Search results for: risk prediction model

Authors: Shi-Yu Xu

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The top three risk factors that have contributed to building collapses during past earthquake events in Taiwan are: "irregular floor plans or elevations," "insufficient columns in single-bay buildings," and the "weak-story problem." Fortunately, these unsound structural characteristics can be directly identified from the floor plans. However, due to the vast number of old buildings, conducting manual inspections to identify these compromised structural features in all existing structures would be time-consuming and prone to human errors. This study aims to develop an algorithm that utilizes artificial intelligence techniques to automatically pinpoint the structural components within a building's floor plans. The obtained spatial information will be utilized to construct a digital structural model of the building. This information, particularly regarding the distribution of columns in the floor plan, can then be used to conduct preliminary seismic assessments of the building. The study employs various image processing and pattern recognition techniques to enhance detection efficiency and accuracy. The study enables a large-scale evaluation of structural vulnerability for numerous old buildings, providing ample time to arrange for structural retrofitting in those buildings that are at risk of significant damage or collapse during earthquakes.

Keywords: structural vulnerability detection, object recognition, seismic capacity assessment, old buildings, artificial intelligence

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Authors: Algia Hammami, Abdelfatteh Bouri

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In this work, our objective is to study the transmission of volatility between oil and stock markets in developed (USA, Germany, Italy, France and Japan) and emerging countries (Tunisia, Thailand, Brazil, Argentina, and Jordan) for the period 1998-2015. Our methodology consists of analyzing the monthly data by the GARCH-BEKK model to capture the effect in terms of volatility in the variation of the oil price on the different stock market. The empirical results in the emerging countries indicate that the relationships are unidirectional from the stock market to the oil market. For the developed countries, we find that the transmission of volatility is unidirectional from the oil market to stock market. For the USA and Italy, we find no transmission between the two markets. The transmission is bi-directional only in Thailand. Following our estimates, we also noticed that the emerging countries influence almost the same extent as the developed countries, while at the transmission of volatility there a bid difference. The GARCH-BEKK model is more effective than the others versions to minimize the risk of an oil-stock portfolio.

Keywords: GARCH, oil prices, stock market, volatility transmission

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Authors: Yolanda Indah Permatasari, Sudarsono Hardjosoekarto

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Public private partnership (PPP) scheme was considered as an alternative source of funding for infrastructure provision. However, the performance of PPP scheme and interest of private sector to participate in the provision of infrastructure was still practically low. This phenomenon motivates the research to reconstruct the form of collaborative governance at the policy level from the perspective of transaction cost of the PPP scheme. Soft-system methodology (SSM)-based action research was used as this research methodology. The result of this study concludes that the emergence of transaction cost sources at the policy level is caused by the absence of a law that governs infrastructure investment, especially the implementation of PPP scheme. This absence is causing the imbalance in risk allocation and risk mitigation between the public and private sector. Thus, this research recommended the formulation of infrastructure investment law that aims to minimize asymmetry information, to anticipate the principal-principal problems, and to provide legal basis that ensures risk certainty and guarantee fair risk allocation between public and private sector.

Keywords: public governance, public private partnership, soft system methodology, transaction cost

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Authors: Jai Heui Kim, Sotheara Veng

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This paper studies the portfolio optimization problem for a pension fund under a hybrid model of stochastic volatility and constant elasticity of variance (CEV) using asymptotic analysis method. When the volatility component is fast mean-reverting, it is able to derive asymptotic approximations for the value function and the optimal strategy for general utility functions. Explicit solutions are given for the exponential and hyperbolic absolute risk aversion (HARA) utility functions. The study also shows that using the leading order optimal strategy results in the value function, not only up to the leading order, but also up to first order correction term. A practical strategy that does not depend on the unobservable volatility level is suggested. The result is an extension of the Merton's solution when stochastic volatility and elasticity of variance are considered simultaneously.

Keywords: asymptotic analysis, constant elasticity of variance, portfolio optimization, stochastic optimal control, stochastic volatility

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Authors: D. Thawinkarn, S. Wongbutlee

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The Social Change Leadership model is strongly aligned with administration’s mission. This research aims to examine the elements of social change leadership, build and develop leadership for social change, and evaluate effectiveness of leadership development model for social change. The research operation has 3 phases: model studies by in-depth interviews and survey research; drafting and creating model which verified by the experts; and trial of model in schools. The results showed that administrators and teachers have the elements of leadership for social change in moderate level. These elements are ranged descending from consciousness of self, common purpose, congruence, collaboration, commitment, citizenship, and controversy with civility. Model of leadership for social change is included the principles, objectives, content, process. Workshop process: Results show that the model of leadership development for social change in administrators and teachers leads to higher score in leadership evaluation prior to administering the operation.

