Search results for: squared prediction risk

Authors: Betty Johanna Garzon Rozo, Jonathan Crook, Fernando Moreira

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Operational risk losses are heavy tailed and are likely to be asymmetric and extremely dependent among business lines/event types. We propose a new methodology to assess, in a multivariate way, the asymmetry and extreme dependence between severity distributions, and to calculate the capital for Operational Risk. This methodology simultaneously uses (i) several parametric distributions and an alternative mix distribution (the Lognormal for the body of losses and the Generalized Pareto Distribution for the tail) via extreme value theory using SAS®, (ii) the multivariate skew t-copula applied for the first time for operational losses and (iii) Bayesian theory to estimate new n-dimensional skew t-copula models via Markov chain Monte Carlo (MCMC) simulation. This paper analyses a newly operational loss data set, SAS Global Operational Risk Data [SAS OpRisk], to model operational risk at international financial institutions. All the severity models are constructed in SAS® 9.2. We implement the procedure PROC SEVERITY and PROC NLMIXED. This paper focuses in describing this implementation.

Keywords: operational risk, loss distribution approach, extreme value theory, copulas

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: Yingkun Huang, Li Guo, Zekang Lan, Kai Tian

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Hard disk drives (HDD) failure of the exascale supercomputing system may lead to service interruption and invalidate previous calculations, and it will cause permanent data loss. Therefore, initiating corrective actions before hard drive failures materialize is critical to the continued operation of jobs. In this paper, a highly accurate analysis model based on CNN-LSTM and oversampling technique was proposed, which can correctly predict the necessity of a disk replacement even ten days in advance. Generally, the learning-based method performs poorly on a training dataset with long-tail distribution, especially fault prediction is a very classic situation as the scarcity of failure data. To overcome the puzzle, a new oversampling was employed to augment the data, and then, an improved CNN-LSTM with the shortcut was built to learn more effective features. The shortcut transmits the results of the previous layer of CNN and is used as the input of the LSTM model after weighted fusion with the output of the next layer. Finally, a detailed, empirical comparison of 6 prediction methods is presented and discussed on a public dataset for evaluation. The experiments indicate that the proposed method predicts disk failure with 0.91 Precision, 0.91 Recall, 0.91 F-measure, and 0.90 MCC for 10 days prediction horizon. Thus, the proposed algorithm is an efficient algorithm for predicting HDD failure in supercomputing.

Keywords: HDD replacement, failure, CNN-LSTM, oversampling, prediction

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Authors: Mohamed Saad Ahmed Hussien

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This paper discusses the risk management in Islamic banks and aims to determine the difference in the practices and methods of risk management in those banks compared to the conventional banks, and to make a case study of the biggest Islamic bank in Egypt (Faisal Islamic Bank of Egypt) to identify the most important financial risks faced and how to manage those risks. It was found that Islamic banks face two types of risks. The first type is similar to the risks in conventional banks; the second type is the additional risks which facing the Islamic banks only as a result of some Islamic modes of financing. With regard to the risk management, Islamic banks such as conventional banks applied the regulatory rules issued by the Central Banks and the Basel Committee; Islamic banks also applied the instructions and procedures issued by the Islamic Financial Services Board (IFSB). Also, Islamic banks are similar to the conventional banks in the practices and methods which they use to manage the risks. And there are some factors that may affect the risk management in Islamic banks, such as the size of the bank and the efficiency of the administration and the staff of the bank.

Keywords: conventional banks, Faisal Islamic Bank of Egypt, Islamic banks, risk management

