Search results for: squared prediction risk

Authors: Taeho Park, Ming Zhou, Sangryul Shim

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With the emergence of the global economy, today’s business environment is getting more competitive than ever in the past. And many supply chain (SC) strategies and operations have significantly been altered over the past decade to overcome more complexities and risks imposed onto the global business. First, offshoring and outsourcing are more adopted as operational strategies. Manufacturing continues to move to better locations for enhancing competitiveness. Second, international operations are a challenge to a company’s SC system. Third, the products traded in the SC system are not just physical goods, but also digital goods (e.g., software, e-books, music, video materials). There are three main flows involved in fulfilling the activities in the SC system: physical flow, information flow, and financial flow. An advance of the Internet and electronic communication technologies has enabled companies to perform the flows of SC activities in electronic formats, resulting in the advent of an electronic supply chain management (e-SCM) system. A SC system for digital goods is somewhat different from the supply chain system for physical goods. However, it involves many similar or identical SC activities and flows. For example, like the production of physical goods, many third parties are also involved in producing digital goods for the production of components and even final products. This research aims at identifying process flows of both physical and digital goods in a SC system, and then investigating all risk elements involved in the physical, information, and financial flows during the fulfilment of SC activities. There are many risks inherent in the e-SCM system. Some risks may have severe impact on a company’s business, and some occur frequently but are not detrimental enough to jeopardize a company. Thus, companies should assess the impact and frequency of those risks, and then prioritize them in terms of their severity, frequency, budget, and time in order to be carefully maintained. We found risks involved in the global trading of physical and digital goods in four different categories: environmental risk, strategic risk, technological risk, and operational risk. And then the significance of those risks was investigated through a survey. The survey asked companies about the frequency and severity of the identified risks. They were also asked whether they had faced those risks in the past. Since the characteristics and supply chain flows of digital goods are varying industry by industry and country by country, it is more meaningful and useful to analyze risks by industry and country. To this end, more data in each industry sector and country should be collected, which could be accomplished in the future research.

Keywords: digital goods, e-SCM, risk analysis, supply chain flows

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Authors: Belalia Douma Omar, Bakhta Boukhatem, Mohamed Ghrici

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Self-compacting concrete (SCC) developed in Japan in the late 80s has enabled the construction industry to reduce demand on the resources, improve the work condition and also reduce the impact of environment by elimination of the need for compaction. Fuzzy logic (FL) approaches has recently been used to model some of the human activities in many areas of civil engineering applications. Especially from these systems in the model experimental studies, very good results have been obtained. In the present study, a model for predicting compressive strength of SCC containing various proportions of fly ash, as partial replacement of cement has been developed by using Adaptive Neuro-Fuzzy Inference System (ANFIS). For the purpose of building this model, a database of experimental data were gathered from the literature and used for training and testing the model. The used data as the inputs of fuzzy logic models are arranged in a format of five parameters that cover the total binder content, fly ash replacement percentage, water content, super plasticizer and age of specimens. The training and testing results in the fuzzy logic model have shown a strong potential for predicting the compressive strength of SCC containing fly ash in the considered range.

Keywords: self-compacting concrete, fly ash, strength prediction, fuzzy logic

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Authors: El Khaddam Safaa, Soulaymani Abdelmajid, Mokhtari Abdelghani, Ouammi Lahcen, Rachida Soulaymani-Beincheikh

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The poisonings represent a problem of health in the world and Morocco, The exact dimensions of this phenomenon are still poorly recorded that we see the lack of exhaustive statistical data. The objective of this retrospective study of a series of cases of the poisonings declared at the level of the region of Tadla-Azilal and collected by the Moroccan Poison Control and Pharmacovigilance Center. An epidemiological profile of the poisonings was to raise, to determine the risk factors influencing the vital preview of the poisoned And to follow the evolution of the incidence, the lethality, and the mortality. During the period of study, we collected and analyzed 9303 cases of poisonings by different incriminated toxic products with the exception of the scorpion poisonings. These poisonings drove to 99 deaths. The epidemiological profile which we raised, showed that the poisoned were of any age with an average of 24.62±16.61 years, The sex-ratio (woman/man) was 1.36 in favor of the women. The difference between both sexes is highly significant (χ2 = 210.5; p<0,001). Most of the poisoned which declared to be of urban origin (60.5 %) (χ2=210.5; p<0,001). Carbon monoxide was the most incriminated among the cases of poisonings (24.15 %), them putting in head, followed by some pesticides and farm produces (21.44 %) and food (19.95 %). The analysis of the risk factors showed that the grown-up patients whose age is between 20 and 74 years have twice more risk of evolving towards the death (RR=1,57; IC95 % = 1,03-2,38) than the other age brackets, so the male genital organ was the most exposed (explained) to the death that the female genital organ (RR=1,59; IC95 % = 1,07-2,38) The patients of rural origin had presented 5 times more risk (RR=4,713; IC95 % = 2,543-8,742). Poisoned by the mineral products had presented the maximum of risk on the vital preview death (RR=23,19, IC95 % = 2,39-224,1). The poisonings by pesticides produce a risk of 9 (RR=9,31; IC95 % = 6,10-14,18). The incidence was 3,3 cases of 10000 inhabitants, and the mortality was 0,004 cases of 1000 inhabitants (that is 4 cases by 1000 000 inhabitants). The rate of lethality registered annually was 10.6 %. The evolution of the indicators of health according to the years showed that the rate of statement measured by the incidence increased by a significant way. We also noted an improvement in the coverage which (who) ended up with a decrease in the rate of the lethality and the mortality during last years. The fight anti-toxic is a work of length time. He asks for a lot of work various levels. It is necessary to attack the delay accumulated by our country on the various legal, institutional and technical aspects. The ideal solution is to develop and to set up a national strategy.

