companies. This study is the first to provide important empirical evidence from Chinese pharmaceutical industry on the association between life cycle and CSR performance, supporting that corporate life cycle is a key factor in CSR performance. The study expands corporate life cycle and CSR literatures and has both empirical and theoretical contributions to the literature. From perspective of empirical contributions, the findings contribute to the argument that whether there is a relationship between CSR performance and various corporate life cycle stages in the literature. This study also provides empirical evidence that companies in different corporate life cycles have difference in CSR performance. From perspective of theoretical contributions, this study relates CSR and stakeholders to corporate life cycle stages and complements the corporate life cycle and CSR literature. This study has important implications for managers and policy makers. First, the results will be helpful for managers to have an understanding in the essence of CSR, and their company’s current and future CSR focus over corporate life cycle. This study provides a reference for their actions and may help them make more wise resources allocation decisions of CSR investment. Second, policy makers (in the government, stock exchanges, and securities commission) may consider corporate life cycle as an important factor in formulating future regulations for companies. Future research can explore the "process-based" differences in CSR performance and more industries.

Keywords: China, corporate life cycle, corporate social responsibility, pharmaceutical industry

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Authors: Zuwaira Abubakar

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Islam is the Divine religion which guides Man ways of leading a prosperous life in this life and the hereafter. This study was conducted in order to investigate the possible relationship between adherence to Islamic principles of success and academic performance of university students. Accordingly, a questionnaire based on Islamized principles of success (referred to as 'Islamic character quotient inventory (ICQi)') was correlated with CGPA (Cumulative Grade Point Averages) of 343 students of Usmanu Danfodiyo University Sokoto. The empirical testing indicates that the total score on ICQi correlated positively and significantly with academic performance of the respondent. Students with either high or medium adherence have a significantly (P<0.01) higher CGPA than their counterparts with the low-adherence level. However, the result did not show a significant relationship between the CGPA of highly adherent individuals and that of those with medium adherence level. This may suggests that Islam is not for spiritual life only but also relevant and useful for our practical life.

Keywords: academic, Islam, principles, success

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Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

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The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

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Authors: I. L. Kim, J. Y. Lee, A. K. Tekile

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In consequence of the very high urbanization rate of Korea, the number of traffic noise damages in areas congested with population and facilities is steadily increasing. The current environmental noise levels data in major cities of the country show that the noise levels exceed the standards set for both day and night times. This research was about comparative analysis in search for optimal soundproof panel shape and design factor that can minimize sound reflection noise. In addition to the normal flat-type panel shape, the reflection noise reduction of swelling-type, combined swelling and curved-type, and screen-type were evaluated. The noise source model Nord 2000, which often provides abundant information compared to models for the similar purpose, was used in the study to determine the overall noise level. Based on vehicle categorization in Korea, the noise levels for varying frequency from different heights of the sound source (directivity heights of Harmonize model) have been calculated for simulation. Each simulation has been made using the ray-tracing method. The noise level has also been calculated using the noise prediction program called SoundPlan 7.2, for comparison. The noise level prediction was made at 15m (R1), 30 m (R2) and at middle of the road, 2m (R3) receiving the point. By designing the noise barriers by shape and running the prediction program by inserting the noise source on the 2nd lane to the noise barrier side, among the 6 lanes considered, the reflection noise slightly decreased or increased in all noise barriers. At R1, especially in the cases of the screen-type noise barriers, there was no reduction effect predicted in all conditions. However, the swelling-type showed a decrease of 0.7~1.2 dB at R1, performing the best reduction effect among the tested noise barriers. Compared to other forms of noise barriers, the swelling-type was thought to be the most suitable for reducing the reflection noise; however, since a slight increase was predicted at R2, further research based on a more sophisticated categorization of related design factors is necessary. Moreover, as swellings are difficult to produce and the size of the modules are smaller than other panels, it is challenging to install swelling-type noise barriers. If these problems are solved, its applicable region will not be limited to other types of noise barriers. Hence, when a swelling-type noise barrier is installed at a downtown region where the amount of traffic is increasing every day, it will both secure visibility through the transparent walls and diminish any noise pollution due to the reflection. Moreover, when decorated with shapes and design, noise barriers will achieve a visual attraction than a flat-type one and thus will alleviate any psychological hardships related to noise, other than the unique physical soundproofing functions of the soundproof panels.