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Authors: Eva Kirzner

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Based on outcome studies published over the past two decades, in 2018, the ACC/AHA published new guidelines for the management of hypercholesterolemia that incorporate the use of coronary artery calcium (CAC) scanning as a decision tool for ascertaining which patients may benefit from statin therapy. This use is based on the recognition that the absence of calcium on CAC scanning (i.e., a CAC score of zero) usually signifies the absence of significant atherosclerotic deposits in the coronary arteries. Specifically, in patients with a high risk for atherosclerotic cardiovascular disease (ASCVD), initiation of statin therapy is generally recommended to decrease ASCVD risk. However, among patients with intermediate ASCVD risk, the need for statin therapy is less certain. However, there is a need for new outcome studies that provide evidence that the management of hypercholesterolemia based on these new ACC/AHA recommendations is safe for patients. Based on a Pub-Med and Google Scholar literature search, four relevant population-based or patient-based cohort studies that studied the relationship between CAC scanning, risk assessment or mortality, and statin therapy that were published between 2017 and 2021 were identified (see references). In each of these studies, patients were assessed for their baseline risk for atherosclerotic cardiovascular disease (ASCVD) using the Pooled Cohorts Equation (PCE), an ACC/AHA calculator for determining patient risk based on assessment of patient age, gender, ethnicity, and coronary artery disease risk factors. The combined findings of these four studies provided concordant evidence that a zero CAC score defines patients who remain at low clinical risk despite the non-use of statin therapy. Thus, these new studies confirm the use of CAC scanning as a safe tool for reducing the potential overuse of statin therapy among patients with zero CAC scores. Incorporating these new data suggest the following best practice: (1) ascertain ASCVD risk according to the PCE in all patients; (2) following an initial attempt trial to lower ASCVD risk with optimal diet among patients with elevated ASCVD risk, initiate statin therapy for patients who have a high ASCVD risk score; (3) if the ASCVD score is intermediate, refer patients for CAC scanning; and (4) and if the CAC score is zero among the intermediate risk ASCVD patients, statin therapy can be safely withheld despite the presence of an elevated serum cholesterol level.

Keywords: cholesterol, cardiovascular disease, statin therapy, coronary calcium

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Authors: Z. Dedovets, M. Rodionov

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The goal of the modern education system is to prepare students to be able to adapt to ever-changing life situations. They must be able to acquire required knowledge independently; apply such knowledge in practice to solve various problems by using modern technologies; think critically and creatively; competently use information; be communicative, work in a team; and develop their own moral values, intellect and cultural awareness. As a result, the status of education significantly increases; new requirements to its quality have been formed. In recent years, the competency-based approach in education has become of significant interest. This approach is a strengthening of applied and practical characteristics of a school education and leads to the forming of the key students’ competencies which define their success in future life. In this article, the authors’ attention focuses on a range of key competencies, educational, informational and communicative and on the possibility to develop such competencies via STEM education. This research shows the change in students’ attitude towards scientific disciplines such as mathematics, general science, technology and engineering as a result of STEM education. Two-staged analyzes questionnaires completed by students of forms II to IV in the republic of Trinidad and Tobago allowed the authors to categorize students between two levels that represent students’ attitude to various disciplines. The significance of differences between selected levels was confirmed with the use of Pearsons’ chi-squared test. In summary, the analysis of obtained data makes it possible to conclude that STEM education has a great potential for development of core students’ competencies and encourages the development of positive student attitude towards the above mentioned above scientific disciplines.

Keywords: STEM, science, technology, engineering, mathematics, students’ competency, Pearson's chi-squared test

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Authors: Ichihan Tai, Bill Olson, Paul Blessner

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Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: early warning system, knowledge management, market prediction, topic modeling.

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Authors: Ghanima Al-Sharrah, Haitham M. Lababidi, Yusuf I. Ali

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The main objective of the current work is to introduce sustainability factors in optimizing the supply chain model for process industries. The supply chain models are normally based on purely economic considerations related to costs and profits. To account for sustainability, two additional factors have been introduced; environment and risk. A supply chain for an entire petroleum organization has been considered for implementing and testing the proposed optimization models. The environmental and risk factors were introduced as indicators reflecting the anticipated impact of the optimal production scenarios on sustainability. The aggregation method used in extending the single objective function to multi-objective function is proven to be quite effective in balancing the contribution of each objective term. The results indicate that introducing sustainability factor would slightly reduce the economic benefit while improving the environmental and risk reduction performances of the process industries.

Keywords: environmental indicators, optimization, risk, supply chain

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Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga

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Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.