Keywords: epidemiology, poisoning, risk factors, indicators of health, Tadla-Azilal grated by anti-toxic fight

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Authors: Vesna Karapetkovska Hristova, M. Ayaz Ahmad, Julijana Tomovska, Biljana Bogdanova Popov, Blagojce Najdovski

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The average total concentrations of heavy metals; (cadmium [Cd], copper [Cu], nickel [Ni], lead [Pb], and zinc [Zn]) were analyzed in soil and vegetables samples collected from the different region of Macedonia during the years 2010-2012. Basic soil properties such as pH, organic matter and clay content were also included in the study. The average concentrations of Cd, Cu, Ni, Pb, Zn in the A horizon (0-30 cm) of agricultural soils were as follows, respectively: 0.25, 5.3, 6.9, 15.2, 26.3 mg kg-1 of soil. We have found that neural networking model can be considered as a tool for prediction and spatial analysis of the processes controlling the metal transfer within the soil-and vegetables. The predictive ability of such models is well over 80% as compared to 20% for typical regression models. A radial basic function network reflects good predicting accuracy and correlation coefficients between soil properties and metal content in vegetables much better than the back-propagation method. Neural Networking / soft computing can support the decision-making processes at different levels, including agro ecology, to improve crop management based on monitoring data and risk assessment of metal transfer from soils to vegetables.

Keywords: soft computing approach, total concentrations, heavy metals, agricultural soils

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Authors: Fatemeh Sadat Sharifi

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In the present study, the capabilities of WAVEWATCH-III and MIKE21-SW for predicting the characteristics of wind waves in Hormuz Strait are evaluated. The GFS wind data (Global Forecast System) were derived. The bathymetry of gride with 2 arc-minute resolution, also were extracted from the ETOPO1. WAVEWATCH-III findings illustrate more valid prediction of wave features comparing to the MIKE-21 SW in deep water. Apparently, in shallow area, the MIKE-21 provides more uniformities with altimetry measurements. This may be due to the merits of the unstructured grid which are used in MIKE-21, leading to better representations of the coastal area. The findings on the direction of waves generated by wind in the modeling area indicate that in some regions, despite the increase in wind speed, significant wave height stays nearly unchanged. This is fundamental because of swift changes in wind track over the Strait of Hormuz. After discussing wind-induced waves in the region, the impact of instability of the surface layer on wave growth has been considered. For this purpose, the average monthly mean air temperature has been used. The results in cold months, when the surface layer is unstable, indicates an acceptable increase in the accuracy of prediction of the indicator wave height.

Keywords: numerical modeling, WAVEWATCH-III, Strait of Hormuz, MIKE21-SW

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Authors: Adriana Postal, Silvio C. Sampaio, Marcio A. Villas Boas, Josué P. Castro

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This study deals with the estimation of reference evapotranspiration (ET₀) in an agricultural context, focusing on efficient irrigation management to meet the growing interest in the sustainable management of water resources. Given the importance of water in agriculture and its scarcity in many regions, efficient use of this resource is essential to ensure food security and environmental sustainability. The methodology used involved the application of artificial intelligence techniques, specifically Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), to predict ET₀ in the state of Paraná, Brazil. The models were trained and validated with meteorological data from the Brazilian National Institute of Meteorology (INMET), together with data obtained from a producer's weather station in the western region of Paraná. Two optimizers (SGD and Adam) and different meteorological variables, such as temperature, humidity, solar radiation, and wind speed, were explored as inputs to the models. Nineteen configurations with different input variables were tested; amidst them, configuration 9, with 8 input variables, was identified as the most efficient of all. Configuration 10, with 4 input variables, was considered the most effective, considering the smallest number of variables. The main conclusions of this study show that MLP ANNs are capable of accurately estimating ET₀, providing a valuable tool for irrigation management in agriculture. Both configurations (9 and 10) showed promising performance in predicting ET₀. The validation of the models with cultivator data underlined the practical relevance of these tools and confirmed their generalization ability for different field conditions. The results of the statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²), showed excellent agreement between the model predictions and the observed data, with MAE as low as 0.01 mm/day and 0.03 mm/day, respectively. In addition, the models achieved an R² between 0.99 and 1, indicating a satisfactory fit to the real data. This agreement was also confirmed by the Kolmogorov-Smirnov test, which evaluates the agreement of the predictions with the statistical behavior of the real data and yields values between 0.02 and 0.04 for the producer data. In addition, the results of this study suggest that the developed technique can be applied to other locations by using specific data from these sites to further improve ET₀ predictions and thus contribute to sustainable irrigation management in different agricultural regions. The study has some limitations, such as the use of a single ANN architecture and two optimizers, the validation with data from only one producer, and the possible underestimation of the influence of seasonality and local climate variability. An irrigation management application using the most efficient models from this study is already under development. Future research can explore different ANN architectures and optimization techniques, validate models with data from multiple producers and regions, and investigate the model's response to different seasonal and climatic conditions.

Keywords: agricultural technology, neural networks in agriculture, water efficiency, water use optimization

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Authors: Yin-Hsuan Wu, Perng-Jy Tsai, Ying-Fang Wang, Shun-Hui Chung

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The present study was conducted to characterize exposure concentrations, concentrations deposited on the different respiratory regions, and resultant health risks associated with heavy metal exposures for road marking workers. Road marking workers of three similar exposure groups (SEGs) were selected, including the paint pouring worker, marking worker, and preparing worker. Personal exposure samples were collected using an inhalable dust sampler (IOM), and the involved particle size distribution samples were estimated using an eight-stage Marple personal cascade impactor during five working days. In total, 25 IOM samples and 20 Marple samples were collected. All collected samples were analyzed for their heavy metal contents using the ICP/MS. The resultant heavy metal particle size distributions were also used to estimate the fractions of particle deposited on the head airways (Chead), tracheobronchial (Cthorac) and alveolar regions (Cresp) of the exposed workers. In addition, Pb and Cr were selected to estimate the incremental cancer risk, and Zn, Ti, and Mo were selected to estimate the corresponding non-cancer risk in the present study. Results show that three heavy metals, including Pb, Cr, and Ti, were found with the highest concentrations for the SEG of the paint pouring worker (=0.585±2.98, 0.307±1.71, 0.902±2.99 μg/m³, respectively). For the fraction of heavy metal particle deposited on the respiratory tract, both alveolar and head regions were found with the highest values (=23-43% and 39-61%, respectively). For both SEGs of the paint pouring and marking, 51% of Cr, 59-61% of Zn, and 48-51% of Ti were found to be deposited on the alveolar region, and 41-43% of Pb was deposited on the head region. Finally, the incremental cancer risk for the SEGs of the paint pouring, marking, and preparing were found as 1.08×10⁻⁵, 2.78×10⁻⁶, and 2.20×10⁻⁶, respectively. In addition, the estimated non-cancer risk for the above three SEGs was found to be consistently less than unity. In conclusion, though the estimated non-cancer risk was less than unity, all resultant incremental cancer risk was greater than 10⁻⁶ indicating the abatement of workers’ exposure is necessary. It is suggested that strategies, including placing on the molten kettle, substitution the currently used paints for less heavy metal containing paints, and wearing fume protecting personal protective equipment can be considered in the future from reducing the worker’s exposure aspect.

