Search results for: reversible jump Markov Chain Monte Carlo (MCMC)

Authors: Adrián D. García-Soto, Alejandro Hernández-Martínez, Jesús G. Valdés-Vázquez, Reyna A. Vizguerra-Alvarez

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Reinforced concrete bridges designed by code are intended to achieve target reliability levels adequate for the geographical environment where the code is applicable. Several methods can be used to estimate such reliability levels. Many of them require the establishment of an explicit limit state function (LSF). When such LSF is not available as a close-form expression, the simulation techniques are often employed. The simulation methods are computing intensive and time consuming. Note that if the reliability of real bridges designed by code is of interest, numerical schemes, the finite element method (FEM) or computational mechanics could be required. In these cases, it can be quite difficult (or impossible) to establish a close-form of the LSF, and the simulation techniques may be necessary to compute reliability levels. To overcome the need for a large number of simulations when no explicit LSF is available, the point estimate method (PEM) could be considered as an alternative. It has the advantage that only the probabilistic moments of the random variables are required. However, in the PEM, fitting of the resulting moments of the LSF to a probability density function (PDF) is needed. In the present study, a very simple alternative which allows the assessment of the reliability levels when no explicit LSF is available and without the need of extensive simulations is employed. The alternative includes the use of the PEM, and its applicability is shown by assessing reliability levels of reinforced concrete bridges in Mexico when a numerical scheme is required. Comparisons with results by using the Monte Carlo simulation (MCS) technique are included. To overcome the problem of approximating the probabilistic moments from the PEM to a PDF, a well-known distribution is employed. The approach mixes the PEM and other classic reliability method (first order reliability method, FORM). The results in the present study are in good agreement whit those computed with the MCS. Therefore, the alternative of mixing the reliability methods is a very valuable option to determine reliability levels when no close form of the LSF is available, or if numerical schemes, the FEM or computational mechanics are employed.

Keywords: structural reliability, reinforced concrete bridges, combined approach, point estimate method, monte carlo simulation

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Authors: Surajit Chakrabarty, Piyush Chauhan, Subhasis Panda, Sujoy Bhattacharya

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A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed.

Keywords: deep learning, hidden Markov model, pothole, speed breaker

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Authors: G. N. Srinivas, K. Raju

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This paper presents Markov processes for determining the reliability indices of distribution system. The continuous Markov modeling is applied to a complex radial distribution system and electrical equivalent circuits are developed for the modeling. In general PSPICE is being used for electrical and electronic circuits and various applications of power system like fault analysis, transient analysis etc. In this paper, the SPICE modeling equivalent circuits which are developed are applied in a novel way to Distribution System reliability analysis. These circuits are simulated using PSPICE software to obtain the state probabilities, the basic and performance indices. Thus the basic indices and the performance indices obtained by this method are compared with those obtained by FMEA technique. The application of the concepts presented in this paper are illustrated and analyzed for IEEE-Roy Billinton Test System (RBTS).

Keywords: distribution system, Markov Model, reliability indices, spice simulation

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Authors: Helen Nelly Naiga, Christopher Garimoi Orach

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Introduction: Long-acting reversible contraceptive (LARC) methods are highly effective. However, LARC use in Uganda is low (13%). We assessed the factors associated with the use of long-acting reversible contraceptives among women of reproductive age (15-49 yrs) in Jinja District. Methods: We conducted a facility-based cross-sectional study. A total of 314 women aged 15–49 years attending public health facilities (1 hospital and 3 health center IV) in Jinja district, were randomly selected. A total of 6 key informants and 6 in-depth interviews were conducted. Logistic regression analysis was conducted using Stata version 14. Qualitative data were analysed using thematic analysis. Results: The study found that 40.45% of the respondents had ever used LARC. The commonest LARC method used was implanting (38.22%). The factors significantly associated with use of LARC were employment (AOR =2.91; 95% CI (1.05-8.08), access to LARC methods (AOR =4.48; 95% CI (1.24-16.21), husband support (AOR =4.90; 95% CI (1.56-15.41), and experience of no side effects (AOR =3.48; 95% CI (1.00-12.19). Conclusion and recommendations: The study showed that 4 in 10 women of reproductive age in Jinja District were using LARC. The factors associated with LARC use were employment, husband support, access to LARC methods, and the lack of side effects. There is a need to strengthen client education, improve accessibility to LARC methods at all levels of health centers, improve male partner’s decision-making in LARC use and manage the side effects effectively.

