Search results for: squared prediction risk

Authors: Maggie Hui

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Risk management is not a new concept; however, it is an uncharted area as applied to the translation process and translator training. Risk managers are responsible for managing risk, i.e. adopting strategies with the intention to minimize loss and maximize gains in spite of uncertainty. Which risk strategy to use often depends on the frequency of an event (i.e. probability) and the severity of its outcomes (i.e. impact). This is basically the way translation/localization project managers handle risk management. Although risk management could involve both positive and negative impacts, impact seems to be always negative in professional translators’ management models, e.g. how many days of project time are lost or how many clients are lost. However, for analysis of translation performance, the impact should be possibly positive (e.g. increased readability of the translation) or negative (e.g. loss of source-text information). In other words, the straight business model of risk management is not directly applicable to the study of risk management in the rendition process. This research aims to explore trainee translators’ risk managing while translating in a simulated setting that involves translator-client relations. A two-cycle experiment involving two roles, the translator and the simulated client, was carried out with a class of translation students to test the effects of the main variable of peer-group interaction. The researcher made use of a user-friendly screen-voice recording freeware to record subjects’ screen activities, including every word the translator typed and every change they made to the rendition, the websites they browsed and the reference tools they used, in addition to the verbalization of their thoughts throughout the process. The research observes the translation procedures subjects considered and finally adopted, and looks into the justifications for their procedures, in order to interpret their risk management. The qualitative and quantitative results of this study have some implications for translator training: (a) the experience of being a client seems to reinforce the translator’s risk aversion; (b) there is a wide gap between the translator’s internal risk management and their external presentation of risk; and (c) the use of role-playing simulation can empower students’ learning by enhancing their attitudinal or psycho-physiological competence, interpersonal competence and strategic competence.

Keywords: risk management, role-playing simulation, translation pedagogy, translator-client relations

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Rachel Karasenty Saltoun, Charlotte Roddick, Chelsea D. Christie, Frances Chen

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Moving has become an integral part of many people’s lives. This research explores whether relocating to a new city is associated with an increase in loneliness and cardiovascular disease risk and if this increased risk diminishes with continued residency. To test this, various psychosocial variables and three cardiovascular disease risk markers (C-reactive protein, albumin, blood pressure) were assessed on two groups of individuals: those who have moved to Vancouver, Canada in the previous 6 weeks (‘Movers’) and those who have lived in Vancouver for at least five years (‘Non-Movers’). It was hypothesized that individuals who had recently relocated would have heightened levels of loneliness, blood pressure (BP), albumin, and C-reactive protein (CRP) compared to those who had not recently relocated. Length of residency was hypothesized to moderate these effects, such that after a few months, loneliness levels and cardiovascular disease risk would decrease among those who had recently relocated. Correlational analysis indicated a trend between the change in CRP and albumin levels and loneliness overtime on an individual level. However, these results must be interpreted with caution due to the small sample size. As Vancouver’s immigration rates continue to grow, this study has important implications regarding the social support resources offered to new immigrants, as well as bringing awareness at the healthcare level of the potential increase in cardiovascular disease risk among those who have recently relocated.

Keywords: cardiovascular disease risk, loneliness, moving, residential mobility

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Talha Ahmed, Ishtiaq Hassan

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Urban areas or metropolitan is portrayed by the very high density of population due to the result of these economic activities. Some critical elements, such as urban expansion and climate change, are driving changes in cities with exposure to the incidence and impacts of pluvial floods. Urban communities are recurrently developed by huge spaces by which water cannot enter impermeable surfaces, such as man-made permanent surfaces and structures, which do not cause the phenomena of infiltration and percolation. Urban sprawl can result in increased run-off volumes, flood stage and flood extents during heavy rainy seasons. The flood risks require a thorough examination of all aspects affecting to severe an event in order to accurately estimate their impacts and other risk factors associated with them. For risk evaluation and its impact due to urbanization, an integrated hydrological modeling approach is used on the study area in Islamabad (Pakistan), focusing on a natural water body that has been adopted in this research. The vulnerability of the physical elements at risk in the research region is analyzed using GIS and SOBEK. The supervised classification of land use containing the images from 1980 to 2020 is used. The modeling of DEM with selected return period is used for modeling a hydrodynamic model for flood event inundation. The selected return periods are 50,75 and 100 years which are used in flood modeling. The findings of this study provided useful information on high-risk places and at-risk properties.