Keywords: health risk assessment, heavy metal, respiratory track deposition, road marking

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Authors: P. V. Pramila , V. Mahesh

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Pulmonary Function Tests are important non-invasive diagnostic tests to assess respiratory impairments and provides quantifiable measures of lung function. Spirometry is the most frequently used measure of lung function and plays an essential role in the diagnosis and management of pulmonary diseases. However, the test requires considerable patient effort and cooperation, markedly related to the age of patients esulting in incomplete data sets. This paper presents, a nonlinear model built using Multivariate adaptive regression splines and Random forest regression model to predict the missing spirometric features. Random forest based feature selection is used to enhance both the generalization capability and the model interpretability. In the present study, flow-volume data are recorded for N= 198 subjects. The ranked order of feature importance index calculated by the random forests model shows that the spirometric features FVC, FEF 25, PEF,FEF 25-75, FEF50, and the demographic parameter height are the important descriptors. A comparison of performance assessment of both models prove that, the prediction ability of MARS with the `top two ranked features namely the FVC and FEF 25 is higher, yielding a model fit of R2= 0.96 and R2= 0.99 for normal and abnormal subjects. The Root Mean Square Error analysis of the RF model and the MARS model also shows that the latter is capable of predicting the missing values of FEV1 with a notably lower error value of 0.0191 (normal subjects) and 0.0106 (abnormal subjects). It is concluded that combining feature selection with a prediction model provides a minimum subset of predominant features to train the model, yielding better prediction performance. This analysis can assist clinicians with a intelligence support system in the medical diagnosis and improvement of clinical care.

Keywords: FEV, multivariate adaptive regression splines pulmonary function test, random forest

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Authors: Bang Haeyong

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Purpose: The objectives of this study were to determine the prevalence, incidence, and risk factors for postoperative fever after lumbar fusion. Methods: This study was a retrospective chart review of 291 patients who underwent lumbar fusion between March 2015 and February 2016 at the Asan Medical Center. Information was extracted from electronic medical records. Postoperative fever was measured at Tmax > 37.7 ℃ and Tmax > 38.3 ℃. The presence of postoperative fever, blood culture, urinary excretion, and/or chest x-ray were evaluated. Patients were evaluated for infection after lumbar fusion. Results: We found 222 patients (76.3%) had a postoperative temperature of 37.7 ℃, and 162 patients (55.7%) had a postoperative temperature of 38.3 ℃ or higher. The percentage of febrile patients trended down following the mean 1.8days (from the first postoperative day to seventh postoperative day). Infection rate was 9 patients (3.1%), respiratory virus (1.7%), urinary tract infection (0.3%), phlebitis (0.3%), and surgical site infection (1.4%). There was no correlation between Tmax > 37.7℃ or Tmax > 38.3℃, and timing of fever, positive blood or urine cultures, pneumonia, or surgical site infection. Risk factors for increased postoperative fever following surgery were confirmed to be delay of defecation (OR=1.37, p=.046), and shorten of remove drainage (OR=0.66, p=.037). Conclusions: The incidence of fever was 76.3% after lumbar fusion and the drainage time was faster in the case of fever. It was thought that the bleeding was absorbed at the operation site and fever occurred. The prevalence of febrile septicemia was higher in patients with long bowel movements before surgery than after surgery. Clinical symptoms should be considered because postoperative fever cannot be determined by fever alone because fever and infection are not significant.

Keywords: lumbar surgery, fever, postoperative, risk factor

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Authors: Erin V. Kelly, Nicola C. Newton, Lexine A. Stapinski, Maree Teesson

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Introduction: There is a lack of targeted prevention programs for reducing bullying and distress among adolescents involved in bullying. The current study aimed to examine the impact of a personality-targeted intervention (Preventure) on bullying (victimization and perpetration) and distress among adolescent victims/bullies with high-risk personality types. Method: A cluster randomized trial (RCT) was conducted in 26 secondary schools (2190 students) in NSW and Victoria, Australia, as part of the Climate Schools and Preventure trial. The schools were randomly allocated to Preventure (13 schools received Preventure, 13 did not). Students were followed up at 4 time points (6, 12, 24 and 36 months post-baseline). Preventure involves two group sessions, based on cognitive behavioral therapy, and tailored to four personality types shown to increase risk of substance misuse and other emotional and behavioural problems, including impulsivity, sensation-seeking, anxiety sensitivity and hopelessness. Students were allocated to the personality-targeted groups based on their scores on the Substance Use Risk Profile Scale. Bullying was measured using an amended version of the Revised Olweus Bully/Victim Scale. Psychological distress was measured using the Kessler Psychological Distress Scale. Results: Among high-risk students classified as victims at baseline, those in Preventure schools reported significantly less victimization and distress over time than those in control schools. Among high-risk students classified as bullies at baseline, those in Preventure schools reported significantly less distress over time than those in control schools (no difference for perpetration). Conclusion: Preventure is a promising intervention for reducing bullying victimization and psychological distress among adolescents involved in bullying.

