Search results for: cumulative variance.

Authors: Nada Jasim Habeeb, Rana Saad Mohammed, Muntaha Khudair Abbass

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For more efficient and fast video summarization, this paper presents a surveillance video summarization method. The presented method works to improve video summarization technique. This method depends on temporal differencing to extract most important data from large video stream. This method uses histogram differencing and Sum Conditional Variance which is robust against to illumination variations in order to extract motion objects. The experimental results showed that the presented method gives better output compared with temporal differencing based summarization techniques.

Keywords: temporal differencing, video summarization, histogram differencing, sum conditional variance

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Authors: A. Alzeyadi, E. Loffill, R. Alkhaddar

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Continuous upflow filters can combine the nutrient (nitrogen and phosphate) and suspended solid removal in one unit process. The contaminant removal could be achieved chemically or biologically; in both processes the filter removal efficiency depends on the interaction between the packed filter media and the influent. In this paper a residence time distribution (RTD) study was carried out to understand and compare the transfer behaviour of contaminants through a selected filter media packed in a laboratory-scale continuous up flow filter; the selected filter media are limestone and white dolomite. The experimental work was conducted by injecting a tracer (red drain dye tracer –RDD) into the filtration system and then measuring the tracer concentration at the outflow as a function of time; the tracer injection was applied at hydraulic loading rates (HLRs) (3.8 to 15.2 m h-1). The results were analysed according to the cumulative distribution function F(t) to estimate the residence time of the tracer molecules inside the filter media. The mean residence time (MRT) and variance σ2 are two moments of RTD that were calculated to compare the RTD characteristics of limestone with white dolomite. The results showed that the exit-age distribution of the tracer looks better at HLRs (3.8 to 7.6 m h-1) and (3.8 m h-1) for limestone and white dolomite respectively. At these HLRs the cumulative distribution function F(t) revealed that the residence time of the tracer inside the limestone was longer than in the white dolomite; whereas all the tracer took 8 minutes to leave the white dolomite at 3.8 m h-1. On the other hand, the same amount of the tracer took 10 minutes to leave the limestone at the same HLR. In conclusion, the determination of the optimal level of hydraulic loading rate, which achieved the better influent distribution over the filtration system, helps to identify the applicability of the material as filter media. Further work will be applied to examine the efficiency of the limestone and white dolomite for phosphate removal by pumping a phosphate solution into the filter at HLRs (3.8 to 7.6 m h-1).

Keywords: filter media, hydraulic loading rate, residence time distribution, tracer

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Authors: David A. Swanson, Lucky M. Tedrow

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Taylor’s Law is a widely observed empirical pattern that relates variances to means in sets of non-negative measurements via an approximate power function, which has found application to human mortality. This study adds to this research by showing that Taylor’s Law leads to a model that reasonably describes the relationship between life expectancy at birth (e0, which also is equal to mean age at death in a life table) and variance at age of death in seven World Bank regional life tables measured at two points in time, 1970 and 2000. Using as a benchmark a non-random sample of four Japanese female life tables covering the period from 1950 to 2004, the study finds that the simple linear model provides reasonably accurate estimates of variance in age at death in a life table from e0, where the latter range from 60.9 to 85.59 years. Employing 2017 life tables from the Human Mortality Database, the simple linear model is used to provide estimates of variance at age in death for six countries, three of which have high e0 values and three of which have lower e0 values. The paper provides a substantive interpretation of Taylor’s Law relative to e0 and concludes by arguing that reasonably accurate estimates of variance in age at death in a period life table can be calculated using this approach, which also can be used where e0 itself is estimated rather than generated through the construction of a life table, a useful feature of the model.

Keywords: empirical pattern, mean age at death in a life table, mean age of a stationary population, stationary population

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Authors: Zheming Cao

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Control loops are the most important components in the wind turbine system. Product quality, operation safety, and the economic performance are directly or indirectly connected to the performance of control systems. This paper proposed a performance evaluation method based on minimum-variance for wind turbine control system. This method can be applied on PID controller for pitch control system in the wind turbine. The good performance result demonstrated in the paper was achieved by retuning and optimizing the controller settings based on the evaluation result. The concepts presented in this paper are illustrated with the actual data of the industrial wind farm.