Keywords: urbanization, flood, flood risk, GIS

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Lisnawati Hutagalung

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Background: Low birth weight (LBW) is one cause of newborn death. Babies with low birth weight tend to have slower cognitive development, growth retardation, more at risk of infectious disease event at risk of death. Objective: Identifying risk factors and dominant factors that influence the incidence of LBW in Indonesia. Method: This research used some database of public health such as Google Scholar, UGM journals, UI journals and UNAND journals in 2012-2015. Data were filtered using keywords ‘Risk Factors’ AND ‘Cause LBW’ with amounts 2757 study. The filtrate obtained 5 public health research that meets the criteria. Results: Risk factors associated with LBW, among other environment factors (exposure to cigarette smoke and residence), social demographics (age and socio-economic) and maternal factors (anemia, placental abnormal, nutritional status of mothers, examinations antenatal, preeclampsia, parity, and complications in pregnancy). Anemia and nutritional status become the dominant factor affecting LBW. Conclusions: The risk factors that affect LBW, most commonly found in the maternal factors. The dominant factors are a big effect on LBW is anemia and nutritional status of the mother during pregnancy.

Keywords: low birth weight, anemia, nutritional status, the dominant factor

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Authors: Maomao Cao

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Background and aims While the association between higher consumption of fish and lower liver cancer risk has been confirmed, however, the association between specific fish intake and liver cancer risk remains unknown. We aimed to identify the association between specific fish consumption and the risk of liver cancer. Methods: Based on a community-based seropositive hepatitis B cohort involving 18404 individuals, face to face interview was conducted by a standardized questionnaire to acquire baseline information. Three common fish types in this study were analyzed, including freshwater fish, sea fish, and small fish (shrimp, crab, conch, and shell). All participants received liver cancer screening, and possible cases were identified by CT or MRI. Multivariable logistic models were applied to estimate the odds ratio (OR) and 95% confidence intervals (CI). Multivariate multiple imputations were utilized to impute observations with missing values. Results: 179 liver cancer cases were identified. Consumption of freshwater fish and sea fish at least once a week had a strong inverse association with liver cancer risk compared with the lowest intake level, with an adjusted OR of 0.53 (95% CI, 0.38-0.75) and 0.38 (95% CI, 0.19-0.73), respectively. This inverse association was also observed after the imputation. There was no statistically significant association between intake of small fish and liver cancer risk (OR=0.58, 95%, CI 0.32-1.08). Conclusions: Our findings suggest that consumption of freshwater fish and sea fish at least once a week could reduce liver cancer risk.

Keywords: cross-sectional study, fish intake, liver cancer, risk factor

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Authors: Ruziana Masiran, Hamidin Awang, Cheah Y. T. Jun, Nor Fauziah Hashim, Archana Premkumar, Mohd. Feizel Aisiddiq, Mohd. Fakharuddin

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Risk behaviours among secondary school students are common and show an increasing trend over the years. Existing attachment styles between the students and their parents influence the psychosocial development of this group of population hence contributing to the adoption of risk behaviours. The aim of this study was to determine the associations between three risk behaviours; smoking, bullying and being bullied among secondary school students and their styles of attachment to parents in a district in Malaysia. Using multistage simple random sampling, a cross-sectional study was designed with the level of significance, α set at 0.05. The validated self-administered Inventory of Parent and Peer Attachment (IPPA) and Youth Risk Behaviours Surveillance Questionnaire focusing on smoking and bullying were utilized. Secondary school students aged 13 to 17 years old from ten schools in the district of Hulu Langat, Malaysia were sampled. Prevalence of smoking was 15.8%, bullying 8.5% and being bully victims 19.0%. It was found that male gender was a significant risk factor for smoking (p < 0.001), while being Chinese (OR=0.156, 95%CI=0.029-0.837, p=0.030) and having married parents (OR=0.490, 95%CI=0.302-0.796, p=0.490) are protective against smoking. Students with insecure attachment to mothers (OR=1.650, 95%CI=1.018-2.675, p=0.042) and fathers (OR=2.039, 95%CI=1.285-3.234, p=0.002) are at 1.6 and 2 times risk respectively to smoke compared to those with secure attachment. The odds of male students bullying is almost twice than that for female students (OR=2.017, 95%CI=1.416-2.873, p < 0.001), and the odds of being bullied is 1.5 times higher for male students (OR=1.519, 95%CI=1.183-1.950, p=0.001). Those who are insecurely attached to fathers are at 1.8 times higher risk to be bullies (OR=1.867, 95%CI=1.272-2.740, p < 0.001) and 1.5 times higher risk to be bullied (OR=1.546, 95%CI=1.026-2.329, p=0.037). In conclusion, insecure attachment shows a strong association with smoking, bullying and being bullied among secondary school students in Malaysia.