Keywords: adolescents, bullying, personality, prevention

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Authors: Souheila Zemmouchi

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Introduction: Diabetes is currently the leading cause of end-stage chronic kidney disease and dialysis, so it adds additional complexity to the management of chronic hemodialysis patients. These patients are extremely fragile because of their multiple cardiovascular and metabolic comorbidities. Clear and complete description of the experience: the management of a diabetic on hemodialysis is particularly difficult due to frequent hypoglycaemia and significant inter and perdialyticglycemic variability that is difficult to predict. The aim of our study is to describe the clinical-biological profile and to assess the cardiovascular risk of diabetics undergoing chronic hemodialysis, and compare them with non-diabetic hemodialysis patients. Methods: This cross-sectional, descriptive, and analytical study was carried out between January 01 and December 31, 2018, involving 309 hemodialysis patients spread over 4 centersThe data were collected prospectively then compiled and analyzed by the SPSS Version 10 software The FRAMINGHAM RISK SCORE has been used to assess cardiovascular risk in all hemodialysis patients Results: The survey involved 309 hemodialysis patients, including 83 diabetics, for a prevalence of 27% The average age 53 ± 10.2 years. The sex ratio is 1.5. 50% of diabetic hemodialysis patients retained residual diuresis against 32% in non-diabetics. In the group of diabetics, we noted more hypertension (70% versus 38% non-diabetics P 0.004), more intradialytichypoglycemia (15% versus 3% non-diabetics P 0.007), initially, vascular exhaustion was found in 4 diabetics versus 2 non-diabetics. 70% of diabetics with anuria had postdialytichyperglycemia. The study found a statistically significant difference between the different levels of cardiovascular risk according to the diabetic status. Conclusion: There are many challenges in the management of diabetics on hemodialysis, both to optimize glycemic control according to an individualized target and to coordinate comprehensive and effective care.

Keywords: hemodialysis, diabetes, chronic renal failure, glycemic control

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Authors: Guilhermina Torrao

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An extensive number of studies have been conducted to determine the factors which influence crash injury risk (CIR); however, uncertainties inherent to selected variables have been neglected. A review of existing literature is required to not only obtain an overview of the variables and measures but also ascertain the implications when comparing studies without a systematic view of variable taxonomy. Therefore, the aim of this literature review is to examine and report on peer-reviewed studies in the field of crash analysis and to understand the implications of broad variations in variable selection in CIR analysis. The objective of this study is to demonstrate the variance in variable selection and classification when modeling injury risk involving occupants of light vehicles by presenting an analytical review of the literature. Based on data collected from 64 journal publications reported over the past 21 years, the analytical review discusses the variables selected by each study across an organized list of predictors for CIR analysis and provides a better understanding of the contribution of accident and vehicle factors to injuries acquired by occupants of light vehicles. A cross-comparison analysis demonstrates that almost half the studies (48%) did not consider vehicle design specifications (e.g., vehicle weight), whereas, for those that did, the vehicle age/model year was the most selected explanatory variable used by 41% of the literature studies. For those studies that included speed risk factor in their analyses, the majority (64%) used the legal speed limit data as a ‘proxy’ of vehicle speed at the moment of a crash, imposing limitations for CIR analysis and modeling. Despite the proven efficiency of airbags in minimizing injury impact following a crash, only 22% of studies included airbag deployment data. A major contribution of this study is to highlight the uncertainty linked to explanatory variable selection and identify opportunities for improvements when performing future studies in the field of road injuries.

Keywords: crash, exploratory, injury, risk, variables, vehicle

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Authors: Zaw Z Aung

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Background: High-risk groups for hepatitis B infection (HBV) are people who injected drugs (PWID), men who have sex with men (MSM), people living with HIV (PLHIV) and persons with hepatitis C (HCV), etc. HBV/HCV coinfected patients are at increased risk of cirrhosis, hepatic decompensation and hepatocellular carcinoma. To the best of author’s knowledge, there is currently no data for hepatitis B vaccine utilization in HCV positive patients and their antibody response. Methodology: From February 2018 to May 2018, consented participants at or above 18 years who came to the clinic in Mandalay were tested with the anti-HCV rapid test. Those who tested HCV positive (n=168) were further tested with hepatitis B profile and asked about their previous hepatitis B vaccination history and risk factors. Results: Out of 168 HCV positive participants, three were excluded for active HBV infections. The remaining 165 were categorized into previously vaccinated 64% (n=106) and unvaccinated 36% (n=59) There were three characteristics groups- PWID monoinfected (n=77), General Population (GP) monoinfected (n=22) and HIV/HCV coinfected participants (n=66). Unvaccinated participants were highest in HIV/HCV, with 68%(n=45) followed by GP (23%, n=5) and PWID (12%, n=9). Among previously vaccinated participants, the highest percentage was PWID (88%, n=68), the second highest was GP (77%, n=17) and lowest in HIV/HCV patients (32%, n=21). 63 participants completed third doses of vaccination (PWID=36, GP=13, HIV/HCV=14). 53% of participants who completed 3 dose of hepatitis B were non-responders (n=34): HIV/HCV (86%, n=12), PWID (44%, n=16), and GP (46%, n=6) Conclusion: Even in the presence of effective and safe hepatitis B vaccine, uptake is low among high risk groups especially PLHIV that needs to be improved. Integration or collaboration of hepatitis B vaccination program, HIV/AIDS and hepatitis C treatment centers is desirable. About half of vaccinated participants were non-responders so that optimal doses, schedule and follow-up testing need to be addressed carefully for those groups.

Keywords: Hepatitis B vaccine, Hepatitis C, HIV, Myanmar

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Authors: M. S. Aparna, Pushparaj Shetty D.