Keywords: reflection noise, shaped noise barriers, sound proof panel, traffic noise

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Authors: Pablo M. S. Vallejos

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The article aims to analyze the autobiographical practices of users on Instagram, observing the instrumentalization of image resources in the construction of visual narratives that make up that archive and digital diary. Through bibliographical review, discourse exploration and case studies, the research also aims to present a new theoretical perception about everyday records - edited with a collage of filters and aesthetic tools - that permeate that social network, understanding it as a platform fictionalizing and an expansion of life. In this way, therefore, the work reflects on possible futures in the elaboration of representations and identities in the context of digital spaces in the 21st century.

Keywords: visual culture, social media, autobiography, image

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Authors: Anne C. Richer-De-Forges, Dominique Arrouays, Songchao Chen, Mercedes Roman Dobarco

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Most of the digital soil mapping (DSM) products rely on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs. However, many other observations (often qualitative, nominal, or ordinal) could be used as proxies of lab measurements or as input data for ML of PTF predictions. DSM and ML are briefly described with some examples taken from the literature. Then, we explore the potential of an ordinal qualitative variable, i.e., the hand-feel soil texture (HFST) estimating the mineral particle distribution (PSD): % of clay (0-2µm), silt (2-50µm) and sand (50-2000µm) in 15 classes. The PSD can also be measured by lab measurements (LAST) to determine the exact proportion of these particle-sizes. However, due to cost constraints, HFST are much more numerous and spatially dense than LAST. Soil texture (ST) is a very important soil parameter to map as it is controlling many of the soil properties and functions. Therefore, comes an essential question: is it possible to use HFST as a proxy of LAST for calibration and/or validation of DSM predictions of ST? To answer this question, the first step is to compare HFST with LAST on a representative set where both information are available. This comparison was made on ca 17,400 samples representative of a French region (34,000 km2). The accuracy of HFST was assessed, and each HFST class was characterized by a probability distribution function (PDF) of its LAST values. This enables to randomly replace HFST observations by LAST values while respecting the PDF previously calculated and results in a very large increase of observations available for the calibration or validation of PTF and ML predictions. Some preliminary results are shown. First, the comparison between HFST classes and LAST analyses showed that accuracies could be considered very good when compared to other studies. The causes of some inconsistencies were explored and most of them were well explained by other soil characteristics. Then we show some examples applying these relationships and the increase of data to several issues related to DSM. The first issue is: do the PDF functions that were established enable to use HSFT class observations to improve the LAST soil texture prediction? For this objective, we replaced all HFST for topsoil by values from the PDF 100 time replicates). Results were promising for the PTF we tested (a PTF predicting soil water holding capacity). For the question related to the ML prediction of LAST soil texture on the region, we did the same kind of replacement, but we implemented a 10-fold cross-validation using points where we had LAST values. We obtained only preliminary results but they were rather promising. Then we show another example illustrating the potential of using HFST as validation data. As in numerous countries, the HFST observations are very numerous; these promising results pave the way to an important improvement of DSM products in all the countries of the world.

Keywords: digital soil mapping, improvement of digital soil mapping predictions, potential of using hand-feel soil texture, soil texture prediction

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Huang Gui

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There can be little doubt that our world is inexorably moving towards being execution-free. However, China is still on the way until now, in other words, China is still a retentionist state in the term of capital punishment but it is developing domestic criminal law toward that goal (eventual abolition of the capital punishment). The alternative sanction to capital punishment, which would be imposed on a criminal who should have been sentenced to death by law, is a substitute for execution and it should be provided with the basis of the present criminal punishment structure and with the premise of abolishing capital punishment or limiting its use. The aim of this paper, therefore, is to explore a substitute for capital punishment in China. For the criminal sanction system in China, the death penalty with suspension, naturally, is an execution, so it wouldn’t be the substitute; life sentences without parole is out of the tune with punishment policy that promoting correction and rehabilitation; life-imprisonment, which is one of the most severe punishment measure in the sanction system, should be a suitable substitute for executing but it needs to be improved, including the term of imprisonment, the commutation and parole conditions.