Keywords: attachment styles, bullied, bullying, insecure attachment, risk behaviours, smoking and attachment

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Authors: Cristian Păuna

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After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

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Authors: Juniarti, Agus Arianto Toly

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This study aims to examine the effect of capital expenditure on the investors’ responses. The respondents were companies with the best stock performance in each sector in 2017. The observation period is 2017 to 2019. Top 10 companies in each sector with the best stock performance in companies listed on the Indonesia Stock Exchange were selected. The main variables are a growth signal which is proxied by growth in capital spending and capital expenditure, and risk and investor response, which is proxied by CAR. Financial performance as measured by ROA is a control variable in this study. The results showed that the signal of growth as measured by capital expenditures responded positively by the market, the risk moderates this influence, companies with high risk will be responded negatively by investors and vice versa. This finding corrects previous findings that only looked at the signal aspect of growth, without linking it to risk. In addition, these findings reinforce the argument that investors buy the future of the company, not a momentary financial performance. This can be seen from the absence of ROA influence on investor response. This study found that companies need to manage risk appropriately, because the risk aspect of the company is a crucial factor for investors. High risks will eliminate the benefits of strategic decisions in this case in the form of capital expenditures.

Keywords: capital expenditure, growth signals, investor response, risk

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the easy creation of an institutional risk profile for endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support risk factors set up with just the most important values that are important for a particular organisation. Subsequently, the risk profile employs fuzzy models and associated configurations for the file format metadata aggregator to support digital preservation experts with a semi-automatic estimation of endangerment level for file formats. Our goal is to make use of a domain expert knowledge base aggregated from a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation and analysis of risk factors for a requried dimension. The proposed methods improve the visibility of risk factor information and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and automatically aggregated file format metadata from linked open data sources. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: digital information management, file format, endangerment analysis, fuzzy models

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Prateep Wajeetongratana

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The objective of this research is to examine the effects of internal audit innovation on firm performance effectiveness influences of financial report reliability, organizational process improvement, and risk management effectiveness. This paper drew upon the survey data collected from 400 employees survey conducted at Nonthaburi province, Thailand. The statistics utilized in this paper included percentage, mean, standard deviation, and regression analysis. The findings revealed that the majority of samples were between 31-40 years old, married, held an undergraduate degree, and had an average income between 10,000-15,000 baht. And also the results show that auditing integration has only influence on financial report reliability. Moreover, corporate risk evaluation has effect on firm performance by risk management effectiveness and control self-assessment has effect influence on firm performance by organizational process improvement and risk management effectiveness as well.

Keywords: corporate risk evaluation, firm performance effectiveness, internal audit innovation, marketing management

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Authors: Marian Sorin Ionescu, Olivia Negoita, Cosmin Dobrin

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This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models.

Keywords: machine learning, business models, convex analysis, online learning

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Authors: Imad Eddine Charif, Wafaa Benchouk, Sidi Mohamed Mekelleche

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The regioselectivity of a series of 16 1,3-dipolar cycloaddition reactions of nitrile oxides with 2(5H)-furanones has been analysed by means of global and local electrophilic and nucleophilic reactivity indices using density functional theory at the B3LYP level together with the 6-31G(d) basis set. The local electrophilicity and nucleophilicity indices, based on Fukui and Parr functions, have been calculated for the terminal sites, namely the C1 and O3 atoms of the 1,3-dipole and the C4 and C5 atoms of the dipolarophile. These local indices were calculated using both Mulliken and natural charges and spin densities. The results obtained show that the C5 atom of the 2(5H)-furanones is the most electrophilic site whereas the O3 atom of the nitrile oxides is the most nucleophilic centre. It turns out that the experimental regioselectivity is correctly reproduced, indicating that both Fukui- and Parr-based indices are efficient tools for the prediction of the regiochemistry of the studied reactions and could be used for the prediction of newly designed reactions of the same kind.