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Nowadays social networking sites have become so popular that the producers or the sellers look for these sites as one of the best options to target the right audience to market their products. There are several tools available to monitor or analyze the social networks. Our task is to identify the right community web pages and find out the behavior analysis of the members by using these tools and formulate an appropriate strategy to market the products or services to achieve the set goals. The advertising becomes more effective when the information of the product/ services come from a known source. The strategy explores great buying influence in the audience on referral marketing. Our methodology proceeds with critical budget analysis and promotes viral influence propagation. In this context, we encompass the vital bits of budget evaluation such as the number of optimal seed nodes or primary influential users activated onset, an estimate coverage spread of nodes and maximum influence propagating distance from an initial seed to an end node. Our proposal for Buyer Prediction mathematical model arises from the urge to perform complex analysis when the probability density estimates of reliable factors are not known or difficult to calculate. Order Statistics and Buyer Prediction mapping function guarantee the selection of optimal influential users at each level. We exercise an efficient tactics of practicing community pages and user behavior to determine the product enthusiasts on social networks. Our approach is promising and should be an elementary choice when there is little or no prior knowledge on the distribution of potential buyers on social networks. In this strategy, product news propagates to influential users on or surrounding networks. By applying the same technique, a user can search friends who are capable to advise better or give referrals, if a product interests him.

Keywords: viral marketing, social network analysis, community web pages, buyer prediction, influence propagation, budget constraints

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Authors: Antonio Horta, Renato Marinho, Raimir Holanda

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Considered as an evolution of the Blockchain, the Ethereum platform, besides allowing transactions of its cryptocurrency named Ether, it allows the programming of decentralised applications (DApps) and smart contracts. However, this functionality into blockchains has raised other types of threats, and the exploitation of smart contracts vulnerabilities has taken companies to experience big losses. This research intends to figure out the number of contracts that are under risk of being drained. Through a deep investigation, more than two hundred thousand smart contracts currently available in the Ethereum platform were scanned and estimated how much money is at risk. The experiment was based in a query run on Google Big Query in July 2022 and returned 50,707,133 contracts published on the Ethereum platform. After applying the filtering criteria, the experimentgot 430,584 smart contracts to download and analyse. The filtering criteria consisted of filtering out: ERC20 and ERC721 contracts, contracts without transactions, and contracts without balance. From this amount of 430,584 smart contracts selected, only 268,103 had source codes published on Etherscan, however, we discovered, using a hashing process, that there were contracts duplication. Removing the duplicated contracts, the process ended up with 20,417 source codes, which were analysed using the open source SAST tool smartbugswith oyente and securify algorithms. In the end, there was nearly $100,000 at risk of being drained from the potentially vulnerable smart contracts. It is important to note that the tools used in this study may generate false positives, which may interfere with the number of vulnerable contracts. To address this point, our next step in this research is to develop an application to test the contract in a parallel environment to verify the vulnerability. Finally, this study aims to alert users and companies about the risk on not properly creating and analysing their smart contracts before publishing them into the platform. As any other application, smart contracts are at risk of having vulnerabilities which, in this case, may result in direct financial losses.

Keywords: blockchain, reentrancy, static application security testing, smart contracts

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Authors: Ayman M. Arfaj, Jose Lauro M. Llamas, Saleh Y Qahtani

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Sulfur Loading Operation (SLO) is an operation that poses risk of exposure to toxic gases such as Hydrogen Sulfid and Sulfur Dioxide during molten sulfur loading operation. In this operation molten sulfur is loaded into a truck tanker in a liquid state and the temperature of the tanker must maintain liquid sulfur within a 43-degree range — between 266 degrees and 309 degrees Fahrenheit in order for safe loading and unloading to occur. Accordingly, in this study, the e potential risk of occupational exposure to the airborne toxic gases was assessed at three sulfur loading facilities. The concentrations of toxic airborne substances such as Hydrogen Sulfide (H2S) and Sulfur Dioxide (SO2), were monitored during operations at the different locations within the sulfur loading operation facilities. In addition to extensive real-time monitoring, over one hundred and fifty samples were collected and analysed at internationally accredited laboratories. The concentrations of H2S, and SO2 were all found to be well below their respective occupational exposure limits. Very low levels of H2S account for the odours observed intermittingly during mixing and application operations but do not pose a considerable health risk and hence these levels are considered a nuisance. These results were comparable to those reported internationally. Aside from observing the usual general safe work practices such as wearing safety glasses, there are no specific occupational health related concerns at the examined sulfur loading facilities.

Keywords: exposure assessment, sulfur loading operation, health risk study, molten sulfur, toxic airborne substances, air contaminants monitoring

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Authors: Nattapong Techarattanased, Paweena Sribunrueng

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The objective of this study is to investigate strategies in customer relationship management and customers’ behavior in making decision on buying car insurance of Southeast Insurance Co. Ltd. in Bangkok. Subjects in this study included 400 customers with the age over 20 years old to complete questionnaires. The data were analyzed by arithmetic mean and multiple regressions. The results revealed that the customers’ opinions on the strategies in customer relationship management, i.e. customer relationship, customer feedback, customer follow-up, useful service suggestions, customer communication, and service channels were in moderate level but on the customer retention was in high level. Moreover, the strategy in customer relationship management, i.e. customer relationship, and customer feedback had an influence on customers’ buying decision on buying car insurance. The two factors above can be used for the prediction at the rate of 34%. In addition, the strategy in customer relationship management, i.e. customer retention, customer feedback, and useful service suggestions had an influence on the customers’ buying decision on period of being customers. The three factors could be used for the prediction at the rate of 45%.

Keywords: strategies, customer relationship management, behavior in buying decision, car insurance

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Authors: Mary Allagoa, Abir Al-Tabbaa

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The study explores the use of binders and additives, such as Portland cement, pulverized fuel ash, ground granulated blast furnace slag, and MgO, to decrease the concentration and leachability of pollutants in contaminated site soils. The research investigates their effectiveness and associated risks of using the binders, with a focus on Total Heavy metals (THM) and Total Petroleum Hydrocarbon (TPH). The goal of this research is to evaluate the performance and effectiveness of binders and additives in remediating soil pollutants. The study aims to assess the suitability of the mixtures for ground improvement purposes, determine the optimal dosage, and investigate the associated risks. The research utilizes physical (unconfined compressive strength) and chemical tests (batch leachability test) to assess the efficacy of the binders and additives. A completely randomized design one-way ANOVA is used to determine the significance within mix binders of THM. The study also employs incremental lifetime cancer risk assessments (ILCR) and other indexes to evaluate the associated risks. The study finds that Ground Granulated Blast Furnace Slag (GGBS): MgO is the most effective binder for remediation, particularly when using low dosages of MgO combined with higher dosages of GGBS binders on TPH. The results indicate that binders and additives can encapsulate and immobilize pollutants, thereby reducing their leachability and toxicity. The mean unconfined compressive strength of the soil ranges from 285.0- 320.5 kPa, while THM levels are less than 10 µg/l in GGBS: MgO and CEM: PFA but below 1 µg/l in CEM I based. The ILCR ranged from 6.77E-02 - 2.65E-01 and 5.444E-01 – 3.20 E+00, with the highest values observed under extreme conditions. The hazard index (HI), Risk allowable daily dose intake (ADI), and Risk chronic daily intake (CDI) were all less than 1 for the THM. The study identifies MgO as the best additive for use in soil remediation.