Keywords: control performance, evaluation, minimum-variance, wind turbine

Procedia PDF Downloads 334

Authors: Tarah Bouaricha

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The impact of worst natural disasters is analysed in terms of insured losses which happened between 2010 and 2014 on S&P insurance index. Event study analysis is used to test whether natural disasters impact insurance index stock market price. There is no negative impact on insurance stock market price around the disasters event. To analyse the reaction of insurance stock market, normal returns (NR), abnormal returns (AR), cumulative abnormal returns (CAR), cumulative average abnormal returns (CAAR) and a parametric test on AR and on CAR are used.

Keywords: study event, natural disasters, insurance, reinsurance, stock market

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Authors: Picha Sriprachan

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The increase in electricity prices affects the cost of high-rise building construction. The objectives of this research are to study the electrical cost, trend of electrical cost and to forecast electrical cost of high-rise building construction. The methods of this research are: 1) to study electrical payment formats, cost data collection methods, and the factors affecting electrical cost of high-rise building construction, 2) to study the quantity and trend of cumulative percentage of the electrical cost, and 3) to forecast the electrical cost for different types of high-rise buildings. The results of this research show that the average proportion between electrical cost and the value of the construction project is 0.87 percent. The proportion of electrical cost for residential, office and commercial, and hotel buildings are closely proportional. If construction project value increases, the proportion of electrical cost and the value of the construction project will decrease. However, there is a relationship between the amount of electrical cost and the value of the construction project. During the structural construction phase, the amount of electrical cost will increase and during structural and architectural construction phase, electrical cost will be maximum. The cumulative percentage of the electrical cost is related to the cumulative percentage of the high-rise building construction cost in the same direction. The amount of service space of the building, number of floors and the duration of the construction affect the electrical cost of construction. The electrical cost of construction forecasted by using linear regression equation is close to the electrical cost forecasted by using the proportion of electrical cost and value of the project.

Keywords: high-rise building construction, electrical cost, construction phase, architectural phase

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Authors: Seyed Mohammad Nasir Mousavi, Amir Mashayekh, Pasha Hejazi, Sanaz Kanani Zadeh Khalkhali

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To determine the stability of the oil percent canola varieties, an experiment was done in a randomized complete block design with four replications in four research stations of the country Shahrood, Esfahan, Kermanshah, Varamin. Analysis of variance showed that there is cultivars considerable variability in the percentage of oil. The results showed that the coefficient of variation of oil Hyola 401 and Hyola308 stability and flexibility are high. Cultivars Cooper and Likord are minimum variance Shukla that stable for the percentage of oil Based on the chart AMMI 1, cultivars Zarfam and Hyola 401 are of oil percentage than other varieties had higher stability. On the chart AMMI2, cultivars Karun and Hyola 308 are identified as stable, also location Isfahan is stable

Keywords: canola, stability, AMMI, variance Shukla

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Authors: Murad A. Bein

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This paper empirically examines the time-varying relationship and spillover effects between the international crude oil price and ten emerging equity markets, namely three oil-exporting countries (Brazil, Mexico, and Russia) and seven Central and Eastern European (CEE) countries (Bulgaria, Croatia, Czech Republic, Hungary, Poland, Romania, and Slovakia). The results revealed that there are spillover effects from oil markets into almost all emerging equity markets save Slovakia. Besides, the oil supply glut had a homogenous effect on the emerging markets, both net oil-exporting, and oil-importing countries (CEE). Further, the time variance drastically increased during financial turmoil. Indeed, the time variance remained high from 2009 to 2012 in response to aggregate demand shocks (global financial crisis and Eurozone debt crisis) and quantitative easing measures. Interestingly, the time variance was slightly higher for the oil-exporting countries than for some of the CEE countries. Decision-makers in emerging economies should therefore seek policy coordination when dealing with financial turmoil.

Keywords: crude oil, spillover effects, emerging equity, time-varying, aggregate demand shock

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Authors: Sukovatov K.Yu., Bezuglova N.N.