Keywords: 1, 3-dipolar cycloaddition, density functional theory, nitrile oxides, regioselectivity, reactivity indices

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Authors: Majid Lotfi Ghahroud, Seyed Jalal Tabatabaei, Ebrahim Karami, AmirArsalan Ghergherechi, Amir Ali Saeidi

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In this study, the relationship between investment growth and stock returns of companies listed in Tehran Stock Exchange and whether their relationship -behavioral or risk factors- are discussed. Generally, there are two perspectives; risk-based approach and behavioral approach. According to the risk-based approach due to increase investment, systemic risk and consequently the stock returns are reduced. But due to the second approach, an excessive optimism or pessimism leads to assuming stock price with high investment growth in the past, higher than its intrinsic value and the price of stocks with lower investment growth, less than its intrinsic value. The investigation period is eight years from 2007 to 2014. The sample consisted of all companies listed on the Tehran Stock Exchange. The method is a portfolio test, and the analysis is based on the t-student test (t-test). The results indicate that there is a negative relationship between investment growth and stock returns of companies and this negative correlation is stronger for firms with higher cash flow. Also, the negative relationship between asset growth and stock returns is due to behavioral factors.

Keywords: behavioral theory, investment growth, risk-based theory, stock returns

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Authors: Hasan Keshavarzian, Tayebeh Nesari

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Bolted rail joint is one of the most vulnerable areas in railway track. A comprehensive approach was developed for studying the reliability of fatigue crack initiation of railroad bolt hole under random axle loads and random material properties. The operation condition was also considered as stochastic variables. In order to obtain the comprehensive probability model of fatigue crack initiation life prediction in railroad bolt hole, we used FEM, response surface method (RSM), and reliability analysis. Combined energy-density based and critical plane based fatigue concept is used for the fatigue crack prediction. The dynamic loads were calculated according to the axle load, speed, and track properties. The results show that axle load is most sensitive parameter compared to Poisson’s ratio in fatigue crack initiation life. Also, the reliability index decreases slowly due to high cycle fatigue regime in this area.

Keywords: rail-wheel tribology, rolling contact mechanic, finite element modeling, reliability analysis

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Authors: Risna Wijayanti, Sumiati, Hanif Iswari

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This study aims to analyze the role of financial risk attitude as a mediation between financial knowledge and business growth. The ability of human resources in managing capital (financial literacy) can be a major milestone for a company's business to grow and build its competitive advantage. This study analyzed the important role of financial risk attitude in bringing about financial knowledge on corporate growth. There have been many discussions arguing that financial knowledge is one of the main abilities of corporate managers in determining the success of managing a company. However, a contrary argument of other scholars also enlightened that financial knowledge did not have a significant influence on corporate growth. This study used literatures' review to analyze whether there is another variable that can mediate the effect of financial knowledge toward corporate growth. Research mapping was conducted to analyze the concept of risk tolerance. This concept was related to people's risk aversion effects when making a decision under risk and the role of financial knowledge on changes in financial income. Understanding and managing risks and investments are complicated, in particular for corporate managers, who are always demanded to maintain their corporate growth. Substantial financial knowledge is extremely needed to identify and take accurate information for corporate financial decision-making. By reviewing several literature, this study hypothesized that financial knowledge of corporate managers would be meaningless without manager's courage to bear risks for taking favorable business opportunities. Therefore, the level of risk aversion from corporate managers will determine corporate action, which is a reflection of corporate-level investment behavior leading to attain corporate success or failure for achieving the company's expected growth rate.