Keywords: family planning, implants, intrauterine device, long-acting reversible contraceptives (LARC)

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Authors: Azar Alizadeh, Mohammad Reza Naserkhaki

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The application of the IT systems for collecting and analyzing the data can have a significant effect on the performance of any company. In recent decade, different advancements and achievements in the field of information technology have changed the industry compared to the previous decade. The adoption and application of the information technology are one of the ways to achieve a distinctive competitive personality to the companies and their supply chain. The acceptance of the IT and its proper implementation cam reinforce and improve the cooperation between different parts of the supply chain by rapid transfer and distribution of the precise information and the application of the informational systems, leading to the increase in the supply chain efficiency. The main objective of this research is to study the effects and applications of the information technology on and in the supply chain management and to introduce the effective factors on the acceptance of information technology in the companies. Moreover, in order to understand the subject, we will investigate the role and importance of the information and electronic commerce in the supply chain and the characteristics of the supply chain based on the information flow approach.

Keywords: electronic commerce, industry, information technology, management, supply chain, system

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Authors: Faisal Mehmood

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In this globalization era, the supply chain management has acquired strategic importance in diverse business environments. In the current highly competitive business environment, the success of any business considerably depends on the efficiency of the supply chain. Management has now realized that due to the inefficiency of any member of supply chain, the profitability of the business will be affected. This paper proposes an analysis of backward supply chain in the beverages industry of Pakistan. Although reuse of products and materials is a common phenomenon, companies have long ignored this important part of the supply chain, known as backward supply chain or reverse logistics. The beverage industry is among the pioneers of backward supply chain or reverse logistics in Pakistan. The empty glass bottles are returned back from the point of consumption to the warehouse for refilling and reusability purposes. Due to the lack of information on reverse flow of logistics and more attention on the forward distribution, beverages industry in Pakistan is facing high rate of inefficiencies and ineffectiveness. Analysis of backward or reverse logistics practiced in beverages industry is the subject of this study in which framework dictating the current needs of market will be developed.

Keywords: backward supply chain, reverse logistics, refilling, re-usability

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Authors: Giriraj Achari, Malay Bhattacharyya

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In this paper, the effectiveness of Copula Markov Switching Multifractal (MSM) models at forecasting Value-at-Risk of a two-stock portfolio is studied. The innovations are allowed to be drawn from distributions that can capture skewness and leptokurtosis, which are well documented empirical characteristics observed in financial returns. The candidate distributions considered for this purpose are Johnson-SU, Pearson Type-IV and α-Stable distributions. The two univariate marginal distributions are combined using the Student-t copula. The estimation of all parameters is performed by Maximum Likelihood Estimation. Finally, the models are compared in terms of accurate Value-at-Risk (VaR) forecasts using tests of unconditional coverage and independence. It is found that Copula-MSM-models with leptokurtic innovation distributions perform slightly better than Copula-MSM model with Normal innovations. Copula-MSM models, in general, produce better VaR forecasts as compared to traditional methods like Historical Simulation method, Variance-Covariance approach and Copula-Generalized Autoregressive Conditional Heteroscedasticity (Copula-GARCH) models.