Keywords: alternative sanctions, capital punishment, life imprisonment, life imprisonment without parole, China

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Authors: Kasim Görenekli, Ali Gülbağ

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This study presents a comprehensive analysis of water consumption patterns in Kocaeli province, Turkey, utilizing various machine learning approaches. We analyzed data from 5,000 water subscribers across residential, commercial, and official categories over an 80-month period from January 2016 to August 2022, resulting in a total of 400,000 records. The dataset encompasses water consumption records, weather information, weekends and holidays, previous months' consumption, and the influence of the COVID-19 pandemic.We implemented and compared several machine learning models, including Linear Regression, Random Forest, Support Vector Regression (SVR), XGBoost, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Particle Swarm Optimization (PSO) was applied to optimize hyperparameters for all models.Our results demonstrate varying performance across subscriber types and models. For official subscribers, Random Forest achieved the highest R² of 0.699 with PSO optimization. For commercial subscribers, Linear Regression performed best with an R² of 0.730 with PSO. Residential water usage proved more challenging to predict, with XGBoost achieving the highest R² of 0.572 with PSO.The study identified key factors influencing water consumption, with previous months' consumption, meter diameter, and weather conditions being among the most significant predictors. The impact of the COVID-19 pandemic on consumption patterns was also observed, particularly in residential usage.This research provides valuable insights for effective water resource management in Kocaeli and similar regions, considering Turkey's high water loss rate and below-average per capita water supply. The comparative analysis of different machine learning approaches offers a comprehensive framework for selecting appropriate models for water consumption prediction in urban settings.

Keywords: mMachine learning, water consumption prediction, particle swarm optimization, COVID-19, water resource management

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Authors: Horesh Reinman Netta

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Objectives: To examine the relationship between stressful life events and recurrent major depressive disorders Methods: Three groups of 50 subjects were assessed. One group had a recurrent major depressive disorder with melancholic features; the second group met the criteria for borderline personality disorder, and the third consisted of healthy controls. The Structured Clinical Interview for AXIS I DSM-IV Disorders sand the Structured Clinical Interview for AXIS II DSM-IV Disorders were used for diagnosis. The Israel Psychiatric Epidemiology Research Interview (IPERI) Life Event Scale and the Coddington Life Events Schedule (CLES) were used to measure life events which were confirmed with a confirmatory semi-structured interview. The Beck Depression Inventory and the Satisfaction from Life scales were also administered. Results : The total number of loss-related events in childhood and in the year preceding the first episode was significantly higher in the affective disorder group than in the two control groups. Total number of LE, uncontrolled and independent events were also more common in the depressed patients in the year preceding the first episode. No category of SLE was differentiated among any of the three groups during any period of time following the first depressive episode. Conclusions: SLE play an important role in the onset of affective disorders. There appear to be specific kinds of SLE occurring in childhood and in the year preceding a first episode that have particular significance. SLE may have a lesser role in the maintenance of this illness.

Keywords: modd dosorders, recurrent depression, stress, life events

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Fatemeh Roostaee, Zahra Nikmanesh

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This study was done to investigate the role of religious coping components on occupational stress and the quality of working life of midwives. The method of study was descriptive-correlation. The sample was comprised of all midwives in maternity hospitals in Zahedan during 1393. Participants were selected through applying census method. The instruments of data collection were three questionnaires: the quality of working life, occupational stress, and religious opposition. For statistical analysis, Pearson correlation and step by step regression analysis methods were used. The results showed that there is a significant negative relationship between the component of religious activities (r=-0/454) and occupational stress, and regression analysis was also shown that the variable of religious activities has been explained 45% of occupational stress variable changes. The Pearson correlation test showed that there isn't any significant relationship between religious opposition components and the quality of life. Therefore, it is necessary to present essential trainings on (the field of) strengthening compatibility strategies and religious activities to reduce occupational stress.