Keywords: financial knowledge, financial risk attitude, corporate growth, risk tolerance

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Authors: Irni Rahmayani Johan, Ira Kamelia

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One of the impacts of the rapid advancement of technology is the rise of digital finance, including peer-to-peer lending (P2P). P2P lending has been widely marketed, including an online student loan that used the P2P platform. This study aims to analyze the effect of financial literacy, perception of debt, and perception of risk toward student willingness to use the online student loan (P2P lending). Using a cross-sectional study design, in collecting the data this study employed an online survey method, with a total sample of 280 undergraduate students of IPB university, Indonesia. This study found that financial literacy, perception of debt, perception of risk, and interest in using online student loans are categorized as low level. While the level of knowledge is found to be the lowest, the first-year students showed a higher level in terms of willingness to use the online student loan. In addition, the second year students recorded a positive perception toward debt. This study showed that level of study, attendance in personal finance course, and student’ GPA is positively related to financial knowledge. While debt perception is negatively related to financial attitudes. Similarly, the negative relationship is found between risk perception and the willingness to use the online student loan. The determinant factor of the willingness to use online student loans is the level of study, debt perception, financial risk perception, and time risk perception. Students with a higher level of study are more likely to have a lower interest in using online student loans. Moreover, students who perceived debt as a financial stimulator, as well as those with higher level of financial risk perceptions and time risk perceptions, tend to show more interest to use the loan.

Keywords: financial literacy, willingness to use, online student loan, perception of risk, perception of debt

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Authors: M. Marsadek, A. Mohamed

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Power system security analysis is the most time demanding process due to large number of possible contingencies that need to be analyzed.  In a power system, any contingency resulting in security violation such as line overload or low voltage may occur for a number of reasons at any time.  To efficiently rank a contingency, both probability and the extent of security violation must be considered so as not to underestimate the risk associated with the contingency. This paper proposed a contingency ranking method that take into account the probabilistic nature of power system and the severity of contingency by using a newly developed method based on risk factor.  The proposed technique is implemented on IEEE 24-bus system.

Keywords: line overload, low voltage, probability, risk factor, severity

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Authors: Amin Amani, Masomeh Reihany Shirvan, Mahla Nabizadeh Mashizi, Mohadese Khoshtinat, Mohammad Elyas Ansarinia

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The purpose of this study is the relationship between a healthy lifestyle and risky behavior in physical education students of Bojnourd schools. The study sample consisted of teenagers studying in second and third grade of Bojnourd's high schools. According to level sampling, 604 students studying in the second grade, and 600 students studying in third grade were tested from physical education schools in Bojnourd. For sample selection, populations were divided into 4 area including north, East, West and South. Then according to the number of students of each area, sample size of each level was determined. Two questionnaires were used to collect data in this study which were consisted of three parts: The demographic data, Iranian teenagers' risk taking (IARS) and prevention methods with emphasize on the importance of family role were examined. The Central and dispersion indices, such as standard deviation, multiple variance analysis, and multivariate regression analysis were used. Results showed that the observed F is significant (P ≤ 0.01) and 21% of variance related to risky behavior is explained by the lack of awareness. Given the significance of the regression, the coefficients of risky behavior in teenagers in prediction equation showed that each of teenagers' risky behavior can have an impact on healthy lifestyle.

Keywords: healthy lifestyle, high-risk behavior, students, physical education

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Authors: M. Arifuzzaman, H. S. Kim

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Multiple sets of variables associated with expanded perlite particle consolidation in foam manufacturing were analyzed to develop a model for predicting perlite foam density. The consolidation of perlite particles based on the flotation method and compaction involves numerous variables leading to the final perlite foam density. The variables include binder content, compaction ratio, perlite particle size, various perlite particle densities and porosities, and various volumes of perlite at different stages of process. The developed model was found to be useful not only for prediction of foam density but also for optimization between compaction ratio and binder content to achieve a desired density. Experimental verification was conducted using a range of foam densities (0.15–0.5 g/cm3) produced with a range of compaction ratios (1.5-3.5), a range of sodium silicate contents (0.05–0.35 g/ml) in dilution, a range of expanded perlite particle sizes (1-4 mm), and various perlite densities (such as skeletal, material, bulk, and envelope densities). A close agreement between predictions and experimental results was found.

Keywords: expanded perlite, flotation method, foam density, model, prediction, sodium silicate

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Authors: Kasirga Yildirak, Ismail Gur

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We propose a model to measure hail risk of an Agricultural Insurance portfolio. Hail is one of the major catastrophic event that causes big amount of loss to an insurer. Moreover, it is very hard to predict due to its strange atmospheric characteristics. We make use of parcel based claims data on apricot damage collected by the Turkish Agricultural Insurance Pool (TARSIM). As our ultimate aim is to compute the loadings assigned to specific parcels, we build a portfolio risk model that makes use of PD and the severity of the exposures. PD is computed by Spherical-Linear and Circular –Linear regression models as the data carries coordinate information and seasonality. Severity is mapped into integer brackets so that Probability Generation Function could be employed. Individual regressions are run on each clusters estimated on different criteria. Loss distribution is constructed by Panjer Recursion technique. We also show that one risk-one crop model can easily be extended to the multi risk–multi crop model by assuming conditional independency.

