Search results for: squared prediction risk

Authors: Adel Ali Elshaibani

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Risk management indicators augment the reporting infrastructure, particularly for the board and senior management, to identify, monitor, and manage risks. This enhancement facilitates improved decision-making throughout the banking organization. Decision tree analysis is a tool that visually outlines potential outcomes, costs, and consequences of complex decisions. It is particularly beneficial for analyzing quantitative data and making decisions based on numerical values. By calculating the expected value of each outcome, decision tree analysis can help assess the best course of action. In the context of banking, decision tree analysis can assist lenders in evaluating a customer’s creditworthiness, thereby preventing losses. However, applying these tools in developing countries may face several limitations, such as data availability, lack of technological infrastructure and resources, lack of skilled professionals, cultural factors, and cost. Moreover, decision trees can create overly complex models that do not generalize well to new data, known as overfitting. They can also be sensitive to small changes in the data, which can result in different tree structures and can become computationally expensive when dealing with large datasets. In conclusion, while risk management indicators and decision tree analysis are beneficial for decision-making in banks, their effectiveness is contingent upon how they are implemented and utilized by the board of directors, especially in the context of developing countries. It’s important to consider these limitations when planning to implement these tools in developing countries.

Keywords: risk management indicators, decision tree analysis, developing countries, board of directors, bank performance, risk management strategy, banking institutions

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Authors: Jossitt Williams Vargas Cruz, Aura Jazmín Pérez Ríos

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The variations of solar dynamics have become a relevant topic of study due to the effects of climate changes generated on the earth. One of the most disconcerting aspects is the variability that the sun has on the climate is the role played by sunspots (extra-atmospheric variable) in the modulation of the Cosmic Rays CR (extra-atmospheric variable). CRs influence the earth's climate by affecting cloud formation (atmospheric variable), and solar cycle influence is associated with the presence of solar storms, and the magnetic activity is greater, resulting in less CR entering the earth's atmosphere. The different methods of climate prediction in Colombia do not take into account the extra-atmospheric variables. Therefore, correlations between atmospheric and extra-atmospheric variables were studied in order to implement a Python code based on neural networks to make the prediction of the extra-atmospheric variable with the highest correlation.

Keywords: correlations, cosmic rays, sun, sunspots and variations.

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Authors: Nguyen Thi Loan, Phan Ngoc Thanh Tra

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Container is one of the techniques used in Eye Movement Desensitization and Reprocessing (EDMR) Therapy. This paper presents the positive results of applying Container technique to “high risk children”. The sample for this research is composed of 60 “high risk children” whose ages range from 11 to 18 years old, housed in Ho Chi Minh City Youth Center. They have been under the program of the Worldwide Orphans Foundation since August 2015 for various reasons such as, loss of parents, anti-social behaviors, homelessness, child labor among others. These “high risk children” are under high levels of stress, anxiety and depression. The subjects were divided into two groups: the control and the experimental with 30 members each. The experimental group was applied Container Technique and the instruments used to measure their levels of stress, anxiety, and depression are DASS-42 and ASEBA. Results show that after applying the Container Technique to the experimental group, there are significant differences between the two groups’ levels of stress, anxiety and depression. The experimental group’s levels of stress, anxiety and depression decreased significantly. The results serve as a basis for the researchers to make an appeal to psychologists to apply Container Technique in doing psychological treatment in a suitable context.

Keywords: anxiety, depression, container technique, EMDR

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Authors: Mohsen Mohammadi, Toshio Fujimi

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High population and irregular urban development in Kabul city, Afghanistan's capital, are among factors that increase its vulnerability to earthquake disasters (on top of its location in a high seismic region); this can lead to widespread economic loss and casualties. This study aims to evaluate earthquake risks in Kabul's 13th district based on scientific data. The research data, which include hazard curves of Kabul, vulnerability curves, and a questionnaire survey through sampling in district 13, have been incorporated to develop risk curves. To estimate potential casualties, we used a set of M parameters in a model developed by Coburn and Spence. The results indicate that in the worst case scenario, more than 90% of district 13, which comprises mostly residential buildings, is exposed to high risk; this may lead to nearly 1000 million USD economic loss and 120 thousand casualties (equal to 25.88% of the 13th district's population) for a nighttime earthquake. To reduce risks, we present the reconstruction of the most vulnerable buildings, which are primarily adobe and masonry buildings. A comparison of risk reduction between reconstructing adobe and masonry buildings indicates that rebuilding adobe buildings would be more effective.