Keywords: genetic algorithm, neural networks, word prediction, machine learning

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Authors: Thirumila Muthukamaru

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The objective of a Health Risk Assessment (HRA) is to achieve a quality evaluation of risk assessments in a timely manner where adequate controls can be in place to protect workers health, especially during turnarounds where the exposure to health hazards is expected to rise during the performance of the many activities that take place, exposing workers to health risk. HRA development requires a competent team comprising experienced subject matter experts in the field, such as Industrial hygienists, Occupational Health Doctors, Turnaround Coordinators, Operation / Maintenance personnel, etc. The conventional way of conducting HRA is not only tedious and time-consuming but also less appreciated when it is not interpreted correctly, which may contribute to inadequate operationalization of it. Simplification can be the essence of timely intervention in managing health risks. This paper is intended as a sharing of the approach taken to simplify the methodology of developing the HRA report and operationalizing it. The approach includes developing a Generic HRA for turnaround activities to be used as a reference document and the empowerment of identified personnel through upskilling sessions to take up the role of facilitating HRA sessions. This empowerment is one of the key approaches towards the successful translation of the HRA into specific turnaround Job Hazard Analysis (JHA) that embed it in the Permit to Work (PTW) process. The approach used here increases awareness and compliance on HRA for turnaround activities through better interpretation and operationalization of the HRA report, adding value to the risk assessment for turnaround activities.

Keywords: industrial hygiene, health risk assessment, HRA, risk assessment

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Authors: Jayden R. Hunter, Brett A. Gordon, Stephen R. Bird, Amanda C. Benson

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The World Health Organisation recognises the workplace as an important setting for exercise promotion, with potential benefits including improved employee health and fitness, and reduced worker absenteeism and presenteeism. Despite these potential benefits to both employee and employer, there is a lack of evidence supporting the long-term effectiveness of workplace exercise programs. There is, therefore, a need for better-informed programs that cater to employee exercise preferences. Specifically, workplace exercise programs should address any time, motivation, internal and external barriers to participation reported by sub-groups of employees. This study sought to compare exercise participation to perceived barriers and facilitators to workplace exercise engagement of university employees. This information is needed to design and implement wider-reaching programs aiming to maximise long-term employee exercise adherence and subsequent health, fitness and productivity benefits. An online survey was advertised at an Australian university with the potential to reach 3,104 full-time employees. Along with exercise participation (International physical activity questionnaire) and behaviour (stage of behaviour change in relation to physical activity questionnaire), perceived barriers (corporate exercise barriers scale) and facilitators to workplace exercise participation were identified. The survey response rate was 8.1% (252 full-time employees; 95% white-collar; 60% female; 79.4% aged 30–59 years; 57% professional and 38% academic). Most employees reported meeting (43.7%) or exceeding (42.9%) exercise guidelines over the previous week (i.e. ⩾30 min of moderate-intensity exercise on most days or ⩾ 25 min of vigorous-intensity exercise on at least three days per week). Reported exercise behaviour over the previous six months showed that 64.7% of employees were in maintenance, 8.3% were in action, 10.9% were in preparation, 12.4% were in contemplation, and 3.8% were in the pre-contemplation stage of change. Perceived barriers towards workplace exercise participation were significantly higher in employees not attaining weekly exercise guidelines compared to employees meeting or exceeding guidelines, including a lack of time or reduced motivation (p < 0.001; partial eta squared = 0.24 (large effect)), exercise attitude (p < 0.05; partial eta squared = 0.04 (small effect)), internal (p < 0.01; partial eta squared = 0.10 (moderate effect)) and external (p < 0.01; partial eta squared = 0.06 (moderate effect)) barriers. The most frequently reported exercise facilitators were personal training (particularly for insufficiently active employees; 33%) and group exercise classes (20%). The most frequently cited preferred modes of exercise were walking (70%), swimming (50%), gym (48%), and cycling (45%). In conclusion, providing additional means of support such as individualised gym, swimming and cycling programs with personal supervision and guidance may be particularly useful for employees not meeting recommended moderate-vigorous volumes of exercise, to help overcome reported exercise barriers in order to improve participation, health, and fitness. While individual biopsychosocial factors should be considered when making recommendations for interventions, the specific barriers and facilitators to workplace exercise participation identified by this study can inform the development of workplace exercise programs aiming to broaden employee engagement and promote greater ongoing exercise adherence. This is especially important for the uptake of less active employees who perceive greater barriers to workplace exercise participation than their more active colleagues.