Keywords: hail insurance, spherical regression, circular regression, spherical clustering

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Authors: Chloe E. Edwards, Yasaman Shahtaheri

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Risk management plays a fundamental role in project planning and delivery. The purpose of incorporating risk management into project management practices is to identify and address uncertainties related to key project-related activities. The uncertainties, known as risk events, can relate to project deliverables that are quantifiable and are often measured by impact to project schedule, cost, or environmental impact. Risk management should be incorporated as an iterative practice throughout the planning, execution, and commissioning phases of a project. This paper specifically examines how risk management contributes to effective project planning and delivery through a case study of a transportation project. This case study focused solely on impacts to project schedule regarding three milestones: readiness for delivery, readiness for testing and commissioning, and completion of the facility. The case study followed the ISO 31000: Risk Management – Guidelines. The key factors that are outlined by these guidelines include understanding the scope and context of the project, conducting a risk assessment including identification, analysis, and evaluation, and lastly, risk treatment through mitigation measures. This process requires continuous consultation with subject matter experts and monitoring to iteratively update the risks accordingly. The risk identification process led to a total of fourteen risks related to design, permitting, construction, and commissioning. The analysis involved running 1,000 Monte Carlo simulations through @RISK 8.0 Industrial software to determine potential milestone completion dates based on the project baseline schedule. These dates include the best case, most likely case, and worst case to provide an estimated delay for each milestone. Evaluation of these results provided insight into which risks were the highest contributors to the projected milestone completion dates. Based on the analysis results, the risk management team was able to provide recommendations for mitigation measures to reduce the likelihood of risks occurring. The risk management team also provided recommendations for managing the identified risks and project activities moving forward to meet the most likely or best-case milestone completion dates.

Keywords: construction management, monte carlo simulation, project delivery, risk assessment, transportation engineering

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Authors: Swathi Sampath, V. Kalaichelvi

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The assessment of the risk posed by a borrower to a lender is one of the common problems that financial institutions have to deal with. Consumers vying for a mortgage are generally compared to each other by the use of a number called the Credit Score, which is generated by applying a mathematical algorithm to information in the applicant’s credit report. The higher the credit score, the lower the risk posed by the candidate, and the better he is to be taken on by the lender. The objective of the present work is to use fuzzy logic and linguistic rules to create a model that generates Credit Scores.

Keywords: credit scoring, fuzzy logic, mortgage, risk assessment

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Authors: Mustafa Dogru, Ruveyda Komurlu

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Companies and professionals in the construction sector face various risks in every project depending on the characteristics, size, complexity, the location of the projects and the techniques used. Some risks’ effects may increase as the project progresses whereas new risks may emerge. Because of the ever-changing nature of the risks, risk management is a cyclical process that needs to be repeated throughout the project. Since the risks threaten the success of the project, risk management is an important part of the entire project management process. The aims of this study are to emphasize the importance of risk management in construction projects, summarize the risk identification process, and introduce a number of methods for preventing risks such as alternative design, checklists, prototyping and test-analysis-correction technique etc. Following the literature review conducted to list the techniques for preventing risks, case studies has been performed to compare and evaluate the success of the techniques in a number of completed projects with the same typology, performed domestic and international. Findings of the study suggest that controlling and minimizing the level of the risks in construction projects, taking optimal precautions for different risks, and mitigating or eliminating the effects of risks are important in order to prevent additional costs for the project. Additionally, focusing on the risks that have highest impact is the most rational way to minimize the effects of the risks on projects.