Keywords: Copula, Markov Switching, multifractal, value-at-risk

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Authors: Seyedeh Mersedeh Banijamali, Ali Rajabzadeh

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Industrial development in recent centuries has been replaced by a sustainable development. The industry executives, particularly in the development countries are looking for procedures to protect the environment, improve their organization's performance. One of these approaches is the green supply chain management. Green supply chain management approach as a comprehensive approach to environmental management that contains all flows from suppliers to producers and ultimately to consumers, in many industries, particularly in the Steel industry, which has a strategic role in the country's industrial and economic development, has been receiving significant attention. The purpose of this study is examining the impact of green supply chain on chain operational performance in the Steel industry and formulating model for it. In this way, first the components of green supply chain (in 5 dimensions, planning, sourcing, making, delivery and return) have been prioritized through TOPSIS decision technique and then impact of these components on operational performance has been modeled with model dynamic systems and Vensim software. This research shows that green supply chain has a positive impact on operational performance and improve it.

Keywords: green supply chain, the dimensions of the green supply chain, operational performance, steel industry, dynamical systems

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Authors: Che Hsiu Chen, Su Yun Chen, Hon Wen Cheng

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Greater core stability may benefit sports performance by providing a foundation for greater force production in the upper and lower extremities. Core stability exercises on instability device (such as the TRX suspension systems) were found to be able to induce higher core muscle activity than performing on a stable surface. However, high intensity interval TRX suspension exercises training on sport performances remain unclear. The purpose of this study was to examine whether high intensity TRX suspension training could improve sport performance. Twenty-four healthy university female students (age 19.0 years, height 157.9 cm, body mass 51.3 kg, fat mass 25.2 %) were voluntarily participated in this study. After a familiarization session, each participant underwent five suspension exercises (e.g., hip abduction in plank alternative, hamstring curl, 45-degree row, lunge and oblique crunch). Each type of exercise was performed for 30 seconds, followed by 30 seconds break, two times per week for eight weeks while each exercise session was increased by 10 seconds every week. The results showed that the fat mass (about 12.92%) decreased significantly, sit and reach test (9%), 1 minute sit-up test (17.5%), standing broad jump (4.8%), physical fitness index (10.3%) increased significantly after 8-week high intensity TRX suspension training. Hence, eight weeks of high intensity interval TRX suspension exercises training can improve hamstring flexibility, trunk endurance, jump ability, aerobic fitness and fat mass percentage decreased substantially.

Keywords: core endurance, jump, flexibility, cardiovascular fitness

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Authors: Zulkifli Mohamed Udin, Lee Khai-Loon, Mohamad Ghozali Hassan

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In today’s dynamic business environment, the competition is no longer between firms, but between supply chains to gain competitive advantages. The global manufacturing sector, especially the textile and apparel industry are essentially known for its supply chain dependency. The delicate nature of its business leads to emphasis on the smooth movement of upstream and downstream supply chain. The nature of this industry, however, result in huge dynamic flow of physical, information, and financial. The dynamic management of these flows requires adoption of supply chain technologies. Even though technology is widely implemented and studied in many industries by researchers, adoption of supply chain technologies in Malaysian textile and apparel industry is limited. There is relatively a handful academic study conducted on recent developments in Malaysian textile and apparel industry and supply chain technology adoption indicate a major gap in supply chain performance studies. Considering the importance given to Third Industrial Master Plan by the government Malaysia, it is necessary to understand the power of supply chain technology adoptions. This study aims to investigate supply chain technology adoption by textile and apparel companies in Malaysia. The result highlighted the benefits perceived by textile and apparel companies from supply chain technologies. The indifference of small and medium enterprises to operation management acts as a major inhibitor to the adoption of supply chain technologies, since they have resource limitations. This study could be used as a precursor for further detailed studies on this issue.