Keywords: exercise barriers, exercise facilitators, physical activity, workplace health

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Authors: Kassaye Balkew Workagegn, Gunnar Klemetsdal, Hans Magnus Gjøen

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In this study, the first objective was to investigate whether non-lethal or non-invasive methods, utilizing body measurements, could be used to efficiently predict fillet weight and fillet yield for a complete diallel cross of three Nile tilapia (Oreochromis niloticus) strains collected from three Ethiopian Rift Valley lakes, Lakes Ziway, Koka and Chamo. The second objective was to estimate heritability of body weight, actual and predicted fillet traits, as well as genetic correlations between these traits. A third goal was to estimate additive, reciprocal, and heterosis effects for body weight and the various fillet traits. As in females, early sexual maturation was widespread, only 958 male fish from 81 full-sib families were used, both for the prediction of fillet traits and in genetic analysis. The prediction equations from body measurements were established by forward regression analysis, choosing models with the least predicted residual error sums of squares (PRESS). The results revealed that body measurements on live Nile tilapia is well suited to predict fillet weight but not fillet yield (R²= 0.945 and 0.209, respectively), but both models were seemingly unbiased. The genetic analyses were carried out with bivariate, multibreed models. Body weight, fillet weight, and predicted fillet weight were all estimated with a heritability ranged from 0.23 to 0.28, and with genetic correlations close to one. Contrary, fillet yield was only to a minor degree heritable (0.05), while predicted fillet yield obtained a heritability of 0.19, being a resultant of two body weight variables known to have high heritability. The latter trait was estimated with genetic correlations to body weight and fillet weight traits larger than 0.82. No significant differences among strains were found for their additive genetic, reciprocal, or heterosis effects, while total heterosis effects were estimated as positive and significant (P < 0.05). As a conclusion, prediction of prediction of fillet weight based on body measurements is possible, but not for fillet yield.

Keywords: additive, fillet traits, genetic correlation, heritability, heterosis, prediction, reciprocal

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Authors: Jayanta Pokharel, Gokarna Aryal, Netra Kanaal, Chris Tsokos

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Investment in stocks and shares aims to seek potential gains while weighing the risk of future needs, such as retirement, children's education etc. Analysis of the behavior of the stock market returns and making prediction is important for investors to mitigate risk on investment. Historically, the normal variance models have been used to describe the behavior of stock market returns. However, the returns of the financial assets are actually skewed with higher kurtosis, heavier tails, and a higher center than the normal distribution. The Laplace distribution and its family are natural candidates for modeling stock returns. The Variance-Gamma (VG) distribution is the most sought-after distributions for modeling asset returns and has been extensively discussed in financial literatures. In this paper, it explore the other Laplace family, such as Asymmetric Laplace, Skewed Laplace, Kumaraswamy Laplace (KS) together with Variance-Gamma to model the weekly returns of the S&P 500 Index and it's eleven business sector indices. The method of maximum likelihood is employed to estimate the parameters of the distributions and our empirical inquiry shows that the Kumaraswamy Laplace distribution performs much better for stock returns modeling among the choice of distributions used in this study and in practice, KS can be used as a strong alternative to VG distribution.

Keywords: stock returns, variance-gamma, kumaraswamy laplace, maximum likelihood

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Authors: Irena Paramita Pramono

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Islamic bank is a financial industry with huge potential to grow in Indonesia. Profit-sharing agreement in the operations of Islamic banks distinguishes Islamic banks with conventional banks. Profit-sharing agreement allows sharing of benefits and risks between shahibul maal and mudharib in islamic bank. This study aimed to observe the patterns of influence between the risk-sharing agreement, financing risk and Profitability in Islamic banks. This research used several Islamic banks as sample and path analysis method. The empirical results of this research shows that the profit-sharing agreement in deposits structure has no direct significant effect to ROA, but it has indirect effect to ROA through profit-sharing financing. On the other hand, profit-sharing financing has direct and indirect influence to ROA through financing risk. This research shows that profit-sharing financing has a positive significant effect to the financing risk and also to the ROA. The research recommends Islamic banks to continue using and developing profit-sharing agreement in its operational activities, hence to create value.

Keywords: Islamic bank, profit-loss sharing agreement, financing risk, profitability

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Authors: Katalin Bodor, Zsolt Bodor, Robert Szep

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The particulate matter (PM) smaller than 2.5 μm are less studied due to the limited availability of PM₂.₅, and less information is available on the health effects attributable to PM₁₀ in Central-Eastern Europe. The objective of the current study was to assess the human health risk and characterize the spatial and temporal variation of PM₂.₅ and PM₁₀ in eight Romanian regions between the 2009-2018 and. The PM concentrations showed high variability over time and spatial distribution. The highest concentration was detected in the Bucharest region in the winter period, and the lowest was detected in West. The relative risk caused by the PM₁₀ for all-cause mortality varied between 1.017 (B) and 1.025 (W), with an average 1.020. The results demonstrate a positive relative risk of cardiopulmonary and lung cancer disease due to exposure to PM₂.₅ on the national average 1.26 ( ± 0.023) and 1.42 ( ± 0.037), respectively.