Keywords: construction projects, construction management, project management, risk management

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Authors: Hary Aprianto Wijaya Siahaan, Bayu Edo Pratama

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Sea fishery's potential to become one of the nation's assets which very contributed to Indonesia's economy. This fishery potential not in spite of the availability of the chlorophyll in the territorial waters of Indonesia. The research was conducted using three methods, namely: statistics, comparative and analytical. The data used include MODIS sea temperature data imaging results in Aqua satellite with a resolution of 4 km in 2002-2015, MODIS data of chlorophyll-a imaging results in Aqua satellite with a resolution of 4 km in 2002-2015, and Imaging results data ASCAT on MetOp and NOAA satellites with 27 km resolution in 2002-2015. The results of the processing of the data show that the incidence of upwelling in the south of East Java Sea began to happen in June identified with sea surface temperature anomaly below normal, the mass of the air that moves from the East to the West, and chlorophyll-a concentrations are high. In July the region upwelling events are increasingly expanding towards the West and reached its peak in August. Chlorophyll-a concentration prediction using multiple linear regression equations demonstrate excellent results to chlorophyll-a concentrations prediction in 2002 until 2015 with the correlation of predicted chlorophyll-a concentration indicate a value of 0.8 and 0.3 with RMSE value. On the chlorophyll-a concentration prediction in 2016 indicate good results despite a decline in the value of the correlation, where the correlation of predicted chlorophyll-a concentration in the year 2016 indicate a value 0.6, but showed improvement in RMSE values with 0.2.

Keywords: satellite, sea surface temperature, upwelling, wind stress

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Authors: Lesa A. Thompson, Yoshinori Ikenaka, Victor Wepener, Mayumi Ishizuka

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One method for controlling malaria in endemic regions is the killing of vector mosquitoes using pesticides such as DDT in indoor residual spraying (IRS). This study was carried out to investigate the presence of and human health risk due to DDT and its metabolites (collectively, DDTs) contaminating human food sources in areas where DDT is used for IRS. Free-range chicken products (meat and eggs) were collected from homesteads in KwaZulu-Natal Province in the northeast of South Africa, and fish meat samples from Maputo Bay in neighbouring Mozambique. Samples were analysed for DDTs (o,p’-DDT, p,p’-DDT, o,p’-DDD, p,p’-DDD, o,p’-DDE and p,p’-DDE) using a gas chromatograph with electron capture detector (GC-ECD). DDTs were detected in all food types, with the predominant congener being p,p’-DDE. The presence of p,p’-DDT confirmed recent release of DDT into the environment. By using concentration levels detected in foods and national consumption levels, the risk to human health through consumption of such food products was calculated. In order of risk level, these were: chicken eggs > chicken meat > fish meat. Human risk (carcinogenic) values greater than one suggest there is an increased health risk through consumption of these foods.

Keywords: DDT, food contamination, human health risk, Mozambique, South Africa

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Authors: P. Serpytis, K. Azukaitis, U. Gargalskaite, R. Navickas, J. Badariene, V. Dzenkeviciute

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Purpose: Total longitudinal displacement (tLoD) of the common carotid artery (CCA) wall is a novel ultrasound marker of vascular function that can be evaluated using modified speckle tracking techniques. Decreased CCA tLoD has already been shown to be associated with diabetes and was shown to predict one year cardiovascular outcome in patients with suspected coronary artery disease (CAD) . The aim of our study was to evaluate if CCA tLoD differ between patients with moderate or high cardiovascular (CV) risk and patients after recent acute myocardial infarction (AMI). Methods: 49 patients (54±6 years) with moderate or high CV risk and 42 patients (58±7 years) after recent AMI were included. All patients were non-diabetic. CCA tLoD was evaluated using GE EchoPAC speckle tracking software and expressed as mean of both sides. Data on systolic blood pressure, total and high density lipoprotein (HDL) cholesterol levels, high sensitivity C-reactive protein (hsCRP) level, smoking status and family history of early CV events was evaluated and assessed for association with CCA tLoD. Results: tLoD of CCA did not differ between patients with moderate or high CV risk and patients with very high CV risk after MI (0.265±0.128 mm vs. 0.237±0.103 mm, p>0.05). Lower tLoD was associated with lower HDL cholesterol levels (r=0.211, p=0.04) and male sex (0.228±0.1 vs. 0.297±0.134, p=0.01). Conclusions: tLoD of CCA did not differ between patients with moderate or high CV risk and patients with very high CV risk after AMI. However, lower CCA tLoD was significantly associated with low HDL cholesterol levels and male sex.

Keywords: total longitudinal displacement, carotid artery, cardiovascular risk, acute myocardial infarction

Procedia PDF Downloads 384