Keywords: leadership, social change model, organization, administrators

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Authors: Ju-Hyung Kim, Dae-Ho Mun, Hong-Gun Park

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When an impact is applied to a floating floor, noise and vibration response of high-frequency range is reduced effectively, while amplifies the response at low-frequency range. This means floating floor can make worse noise condition when heavy-weight impact is applied. The amplified response is the result of interaction between finishing layer (mortar plate) and concrete slab. Because an impact force is not directly delivered to concrete slab, the impact force waveform or spectrum can be changed. In this paper, the changed impact spectrum was derived from several floating floor vibration tests. Based on the measured data, numerical modeling can describe the floating floor response, especially at low-frequency range. As a result, heavy-weight impact noise can be predicted using modified impact spectrum.

Keywords: floating floor, heavy-weight impact, prediction, vibration

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Authors: Mina Henneb, Rafik Belabbas, Safia Zenia

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The objective of this study was to determine the prevalence and to identify the risk factors of Eimeria spp. and Giardia spp. infection in rabbits from the local population of four localities in northern Algeria. Dung samples were collected from 16 farms, totalling 111 rabbits, and were analysed by the flotation method. Additional, data regarding the farms and management practices were obtained by means of a questionnaire used in the surveys and interviews. The results revealed that the prevalence of Eimerias pp. contamination was 68.75% (11/16) for farms and 58.56% (65/111) for rabbits, respectively. The prevalence of Giardia spp. was respectively 56.25% (9/16) for farms and 11.7% (13/111) for rabbits. The analyses showed that the prevalence of Eimeria spp. was significantly higher in the farms that did not comply with hygiene and non-conventional feeding and watering. However, the prevalence of Giardia spp. was significant in rabbits kept in poor conditions of rearing. In conclusion, this study showed that the prevalence of these two parasites in rabbits from the local population is relevant and may have important implications for the rabbit industry and public health, especially in rural areas.

Keywords: Algeria, digestive parasites, prevalence, rabbits, risk factors

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Authors: Erez Yerushalmi, Sani Ziv

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The rising demand for healthcare services, without a corresponding rise in public supply, led to a debate on whether to increase private healthcare provision - especially in hospital services and second-tier healthcare. Proponents for increasing private healthcare highlight gains in efficiency, while opponents its risk to social welfare. None, however, provide a measure of the social value and its impact on the economy in terms of a monetary value. In this paper, we impute a minimum social value of public healthcare that corresponds to indifference between gains in efficiency, with losses to social welfare. Our approach resembles contingent valuation methods that introduce a hypothetical market for non-commodities, but is different from them because we use numerical simulation techniques to exploit certain market failure conditions. In this paper, we develop a general equilibrium model that distinguishes between public-private healthcare services and public-private financing. Furthermore, the social value is modelled as a by product of healthcare services. The model is then calibrated to our unique health focused Social Accounting Matrix of Israel, and simulates the introduction of a hypothetical health-labour market - given that it is heavily regulated in the baseline (i.e., the true situation in Israel today). For baseline parameters, we estimate the minimum social value at around 18% public healthcare financing. The intuition is that the gain in economic welfare from improved efficiency, is offset by the loss in social welfare due to a reduction in available social value. We furthermore simulate a deregulated healthcare scenario that internalizes the imputed value of social value and searches for the optimal weight of public and private healthcare provision.

Keywords: contingent valuation method (CVM), general equilibrium model, hypothetical market, private-public healthcare, social value of public healthcare

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Authors: Katarzyna Niewiadomska

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Many technology startups fail to achieve a worthwhile return on investment for their funders, founders, and employees. Failures in product development, to-market strategy, sales, and delivery are commonly recognized. Founder failures are not as obvious and harder to identify. This paper explores six critical failure points that entrepreneurs and innovators are susceptible to and aims to link their emotional intelligence and behavioral profile to the points at which they experienced self-perceived failure. A model of six failure points from the perspective of the technology entrepreneur ranging from pre-startup to maturity is provided. By analyzing emotional and behavioral profile data from entrepreneurs and recording in-person accounts, certain key emotional and behavioral clusters contributing to each failure point are determined, and several underlying factors are defined and discussed. Recommendations that support entrepreneurs and innovators stalling at each failure point are given. This work can enable stakeholders to evaluate founder emotional and behavioral profiles and to take risk-mitigating action, either through coaching or through more robust team creation, to avoid founder-related company failure. The paper will be of interest to investors funding startups, executives leading them and mentors supporting them.