Keywords: earthquake risk evaluation, Kabul, mitigation, vulnerability

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Authors: Nur Atiqah Adam, Mor Jack Ng, Bernard Chern, Kok Hian Tan

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Introduction: Gestational diabetes (GDM) is a common pregnancy complication with short and long-term health consequences for both mother and fetus. Factors such as family history of diabetes mellitus, maternal obesity, maternal age, ethnicity and parity have been reported to influence the risk of GDM. In a multi-racial country like Singapore, it is worthwhile to study the GDM prevalences of different ethnicities. We aim to investigate the influence of ethnicity on the racial prevalences of GDM in Singapore. This is important as it may help us to improve guidelines on GDM healthcare services according to significant risk factors unique to Singapore. Materials and Methods: Obstetric cohort data of 926 singleton deliveries in KK Women’s and Children’s Hospital (KKH) from 2011 to 2013 was obtained. Only patients aged 18 and above and without complicated pregnancies or chronic illnesses were targeted. Factors such as ethnicity, maternal age, parity and maternal body mass index (BMI) at booking visit were studied. A multivariable logistic regression model, adjusted for confounders, was used to determine which of these factors are significantly associated with an increased risk of GDM. Results: The overall GDM prevalence rate based on WHO 1999 criteria & at risk screening (race alone not a risk factor) was 8.86%. GDM rates were higher among women above 35 years old (15.96%), obese (15.15%) and multiparous women (10.12%). Indians had a higher GDM rate (13.0 %) compared to the Chinese (9.57%) and Malays (5.20%). However, using multiple logistic regression model, variables that are significantly related to GDM rates were maternal age (p < 0.001) and maternal BMI at booking visit (p = 0.006). Conclusion: Maternal age (p < 0.001) and maternal booking BMI (p = 0.006) are the strongest risk factors for GDM. Ethnicity per se does not seem to have a significant influence on the prevalence of GDM in Singapore (p = 0.064). Hence we should tailor guidelines on GDM healthcare services according to maternal age and booking BMI rather than ethnicity.

Keywords: ethnicity, gestational diabetes, healthcare, pregnancy

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Authors: Kyriacos Clerides, Panagiotis Herodotou, Constantina Polycarpou, Evagoras Xydas

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Background: Musculoskeletal disorders (MSDs) in the workplace is a very common problem in Europe which are caused by multiple risk factors. In recent years, wearable devices and exoskeletons for the workplace have been trying to address the various risk factors that are associated with strenuous tasks in the workplace. The logistics sector is a huge sector that includes warehousing, storage, and transportation. However, the task associated with logistics is not well-studied in terms of MSDs risk. This study was aimed at looking into the MSDs affecting workers of logistics companies. It compares the prevalence of MSDs among workers and evaluates multiple risk factors that contribute to the development of MSDs. Moreover, this study seeks to obtain user feedback on the adoption of exoskeletons in such a work environment. Materials and Methods: The study was conducted among workers in logistics companies in Nicosia, Cyprus, from July to September 2022. A set of standardized questionnaires was used for collecting different types of data. Results: A high proportion of logistics professionals reported MSDs in one or more other body regions, the lower back being the most commonly affected area. Working in the same position for long periods, working in awkward postures, and handling an excessive load, were found to be the most commonly reported job risk factor that contributed to the development of MSDs, in this study. A significant number of participants consider the back region as the most to be benefited from a wearable exoskeleton device. Half of the participants would like to have at least a 50% reduction in their daily effort. The most important characteristics for the adoption of exoskeleton devices were found to be how comfortable the device is and its weight. Conclusion: Lower back and posture were the highest risk factors among all logistics professionals assessed in this study. A larger scale study using quantitative analytical tools may give a more accurate estimate of MSDs, which would pave the way for making more precise recommendations to eliminate the risk factors and thereby prevent MSDs. A follow-up study using exoskeletons in the workplace should be done to assess whether they assist in MSD prevention.

Keywords: musculoskeletal disorders, occupational health, safety, occupational risk, logistic companies, workers, Cyprus, industrial exoskeletons, wearable devices

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Authors: Dhahri Maher, Aouinet Hana

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The presence of bubbles in the boundary layer introduces corrections into the log law, which must be taken into account. In this work, a logarithmic wall law was presented for bubbly two phase flows. The wall law presented in this work was based on the postulation of additional turbulent viscosity associated with bubble wakes in the boundary layer. The presented wall law contained empirical constant accounting both for shear induced turbulence interaction and for non-linearity of bubble. This constant was deduced from experimental data. The wall friction prediction achieved with the wall law was compared to the experimental data, in the case of a turbulent boundary layer developing on a vertical flat plate in the presence of millimetric bubbles. A very good agreement between experimental and numerical wall friction prediction was verified. The agreement was especially noticeable for the low void fraction when bubble induced turbulence plays a significant role.