Keywords: supply chain technology adoption, supply chain performance, textile, apparel industry

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Authors: Salah Alrabeei, Mohammad Yousuf

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The goal of option pricing theory is to help the investors to manage their money, enhance returns and control their financial future by theoretically valuing their options. Modeling option pricing by Black-School models with jumps guarantees to consider the market movement. However, only numerical methods can solve this model. Furthermore, not all the numerical methods are efficient to solve these models because they have nonsmoothing payoffs or discontinuous derivatives at the exercise price. In this paper, the exponential time differencing (ETD) method is applied for solving partial integrodifferential equations arising in pricing European options under Merton’s and Kou’s jump-diffusion models. Fast Fourier Transform (FFT) algorithm is used as a matrix-vector multiplication solver, which reduces the complexity from O(M2) into O(M logM). A partial fraction form of Pad`e schemes is used to overcome the complexity of inverting polynomial of matrices. These two tools guarantee to get efficient and accurate numerical solutions. We construct a parallel and easy to implement a version of the numerical scheme. Numerical experiments are given to show how fast and accurate is our scheme.

Keywords: Integral differential equations, , L-stable methods, pricing European options, Jump–diffusion model

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Authors: Ammatu Alhassan

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Quality healthcare delivery is a crucial indicator in assessing the overall developmental status of a country. There are many limitations in the Ghanaian healthcare supply chain due to the lack of studies about the correlation between quality health service and the healthcare supply chain. Patients who visit various healthcare providers face unpleasant experiences such as delays in the availability of their medications. In this study, an assessment of the quality of services provided to Ghanaian outpatients who visit public healthcare providers was investigated to establish its effect on the healthcare supply chain using a conceptual model. The Donabedian’s structure, process, and outcome theory for service quality evaluation were used to analyse 20 Ghanaian hospitals. The data obtained was tested using the structural equation model (SEM). The findings from this research will help us to improve the overall quality of the Ghanaian healthcare supply chain. The model which will be developed will help us to understand better the linkage between quality healthcare and the healthcare supply chain as well as serving as a reference tool for future healthcare research in Ghana.

Keywords: Ghana, healthcare, outpatients, supply chain

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Authors: Mani Venkatesh, Catarina Delgado, Purvishkumar Patel

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The sustainable supply chain is gaining popularity among practitioners because of increased environmental degradation and stakeholder awareness. On the other hand supply chain, risk management is very crucial for the practitioners as it potentially disrupts supply chain operations. Prediction and addressing the risk caused by social issues in the supply chain is paramount importance to the sustainable enterprise. More recently, the usage of Big data analytics for forecasting business trends has been gaining momentum among professionals. The aim of the research is to explore the application of big data, predictive analytics in successfully mitigating supply chain social risk and demonstrate how such mitigation can help in achieving sustainability (environmental, economic & social). The method involves the identification and validation of social issues in the supply chain by an expert panel and survey. Later, we used a case study to illustrate the application of big data in the successful identification and mitigation of social issues in the supply chain. Our result shows that the company can predict various social issues through big data, predictive analytics and mitigate the social risk. We also discuss the implication of this research to the body of knowledge and practice.

Keywords: big data, sustainability, supply chain social sustainability, social risk, case study

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Authors: A. Mohammadi, M. Moghimi, S. Mohammadi

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Engineers need computational methods which could provide solutions less sensitive to the environmental effects, so the techniques should be used which take the uncertainty to account to control and minimize the risk associated with design and operation. In order to consider uncertainty in engineering problem, the optimization problem should be solved for a suitable range of the each uncertain input variable instead of just one estimated point. Using deterministic optimization problem, a large computational burden is required to consider every possible and probable combination of uncertain input variables. Several methods have been reported in the literature to deal with problems under uncertainty. In this paper, different methods presented and analyzed.