Keywords: behavior, emotional intelligence, entrepreneur, failure

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Authors: Muhamad Aris Burhanudin, Angga Firmansyas, Bagus Jaya Santosa

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Earthquakes are natural hazard that can trigger the most dangerous hazard, tsunami. 26 December 2004, a giant earthquake occurred in north-west Andalas Island. It made giant tsunami which crushed Sumatra, Bangladesh, India, Sri Lanka, Malaysia and Singapore. More than twenty thousand people dead. The occurrence of earthquake and tsunami can not be avoided. But this hazard can be mitigated by earthquake forecasting. Early preparation is the key factor to reduce its damages and consequences. We aim to investigate quantitatively on pattern of earthquake. Then, we can know the trend. We study about earthquake which has happened in Andalas island, Indonesia one last decade. Andalas is island which has high seismicity, more than a thousand event occur in a year. It is because Andalas island is in tectonic subduction zone of Hindia sea plate and Eurasia plate. A tsunami forecasting is needed to mitigation action. Thus, a Tsunami Forecasting Method is presented in this work. Neutral Network has used widely in many research to estimate earthquake and it is convinced that by using Backpropagation Method, earthquake can be predicted. At first, ANN is trained to predict Tsunami 26 December 2004 by using earthquake data before it. Then after we get trained ANN, we apply to predict the next earthquake. Not all earthquake will trigger Tsunami, there are some characteristics of earthquake that can cause Tsunami. Wrong decision can cause other problem in the society. Then, we need a method to reduce possibility of wrong decision. Fuzzy TOPSIS is a statistical method that is widely used to be decision seconder referring to given parameters. Fuzzy TOPSIS method can make the best decision whether it cause Tsunami or not. This work combines earthquake prediction using neural network method and using Fuzzy TOPSIS to determine the decision that the earthquake triggers Tsunami wave or not. Neural Network model is capable to capture non-linear relationship and Fuzzy TOPSIS is capable to determine the best decision better than other statistical method in tsunami prediction.

Keywords: earthquake, fuzzy TOPSIS, neural network, tsunami

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Authors: Pema Choejey, David Murray, Chun Che Fung

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Bhutan is becoming increasingly dependent on Information and Communications Technologies (ICTs), especially the Internet for performing the daily activities of governments, businesses, and individuals. Consequently, information systems and networks are becoming more exposed and vulnerable to cybersecurity threats. This paper highlights the findings of the survey study carried out to understand the perceptions of cybersecurity implementation among government organizations in Bhutan. About 280 ICT personnel were surveyed about the effectiveness of cybersecurity implementation in their organizations. A questionnaire based on a 5 point Likert scale was used to assess the perceptions of respondents. The questions were asked on cybersecurity practices such as cybersecurity policies, awareness and training, and risk management. The survey results show that less than 50% of respondents believe that the cybersecurity implementation is effective: cybersecurity policy (40%), risk management (23%), training and awareness (28%), system development life cycle (34%); incident management (26%), and communications and operational management (40%). The findings suggest that many of the cybersecurity practices are inadequately implemented and therefore, there exist a gap in achieving a required cybersecurity posture. This study recommends government organizations to establish a comprehensive cybersecurity program with emphasis on cybersecurity policy, risk management, and awareness and training. In addition, the research study has practical implications to both government and private organizations for implementing and managing cybersecurity.

Keywords: awareness and training, cybersecurity policy, risk management, security risks

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

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Authors: Chantal Hendriks, Harry Gibson, Anna Trett, Penny Hancock, Catherine Moyes

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The use of modern inputs for crop protection, such as insecticides, is strongly underestimated in Sub-Saharan Africa. Several studies measured toxic concentrations of insecticides in fruits, vegetables and fish that were cultivated in Sub-Saharan Africa. The use of agricultural insecticides has impact on human and environmental health, but it also has the potential to impact on insecticide resistance in malaria transmitting mosquitos. To analyse associations between historic use of agricultural insecticides and the distribution of insecticide resistance through space and time, the use and environmental fate of agricultural insecticides needs to be mapped through the same time period. However, data on the use and environmental fate of agricultural insecticides in Africa are limited and therefore risk maps on the spatiotemporal occurrence of agricultural insecticides are created using environmental data. Environmental data on crop density and crop type were used to select the areas that most likely receive insecticides. These areas were verified by a literature review and expert knowledge. Pesticide fate models were compared to select most dominant processes that are involved in the environmental fate of insecticides and that can be mapped at a continental scale. The selected processes include: surface runoff, erosion, infiltration, volatilization and the storing and filtering capacity of soils. The processes indicate the risk for insecticide accumulation in soil, water, sediment and air. A compilation of all available data for traces of insecticides in the environment was used to validate the maps. The risk maps can result in space and time specific measures that reduce the risk of insecticide exposure to non-target organisms.