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Winter precipitation variability over the Altai Krai was investigated by retrieving temporal patterns. The spectral singular analysis was used to describe the variance distribution and to reduce the precipitation data into a few components (modes). The associated time series were related to large-scale atmospheric and oceanic circulation indices by using lag cross-correlation and wavelet-coherence analysis. GPCC monthly precipitation data for rectangular field limited by 50-550N, 77-880E and monthly climatological circulation index data for the cold season were used to perform SSA decomposition and retrieve statistics for analyzed parameters on the time period 1951-2017. Interannual variability of winter precipitation over the Altai Krai are mostly caused by three natural factors: intensity variations of momentum exchange between mid and polar latitudes over the North Atlantic (explained variance 11.4%); wind speed variations in equatorial stratosphere (quasi-biennial oscillation, explained variance 15.3%); and surface temperature variations for equatorial Pacific sea (ENSO, explained variance 2.8%). It is concluded that under the current climate conditions (Arctic amplification and increasing frequency of meridional processes in mid-latitudes) the second and the third factors are giving more significant contribution into explained variance of interannual variability for cold season atmospheric precipitation over the Altai Krai than the first factor.

Keywords: interannual variability, winter precipitation, Altai Krai, wavelet-coherence

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Authors: Autcha Araveeporn

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This paper is to compare the parameter estimation of the mean in normal distribution by Maximum Likelihood (ML), Bayes, and Markov Chain Monte Carlo (MCMC) methods. The ML estimator is estimated by the average of data, the Bayes method is considered from the prior distribution to estimate Bayes estimator, and MCMC estimator is approximated by Gibbs sampling from posterior distribution. These methods are also to estimate a parameter then the hypothesis testing is used to check a robustness of the estimators. Data are simulated from normal distribution with the true parameter of mean 2, and variance 4, 9, and 16 when the sample sizes is set as 10, 20, 30, and 50. From the results, it can be seen that the estimation of MLE, and MCMC are perceivably different from the true parameter when the sample size is 10 and 20 with variance 16. Furthermore, the Bayes estimator is estimated from the prior distribution when mean is 1, and variance is 12 which showed the significant difference in mean with variance 9 at the sample size 10 and 20.

Keywords: Bayes method, Markov chain Monte Carlo method, maximum likelihood method, normal distribution

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Authors: A. A. Hussen, I. N. Jleta

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Micro-electromechanical system (MEMS) accelerometers and gyroscopes are suitable for the inertial navigation system (INS) of many applications due to the low price, small dimensions and light weight. The main disadvantage in a comparison with classic sensors is a worse long term stability. The estimation accuracy is mostly affected by the time-dependent growth of inertial sensor errors, especially the stochastic errors. In order to eliminate negative effect of these random errors, they must be accurately modeled. Where the key is the successful implementation that depends on how well the noise statistics of the inertial sensors is selected. In this paper, the Allan variance technique will be used in modeling the stochastic errors of the inertial sensors. By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial-sensor output data.

Keywords: Allan variance, accelerometer, gyroscope, stochastic errors

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Authors: S. Ravindran, Y.Liu, I. Gratchev, D.Jeng

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Rainfall-induced shallow landslides are more common in the northern New South Wales (NSW), Australia. From 2009 to 2017, around 105 rainfall-induced landslides occurred along the road corridors and caused temporary road closures in the northern NSW. Rainfall causing shallow landslides has different distributions of rainfall varying from uniform, normal, decreasing to increasing rainfall intensity. The duration of rainfall varied from one day to 18 days according to historical data. The objective of this research is to analyse slope instability of some of the sites in the northern NSW by varying cumulative rainfall using SLOPE/W and SEEP/W and compare with field data of rainfall causing shallow landslides. The rainfall data and topographical data from public authorities and soil data obtained from laboratory tests will be used for this modelling. There is a likelihood of shallow landslides if the cumulative rainfall is between 100 mm to 400 mm in accordance with field data.

Keywords: landslides, modelling, rainfall, suction

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Authors: Wael M. Bazzi, Amir Rastegarnia, Azam Khalili

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In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based on incremental distributed least mean-square (IDLMS) algorithm. The proposed method contains two phases: I) Estimation of each sensors observation noise variance, and II) Estimation of the desired parameter using the estimated observation variances. To deal with the reliability of measurements, in the second phase of the proposed algorithm, the step-size parameter is adjusted for each sensor according to its observation noise variance. As our simulation results show, the proposed algorithm considerably improves the performance of the IDLMS algorithm in the same condition.