Keywords: uncertainty, Monte Carlo simulated, stochastic programming, scenario method

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Authors: Ali Mohaghar, Iman Ghasemian Sahebi, Alireza Arab

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In the last decades, increasing in human and natural disaster occurrence had very irreparable effects on human life. Hence, one of the important issues in humanitarian supply chain management is identifying and prioritizing the different risks and finding suitable solutions for encountering them at the time of disaster occurrence. This study is an attempt to provide a comprehensive review of humanitarian supply chain risks in a case study of Tehran Red Crescent Societies. For this purpose, Best-Worst method (BWM) has been used for analyzing the risks of the humanitarian supply chain. 22 risks of the humanitarian supply chain were identified based on the literature and interviews with four experts. According to BWM method, the importance of each risk was calculated. The findings showed that culture contexts, little awareness of people, and poor education system are the most important humanitarian supply chain risks. This research provides a useful guideline for managers so that they can benefit from the results to prioritize their solutions.

Keywords: Best-Worst Method, humanitarian logistics, humanitarian supply chain, risk management

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Authors: M. Bevilacqua, F. E. Ciarapica, G. Marcucci

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Supply Chain Resilience has been broadly studied during the last decade, focusing the research on many aspects of Supply Chain performance. Consequently, different definitions of Supply Chain Resilience have been developed by the research community, drawing inspiration also from other fields of study such as ecology, sociology, psychology, economy et al. This way, the definitions so far developed in the extant literature are therefore very heterogeneous, and many authors have pointed out a lack of consensus in this field of analysis. The aim of this research is to find common points between these definitions, through the development of a framework of study: the Resilience Triangle. The Resilience Triangle is a tool developed in the field of civil engineering, with the objective of modeling the loss of resilience of a given structure during and after the occurrence of a disruption such as an earthquake. The Resilience Triangle is a simple yet powerful tool: in our opinion, it can summarize all the features that authors have captured in the Supply Chain Resilience definitions over the years. This research intends to recapitulate within this framework all these heterogeneities in Supply Chain Resilience research. After collecting a various number of Supply Chain Resilience definitions present in the extant literature, the methodology approach provides a taxonomy step with the scope of collecting and analyzing all the data gathered. The next step provides the comparison of the data obtained with the plotting of a disruption profile, in order to contextualize the Resilience Triangle in the Supply Chain context. The tool and the results developed in this research will allow to lay the foundation for future Supply Chain Resilience modeling and measurement work.

Keywords: supply chain resilience, resilience definition, supply chain resilience triangle

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Authors: Yaser Miaji, Mohammad Sabbagh

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Radio Frequency Identification (RFID) is a technology which shares a similar concept with bar code. With RFID, the electromagnetic or electrostatic coupling in the RF portion of the electromagnetic spectrum is used to transmit signals. Supply chain management is aimed to keep going long-term performance of individual companies and the overall supply chain by maximizing customer satisfaction with minimum costs. One of the major issues in the supply chain management is product loss or shrinkage. In order to overcome this problem, this system which uses Radio Frequency Identification (RFID) technology will be able to RFID track and identify where losses are occurring and enable effective traceability. RFID brings a new dimension to supply chain management by providing a more efficient way of being able to identify and track items at the various stages throughout the supply chain. This system has been developed and tested to prove that RFID technology can be used to improve traceability in supply chain at low cost. Due to its simplicity in interface program and database management system using Visual Basic and MS Excel or MS Access the system can be more affordable and implemented even by small and medium scale industries.

Keywords: supply chain, RFID, tractability, radio frequency identification

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Authors: A. Anastasopoulou, A. Kolios, T. Somorin, A. Sowale, Y. Jiang, B. Fidalgo, A. Parker, L. Williams, M. Collins, E. J. McAdam, S. Tyrrel