Keywords: crop protection, pesticide fate, tropics, insecticide resistance

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Authors: Ali Kassem

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Background: In the last few years, factors such as insulin resistance (IR) and hepatic steatosis have been linked to progression of hepatic fibrosis.Patients with chronic liver disease, and cirrhosis in particular, are known to be prone to IR. However, chronic HCV (hepatitis C) infection may induce IR, regardless of the presence of liver cirrhosis. Our aims are to study insulin resistance (IR) assessed by HOMA-IR (Homeostatic Model Assessment Insulin Resistance) as a possible risk factor in disease progression in cirrhotic patients and to evaluate the role of IR in hepatic fibrosis progression. The correlations of HOMA-IR values to laboratory, virological and histopathological parameters of chronic HCV are also examined. Methods: The study included 50 people divided into 30 adult chronic hepatitis C patients diagnosed by PCR (polymerase chain reaction) within previous 6 months and 20 healthy controls. The functional and morphological status of the liver were evaluated by ultrasonography and laboratory investigations including liver function tests and by liver biopsy. Fasting blood glucose and fasting insulin levels were measured and body mass index and insulin resistance were calculated. Patients having HOMA-IR >2.5 were labeled as insulin resistant. Results: Chronic hepatitis C patients with IR showed significantly higher mean values of BMI (body mass index) and fasting insulin than those without IR (P < 0.000). Patients with IR were more likely to have steatosis (p = 0.006), higher necroinflammatory activity (p = 0.05). No significant differences were found between the two groups regarding hepatic fibrosis. Conclusion: HOMA-IR measurement could represent a novel marker to identify the cirrhotic patients at greater risk for the progression of liver disease. As IR is a potentially modifiable risk factor, these findings may have important prognostic and therapeutic implications. Assessment of IR by HOMA-IR and improving insulin sensitivity are recommended in patients with HCV and related chronic liver disease.

Keywords: hepatic fibrosis, hepatitis C virus infection, hepatic steatosis, insulin resistance

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Authors: J. Ticona Chambi, E. A. De Almeida, C. A. Andrade Raymundo Gaiotto, A. M. Do Espírito Santo, L. Infantes, S. L. Cuffini

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The solubility of active pharmaceutical ingredients (APIs) is challenging for the pharmaceutical industry. The new multicomponent crystalline forms as cocrystal and solvates present an opportunity to improve the solubility of APIs. Commonly, the procedure to obtain multicomponent crystalline forms of a drug starts by screening the drug molecule with the different coformers/solvents. However, it is necessary to develop methods to obtain multicomponent forms in an efficient way and with the least possible environmental impact. The Hansen Solubility Parameters (HSPs) is considered a tool to obtain theoretical knowledge of the solubility of the target compound in the chosen solvent. H-Bond Propensity (HBP), Molecular Complementarity (MC), Coordination Values (CV) are tools used for statistical prediction of cocrystals developed by the Cambridge Crystallographic Data Center (CCDC). The HSPs and the CCDC tools are based on inter- and intra-molecular interactions. The curcumin (Cur), target molecule, is commonly used as an anti‐inflammatory. The demethoxycurcumin (Demcur) and bisdemethoxycurcumin (Bisdcur) are natural analogues of Cur from turmeric. Those target molecules have differences in their solubilities. In this way, the work aimed to analyze and compare different tools for multicomponent forms prediction (solvates) of Cur, Demcur and Biscur. The HSP values were calculated for Cur, Demcur, and Biscur using the chemical group contribution methods and the statistical optimization from experimental data. The HSPmol software was used. From the HSPs of the target molecules and fifty solvents (listed in the HSP books), the relative energy difference (RED) was determined. The probability of the target molecules would be interacting with the solvent molecule was determined using the CCDC tools. A dataset of fifty molecules of different organic solvents was ranked for each prediction method and by a consensus ranking of different combinations: HSP, CV, HBP and MC values. Based on the prediction, 15 solvents were selected as Dimethyl Sulfoxide (DMSO), Tetrahydrofuran (THF), Acetonitrile (ACN), 1,4-Dioxane (DOX) and others. In a starting analysis, the slow evaporation technique from 50°C at room temperature and 4°C was used to obtain solvates. The single crystals were collected by using a Bruker D8 Venture diffractometer, detector Photon100. The data processing and crystal structure determination were performed using APEX3 and Olex2-1.5 software. According to the results, the HSPs (theoretical and optimized) and the Hansen solubility sphere for Cur, Demcur and Biscur were obtained. With respect to prediction analyses, a way to evaluate the predicting method was through the ranking and the consensus ranking position of solvates already reported in the literature. It was observed that the combination of HSP-CV obtained the best results when compared to the other methods. Furthermore, as a result of solvent selected, six new solvates, Cur-DOX, Cur-DMSO, Bicur-DOX, Bircur-THF, Demcur-DOX, Demcur-ACN and a new Biscur hydrate, were obtained. Crystal structures were determined for Cur-DOX, Biscur-DOX, Demcur-DOX and Bicur-Water. Moreover, the unit-cell parameter information for Cur-DMSO, Biscur-THF and Demcur-ACN were obtained. The preliminary results showed that the prediction method is showing a promising strategy to evaluate the possibility of forming multicomponent. It is currently working on obtaining multicomponent single crystals.