Keywords: PM₂.₅, PM₁₀, relative risk, health effect

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Authors: Longqing Li

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The paper addresses the inefficiency of the classical model in measuring the Value-at-Risk (VaR) using a normal distribution or a Student’s t distribution. Specifically, the paper focuses on the one day ahead Value-at-Risk (VaR) of major stock market’s daily returns in US, UK, China and Hong Kong in the most recent ten years under 95% confidence level. To improve the predictable power and search for the best performing model, the paper proposes using two leading alternatives, Extreme Value Theory (EVT) and a family of GARCH models, and compares the relative performance. The main contribution could be summarized in two aspects. First, the paper extends the GARCH family model by incorporating EGARCH and TGARCH to shed light on the difference between each in estimating one day ahead Value-at-Risk (VaR). Second, to account for the non-normality in the distribution of financial markets, the paper applies Generalized Error Distribution (GED), instead of the normal distribution, to govern the innovation term. A dynamic back-testing procedure is employed to assess the performance of each model, a family of GARCH and the conditional EVT. The conclusion is that Exponential GARCH yields the best estimate in out-of-sample one day ahead Value-at-Risk (VaR) forecasting. Moreover, the discrepancy of performance between the GARCH and the conditional EVT is indistinguishable.

Keywords: Value-at-Risk, Extreme Value Theory, conditional EVT, backtesting

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Authors: Rahmoune Abdelhaq

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Systemic risk is a key concern for central banks charged with safeguarding overall financial stability. In this work, we investigate how systemic risk is affected by the structure of the financial system. We construct banking systems that are composed of a number of banks that are connected by interbank linkages. We then vary the key parameters that define the structure of the financial system — including its level of capitalization, the degree to which banks are connected, the size of interbank exposures and the degree of concentration of the system — and analyses the influence of these parameters on the likelihood of contagious (knock-on) defaults. First, we find that the better-capitalized banks are, the more resilient is the banking system against contagious defaults and this effect is non-linear. Second, the effect of the degree of connectivity is non-monotonic, that is, initially a small increase in connectivity increases the contagion effect; but after a certain threshold value, connectivity improves the ability of a banking system to absorb shocks. Third, the size of interbank liabilities tends to increase the risk of knock-on default, even if banks hold capital against such exposures. Fourth, more concentrated banking systems are shown to be prone to larger systemic risk, all else equal. In an extension to the main analysis, we study how liquidity effects interact with banking structure to produce a greater chance of systemic breakdown. We finally consider how the risk of contagion might depend on the degree of asymmetry (tier) inherent in the structure of the banking system. A number of our results have important implications for public policy, which this paper also draws out. This paper also discusses why bank risk management is needed to get the optimal one.

Keywords: financial stability, contagion, liquidity risk, optimal risk

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Authors: Hongmeng Su, Luyu Zhao, Yanyan Qian, Hong Fan

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Aim: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors that endanger human health seriously. RNA methylation, especially N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), a crucial epigenetic transcriptional regulatory mechanism, plays an important role in tumorigenesis, progression and prognosis. This research aims to systematically evaluate the prognostic value of m⁶A and m⁵C modulators in HCC patients. Methods: Twenty-four modulators of m⁶A and m⁵C were candidates to analyze their expression level and their contribution to predict the prognosis of HCC. Consensus clustering analysis was applied to classify HCC patients. Cox and LASSO regression were used to construct the risk model. According to the risk score, HCC patients were divided into high-risk and low/medium-risk groups. The clinical pathology factors of HCC patients were analyzed by univariate and multivariate Cox regression analysis. Results: The HCC patients were classified into 2 clusters with significant differences in overall survival and clinical characteristics. Nine-gene risk model was constructed including METTL3, VIRMA, YTHDF1, YTHDF2, NOP2, NSUN4, NSUN5, DNMT3A and ALYREF. It was indicated that the risk score could serve as an independent prognostic factor for patients with HCC. Conclusion: This study constructed a Nine-gene risk model by modulators of m⁶A and m⁵C and investigated its effect on the clinical prognosis of HCC. This model may provide important consideration for the therapeutic strategy and prognosis evaluation analysis of patients with HCC.