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Developing countries are nowadays confronted with great challenges related to domestic sanitation services in view of the imminent water scarcity. Contemporary sanitation technologies established in these countries are likely to pose health risks unless waste management standards are followed properly. This paper provides a solution to sustainable sanitation with the development of an innovative toilet system, called Nano Membrane Toilet (NMT), which has been developed by Cranfield University and sponsored by the Bill & Melinda Gates Foundation. The particular technology converts human faeces into energy through gasification and provides treated wastewater from urine through membrane filtration. In order to evaluate the environmental profile of the NMT system, a deterministic life cycle assessment (LCA) has been conducted in SimaPro software employing the Ecoinvent v3.3 database. The particular study has determined the most contributory factors to the environmental footprint of the NMT system. However, as sensitivity analysis has identified certain critical operating parameters for the robustness of the LCA results, adopting a stochastic approach to the Life Cycle Inventory (LCI) will comprehensively capture the input data uncertainty and enhance the credibility of the LCA outcome. For that purpose, Monte Carlo simulations, in combination with an artificial neural network (ANN) model, have been conducted for the input parameters of raw material, produced electricity, NO_X emissions, amount of ash and transportation of fertilizer. The given analysis has provided the distribution and the confidence intervals of the selected impact categories and, in turn, more credible conclusions are drawn on the respective LCIA (Life Cycle Impact Assessment) profile of NMT system. Last but not least, the specific study will also yield essential insights into the methodological framework that can be adopted in the environmental impact assessment of other complex engineering systems subject to a high level of input data uncertainty.

Keywords: sanitation systems, nano-membrane toilet, lca, stochastic uncertainty analysis, Monte Carlo simulations, artificial neural network

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Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

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Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

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Authors: Kristina Pflug, Markus Busch

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Being able to predict polymer properties and processing behavior based on the applied operating reaction conditions in one of the key challenges in modern polymer reaction engineering. Especially, for cost-intensive processes such as the high-pressure polymerization of low-density polyethylene (LDPE) with high safety-requirements, the need for simulation-based process optimization and product design is high. A multi-scale modelling approach was set-up and validated via a series of high-pressure mini-plant autoclave reactor experiments. The approach starts with the numerical modelling of the complex reaction network of the LDPE polymerization taking into consideration the actual reaction conditions. While this gives average product properties, the complex polymeric microstructure including random short- and long-chain branching is calculated via a hybrid Monte Carlo-approach. Finally, the processing behavior of LDPE -its melt flow behavior- is determined in dependence of the previously determined polymeric microstructure using the branch on branch algorithm for randomly branched polymer systems. All three steps of the multi-scale modelling approach can be independently validated against analytical data. A triple-detector GPC containing an IR, viscosimetry and multi-angle light scattering detector is applied. It serves to determine molecular weight distributions as well as chain-length dependent short- and long-chain branching frequencies. 13C-NMR measurements give average branching frequencies, and rheological measurements in shear and extension serve to characterize the polymeric flow behavior. The accordance of experimental and modelled results was found to be extraordinary, especially taking into consideration that the applied multi-scale modelling approach does not contain parameter fitting of the data. This validates the suggested approach and proves its universality at the same time. In the next step, the modelling approach can be applied to other reactor types, such as tubular reactors or industrial scale. Moreover, sensitivity analysis for systematically varying process conditions is easily feasible. The developed multi-scale modelling approach finally gives the opportunity to predict and design LDPE processing behavior simply based on process conditions such as feed streams and inlet temperatures and pressures.

Keywords: low-density polyethylene, multi-scale modelling, polymer properties, reaction engineering, rheology

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Authors: Mohammad Yousuf Khan, Bhavya Alankar

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Blockchain is a promising technology with its features such as immutability and decentralized database. It has applications in various fields such as pharmaceutical, finance, & the food industry. At the core of its heart lies its feature, traceability which is the most desired key in supply chains. However, supply chains have always been hit rock bottom by scandals and controversies. In this review paper, we have explored the advancement and research gaps of blockchain technology (BT) in supply chain management (SCM). We have used the Prisma framework for systematic literature review (SLR) and included a minuscule amount of grey literature to reduce publication bias. We found that supply chain traceability and transparency is the most researched objective in SCM. There was hardly any research in supply chain resilience. Further, we found that 40 % of the papers were application based. Most articles have focused on the advantages of BT, rather than analyzing it critically. This study will help identify gaps and suitable actions to be followed for an efficient implementation of BT in SCM.