Keywords: curcumin, HSPs, prediction, solvates, solubility

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Authors: Yousef Naserzadeh, Niloufar Mahmoudi

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The spotted-wing drosophila, Drosophila suzukii (Matsumura) (Diptera: Drosophilidae), a vinegar fly native to South East Asia, has recently invaded Europe, North- and South America and is spreading rapidly. Species distribution modeling has been widely employed to indicate probable areas of invasion and to guide management strategies. Drosophila sp. is native to Asia, but since 2015, it has invaded almost every country in the world, including Africa, Australia, India, and most recently, the Americas. The growth of this species of Drosophila suzukii has been rapidly multiplying and spreading in the last decade. In fact, we examine and model the potential geographical distribution of D. suzukii for both present and future scenarios. Finally, we determine the environmental variables that affect its distribution, as well as assess the risk of encroachment on protected areas. D.suzukii has the potential to expand its occurrence, especially on continents that have already been invaded. The predictive models obtained in this study indicate potential regions that could be at risk of invasion by D. suzukii, including protected areas. These results are important and can assist in the establishment of management plans to avoid the possible harm caused by biological invasions.

Keywords: climate change, Drosophila suzukii, environmental variables, host preference, host plant, nutrition

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Authors: A. Lakrim, D. Tahri

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This paper sets out a behavioral macro-model of a Merged PiN and Schottky (MPS) diode based on silicon carbide (SiC). This model holds good for both static and dynamic electrothermal simulations for industrial applications. Its parameters have been worked out from datasheets curves by drawing on the optimization method: Simulated Annealing (SA) for the SiC MPS diodes made available in the industry. The model also adopts the Analog Behavioral Model (ABM) of PSPICE in which it has been implemented. The thermal behavior of the devices was also taken into consideration by making use of Foster’ canonical network as figured out from electro-thermal measurement provided by the manufacturer of the device.

Keywords: SiC MPS diode, electro-thermal, SPICE model, behavioral macro-model

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Authors: Oksana Domkina

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According to the modern economic theory, human capital became one of the main production factors and the most promising direction of investment, as such investment provides opportunity of obtaining high and long-term economic and social effects. Informational technology (IT) sector is the representative of this new economy which is most dependent on human capital as the main competitive factor. So the question for this sector is not whether investment in development of personal should be made, but what are the most effective ways of executing it and who has to pay for the education: Worker, company or government. In this paper we examine the IT sector, describe the labor market of IT workers and its development, and analyze the risks that IT companies may face if they invest in the development of their workers and what factors influence it. The main problem and difficulty of quantitative estimation of risk of investment in human capital of a company and its forecasting is human factor. Human behavior is often unpredictable and complex, so it requires specific approaches and methods of assessment. To build a comprehensive method of estimation of the risk of investment in human capital of a company considering human factor, we decided to use the method of analytic hierarchy process (AHP), that initially was created and developed. We separated three main group of factors: Risks related to the worker, related to the company, and external factors. To receive data for our research, we conducted a survey among the HR departments of Ukrainian IT companies used them as experts for the AHP method. Received results showed that IT companies mostly invest in the development of their workers, although several hire only already qualified personnel. According to the results, the most significant risks are the risk of ineffective training and the risk of non-investment that are both related to the firm. The analysis of risk factors related to the employee showed that, the factors of personal reasons, motivation, and work performance have almost the same weights of importance. Regarding internal factors of the company, there is a high role of the factor of compensation and benefits, factors of interesting projects, team, and career opportunities. As for the external environment, one of the most dangerous factor of risk is competitor activities, meanwhile the political and economical situation factor also has a relatively high weight, which is easy to explain by the influence of severe crisis in Ukraine during 2014-2015. The presented method allows to take into consideration all main factors that affect the risk of investment in human capital of a company. This gives a base for further research in this field and allows for a creation of a practical framework for making decisions regarding the personnel development strategy and specific employees' development plans for the HR departments.

Keywords: risks, personnel development, investment in development, factors of risk, risk of investment in development, IT, analytic hierarchy process, AHP

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Authors: Yi-Min Lan, Hung-Wen Cheng, Hsuan-Ling Chang, Jou-Ping Yu

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The study applies machine learning models to construct momentum strategies and utilizes the information coefficient as an indicator for selecting stocks with strong and weak momentum characteristics. Through this approach, the study has built investment portfolios capable of generating superior returns and conducted a thorough analysis. Compared to existing research on momentum strategies, machine learning is incorporated to capture non-linear interactions. This approach enhances the conventional stock selection process, which is often impeded by difficulties associated with timeliness, accuracy, and efficiency due to market risk factors. The study finds that implementing bidirectional momentum strategies outperforms unidirectional ones, and momentum factors with longer observation periods exhibit stronger correlations with returns. Optimizing the number of stocks in the portfolio while staying within a certain threshold leads to the highest level of excess returns. The study presents a novel framework for momentum strategies that enhances and improves the operational aspects of asset management. By introducing innovative financial technology applications to traditional investment strategies, this paper can demonstrate significant effectiveness.