Keywords: hepatocellular carcinoma, m⁶A, m⁵C, prognosis, RNA methylation

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Authors: Ezeabasili Chibuike Patrick

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The complex situation of construction industries in Nigeria and the risk of failures involved includes cost overrun, time overrun, Corruption, Government influence, Subcontractor challenges, Political influence and Instability, Cultural differences, Human resources deficiencies, cash flow Challenges, foreign exchange issues, inadequate design, Safety, low productivity, late payment, Quality control issues, project management issues, Environmental issues, Force majeure Competition amongst others has made the industry prone to risk and failures. Good project management remains effective in improving decision-making, which minimizes these risk events. This study was done to address these project risks and good decision-making to avert them. A mixed-method approach to research was used to do this study. Data collected by questionnaires and interviews on thirty-two (32) construction professionals was used in analyses to aid the knowledge and management of risks that were identified. The study revealed that there is no good risk management expertise in Nigeria. Also, that the economic/political situation and the recent COVID-19 pandemic has added to the risk and poor management strategies. The contingency theory and cost has therefore surfaced to be the most strategic management method used to reduce these risk issues and they seem to be very effective.

Keywords: strategies, risk management, contingency theory, Nigeria

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Authors: Ru-Yin Yang, Chi Wu, Cheng-Yu Tsai, Yin-Tzu Lin, Wen-Te Liu

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Background: Sleep breathing disorder (SDB) is a condition demonstrated by recurrent episodes of the airway obstruction leading to intermittent hypoxia and quality fragmentation during sleep time. However, the procedures for SDB severity examination remain complicated and costly. Objective: The objective of this study is to establish a simplified examination method for SDB by the respiratory impendence pattern sensor combining the signal processing and machine learning model. Methodologies: We records heart rate variability by the electrocardiogram and respiratory pattern by impendence. After the polysomnography (PSG) been done with the diagnosis of SDB by the apnea and hypopnea index (AHI), we calculate the episodes with the absence of flow and arousal index (AI) from device record. Subjects were divided into training and testing groups. Neuron network was used to establish a prediction model to classify the severity of the SDB by the AI, episodes, and body profiles. The performance was evaluated by classification in the testing group compared with PSG. Results: In this study, we enrolled 66 subjects (Male/Female: 37/29; Age:49.9±13.2) with the diagnosis of SDB in a sleep center in Taipei city, Taiwan, from 2015 to 2016. The accuracy from the confusion matrix on the test group by NN is 71.94 %. Conclusion: Based on the models, we established a prediction model for SDB by means of the wearable sensor. With more cases incoming and training, this system may be used to rapidly and automatically screen the risk of SDB in the future.

Keywords: sleep breathing disorder, apnea and hypopnea index, body parameters, neuron network

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Authors: Nataša Kalajdžija, Strahinja Kovačević, Davor Lončar, Sanja Podunavac Kuzmanović, Lidija Jevrić

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In order to investigate the relationship between retention and structure, a quantitative Structure Retention Relationships (QSRRs) study was applied for the prediction of retention times of a set of 23 secoestrane derivatives in a reversed-phase thin-layer chromatography. After the calculation of molecular descriptors, a suitable set of molecular descriptors was selected by using step-wise multiple linear regressions. Artificial Neural Network (ANN) method was employed to model the nonlinear structure-activity relationships. The ANN technique resulted in 5-6-1 ANN model with the correlation coefficient of 0.98. We found that the following descriptors: Critical pressure, total energy, protease inhibition, distribution coefficient (LogD) and parameter of lipophilicity (miLogP) have a significant effect on the retention times. The prediction results are in very good agreement with the experimental ones. This approach provided a new and effective method for predicting the chromatographic retention index for the secoestrane derivatives investigated.