Keywords: the quality of working life, occupational stress, religious, midwife

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Authors: Hsien-Te Lin

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The purpose of this paper is to introduce the BCFES (building carbon footprint evaluation system), which is a LCA (life cycle assessment) tool developed by the Low Carbon Building Alliance (LCBA) in Taiwan. A qualified BCFES for the building industry should fulfill the function of evaluating carbon footprint throughout all stages in the life cycle of building projects, including the production, transportation and manufacturing of materials, construction, daily energy usage, renovation and demolition. However, many existing BCFESs are too complicated and not very designer-friendly, creating obstacles in the implementation of carbon reduction policies. One of the greatest obstacle is the misapplication of the carbon footprint inventory standards of PAS2050 or ISO14067, which are designed for mass-produced goods rather than building projects. When these product-oriented rules are applied to building projects, one must compute a tremendous amount of data for raw materials and the transportation of construction equipment throughout the construction period based on purchasing lists and construction logs. This verification method is very cumbersome by nature and unhelpful to the promotion of low carbon design. With a view to provide an engineer-oriented BCFE with pre-diagnosis functions, a component input/output (I/O) database system and a scenario simulation method for building energy are proposed herein. Most existing BCFESs base their calculations on a product-oriented carbon database for raw materials like cement, steel, glass, and wood. However, data on raw materials is meaningless for the purpose of encouraging carbon reduction design without a feedback mechanism, because an engineering project is not designed based on raw materials but rather on building components, such as flooring, walls, roofs, ceilings, roads or cabinets. The LCBA Database has been composited from existing carbon footprint databases for raw materials and architectural graphic standards. Project designers can now use the LCBA Database to conduct low carbon design in a much more simple and efficient way. Daily energy usage throughout a building's life cycle, including air conditioning, lighting, and electric equipment, is very difficult for the building designer to predict. A good BCFES should provide a simplified and designer-friendly method to overcome this obstacle in predicting energy consumption. In this paper, the author has developed a simplified tool, the dynamic Energy Use Intensity (EUI) method, to accurately predict energy usage with simple multiplications and additions using EUI data and the designed efficiency levels for the building envelope, AC, lighting and electrical equipment. Remarkably simple to use, it can help designers pre-diagnose hotspots in building carbon footprint and further enhance low carbon designs. The BCFES-LCBA offers the advantages of an engineer-friendly component I/O database, simplified energy prediction methods, pre-diagnosis of carbon hotspots and sensitivity to good low carbon designs, making it an increasingly popular carbon management tool in Taiwan. To date, about thirty projects have been awarded BCFES-LCBA certification and the assessment has become mandatory in some cities.

Keywords: building carbon footprint, life cycle assessment, energy use intensity, building energy

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Authors: Mohammed Russedul Islam, Shah M. Muniruzzaman, M. Kamrul-Al-Masud, Syed Sadat Morshed

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Bangladesh is one of the most climate vulnerable countries in the world. According to scientists it is predicted that temperature will raise 1-3% and precipitation 20% by 2050 in Bangladesh. Increased temperature and precipitation will deteriorate pavement structure in an accelerated rate. The study has found that pavement life will reduce significantly due to rise in temperature and precipitation in in a coastal road in Bangladesh. It will cause to increase the maintenance cost of the road. The study has found that reduction in pavement life will be caused due the decrease in stiffness and strength parameters of the pavement material due to high temperature and precipitation. It has found that use of new material costlier than the existing one will be necessary to prevent the reduction of pavement life. Eventually it will increase the re-construction cost of the road. The study has used mechanistic-empirical analysis method with a software GAMES (General analysis on multi-layered elastic systems) to find out the effect of temperature and precipitation rise on the pavement life. The study will help to guide road engineers of Bangladesh to prepare in advance to fight with the climate change effect.