Keywords: information coefficient, machine learning, momentum, portfolio, return prediction

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Authors: Ahmed T. Farid, Muhammed Rizwan

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Geotechnical study for evaluating soil or rock permeability is a highly important parameter. Permeability values for rock formations are more difficult for determination than soil formation as it is an effect of the rock quality and its fracture values. In this research, the prediction of in situ permeability of limestone rock formations was predicted. The limestone rock permeability was evaluated using Lugeon tests (in-situ packer permeability). Different sites which spread all over the Riyadh region of Saudi Arabia were chosen to conduct our study of predicting the in-situ permeability of limestone rock. Correlations were deducted between the values of in-situ permeability of the limestone rock with the value of the rock quality designation (RQD) calculated during the execution of the boreholes of the study areas. The study was performed for different ranges of RQD values measured during drilling of the sites boreholes. The developed correlations are recommended for the onsite determination of the in-situ permeability of limestone rock only. For the other sedimentary formations of rock, more studies are needed for predicting the actual correlations related to each type.

Keywords: In situ, packer, permeability, rock, quality

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Authors: Piotr Fiszeder, Marcin Fałdziński, Peter Molnár

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The dynamic conditional correlation model of Engle (2002) is one of the most popular multivariate volatility models. However, this model is based solely on closing prices. It has been documented in the literature that the high and low price of the day can be used in an efficient volatility estimation. We, therefore, suggest a model which incorporates high and low prices into the dynamic conditional correlation framework. Empirical evaluation of this model is conducted on three datasets: currencies, stocks, and commodity exchange-traded funds. The utilisation of realized variances and covariances as proxies for true variances and covariances allows us to reach a strong conclusion that our model outperforms not only the standard dynamic conditional correlation model but also a competing range-based dynamic conditional correlation model.

Keywords: volatility, DCC model, high and low prices, range-based models, covariance forecasting

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Authors: Mohammed Aljoma, Eblal Zakzok

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Safety is a science that seeks to protect and avoid humans from risks in any field and prevent losses in properties and lives as much as possible. On the other hand, occupational safety goals aim to protect workers from risks which can occur during work execution. The main purpose of occupational safety is to ultimately protect people, properties and the environment by reducing accidents and injuries that may cause losses and damages. To achieve this goal, we must remove the direct and indirect reasons which cause accidents and injuries; some of the reasons of accidents are the unsafe cases and inept behavior or both of them. This research focuses on the manner of providing instant protection from the very first beginning to people, properties and the environment by: -Inserting safety demands in the planning and designing works by identifying risk levels in every task of the project, -Using a new risk managing system or modifying or changing a previously-used one.

Keywords: planning, scheduling, risk management, project duration, site safety

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Authors: C. Shih, J. R. Wang, H. C. Chang, S. W. Chen, S. C. Chiang, T. Y. Yu

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After the measurement uncertainty recapture (MUR) power uprates, Kuosheng nuclear power plant (NPP) was uprated the power from 2894 MWt to 2943 MWt. For power upgrade, several codes (e.g., TRACE, RELAP5, etc.) were applied to assess the safety of Kuosheng NPP. Hence, the main work of this research is to establish a RELAP5/MOD3.3 model of Kuosheng NPP with SNAP interface. The establishment of RELAP5/SNAP model was referred to the FSAR, training documents, and TRACE model which has been developed and verified before. After completing the model establishment, the startup test scenarios would be applied to the RELAP5/SNAP model. With comparing the startup test data and TRACE analysis results, the applicability of RELAP5/SNAP model would be assessed.

Keywords: RELAP5, TRACE, SNAP, BWR

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Authors: Alma Shehu, Majlinda Vasjari, Sonila Duka, Loreta Vallja, Nevila Broli