Keywords: lipophilicity, QSRR, RP TLC retention, secoestranes

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Authors: Khaled Alkadamani

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Using the simultaneous equations model, this paper examines the impact of capital requirements on bank risk-taking during the recent financial crisis. It also explores the relationship between capital and risk decisions and the impact of economic instability on this relationship. By analyzing the data of 20 Islamic commercial banks between 2004 and 2014 from four Middle East countries, the study concludes a positive effect of regulatory pressure on bank capital in Saudi Arabia and UAE and a negative effect in Jordan and Kuwait. Moreover, the results show a negative impact of regulatory pressure on bank risk taking in Saudi Arabia, Jordan and UAE. The findings reveal also that banks close to the minimum regulatory capital requirements improve their capital adequacy by increasing their capital and decreasing their risk taking. Furthermore, the results show that economic crisis negatively affects bank risk changes, suggesting that banks react to the impact of uncertainty by reducing their risk taking. Finally, the estimations show a negative correlation between banks profitability and capital adequacy ratio (CAR), implying that as more capital is set aside as a buffer for banks safety; it affects the performance of Islamic banks.

Keywords: bank capital, bank regulation, crisis, Islamic banks, risk taking

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Authors: Yeaji Seok, Myung Kyung Lee

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Background: Even if patients survive after a stroke, stroke patients may experience disability in mobility, sensation, cognition, and speech and language. Stroke patients require rehabilitation for functional recovery and daily life for a considerable time. During rehabilitation, the role of caregivers is important. However, the stroke patients’ quality of life may deteriorate due to family caregivers’ non-preparedness and increased role burden. Purpose: To investigate the prediction of caregivers' preparedness and role burden on stroke patients’ quality of life. Methods: The target population was stroke patients who were hospitalized for rehabilitation and their family care providers. A total of 153 patient-family caregiver dyads were recruited from June to August 2021. Data were collected from self-reported questionnaires and analyzed using descriptive statistics, t-tests, chi-squared test, one-way analysis of variance, Pearson’s correlation coefficients, and multiple regression with SPSS statistics 28 programs. Results: Family caregivers’ preparedness affected stroke patients’ mobility (β = .20, p < 0.05) and character (β = -.084, p < 0.05) and production activities (β = -.197, p < 0.05) in quality of life. The role burden of family caregivers affected language skills (β = .310, p<0.05), visual functions (β=-.357, p < 0.05), thinking skills (β = 0.443, p = 0.05), mood conditions (β = 0.565, p < 0.001), family roles (β = -0.361, p < 0.001), and social roles (β = -0.304, p < 0.001), while the caregivers’ burden of performing self-protection negatively affected patients’ social roles (β = .180, p=.048). In addition, caregivers’ role burden of personal life sacrifice affected patients’ mobility (β = .311, p < 0.05), self-care (β =.232, p < 0.05) and energy (β = .239, p < 0.05). Conclusion: This study indicated that family caregivers' preparedness and role burden affected stroke patients’ quality of life. The results of this study suggested that intervention to improve family caregivers’ preparedness and to reduce role burden should be required for quality of life in stroke patients.

Keywords: quality of life, preparedness, role burden, caregivers, stroke

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Authors: J. R. Wang, W. Y. Cheng, J. S. Yeh, S. W. Chen, Y. M. Ferng, J. H. Yang, W. S. Hsu, C. Shih

Abstract:

To understand the risk for dry storage concrete casks in the cask loading, transfer, and storage phase, the purpose of this research is to establish the probabilistic risk assessment (PRA) analysis methodology for dry storage concrete casks by using SAPHIRE 8 code. This analysis methodology is used to perform the study of Taiwan nuclear power plants (NPPs) dry storage system. The process of research has three steps. First, the data of the concrete casks and Taiwan NPPs are collected. Second, the PRA analysis methodology is developed by using SAPHIRE 8. Third, the PRA analysis is performed by using this methodology. According to the analysis results, the maximum risk is the multipurpose canister (MPC) drop case.

Keywords: PRA, dry storage, concrete cask, SAPHIRE

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Authors: Abdessalem Abbassi, Ahlem Dakhlaoui, Lota D. Tamini

Abstract:

This article analyses management of hydropower dams within two different industrial structures: monopolistic and oligopolistic; when hydroelectricity producers are risk averse and face demand uncertainty. In each type of market structure we determine the water release path in closed-loop equilibrium. We show how a monopoly can manage its hydropower dams by additional pumping or storage depending on the relative abundance of water between different regions to smooth the effect of uncertainty on electricity prices. In the oligopolistic case with symmetric rates of risk aversion, we determine the conditions under which the relative scarcity (abundance) of water in the dam of a hydroelectric operator can favor additional strategic pumping (storage) in its competitor’s dams. When there is asymmetry of the risk aversion coefficient, the firm’s hydroelectricity production increases as its competitor’s risk aversion increases, if and only if the average recharge speed of the competitor’s dam exceeds a certain threshold, which is an increasing function of its average water inflows.