Keywords: bubbly flows, log law, boundary layer, CFD

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Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

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Authors: Emad A. Mohammed

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Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

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Authors: Muhammad Waqar Ashraf

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Selected heavy metals, namely Cu, Zn, Fe, Mn, Cd, Pb, and As, in seven popular varieties of edible vegetable oils collected from Saudi Arabia, were determined by graphite furnace atomic absorption spectrometry (GF-AAS) using microwave digestion. The accuracy of procedure was confirmed by certified reference materials (NIST 1577b). The concentrations for copper, zinc, iron, manganese, lead and arsenic were observed in the range of 0.035 - 0.286, 0.955 - 3.10, 17.3 - 57.8, 0.178 - 0.586, 0.011 - 0.017 and 0.011 - 0.018 µg/g, respectively. Cadmium was found to be in the range of 2.36 - 6.34 ng/g. The results are compared internationally and with standards laid down by world health agencies. A risk assessment study has been carried out to assess exposure to these metals via consumption of vegetable oils. A comparison has been made with safety intake levels for these heavy metals recommended by Institute of Medicine of the National Academies (IOM), US Environmental Protection Agency (US EPA) and Joint FAO/WHO Expert Committee on Food Additives (JECFA). The results indicated that the dietary intakes of the selected heavy metals from daily consumption of 25 g of edible vegetable oils for a 70 kg individual should pose no significant health risk to local population.

Keywords: vegetable oils, heavy metals, contamination, health risk assessment

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Authors: Tony Jehi, Sujatha Rajaram, Nader majzoub, Joan Sabate

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Aging significantly increases the incidence of various cardiometabolic diseases, including cardiovascular disease (CVD). To combat CVD and its associated risk factors, it is imperative to adopt a healthy dietary pattern that is rife with beneficial nutrient and non-nutrient compounds. Unsaturated fats, specifically n-3 polyunsaturated fatty acids (n-3 PUFA), have cardio-protective effects; the opposite is true for saturated fatty acids. What role, if any, does the biomarker of fatty acid intake (specific fatty acids in the erythrocyte) play in predicting cardiometabolic risk among the elderly, a population highly susceptible to increased mortality and morbidity from CVD risk factors, remains unclear. This was a secondary analysis of the Walnuts and Healthy Aging Study. Briefly, elderly (n=192, mean age 69 y) participants followed their usual diet and were randomized into two groups to either eat walnuts daily or abstain from eating walnuts for a period of 2 years. The purpose was to identify potential associations between erythrocyte membrane fatty acids and cardiometabolic risk factors (body weight, blood pressure, blood lipids, and fasting glucose). Erythrocyte n-3 PUFA were inversely associated with total cholesterol (ß = -3.83; p= 0.02), triglycerides (ß = -7.66; p= <0.01), and fasting glucose (ß = -0.19; p=0.03). Specifically, erythrocyte ALA (ß= -1.59; P = 0.04) and DPA (ß= -0.62; P=0.04) were inversely associated with diastolic blood pressure and fasting glucose, respectively. N-6 PUFAs were positively associated with systolic blood pressure (ß=1.10; P=0.02). Mono-unsaturated fatty acids were positively associated with TAG (ß = 4.16; P=0.03). Total saturated fatty acids were not associated with any cardiometabolic risk factors. No association was found between any erythrocyte fatty acid and body weight. In conclusion, erythrocyte n-3 PUFA may be used as a biomarker to predict the cardiometabolic risk among healthy elders, providing support for the American Heart Association guidelines for including n-3 PUFA for preventing CVD.

Keywords: cardiometabolic diseases, erythrocyte fatty acids, elderly, n-3 PUFA

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Authors: Mohammad A. Alolayan, John S. Evans, James K. Hammitt

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The value of statistical life has been estimated for a middle eastern country with high economical subsidization system. In this study, in-person interviews were conducted on a stratified random sample to estimate the value of mortality risk. Double-bounded dichotomous choice questions followed by open-ended question were used in the interview to investigate the willingness to pay of the respondent for mortality risk reduction. High willingness to pay was found to be associated with high income and education. Also, females were found to have lower willingness to pay than males. The estimated value of statistical life is larger than the ones estimated for western countries where taxation system exists. This estimate provides a baseline for monetizing the health benefits for proposed policy or program to the decision makers in an eastern country. Also, the value of statistical life for a country in the region can be extrapolated from this this estimate by using the benefit transfer method.