Keywords: risk ADI, risk CDI, ILCR, novel binders, additives binders, hazard index

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Authors: Chau-Kuang Chen, John Hughes, Jr., A. Dexter Samuels

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The prediction models for the United States Medical Licensure Examination (USMLE) Steps 1 and 2 performances were constructed by the Monte Carlo simulation modeling approach via linear regression. The purpose of this study was to build robust simulation models to accurately identify the most important predictors and yield the valid range estimations of the Steps 1 and 2 scores. The application of simulation modeling approach was deemed an effective way in predicting student performances on licensure examinations. Also, sensitivity analysis (a/k/a what-if analysis) in the simulation models was used to predict the magnitudes of Steps 1 and 2 affected by changes in the National Board of Medical Examiners (NBME) Basic Science Subject Board scores. In addition, the study results indicated that the Medical College Admission Test (MCAT) Verbal Reasoning score and Step 1 score were significant predictors of the Step 2 performance. Hence, institutions could screen qualified student applicants for interviews and document the effectiveness of basic science education program based on the simulation results.

Keywords: prediction model, sensitivity analysis, simulation method, USMLE

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Mathewos Temesgen, Lemi Geleta

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Lake Koka is one of the Ethiopian Central Rift Valleys lakes, where the absorbance of domestic, agricultural, and industrial waste from the nearby industrial and agro-industrial activities is very common. The aim of this research was to assess the heavy metal bioaccumulation in edible parts of common carp (Cyprinus carpio) in Lake Koka and the health risks associated with the dietary intake of the fish. Three sampling sites were selected randomly for primary data collection. Physicochemical parameters (pH, Total Dissolved Solids, Dissolved Oxygen and Electrical Conductivity) were measured in-situ. Four heavy metals (Cd, Cr, Pb, and Zn) in water and bio-accumulation in the edible parts of the fish were analyzed with flame atomic absorption spectrometry. The mean values of TDS, EC, DO and pH of the lake water were 458.1 mg/L, 905.7 µ s/cm, 7.36 mg/L, and 7.9, respectively. The mean concentrations of Zn, Cr, and Cd in the edible part of fish were also 0.18 mg/kg, ND-0.24 mg/kg, and ND-0.03 mg/kg, respectively. Pb was, however, not identified. The amount of Cr in the examined fish muscle was above the level set by FAO, and the accumulation of the metals showed marked differences between sampling sites (p<0.05). The concentrations of Cd, Pb and were below the maximum permissible limit. The results also indicated that Cr has a high transfer factor value and Zn has the lowest. The carcinogenic hazard ratio values were below the threshold value (<1) for the edible parts of fish. The estimated weekly intake of heavy metals from fish muscles ranked as Cr>Zn>Cd, but the values were lower than the Reference Dose limit for metals. The carcinogenic risk values indicated a low health risk due to the intake of individual metals from fish. Furthermore, the hazard index of the edible part of fish was less than unity. Generally, the water quality is not a risk for the survival and reproduction of fish, and the heavy metal contents in the edible parts of fish exhibited low carcinogenic risk through the food chain.

Keywords: bio-accumulation, cyprinus carpio, hazard index, heavy metals, Lake Koka

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Authors: Triveni Gogoi, Rima Chatterjee

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Shear wave velocity (Vs) estimation is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. There are varying methods for prediction of S-wave velocity, if recorded S-wave log is not available. But all the available methods for Vs prediction are empirical mathematical models. Shear wave velocity can be estimated using P-wave velocity by applying Castagna’s equation, which is the most common approach. The constants used in Castagna’s equation vary for different lithologies and geological set-ups. In this study, multiple regression analysis has been used for estimation of S-wave velocity. The EMERGE module from Hampson-Russel software has been used here for generation of S-wave log. Both single attribute and multi attributes analysis have been carried out for generation of synthetic S-wave log in Upper Assam basin. Upper Assam basin situated in North Eastern India is one of the most important petroleum provinces of India. The present study was carried out using four wells of the study area. Out of these wells, S-wave velocity was available for three wells. The main objective of the present study is a prediction of shear wave velocities for wells where S-wave velocity information is not available. The three wells having S-wave velocity were first used to test the reliability of the method and the generated S-wave log was compared with actual S-wave log. Single attribute analysis has been carried out for these three wells within the depth range 1700-2100m, which corresponds to Barail group of Oligocene age. The Barail Group is the main target zone in this study, which is the primary producing reservoir of the basin. A system generated list of attributes with varying degrees of correlation appeared and the attribute with the highest correlation was concerned for the single attribute analysis. Crossplot between the attributes shows the variation of points from line of best fit. The final result of the analysis was compared with the available S-wave log, which shows a good visual fit with a correlation of 72%. Next multi-attribute analysis has been carried out for the same data using all the wells within the same analysis window. A high correlation of 85% has been observed between the output log from the analysis and the recorded S-wave. The almost perfect fit between the synthetic S-wave and the recorded S-wave log validates the reliability of the method. For further authentication, the generated S-wave data from the wells have been tied to the seismic and correlated them. Synthetic share wave log has been generated for the well M2 where S-wave is not available and it shows a good correlation with the seismic. Neutron porosity, density, AI and P-wave velocity are proved to be the most significant variables in this statistical method for S-wave generation. Multilinear regression method thus can be considered as a reliable technique for generation of shear wave velocity log in this study.