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Humans consume drinking water from several sources, including tap water, bottled water, natural springs, filtered tap water, etc. The quality of drinking water is crucial for human survival given the fact that the consumption of contaminated drinking water is related to many diseases and deaths all over the world. This study represents the investigation of the quality and health risks of different types of drinking waters being consumed by the population in Albania, arising from heavy metals content. Investigated water included industrialized water, tap water, and spring water. In total, 20 samples were analyzed for the content of Pb, Cd, Cr, Ni, Cu, Fe, Zn, Al, and Mn. Determination of each metal concentration in selected samples was conducted by atomic absorption spectroscopy method with electrothermal atomization, GFAAS. Water quality was evaluated by comparing the obtained metals concentrations with the recommended maximum limits, according to the European Directive (98/83/EC) and Guidelines for Drinking Water Quality (WHO, 2017). Metal Index (MI) was used to assess the overall water quality due to heavy metals content. Health risk assessment was conducted based on the recommendations of the USEPA (1996), human health risk assessment, via ingestion. Results of this investigation showed that Al, Ni, Fe, and Cu were the metals found in higher concentrations while Cd exhibited the lowest concentration. Among the analyzed metals, Al (one sample) and Ni (in five samples) exceeded the maximum allowed limit. Based on the pollution metal index, it was concluded that the overall quality of Glina bottled water can be considered as toxic to humans, while the quality of bottled water (Trebeshina) was classified as moderately toxic. Values of health risk quotient (HQ) varied between 1x10⁻⁶-1.3x10⁻¹, following the order Ni > Cd > Pb > Cu > Al > Fe > Zn > Mn. All the values were lower than 1, which suggests that the analyzed samples exhibit no health risk for humans.

Keywords: drinking water, health risk assessment, heavy metals, pollution index

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Authors: John Davidson, Matteo Castelletti, Ismael Torres, Victor Terente, Jamie Irvine, Sylvie Raymackers

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The current industry approach to pile driving predictions consists of developing a model of the hammer-pile-soil system which simulates the relationship between soil resistance to driving (SRD) and blow counts (or pile penetration per blow). The SRD methods traditionally used are broadly based on static pile capacity calculations. The SRD is used in combination with the one-dimensional wave equation model to indicate the anticipated blowcounts with depth for specific hammer energy settings. This approach has predominantly been calibrated on relatively long slender piles used in the oil and gas industry but is now being extended to allow calculations to be undertaken for relatively short rigid large diameter monopile foundations. This paper evaluates the accuracy of current industry practice when applied to a site where large diameter monopiles were installed in predominantly stiff fissured clay. Actual geotechnical and pile installation data, including pile driving records and signal matching analysis (based upon pile driving monitoring techniques), were used for the assessment on the case study site.

Keywords: driven piles, fissured clay, London clay, monopiles, offshore foundations

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Authors: Vladyslav Povoroznyuk, Tetiana Orlуk, Nataliia Dzerovych

Abstract:

Introduction: Back pain is an extremely common health care problem worldwide. Many studies show a link between an obesity and risk of lower back pain. The aim is to study correlation and peculiarities of vertebral pain in women of different age depending on their anthropometric indicators. Materials: 1886 women aged 25-89 years were examined. The patients were divided into groups according to age (25-44, 45-59, 60-74, 75-89 years old) and body mass index (BMI: to 18.4 kg/m2 (underweight), 18.5-24.9 kg/m2 (normal), 25-30 kg/m2 (overweight) and more than 30.1 kg/m2 (obese). Methods: The presence and intensity of pain was evaluated in the thoracic and lumbar spine using a visual analogue scale (VAS). BMI is calculated by the standard formula based on body weight and height measurements. Statistical analysis was performed using parametric and nonparametric methods. Significant changes were considered as p <0.05. Results: The intensity of pain in the thoracic spine was significantly higher in the underweight women in the age groups of 25-44 years (p = 0.04) and 60-74 years (p=0.005). The intensity of pain in the lumbar spine was significantly higher in the women of 45-59 years (p = 0.001) and 60-74 years (p = 0.0003) with obesity. In the women of 45-74 years BMI was significantly positively correlated with the level of pain in the lumbar spine. Obesity significantly increases the relative risk of pain in the lumbar region (RR=0.07 (95% CI: 1.03-1.12; p=0.002)), while underweight significantly increases the risk of pain in the thoracic region (RR=1.21 (95% CI: 1.00-1.46; p=0.05)). Conclusion: In women, vertebral pain syndrome may be related to the anthropometric characteristics (e.g., BMI). Underweight may indirectly influence the development of pain in the thoracic spine and increase the risk of pain in this part by 1.21 times. Obesity influences the development of pain in the lumbar spine increasing the risk by 1.07 times.

Keywords: body mass index, age, pain in thoracic and lumbar spine, women

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Authors: Hoda Ghavamipoor, S. Alireza Hashemi Golpayegani

Abstract:

An approach to model the customer interaction with e-commerce websites is presented. Considering the service quality level as a predictive feature, we offer an improved method based on the Customer Behavior Model Graph (CBMG), a state-transition graph model. To derive the Quality of Service sensitive-CBMG (QoS-CBMG) model, process-mining techniques is applied to pre-processed website server logs which are categorized as ‘buy’ or ‘visit’. Experimental results on an e-commerce website data confirmed that the proposed method outperforms CBMG based method.