Keywords: mortality, risk, VSL, willingness-to-pay

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Authors: András Rövid, Takeshi Hashimoto

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The paper focuses on the problem of the point correspondence matching in stereo images. The proposed matching algorithm is based on the combination of simpler methods such as normalized sum of squared differences (NSSD) and a more complex phase correlation based approach, by considering the noise and other factors, as well. The speed of NSSD and the preciseness of the phase correlation together yield an efficient approach to find the best candidate point with sub-pixel accuracy in stereo image pairs. The task of the NSSD in this case is to approach the candidate pixel roughly. Afterwards the location of the candidate is refined by an enhanced phase correlation based method which in contrast to the NSSD has to run only once for each selected pixel.

Keywords: stereo matching, sub-pixel accuracy, phase correlation, SVD, NSSD

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Authors: Sayed Amir Hamzeh Mirkheshti

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Offshore wind energy as a strategic renewable energy, has been growing rapidly due to availability, abundance and clean nature of it. On the other hand, budget of this project is incredibly higher in comparison with other renewable energies and it takes more duration. Accordingly, precise estimation of time and cost is needed in order to promote awareness in the developers and society and to convince them to develop this kind of energy despite its difficulties. Occurrence risks during on project would cause its duration and cost constantly changed. Therefore, to develop offshore wind power, it is critical to consider all potential risks which impacted project and to simulate their impact. Hence, knowing about these risks could be useful for the selection of most influencing strategies such as avoidance, transition, and act in order to decrease their probability and impact. This paper presents an evaluation of the feasibility of 500 MV offshore wind project in the Persian Gulf and compares its situation with uncertainty resources and risk. The purpose of this study is to evaluate time and cost of offshore wind project under risk circumstances and uncertain resources by using Monte Carlo simulation. We analyzed each risk and activity along with their distribution function and their effect on the project.

Keywords: wind energy project, uncertain resources, risks, Monte Carlo simulation

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Authors: Sanari Chalin Moriri, Kwabena Kingsley Ayisi, Alina Mofokeng

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Dryland smallholder farmers in South Africa are vulnerable to all kinds of risks, and it negatively affects crop productivity and profit. Pigeonpea is a leguminous and multipurpose crop that provides food, fodder, and wood for smallholder farmers. The majority of these farmers are still growing pigeonpea from traditional unimproved seeds, which comprise a mixture of genotypes. The objectives of the study were to identify the key risk factors that affect pigeonpea productivity and to develop management strategies on how to alleviate the risk factors in pigeonpea production. The study was conducted in two provinces (Limpopo and Mpumalanga) of South Africa in six municipalities during the 2020/2021 growing seasons. The non-probability sampling method using purposive and snowball sampling techniques were used to collect data from the farmers through a structured questionnaire. A total of 114 pigeonpea producers were interviewed individually using a questionnaire. Key stakeholders in each municipality were also identified, invited, and interviewed to verify the information given by farmers. Data collected were subjected to SPSS statistical software 25 version. The findings of the study were that majority of farmers affected by risk factors were women, subsistence, and old farmers resulted in low food production. Drought, unavailability of improved pigeonpea seeds for planting, access to information, and processing equipment were found to be the main risk factors contributing to low crop productivity in farmer’s fields. Above 80% of farmers lack knowledge on the improvement of the crop and also on the processing techniques to secure high prices during the crop off-season. Market availability, pricing, and incidence of pests and diseases were found to be minor risk factors which were triggered by the major risk factors. The minor risk factors can be corrected only if the major risk factors are first given the necessary attention. About 10% of the farmers found to use the crop as a mulch to reduce soil temperatures and to improve soil fertility. The study revealed that most of the farmers were unaware of its utilisation as fodder, much, medicinal, nitrogen fixation, and many more. The risk of frequent drought in dry areas of South Africa where farmers solely depend on rainfall poses a serious threat to crop productivity. The majority of these risk factors are caused by climate change due to unrealistic, low rainfall with extreme temperatures poses a threat to food security, water, and the environment. The use of drought-tolerant, multipurpose legume crops such as pigeonpea, access to new information, provision of processing equipment, and support from all stakeholders will help in addressing food security for smallholder farmers. Policies should be revisited to address the prevailing risk factors faced by farmers and involve them in addressing the risk factors. Awareness should be prioritized in promoting the crop to improve its production and commercialization in the dryland farming system of South Africa.