Keywords: Castagna's equation, multi linear regression, multi attribute analysis, shear wave logs

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Authors: Aksana N. Popel, Volha A. Sujayeva, Olga V. Kоshlataja, Irеna S. Karpava

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Introduction: It is a known fact that cardiovascular pathology and its complications cause a more severe course and worse prognosis in patients with comorbid kidney pathology. Chronic kidney disease (CKD) is associated with inflammation, endothelial dysfunction, and increased activity of the sympathoadrenal system. This circumstance increases the risk of cardiovascular diseases and the progression of kidney pathology. The above determines the need to identify cardiorenal changes at early stages to reduce the risks of cardiovascular complications and the progression of CKD. Objective: To identify risk factors (RF) for the development of CKD in elderly patients with chronic ischemic heart disease (CIHD). Methods: The study included 64 patients (40 women and 24 men) with a mean age of 74.4±4.5 years with coronary heart disease, without a history of structural kidney pathology and CKD. All patients underwent transthoracic echocardiography (TTE) and kidney ultrasound (KU) using GE Vivid 9 equipment (GE HealthCare, USA), and cardiac computed tomography (CCT) using Siemens Somatom Force equipment (Siemens Healthineers AG, Germany) in 3 months and in 1 year. Data obtained were analyzed using multiple regression analysis and nonparametric Mann-Whitney test. Statistical analysis was performed using the STATISTICA 12.0 program (StatSoft Inc.). Results: Initially, CKD was not diagnosed in all patients. In 3 months, CKD was diagnosed: stage C1 had 11 people (18%), stage C2 had 4 people (6%), stage C3A had 11 people (18%), stage C3B had 2 people (3%). After 1 year, CKD was diagnosed: stage C1 had 22 people (35%), stage C2 had 5 people (8%), stage C3A had 17 people (27%), stage C3B had 10 people (15%). In 3 months, statistically significant (p<0.05) risk factors were: 1) according to TTE: mitral peak E-wave velocity (U=678, p=0.039), mitral E-velocity DT (U=514, p=0.0168), mitral peak A-wave velocity (U=682, p=0.013). In 1 year, statistically significant (p<0.05) risk factors were: according to TTE: left ventricular (LV) end-systolic volume in B-mode (U=134, p=0.006), LV end-diastolic volume in B-mode (U=177, p=0.04), LV ejection fraction in B-mode (U=135, p=0.006), left atrial volume (U=178, p=0.021), LV hypertrophy (U=294, p=0.04), mitral valve (MV) fibrosis (U=328, p=0.01); according CCT: epicardial fat thickness (EFT) on the right ventricle (U=8, p=0.015); according to KU: interlobar renal artery resistance index (RI) (U=224, p=0.02), segmental renal artery RI (U=409, p=0.016). Conclusions: Both TTE and KU are very informative methods to determine the additional risk factors of CKD development and progression. The most informative risk factors were LV global systolic and diastolic functions, LV and LA volumes. LV hypertrophy, MV fibrosis, interlobar renal artery and segmental renal artery RIs, EFT.

Keywords: chronic kidney disease, ischemic heart disease, prognosis, risk factors

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Authors: Paul Han, Jun-Geol Baek

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In the flat panel display industry, the scheduler and dispatching system to meet production target quantities and the deadline of production are the major production management system which controls each facility production order and distribution of WIP (Work in Process). In dispatching system, delivery time is a key factor for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors and a forecasting model of delivery time. Of survival analysis techniques to select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the Accelerated Failure Time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the Mean Square Error (MSE) criteria, the AFT model decreased by 33.8% compared to the existing prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing a delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

Keywords: delivery time, survival analysis, Cox PH model, accelerated failure time model

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Authors: Macie Matta, Ahmad Jabri, Stephanie Jackson

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Introduction: In the absence of hypotension, pulmonary embolism (PE) causing right ventricular dysfunction or strain, whether confirmed by imaging or cardiac biomarkers, is deemed to be an intermediate-risk category. Urgent treatment of intermediate-risk PE can prevent progression to hemodynamic instability and death. Management options include thrombolysis, thrombectomy, or systemic anticoagulation. We aim to evaluate the short-term outcomes of a half-dose tissue plasminogen activator (tPA) for the management of intermediate-risk PE. Methods: We retrospectively identified adult patients diagnosed with intermediate-risk PE between the years 2000 and 2021. Demographic data, lab values, imaging, treatment choice, and outcomes were all obtained through chart review. Primary outcomes measured include major bleeding events and in-hospital mortality. Patients on standard systemic anticoagulation without receiving thrombolysis or thrombectomy served as controls. Patient data were analyzed using SAS®️ Software (version 9.4; Cary, NC) to compare individuals that received half-dose tPA with controls, and statistical significance was set at a p-value of 0.05. Results: We included 57 patients in our final analysis, with 19 receiving tPA. Patient characteristics and comorbidities were comparable between both groups. There was a significant difference between PE location, presence of acute deep vein thrombosis, and peak troponin level between both groups. The thrombolytic cohort was more likely to demonstrate a 60/60 sign and thrombus in transit finding on echocardiography than controls. The thrombolytic group was more likely to have major bleeding (17% vs 7.9%, p= 0.4) and in-hospital mortality (5.3% vs 0%, p=0.3); however, this was not statistically significant. Patients who received half-dose tPA had non-significantly higher rates of major bleeding and in-hospital mortality. Larger scale, randomized control trials are needed to establish the benefit and safety of thrombolytics in patients with intermediate-risk PE.