Keywords: customer behavior model, electronic commerce, quality of service, customer behavior model graph, process mining

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Authors: Ella Tyuryumina, Alexey Neznanov

Abstract:

This study is an attempt to obtain reliable data on the natural history of breast cancer growth. We analyze the opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) to try to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. The research aim is to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoMPaS and corresponding software. We are interested in: 1) modelling the whole natural history of the primary tumor and the secondary distant metastases; 2) developing adequate and precise CoMPaS which reflects relations between the primary tumor and the secondary distant metastases; 3) analyzing the CoMPaS scope of application; 4) implementing the model as a software tool. The foundation of the CoMPaS is the exponential tumor growth model, which is described by determinate nonlinear and linear equations. The CoMPaS corresponds to TNM classification. It allows to calculate different growth periods of the primary tumor and the secondary distant metastases: 1) ‘non-visible period’ for the primary tumor; 2) ‘non-visible period’ for the secondary distant metastases; 3) ‘visible period’ for the secondary distant metastases. The CoMPaS is validated on clinical data of 10-years and 15-years survival depending on the tumor stage and diameter of the primary tumor. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer growth models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. The CoMPaS model and predictive software: a) fit to clinical trials data; b) detect different growth periods of the primary tumor and the secondary distant metastases; c) make forecast of the period of the secondary distant metastases appearance; d) have higher average prediction accuracy than the other tools; e) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoMPaS: the number of doublings for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases. The CoMPaS enables, for the first time, to predict ‘whole natural history’ of the primary tumor and the secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on the primary tumor sizes. Summarizing: a) CoMPaS describes correctly the primary tumor growth of IA, IIA, IIB, IIIB (T1-4N0M0) stages without metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and inception of the secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, metastases in lymph nodes, primary tumor, survival

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Authors: Gabrielle Peck, Ryan Hayes

Abstract:

This research presents the design and creation of a drone gas analyser, aimed at addressing the need for independent data collection and analysis of gas emissions during large-scale fires, particularly wasteland fires. The analyser drone, comprising a lightweight gas analysis system attached to a remote-controlled drone, enables the real-time assessment of smoke toxicity and the monitoring of gases released into the atmosphere during such incidents. The key components of the analyser unit included two gas line inlets connected to glass wool filters, a pump with regulated flow controlled by a mass flow controller, and electrochemical cells for detecting nitrogen oxides, hydrogen cyanide, and oxygen levels. Additionally, a non-dispersive infrared (NDIR) analyser is employed to monitor carbon monoxide (CO), carbon dioxide (CO₂), and hydrocarbon concentrations. Thermocouples can be attached to the analyser to monitor temperature, as well as McCaffrey probes combined with pressure transducers to monitor air velocity and wind direction. These additions allow for monitoring of the large fire and can be used for predictions of fire spread. The innovative system not only provides crucial data for assessing smoke toxicity but also contributes to fire prediction and management. The remote-controlled drone's mobility allows for safe and efficient data collection in proximity to the fire source, reducing the need for human exposure to hazardous conditions. The data obtained from the gas analyser unit facilitates informed decision-making by emergency responders, aiding in the protection of both human health and the environment. This abstract highlights the successful development of a drone gas analyser, illustrating its potential for enhancing smoke toxicity analysis and fire prediction capabilities. The integration of this technology into fire management strategies offers a promising solution for addressing the challenges associated with wildfires and other large-scale fire incidents. The project's methodology and results contribute to the growing body of knowledge in the field of environmental monitoring and safety, emphasizing the practical utility of drones for critical applications.

Keywords: fire prediction, drone, smoke toxicity, analyser, fire management

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Authors: Eftekhari M. H., Mozaffari-Khosravi H., Shidfar F.

Abstract:

Background: Many Iranian adolescent girls are iron deficient, but it is unclear whether the iron deficiency is associated with other nutritional risk indicators. Objective: we aimed to investigate the association between iron deficiency and weight status (measured as BMI) among a reprehensive sample of teenage girls. Methods: A cross-sectional study was performed in a region of southern I.R.Iran. One hundred eighty-seven iron-deficient participants (aged between 11 to 14) were selected by systematic random sampling among all students in grades 1 to 3 from high schools for girls. We assayed hemoglobin, hematocrit, serum ferritin, iron and total iron binding capacity and measured weight and height. Body mass index was calculated according to age and gender-specific BMI growth charts for children 2 to 20 years of age. Results: 13% were at risk for being overweight and 8.3% were overweight. The severity of iron deficiency increased as BMI increased from normal to at risk for overweight and overweight. Iron deficiency anemia was most prevalent among overweight adolescents than at risk for overweight and normal weight adolescents (28%, 18%, and 13%, respectively). Conclusions: The results of this study showed an inverse association of BMI with serum ferritin. Overweight adolescents demonstrated an increased prevalence of anemia. Because of the potentially harmful effects of iron deficiency, obese adolescents should be routinely screened and treated as necessary.

Keywords: adolescent, over weight, iron deficiency, Iran

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