Keywords: blockchain technology, supply chain management, supply chain transparency, supply chain resilience

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Authors: Sarbjit Singh

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This framework development explored the status of GSCM in manufacturing SMEs and concluded that there was a significant gap w.r.t carbon emissions measurement in the supply chain activities. The measurement of carbon emissions within supply chains is important green initiative toward its reduction. The majority of the SMEs were facing the problem to quantify the green house gas emissions in its supply chain & to make it a low carbon supply chain or GSCM. Thus, the carbon management initiatives were amalgamated with the supply chain activities in order to measure and reduce the carbon emissions, confirming the GHG protocol scopes. Henceforth, it covers the development of carbon management software (CMS) tool to quantify carbon emissions for effective carbon management. This tool is cheap and easy to use for the industries for the management of their carbon emissions within the supply chain.

Keywords: w.r.t carbon emissions, carbon management software, supply chain management, Indian Industry

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Authors: Mohammed El-Arbi Khalfallah, Mohamed Lakhdar Hadji

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Managing and controlling risk is a topic research in the world of finance. Before a risky situation, the stakeholders need to do comparison according to the positions and actions, and financial institutions must take measures of a particular market risk and credit. In this work, we study a model of risk measure in finance: Value at Risk (VaR), which is a new tool for measuring an entity's exposure risk. We explain the concept of value at risk, your average, tail, and describe the three methods for computing: Parametric method, Historical method, and numerical method of Monte Carlo. Finally, we briefly describe advantages and disadvantages of the three methods for computing value at risk.

Keywords: average value at risk, conditional value at risk, tail value at risk, value at risk

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Authors: Mohamadreza Hatefi, Malihe Hadadnezhad

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It is well established that jump-landing-related biomechanical deficiencies, such as dynamic knee valgus (DKV), can be improved by using various forms of feedback; However, the effectiveness of these interventions synchronously on athletes' jumping performance remains unknown. Twenty-one recreational athletes with DKV performed countermovement jump (CMJ) and drop vertical jump (DVJ) tasks before and after feedback intervention while the kinematic, force plate and electromyography data of the lower extremity were synchronously captured. The athletes’ jumping performance was calculated by using the reactive strength index-modified (RSIₘₒ𝒹). The athletes at the post-intervention exhibited significantly less hip adduction and more tibial internal rotation during both CMJ and DVJ tasks and maximum knee flexion just during DVJ task. Moreover, athletes exhibited increased time to take-off and consequently decreased RSIₘₒ𝒹 during DVJ task, but no difference was observed in CMJ task. Feedback immediately improved DKV without disturbing the athletes’ jumping height during both tasks, But athletes exhibited increased time to take-off and consequently decreased RSIₘₒ𝒹 only during DVJ task, which suggests that the results may differ according to the nature of jumping task. Nevertheless, the effectiveness of landing-related biomechanical deficiencies improvement on athletes' jumping performance must be investigated in the long-term as a new movement pattern.

Keywords: reactive strength index, feedback, biomechanics, dynamic knee valgus

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Authors: Suparat Niwitpong, Sa-aat Niwitpong

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Statistical inference of normal mean with known coefficient of variation has been investigated recently. This phenomenon occurs normally in environment and agriculture experiments when the scientist knows the coefficient of variation of their experiments. In this paper, we constructed new confidence intervals for the normal population mean with known coefficient of variation. We also derived analytic expressions for the coverage probability of each confidence interval. To confirm our theoretical results, Monte Carlo simulation will be used to assess the performance of these intervals based on their coverage probabilities.