Keywords: management strategies, pigeonpea, risk factors, smallholder farmers

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Authors: Asma Qudrat, Abid Ullah, Rafi Ullah, Ali Raza, Shah Zeb, Syed Ali Shan Ul-Haq, Shahkar Ahmed Shah, Attiya Hameed Khan, Saad Zaheer, Umama Qasim, Kiran Jamal, Zahoor khan

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Objectives: Coronary artery disease (CAD) is the major public health issue associated with high mortality and morbidity rate worldwide. Young patients with ACS have unique characteristics with different demographic profiles and risk factors. The precise diagnosis and early risk stratification is important in guiding treatment and predicting the prognosis of young patients with ACS. To evaluate the associated demographics, risk factors, and outcomes profile of ACS in young age patients. Methods: The research follow a retrospective design, the single centre study of patients diagnosis with the first event of ACS in young age (>18 and <40) were included. Data collection included demographic profiles, risk factors, and in-hospital outcomes of young ACS patients. The patient’s data was retrieved through Electronic Medical Records (EMR) of Peshawar Institute of Cardiology (PIC), and all characteristic were assessed. Results: In this study, 77% were male, and 23% were female patients. The risk factors were assessed with CAD and shown significant results (P < 0.01). The most common presentation was STEMI, with (45%) most in ACS young patients. The angiographic pattern showed single vessel disease (SVD) in 49%, double vessel disease (DVD) in 17% and triple vessel disease (TVD) was found in 10%, and Left Artery Disease (LAD) (54%) was present to be the most common involved artery. Conclusion: It is concluded that the male sex was predominant in ACS young age patients. SVD was the common coronary angiographic finding. Risk factors showed significant results towards CAD and common presentations.

Keywords: coronary artery disease, Non-ST elevation myocardial infarction, ST elevation myocardial infarction, unstable angina, acute coronary syndrome

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Authors: Kun Huang

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This paper investigates whether the combination of local and stochastic volatility models can be calibrated exactly to any arbitrage-free implied volatility surface of European option. The risk neutral Brownian Bridge density is applied for calibration of the leverage function of our Hybrid model. Furthermore, the tails of marginal risk neutral density are generated by Generalized Extreme Value distribution in order to capture the properties of asset returns. The local volatility is generated from the arbitrage-free implied volatility surface using stochastic volatility inspired parameterization.

Keywords: arbitrage free implied volatility, calibration, extreme value distribution, hybrid model, local volatility, risk-neutral density, stochastic volatility

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Rainfall and runoff phenomenon is a chaotic and complex outcome of nature which requires sophisticated modelling and simulation methods for explanation and use. Time Series modelling allows runoff data analysis and can be used as forecasting tool. In the paper attempt is made to model river runoff data and predict the future behavioural pattern of river based on annual past observations of annual river runoff. The river runoff analysis and predict are done using ARIMA model. For evaluating the efficiency of prediction to hydrological events such as rainfall, runoff and etc., we use the statistical formulae applicable. The good agreement between predicted and observation river runoff coefficient of determination (R²) display that the ARIMA (4,1,1) is the suitable model for predicting Karkheh River runoff at Iran.

Keywords: time series modelling, ARIMA model, river runoff, Karkheh River, CLS method

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

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In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: Kaoutar Abbahaddou, Mohammed Salah Chiadmi

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The need to find a relevant way to predict the next-day price of a stock index is a real concern for many financial stakeholders and researchers. We have known across years the proliferation of several methods. Nevertheless, among all these methods, the most controversial one is a machine learning algorithm that claims to be reliable, namely neural networks. Thus, the purpose of this article is to study the prediction power of neural networks in the particular case of Islamic finance as it is an under-looked area. In this article, we will first briefly present a review of the literature regarding neural networks and Islamic finance. Next, we present the architecture and principles of artificial neural networks most commonly used in finance. Then, we will show its empirical application on two Islamic stock indexes. The accuracy rate would be used to measure the performance of the algorithm in predicting the right price the next day. As a result, we can conclude that artificial neural networks are a reliable method to predict the next-day price for Islamic indices as it is claimed for conventional ones.