Keywords: climate change, maintenance cost, mechanistic-empirical method, pavement life

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Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong

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To improve the satisfaction of residents' travel, this paper analyzes the characteristics and influencing factors of urban residents' travel behavior. First, a Multinominal Logit Model (MNL) model is built to analyze the characteristics of residents' travel behavior, reveal the influence of individual attributes, family attributes and travel characteristics on the choice of travel mode, and identify the significant factors. Then put forward suggestions for policy improvement. Finally, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models are introduced to evaluate the policy effect. This paper selects Futian Street in Futian District, Shenzhen City for investigation and research. The results show that gender, age, education, income, number of cars owned, travel purpose, departure time, journey time, travel distance and times all have a significant influence on residents' choice of travel mode. Based on the above results, two policy improvement suggestions are put forward from reducing public transportation and non-motor vehicle travel time, and the policy effect is evaluated. Before the evaluation, the prediction effect of MNL, SVM and MLP models was evaluated. After parameter optimization, it was found that the prediction accuracy of the three models was 72.80%, 71.42%, and 76.42%, respectively. The MLP model with the highest prediction accuracy was selected to evaluate the effect of policy improvement. The results showed that after the implementation of the policy, the proportion of public transportation in plan 1 and plan 2 increased by 14.04% and 9.86%, respectively, while the proportion of private cars decreased by 3.47% and 2.54%, respectively. The proportion of car trips decreased obviously, while the proportion of public transport trips increased. It can be considered that the measures have a positive effect on promoting green trips and improving the satisfaction of urban residents, and can provide a reference for relevant departments to formulate transportation policies.

Keywords: neural network, travel characteristics analysis, transportation choice, travel sharing rate, traffic resource allocation

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Authors: Priyadarsini Samal, Rajesh Singla

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Many mobile games come with the benefits of entertainment by introducing stress to the human brain. In recognizing this mental stress, the brain-computer interface (BCI) plays an important role. It has various neuroimaging approaches which help in analyzing the brain signals. Electroencephalogram (EEG) is the most commonly used method among them as it is non-invasive, portable, and economical. Here, this paper investigates the pattern in brain signals when introduced with mental stress. Two healthy volunteers played a game whose aim was to search hidden words from the grid, and the levels were chosen randomly. The EEG signals during gameplay were recorded to investigate the impacts of stress with the changing levels from easy to medium to hard. A total of 16 features of EEG were analyzed for this experiment which includes power band features with relative powers, event-related desynchronization, along statistical features. Support vector machine was used as the classifier, which resulted in an accuracy of 93.9% for three-level stress analysis; for two levels, the accuracy of 92% and 98% are achieved. In addition to that, another game that was similar in nature was played by the volunteers. A suitable regression model was designed for prediction where the feature sets of the first and second game were used for testing and training purposes, respectively, and an accuracy of 73% was found.

Keywords: brain computer interface, electroencephalogram, regression model, stress, word search

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Authors: Atefeh Bagherianziarat, Dana Hamplova

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The inverse social gradient in life satisfaction (LS) is a well-established research finding. To estimate the influence of inequality on LS, most of the studies have explored the effect of the objective aspects of inequality or individuals’ socioeconomic status (SES). However, relatively fewer studies have confirmed recently the significant effect of the subjective aspect of inequality or subjective socioeconomic status (SSS) on life satisfaction over and above SES. In other words, it is confirmed by some studies that individuals’ perception of their unequal status in society or SSS can moderate the impact of their absolute unequal status on their life satisfaction. Nevertheless, this newly confirmed moderating link has not been affirmed to work likewise in societies with different levels of social inequality and also for people who believe in the value of equality, at different levels. In this study, we compared the moderative influence of subjective inequality on the link between objective inequality and life satisfaction. In particular, we focus on differences across welfare state regimes based on Esping-Andersen's theory. Also, we explored the moderative role of believing in the value of equality on the link between objective and subjective inequality on LS in the given societies. Since our studied variables were measured at both individual and country levels, we applied a multilevel analysis to the European Social Survey data (round 9). The results showed that people in deferent regimes reported statistically meaningful different levels of life satisfaction that is explained to different extends by their household income and their perception of their income inequality. The findings of the study supported the previous findings of the moderator influence of perceived inequality on the link between objective inequality and LS. However, this link is different in various welfare state regimes. The results of the multilevel modeling showed that country-level subjective equality is a positive predictor for individuals’ life satisfaction, while the GINI coefficient that was considered as the indicator of absolute inequality has a smaller effect on life satisfaction. Also, country-level subjective equality moderates the confirmed link between individuals’ income and their life satisfaction. It can be concluded that both individual and country-level subjective inequality slightly moderate the effect of individuals’ income on their life satisfaction.