Keywords: asymmetric risk aversion, closed-loop Cournot competition, electricity wholesale market, hydropower dams

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Authors: Nataliya V. Dolgushina, Elena A. Gorodnova, Olga S. Beznoshenco, Andrey Yu Romanov, Irina V. Menzhinskaya, Lyubov V. Krechetova, Gennady T. Suchich

Abstract:

In patients with COVID-19, generalized hypercoagulability can lead to the development of severe coagulopathy. This event is accompanied by the development of a pronounced inflammatory reaction. The observational prospective study included 39 patients with mild COVID-19 and 102 patients with moderate and severe COVID-19. Patients were then stratified into groups depending on the risk of venous thromboembolism. vWF to ADAMTS-13 concentrations and activity ratios were significantly higher in patients with a high venous thromboembolism risks in patients with moderate and severe forms COVID-19.

Keywords: ADAMTS-13, COVID-19, hypercoagulation, thrombosis, von Willebrand factor

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

Abstract:

Diabetes in India is growing at an alarming rate and the complications caused by it need to be controlled. Coronary heart disease (CHD) is one of the complications that will be discussed for prediction in this study. India has the second most number of diabetes patients in the world. To the best of our knowledge, there is no CHD risk score for Indian type 2 diabetes patients. Any form of CHD has been taken as the event of interest. A sample of 750 was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of CHD. Predictive risk scores of CHD events are designed by cox proportional hazard regression. Model calibration and discrimination is assessed from Hosmer Lemeshow and area under receiver operating characteristic (ROC) curve. Overfitting and underfitting of the model is checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Youden’s index is used to choose the optimal cut off point from the scores. Five year probability of CHD is predicted by both survival function and Markov chain two state model and the better technique is concluded. The risk scores for CHD developed can be calculated by doctors and patients for self-control of diabetes. Furthermore, the five-year probabilities can be implemented as well to forecast and maintain the condition of patients.

Keywords: coronary heart disease, cox proportional hazard regression, ROC curve, type 2 diabetes Mellitus

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

Abstract:

In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

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Authors: Henok Hailemariam, Frank Wuttke

Abstract:

Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

Procedia PDF Downloads 339

Authors: Yuwaratu Syafira, Wahyu K. Y. Putra, Kusharisupeni Djokosujono

Abstract:

Background/Objective: Body Mass Index (BMI) serves various purposes, including measuring the prevalence of obesity in a population, and also in formulating a patient’s diet at a hospital, and can be calculated with the equation = body weight (kg)/body height (m)². However, the BMI of an individual with difficulties in carrying their weight or standing up straight can not necessarily be measured. The aim of this study was to form a prediction model for the BMI of young adult students of Public Health Faculty of University of Indonesia. Subject/Method: This study used a cross sectional design, with a total sample of 132 respondents, consisted of 58 males and 74 females aged 21- 30. The dependent variable of this study was BMI, and the independent variables consisted of sex and anthropometric measurements, which included ulna length, arm length, tibia length, knee height, mid-upper arm circumference, and calf circumference. Anthropometric information was measured and recorded in a single sitting. Simple and multiple linear regression analysis were used to create the prediction equation for BMI. Results: The male respondents had an average BMI of 24.63 kg/m² and the female respondents had an average of 22.52 kg/m². A total of 17 variables were analysed for its correlation with BMI. Bivariate analysis showed the variable with the strongest correlation with BMI was Mid-Upper Arm Circumference/√Ulna Length (MUAC/√UL) (r = 0.926 for males and r = 0.886 for females). Furthermore, MUAC alone also has a very strong correlation with BMI (r = 0,913 for males and r = 0,877 for females). Prediction models formed from either MUAC/√UL or MUAC alone both produce highly accurate predictions of BMI. However, measuring MUAC/√UL is considered inconvenient, which may cause difficulties when applied on the field. Conclusion: The prediction model considered most ideal to estimate BMI is: Male BMI (kg/m²) = 1.109(MUAC (cm)) – 9.202 and Female BMI (kg/m²) = 0.236 + 0.825(MUAC (cm)), based on its high accuracy levels and the convenience of measuring MUAC on the field.

Keywords: body mass index, mid-upper arm circumference, prediction model, ulna length

Procedia PDF Downloads 201