Keywords: Islamic finance, stock price prediction, artificial neural networks, machine learning

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Authors: Ognen Kostovski, Svetozar Antovic, Rubens Jovanovic, Irena Kostovska, Nikola Jankulovski

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Introduction:Colorectal carcinoma (CRC) is one of the most common malignancies in the world. The cancer stem cell (CSC) markers are associated with aggressive cancer types and poor prognosis. The aim of study was to determine whether the expression of colorectal cancer stem cell markers CD133 and CD44 could be significant in prediction of clinically aggressive form of CRC. Materials and methods: Our study included ninety patients (n=90) with CRC. Patients were divided into two subgroups: with metatstatic CRC and non-metastatic CRC. Tumor samples were analyzed with standard histopathological methods, than was performed immunohistochemical analysis with monoclonal antibodies against CD133 and CD44 stem cell markers. Results: High coexpression of CD133 and CD44 was observed in 71.4% of patients with metastatic disease, compared to 37.9% in patients without metastases. Discordant expression of both markers was found in 8% of the subgroup with metastatic CRC, and in 13.4% of the subgroup without metastatic CRC. Statistical analyses showed a significant association of increased expression of CD133 and CD44 with the disease stage, T - category and N - nodal status. With multiple regression analysis the stage of disease was designate as a factor with the greatest statistically significant influence on expression of CD133 (p <0.0001) and CD44 (p <0.0001). Conclusion: Our results suggest that the coexpression of CD133 and CD44 have an important role in prediction of clinically aggressive form of CRC. Both stem cell markers can be routinely implemented in standard pathohistological diagnostics and can be useful markers for pre-therapeutic oncology screening.

Keywords: colorectal carcinoma, stem cells, CD133+, CD44+

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Authors: L. Sierra, N. Gambetta, M. A. Garcia-Benau, M. Orta

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Financial scandals and financial crisis have led to an international debate on the value of auditing. In recent years there have been significant legislative reforms aiming to increase markets’ confidence in audit services. In particular, there has been a significant debate on the need to improve the communication of auditors with audit reports users as a way to improve its informative value and thus, to improve audit quality. The International Auditing and Assurance Standards Board (IAASB) has proposed changes to the audit report standards. The International Standard on Auditing 701, Communicating Key Audit Matters (KAM) in the Independent Auditor's Report, has introduced new concepts that go beyond the auditor's opinion and requires to disclose the risks that, from the auditor's point of view, are more significant in the audited company information. Focusing on the companies included in the Financial Times Stock Exchange 100 index, this study aims to focus on the analysis of the determinants of the number of KAM disclosed by the auditor in the audit report and moreover, the analysis of the determinants of the different type of KAM reported during the period 2013-2015. To test the hypotheses in the empirical research, two different models have been used. The first one is a linear regression model to identify the client’s characteristics, industry sector and auditor’s characteristics that are related to the number of KAM disclosed in the audit report. Secondly, a logistic regression model is used to identify the determinants of the number of each KAM type disclosed in the audit report; in line with the risk-based approach to auditing financial statements, we categorized the KAM in 2 groups: Entity-level KAM and Accounting-level KAM. Regarding the auditor’s characteristics impact on the KAM disclosure, the results show that PwC tends to report a larger number of KAM while KPMG tends to report less KAM in the audit report. Further, PwC reports a larger number of entity-level risk KAM while KPMG reports less account-level risk KAM. The results also show that companies paying higher fees tend to have more entity-level risk KAM and less account-level risk KAM. The materiality level is positively related to the number of account-level risk KAM. Additionally, these study results show that the relationship between client’s characteristics and number of KAM is more evident in account-level risk KAM than in entity-level risk KAM. A highly leveraged company carries a great deal of risk, but due to this, they are usually subject to strong capital providers monitoring resulting in less account-level risk KAM. The results reveal that the number of account-level risk KAM is strongly related to the industry sector in which the company operates assets. This study helps to understand the UK audit market, provides information to auditors and finally, it opens new research avenues in the academia.

Keywords: FTSE 100, IAS 701, key audit matters, auditor’s characteristics, client’s characteristics

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Authors: Li Chen, Alex Skvortsov, Chris Norwood

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Gas releasing into water can be found in for many industrial situations. This process results in the formation of bubbles and acoustic emission which depends upon the bubble characteristics. If the bubble creation rates (bubble volume flow rate) are of interest, an inverse method has to be used based on the measurement of acoustic emission. However, there will be sound attenuation through the bubbly plume which will influence the measurement and should be taken into consideration in the model. The sound transmission through the bubbly plume depends on the characteristics of the bubbly plume, such as the shape and the bubble distributions. In this study, the bubbly plume shape is modelled using a self-similarity model, which has been normally applied for a single phase buoyant plume. The prediction is compared with the experimental data. It has been found the model can be applied to a buoyant plume of gas-liquid mixture. The influence of the gas flow rate and discharge nozzle size is studied.