Keywords: individual values, life satisfaction, multilevel analysis, objective inequality, subjective inequality, welfare regimes status

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Authors: Samaila Muazu Bawa

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Hydration process, crack potential and setting time of concrete grade C30, C40 and C50 were separately monitored using non-contact electrical resistivity apparatus, a plastic ring mould and penetration resistance method respectively. The results show highest resistivity of C30 at the beginning until reaching the acceleration point when C50 accelerated and overtaken the others, and this period corresponds to its final setting time range, from resistivity derivative curve, hydration process can be divided into dissolution, induction, acceleration and deceleration periods, restrained shrinkage crack and setting time tests demonstrated the earliest cracking and setting time of C50, therefore, this method conveniently and rapidly determines the concrete’s crack potential. The highest inflection time (ti), the final setting time (tf) were obtained and used with crack time in coming up with mathematical models for the prediction of concrete’s cracking age for the range being considered. Finally, ANSYS numerical simulations supports the experimental findings in terms of the earliest crack age of C50 and the crack location that, highest stress concentration is always beneath the artificially introduced expansion joint of C50.

Keywords: concrete hydration, electrical resistivity, restrained shrinkage crack, ANSYS simulation

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Authors: Jan-Peter Mund, Christian Kind

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In view of the ongoing climate change and global warming, fires along railways in Germany are occurring more frequently, with sometimes massive consequences for railway operations and affected railroad infrastructure. In the absence of systematic studies within the infrastructure network of German Rail, little is known about the causes of such embankment fires. Since a further increase in these hazards is to be expected in the near future, there is a need for a sound knowledge of triggers and drivers for embankment fires as well as methodical knowledge of prediction tools. Two predictable future trends speak for the increasing relevance of the topic: through the intensification of the use of rail for passenger and freight transport (e.g..: doubling of annual passenger numbers by 2030, compared to 2019), there will be more rail traffic and also more maintenance and construction work on the railways. This research project approach uses satellite data to identify historical embankment fires along rail network infrastructure. The team links data from these fires with infrastructure and weather data and trains a machine-learning model with the aim of predicting fire hazards on sections of the track. Companies reflect on the results and use them on a pilot basis in precautionary measures.

Keywords: embankment fires, railway maintenance, machine learning, remote sensing, VIIRS data

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Authors: Sherine A. El Baradei

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Global warming and climatic change are very important topics that are being studied and investigated nowadays as they have lots of diverse impacts on mankind, water quality, aquatic life, wildlife,…etc. Also, many water and hydraulics structures like dams and barrages are being built every day to satisfy water consumption needs, irrigation purposes and power generating purposes. Each of global warming and water structures alone has diversity of impacts on water quality and aquatic life in rivers. This research is investigating the dual combined effect of both water structures and global warming on the water quality and aquatic life through mathematical modeling. A case study of the Esna Barrage on the Nile River in Egypt is being studied. This research study is taking into account the effects of both seasons; namely, winter and summer and their effects on air and hence water temperature of the Nile reach under study. To do so, the study is conducted on the last 23 years to investigate the effect of global warming and climatic change on the studied river water. The mathematical model is then combining the dual effect of the Esna barrage and the global warming on the water quality; as well as, on aquatic life of the Nile reach under study. From the results of the mathematical model, it could be concluded that the dual effect of water structures and global warming is very negative on the water quality and the aquatic life in rivers upstream those structures.