Keywords: pulmonary embolism, half dose thrombolysis, tissue plasminogen activator, cardiac biomarkers, echocardiographic findings, major bleeding event

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Authors: F. J. Ma, A. K. H. Kwan

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Crack caused by shrinkage movement of concrete is a serious problem especially when restraint is provided. It may cause severe serviceability and durability problems. The existing prediction methods for crack width of concrete due to shrinkage movement are mainly numerical methods under simplified circumstances, which do not agree with each other. To get a more unified prediction method applicable to more sophisticated circumstances, finite element crack width analysis for shrinkage effect should be developed. However, no existing finite element analysis can be carried out to predict the crack width of concrete due to shrinkage movement because of unsolved reasons of conventional finite element analysis. In this paper, crack width analysis implemented by finite element analysis is presented with pseudo-discrete crack model, which combines traditional smeared crack model and newly proposed crack queuing algorithm. The proposed pseudo-discrete crack model is capable of simulating separate and single crack without adopting discrete crack element. And the improved finite element analysis can successfully simulate the stress redistribution when concrete is cracked, which is crucial for predicting crack width, crack spacing and crack number.

Keywords: crack queuing algorithm, crack width analysis, finite element analysis, shrinkage effect

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Mohammad E. Nikoofal, Mehmet Gumus

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Besides the many benefits of outsourcing, firms are still concerned about the lack of critical information regarding both the risk levels and actions of their suppliers that are just a few links away. In this paper, we study the effectiveness of audit for the manufacturer in managing her supplier’s process improvement effort when the supplier is privately informed about his disruption risk and actions. By comparing the agency costs associated with the optimal menu of contracts with and without audit, we completely characterize the value of audit for all the cases from the perspectives of both manufacturer, and supplier as well as total supply chain. First, the analysis of value of audit from the manufacturer’s perspective shows that she can strictly benefit from auditing her supplier’s actions. To the best of our knowledge, this result has not been documented before in the principal-agent literature under a standard setting where the agent is assumed to be risk-neutral and not protected by limited liability constraints. Second, we find that not only the manufacturer but also the supplier can strictly benefit from audit. Third, the audit enables the manufacturer to customize her contract offerings based on the reliability of the supplier. Finally, by analyzing the impact of problem parameters on the value of audit, we identify the conditions under which an audit would be beneficial for individual supply chain parties as well as total supply chain.

Keywords: supply disruption, adverse selection, moral hazard incentives, audit

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Authors: Seyed Fakhreddin Hejazi, Leili Iranirad, Mohammad Sadeghi, Mohsen Talebizadeh

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Background: Contrast-induced nephropathy (CIN) remains to be a potentially serious complication of radiographic procedures. We performed this clinical trial to assess the preventive effect of allopurinol against CIN in high-risk patients undergoing coronary angiography. Methods: In this prospective randomized controlled trial, 140 patients with at least two risk factors for CIN undergoing coronary angiography were randomly assigned to either the allopurinol group or the control group. Patients in the allopurinol group received 300 mg allopurinol 24 hours before a procedure and intravenous hydration for 12 hours before and after coronary angiography, whereas patients in the control group received intravenous hydration. Serum creatinine (SCr), blood urea nitrogen (BUN) and uric acid were measured before contrast exposure and at 48 hours. CIN was defined as an increase of 25% in serum creatinine (SCr) or >0.5 mg/dl 48 hours after contrast administration. Results: CIN occurred in 11 out of 70 (7.9%) patients in the control group and in 8 out of 70 (5.7%) patients in the allopurinol group. There was no significant difference in the incidence of CIN between the two groups at 48 hours after administering the radiocontrast agent (p = 0.459). However, there were significant differences between the two groups in SCr, BUN, uric acid, and eGFR 48 hours after radiocontrast administration (p < 0.05). Conclusion: Our findings revealed that allopurinol had no substantial efficacy over hydration protocol in high-risk patients for the development of CIN.

Keywords: contrast-induced nephropathy, allopurinol, coronary angiography, contrast agent

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Authors: Mehdi Janbaz

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The global financial crisis and the credit crunch that followed magnified the importance of credit risk management and its crucial role in the stability of all financial sectors and the whole of the system. Many believe that risks faced by the sovereign sector are highly interconnected with banking risks and most likely to trigger and reinforce each other. This study aims to examine (1) the impact of banking and interbank risk factors on the sovereign credit risk of Eurozone, and (2) how the EU Credit Default Swaps spreads dynamics are affected by the Crude Oil price fluctuations. The hypothesizes are tested by employing fitting risk measures and through a four-staged linear modeling approach. The sovereign senior 5-year Credit Default Swap spreads are used as a core measure of the credit risk. The monthly time-series data of the variables used in the study are gathered from the DataStream database for a period of 2008-2019. First, a linear model test the impact of regional macroeconomic and market-based factors (STOXX, VSTOXX, Oil, Sovereign Debt, and Slope) on the CDS spreads dynamics. Second, the bank-specific factors, including LIBOR-OIS spread (the difference between the Euro 3-month LIBOR rate and Euro 3-month overnight index swap rates) and Euribor, are added to the most significant factors of the previous model. Third, the global financial factors including EURO to USD Foreign Exchange Volatility, TED spread (the difference between 3-month T-bill and the 3-month LIBOR rate based in US dollars), and Chicago Board Options Exchange (CBOE) Crude Oil Volatility Index are added to the major significant factors of the first two models. Finally, a model is generated by a combination of the major factor of each variable set in addition to the crisis dummy. The findings show that (1) the explanatory power of LIBOR-OIS on the sovereign CDS spread of Eurozone is very significant, and (2) there is a meaningful adverse co-movement between the Crude Oil price and CDS price of Eurozone. Surprisingly, adding TED spread (the difference between the three-month Treasury bill and the three-month LIBOR based in US dollars.) to the analysis and beside the LIBOR-OIS spread (the difference between the Euro 3M LIBOR and Euro 3M OIS) in third and fourth models has been increased the predicting power of LIBOR-OIS. Based on the results, LIBOR-OIS, Stoxx, TED spread, Slope, Oil price, OVX, FX volatility, and Euribor are the determinants of CDS spreads dynamics in Eurozone. Moreover, the positive impact of the crisis period on the creditworthiness of the Eurozone is meaningful.

Keywords: CDS, crude oil, interbank risk, LIBOR-OIS, OVX, sovereign credit risk, TED

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