Keywords: bubbly plume, buoyant plume, bubble acoustics, self-similarity model

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Authors: Afifa Aslam, Muhammad Ibrahim, Abid Mahmood, Muhammad Usman Alvi, Fariha Jabeen, Umara Tabassum

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Many factors cause air pollution in Pakistan, which poses a significant threat to human health. Diesel fuel and gasoline motor vehicles, as well as industrial companies, pollute the air in Pakistan's cities. The study's goal is to determine the level of air pollution in a Pakistani industrial city and to establish risk levels for the health of the population. We measured the intensity of air pollution by chemical characterization and examination of air samples collected at stationary remark sites. The PM10 levels observed at all sampling sites, including residential, commercial, high-traffic, and industrial areas were well above the limits imposed by Pakistan EPA, the United States EPA, and WHO. We assessed the health risk via chemical factors using a methodology approved for risk assessment. All Igeo index values greater than one were considered moderately contaminated or moderately to severely contaminated. Heavy metals have a substantial risk of acute adverse effects. In Faisalabad, Pakistan, there was an enormously high risk of chronic effects produced by a heavy metal acquaintance. Concerning specified toxic metals, intolerable levels of carcinogenic risks have been determined for the entire population. As a result, in most of the investigated areas of Faisalabad, the indices and hazard quotients for chronic and acute exposure exceeded the permissible level of 1.0. In the current study, re-suspended roadside mineral dust, anthropogenic exhaust emissions from traffic and industry, and industrial dust were identified as major emission sources of elemental particulate contents. Because of the unacceptable levels of risk in the research area, it is strongly suggested that a comprehensive study of the population's health status as a result of air pollution should be conducted for policies to be developed against these risks.

Keywords: elemental composition, particulate pollution, Igeo index, health risk assessment, hazard quotient

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Authors: Wahab Ali, Oyebade K. Oyedotun, Adnan Khashman

Abstract:

Breast cancer remains a threat to the woman’s world in view of survival rates, it early diagnosis and mortality statistics. So far, research has shown that many survivors of breast cancer cases are in the ones with early diagnosis. Breast cancer is usually categorized into stages which indicates its severity and corresponding survival rates for patients. Investigations show that the farther into the stages before diagnosis the lesser the chance of survival; hence the early diagnosis of breast cancer becomes imperative, and consequently the application of novel technologies to achieving this. Over the year, mammograms have used in the diagnosis of breast cancer, but the inconclusive deductions made from such scans lead to either false negative cases where cancer patients may be left untreated or false positive where unnecessary biopsies are carried out. This paper presents the application of artificial neural networks in the prediction of severity of breast tumour (whether benign or malignant) using mammography reports and other factors that are related to breast cancer.

Keywords: breast cancer, intelligent classification, neural networks, mammography

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Authors: Karuna Tuchinda, Sasithon Bland

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This work investigates the wear of a steam turbine blade coated with titanium nitride (TiN), and compares to the wear of uncoated blades. The coating is deposited on by physical vapor deposition (PVD) method. The working conditions of the blade were simulated and surface temperature and pressure values as well as flow velocity and flow direction were obtained. This data was used in the finite element wear model developed here in order to predict the wear of the blade. The wear mechanisms considered are erosive wear due to particle impingement and fluid jet, and fatigue wear due to repeated impingement of particles and fluid jet. Results show that the life of the TiN-coated blade is approximately 1.76 times longer than the life of the uncoated one.

Keywords: physical vapour deposition, steam turbine blade, titanium-based coating, wear prediction

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Authors: Hui-Chen Tsai, Chien-Jen Ho, Bo-Wei Power Liang, Ying Shen, Yi-Hsin Lai

Abstract:

Former Taiwan Metal Mining Corporation and its associated 3 wasted flue gas tunnels, hereinafter referred to as 'TMMC', was contaminated with heavy metals, Polychlorinated biphenyls (PCBs) and Total Petroleum Hydrocarbons (TPHs) in soil. Since the contamination had been exposed and unmanaged in the environment for more than 40 years, the extent of the contamination area is estimated to be more than 25 acres. Additionally, TMMC is located in a remote, mountainous area where almost no residents are residing in the 1-km radius area. Thus, it was deemed necessary to conduct an ecological risk assessment in order to evaluate the details of future contaminated site management plan. According to the winter and summer, ecological investigation results, one type of endangered, multiple vulnerable and near threaten plant was discovered, as well as numerous other protected species, such as Crested Serpent Eagle, Crested Goshawk, Black Kite, Brown Shrike, Taiwan Blue Magpie were observed. Ecological soil screening level (Eco-SSLs) developed by USEPA was adopted as a reference to conduct screening assessment. Since all the protected species observed surrounding TMMC site were birds, screening ecological risk assessment was conducted on birds only. The assessment was assessed mainly based on the chemical evaluation, which the contamination in different environmental media was compared directly with the ecological impact levels (EIL) of each evaluation endpoints and the respective hazard quotient (HQ) and hazard index (HI) could be obtained. The preliminary ecological risk assessment results indicated HI is greater than 1. In other words, the biological stressors (birds) were exposed to the contamination, which was already exceeded the dosage that could cause unacceptable impacts to the ecological system. This result was mainly due to the high concentration of arsenic, metal and lead; thus it was suggested the above mention contaminants should be remediated as soon as possible or proper risk management measures should be taken.