Keywords: aquatic life, barrages, climatic change, dissolved oxygen, global warming, river, water quality, water structures

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Authors: Maria Malliarou, Efrossini Lyraraki, Pavlos Sarafis, Theodosios Paralikas, Styliani Kotrotsiou, Evangelia Kotrotsiou, Mairy Gouva

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Women with breast cancer have to adapt to physical malformations, side effects of chemotherapy, emotional insecurity, and changes in social roles. Inability to recognize the co-morbidity of psychiatric conditions can have an aggravating effect on patient compliance in therapeutic interventions, resulting in treatment delays and an impact on overall survival. The purpose of this study was to identify the quality of life of breast cancer patients undergoing external radiation therapy and to correlate it with depression and anxiety. Patients were asked to respond to an anonymous questionnaire with general demographic and clinical questions, followed by the EORTCQLQ-C30 questionnaire for assessing the quality of life of patients with breast cancer. Hospital Anxiety and Depression Scale (HADS) as well as the Depression, Anxiety and Stress Scale (DASS-21) was also administered. The statistical analysis of the data was done in IBM SPSS. Results indicated that the incidence of anxiety and depression in breast cancer patients is high both in HADS (37.5 % with mild to moderate depression and 62.5 % with significant to severe depression) and DASS - 21 (39.2 % mild to moderate depression and 60.8 % significant to severe) scales. The correlation of anxiety and depression with life quality was negative for HADS (r = -, 810, p = .000) as well as for DASS-21 (r = -, 682, p = .000). The psychological impact of breast cancer on patients is important. Its correlation with the quality of life may lead to better tolerance to treatment and better effectiveness of the therapeutic approach.

Keywords: anxiety, breast cancer, depression, quality of life

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Use of speed prediction models sometimes appears as a feasible alternative to laborious field measurement particularly, in case when field data cannot fulfill designer’s requirements. However, developing speed models is a challenging task specifically in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way without any segregation. Here the traffic composition plays a significant role in determining the vehicular speed. The present research was carried out to examine the effects of traffic volume and its composition on vehicular speed under mixed traffic conditions. Classified traffic volume and speed data were collected from different geometrically identical six lane divided arterials in New Delhi. Based on these field data, speed prediction models were developed for individual vehicle category adopting Kriging approximation technique, an alternative for commonly used regression. These models are validated with the data set kept aside earlier for validation purpose. The predicted speeds showed a great deal of agreement with the observed values and also the model outperforms all other existing speed models. Finally, the proposed models were utilized to evaluate the effect of traffic volume and its composition on speed.

Keywords: speed, Kriging, arterial, traffic volume

Procedia PDF Downloads 357

Authors: Sumaia Jawad, Shalaweh Salem, Walid Kamal, Nicolette Roman

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Background: HIV patients have many social problems like depression, which adversely affects their quality of life. HIV infection is linked to psychological distress such as anxiety. In terms of coping styles, avoidant emotion-focused strategies such as fatalism, wishful thinking and self-blame are associated with higher levels of psychological distress in persons with HIV. In Cairo, Egypt current services are not adapted to provide advice and psychological support to people living with HIV to help them develop problem-solving skills to cope with the stress of living with HIV. Yet, no studies have examined the relationship between quality of life, psychological distress and coping strategies of persons living with HIV/AIDS in Egypt. Therefore, the purpose of this study was to examine the relationship between quality of life, psychological distress and coping strategies of persons living with HIV/AIDS in Cairo, Egypt. Methods: This study used a quantitative methodology with a cross-sectional correlational design. The data was collected using: Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q), Depression, Anxiety and Stress Scale (DASS) and Cope Inventory. The sample consisted of 202 participants who accessed the National AIDS Program (NAP). The data was analysed using the Statistical Program for Social Science V23 (SPSS). Results: The results show that psychological distress and certain coping styles such as substance abuse and behavioural disengagement negatively predict the quality of life of patients with HIV/AIDS. Positive predictors included coping styles such as active coping, self-distraction, venting, positive reframing, humor, acceptance, and religion. Conclusions: It would probably be best to reduce psychological distress and increase coping styles in order to improve the quality of life of patients with HIV/AIDS.

Keywords: HIV/AIDS, quality of life, psychological distress, coping strategies

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