Keywords: confidence interval, coverage probability, expected length, known coefficient of variation

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Authors: Vaibhav Barve

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Reversible information hiding has drawn a lot of interest as of late. Being reversible, we can restore unique computerized data totally. It is a plan where mystery data is put away in digital media like image, video, audio to maintain a strategic distance from unapproved access and security reason. By and large JPEG bit stream is utilized to store this key data, first JPEG bit stream is encrypted into all around sorted out structure and then this secret information or key data is implanted into this encrypted region by marginally changing the JPEG bit stream. Valuable pixels suitable for information implanting are computed and as indicated by this key subtle elements are implanted. In our proposed framework we are utilizing RC4 algorithm for encrypting JPEG bit stream. Encryption key is acknowledged by framework user which, likewise, will be used at the time of decryption. We are executing enhanced least significant bit supplanting steganography by utilizing genetic algorithm. At first, the quantity of bits that must be installed in a guaranteed coefficient is versatile. By utilizing proper parameters, we can get high capacity while ensuring high security. We are utilizing logistic map for shuffling of bits and utilization GA (Genetic Algorithm) to find right parameters for the logistic map. Information embedding key is utilized at the time of information embedding. By utilizing precise picture encryption and information embedding key, the beneficiary can, without much of a stretch, concentrate the incorporated secure data and totally recoup the first picture and also the original secret information. At the point when the embedding key is truant, the first picture can be recouped pretty nearly with sufficient quality without getting the embedding key of interest.

Keywords: data embedding, decryption, encryption, reversible data hiding, steganography

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Authors: Chloe E. Edwards, Yasaman Shahtaheri

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

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

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Authors: W. Benstaali, A. Belasri

Abstract:

In this work we study a planar lamp filled with neon-xenon gas. We use a one-dimensional particle in a cell with Monte Carlo simulation (PIC-MCC) to investigate the effect xenon concentration on the energy deposited on excitation, ionization and ions. A Xe-Ne discharge is studied for a gas pressure of 400 torr. The results show an efficient Xe20-Ne mixture with an applied voltage of 1.2KV; the xenon excitation energy represents 65% form total energy dissipated in the discharge. We have also studied electrical properties and the energy balance a discharge for Xe50-Ne which needs a voltage of 2kv; the xenon energy is than more important.

Keywords: dielectric barrier discharge, efficiency, excitation, lamps

Procedia PDF Downloads 136

Authors: Gubhinder Kundhi, Paul Rilstone

Abstract:

It is well established that Instrumental Variable (IV) estimators in the presence of weak instruments can be poorly behaved, in particular, be quite biased in finite samples. Finite sample approximations to the distributions of these estimators are obtained using Edgeworth and Saddlepoint expansions. Departures from normality of the distributions of these estimators are analyzed using higher order analytical corrections in these expansions. In a Monte-Carlo experiment, the performance of these expansions is compared to the first order approximation and other methods commonly used in finite samples such as the bootstrap.

Keywords: bootstrap, Instrumental Variable, Edgeworth expansions, Saddlepoint expansions

Procedia PDF Downloads 284

Authors: A. Abbou, V. Makis, N. Salari

Abstract:

We study the condition-based maintenance (CBM) problem of a system subject to stochastic deterioration. The system is composed of three units (or modules): (i) Module 1 deterioration follows a Markov process with two operational states and one failure state. The operational states are partially observable through periodic condition monitoring. (ii) Module 2 deterioration follows a Gamma process with a known failure threshold. The deterioration level of this module is fully observable through periodic inspections. (iii) Only the operating age information is available of Module 3. The lifetime of this module has a general distribution. A CBM policy prescribes when to initiate a maintenance intervention and which modules to repair during intervention. Our objective is to determine the optimal CBM policy minimizing the long-run expected average cost of operating the system. This is achieved by formulating a Markov decision process (MDP) and developing the value iteration algorithm for solving the MDP. We provide numerical examples illustrating the cost-effectiveness of the optimal CBM policy through a comparison with heuristic policies commonly found in the literature.

Keywords: reliability, maintenance optimization, Markov decision process, heuristics

Procedia PDF Downloads 190