Keywords: screening, ecological risk assessment, ecological impact levels, risk management

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Authors: Muhammad Shahzad Iqbal, Zobia Sarwar, Salah-ud-Din

Abstract:

Tomato spotted wilt virus (TSWV) belongs to the genus Tospoviruses (family Bunyaviridae). It is one of the most devastating pathogens of tomato (Solanum Lycopersicum) and heavily damages the crop yield each year around the globe. In this study, we retrieved 329 mature miRNA sequences from two microRNA databases (miRBase and miRSoldb) and checked the putative target sites in the downloaded-genome sequence of TSWV. A consensus of three miRNA target prediction tools (RNA22, miRanda and psRNATarget) was used to screen the false-positive microRNAs targeting sites in the TSWV genome. These tools calculated different target sites by calculating minimum free energy (mfe), site-complementarity, minimum folding energy and other microRNA-mRNA binding factors. R language was used to plot the predicted target-site data. All the genes having possible target sites for different miRNAs were screened by building a consensus table. Out of these 329 mature miRNAs predicted by three algorithms, only eight miRNAs met all the criteria/threshold specifications. MC-Fold and MC-Sym were used to predict three-dimensional structures of miRNAs and further analyzed in USCF chimera to visualize the structural and conformational changes before and after microRNA-mRNA interactions. The results of the current study show that the predicted eight miRNAs could further be evaluated by in vitro experiments to develop TSWV-resistant transgenic tomato plants in the future.

Keywords: tomato spotted wild virus (TSWV), Solanum lycopersicum, plant virus, miRNAs, microRNA target prediction, mRNA

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Authors: Erez Kita, Gil Luria

Abstract:

This study examines gender as a boundary condition for the relationship between the psychological variable of mindfulness and the social variable of income with regards to the use of smartphones by young drivers. The use of smartphones while driving increases the likelihood of a car accident, endangering young drivers and other road users. The study sample included 186 young drivers who were legally permitted to drive without supervision. The subjects were first asked to complete questionnaires on mindfulness and income. Next, their smartphone use while driving was monitored over a one-month period. This study is unique as it used an objective smartphone monitoring application (rather than self-reporting) to count the number of times the young participants actually touched their smartphones while driving. The findings show that gender moderates the effects of social and personal factors (i.e., income and mindfulness) on the use of smartphones while driving. The pattern of moderation was similar for both social and personal factors. For men, mindfulness and income are negatively associated with the use of smartphones while driving. These factors are not related to the use of smartphones by women drivers. Mindfulness and income can be used to identify male populations that are at risk of using smartphones while driving. Interventions that improve mindfulness can be used to reduce the use of smartphones by male drivers.

Keywords: mindfulness, using smartphones while driving, income, gender, young drivers

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Authors: Beng Yew Low, Cher Liang Cha, Cheng Yong Teoh

Abstract:

Due to a lack of continual assessment or grade related data, identifying first-year engineering students in a polytechnic education at risk of failing is challenging. Our experience over the years tells us that there is no strong correlation between having good entry grades in Mathematics and the Sciences and excelling in hardcore engineering subjects. Hence, identifying students at risk of failure cannot be on the basis of entry grades in Mathematics and the Sciences alone. These factors compound the difficulty of early identification and intervention. This paper describes the development of a predictive analytics model in the early detection of students at risk of failing and evaluates its effectiveness. Data from continual assessments conducted in term one, supplemented by data of student psychological profiles such as interests and study habits, were used. Three classification techniques, namely Logistic Regression, K Nearest Neighbour, and Random Forest, were used in our predictive model. Based on our findings, Random Forest was determined to be the strongest predictor with an Area Under the Curve (AUC) value of 0.994. Correspondingly, the Accuracy, Precision, Recall, and F-Score were also highest among these three classifiers. Using this Random Forest Classification technique, students at risk of failure could be identified at the end of term one. They could then be assigned to a Learning Support Programme at the beginning of term two. This paper gathers the results of our findings. It also proposes further improvements that can be made to the model.

Keywords: continual assessment, predictive analytics, random forest, student psychological profile

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