Keywords: adaptive filter, distributed estimation, sensor network, IDLMS algorithm

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Authors: Jodie Stevenson, Lisa-Marie Emerson, Abigail Millings

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Mindfulness has been conceptualized as a dispositional trait, which is different across individuals. Previous research has independently identified both adult attachment orientations and emotion regulation abilities as correlates of dispositional mindfulness. Research has also presented a two-factor model of the relationship between these three constructs. The present study aimed to further develop this model and investigated theses relationships in a sample of 186 participants. Participants completed the Five Factor Mindfulness Questionnaire Short Form (FFMQ-SF), the Experiences in Close Relationships Scale for global attachment (ECR), the Emotion Regulation Questionnaire (ERC), and the Adult Disorganized Attachment scale (ADA). Exploratory factor analysis revealed a 3-factor solution accounting for 59% of the variance across scores on these measures. The first factor accounted for 32% of the variance and loaded highly on attachment and mindfulness subscales. The second factor accounted for 15% of the variance with strong loadings on emotion regulation subscales. The third factor accounted for 12% of the variance with strong loadings on disorganized attachment, and the mindfulness observes subscale. The results further confirm the relationship between attachment, mindfulness, and emotion regulation along with the unique addition of disorganized attachment. The extracted factors will then be used to predict well-being outcomes for an undergraduate student population.

Keywords: adult attachment, emotion regulation, mindfulness, well-being

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Authors: Aakanchha Jain, D. V. Kohli

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Cubosomes are nanoparticles but instead of the solid particles, cubosomes are self-assembled liquid crystalline particles of certain surfactant with proper ratio of water with a microstructure that provides unique properties of practical interest. Cubosomes encapsulating Fluconazole were prepared by emulsification method and characterized for particle size, entrapment efficiency. The cubosomes prepared were 257.2±2.94 nm in size with drug entrapment efficiency of 66.2±2.69%. The optimized formulation characterized for shape and surface morphology by TEM and SEM analysis. SEM photograph showed the smooth surface of optimized cubosomes and TEM photograph revealed square somewhat circular intact shapes of cubosomes. MIC was determined by XTT based method and antifungal activity was determined in vitro. The cumulative percentage of Fnz from cubosomes permeated via dialysis membrane (MWCO 12-14 KD) showed a percent cumulative drug release of 76.86% while Fnz solution showed release up to 91.04% in 24 hours in PBS (pH 6.5)(p < 0.005).

Keywords: Candids albicans, cubosomes, fluconazole, topical delivery

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Authors: Abiodun Musbau Lawal, Sunday Samson Babalola, Uzor Friday Ordu

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Issue of employees counterproductive work behavior in government owned organization in emerging economies has continued to be a major concern. This study investigated the factors of pay satisfaction, intent to leave and age as predictors of counterproductive work behavior among non-academic employee in a Nigerian federal government owned university. A sample of 200 non-academic employees completed questionnaires. Hierarchical multiple regression was conducted to determine the contribution of each of the predictor variables on the criterion variable on counterproductive work behavior. Results indicate that age of participants (β = -.18; p < .05) significantly independently predicted CWB by accounting for 3% of the explained variance. Addition of pay satisfaction (β = -.14; p < .05) significantly accounted for 5% of the explained variance, while intent to leave (β = -.17; p < .05) further resulted in 8% of the explained variance in counterproductive work behavior. The importance of these findings with regards to reduction in counterproductive work behavior is highlighted.

Keywords: counterproductive, work behaviour, pay satisfaction, intent to leave

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

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

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

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Authors: Khalid Mujaljal, Hassan Alhajhoj

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This paper empirically investigates the influence of oil price fluctuations on the key macroeconomic variables of the Kingdom of Saudi Arabia using unrestricted VAR methodology. Two analytical tools- Granger-causality and variance decomposition are used. The Granger-causality test reveals that almost all specifications of oil price shocks significantly Granger-cause GDP and demonstrates evidence of causality between oil price changes and money supply (M3) and consumer price index percent (CPIPC) in the case of positive oil price shocks. Surprisingly, almost all specifications of oil price shocks do not Granger-cause government expenditure. The outcomes from variance decomposition analysis suggest that positive oil shocks contribute about 25 percent in causing inflation in the country. Also, contribution of symmetric linear oil price shocks and asymmetric positive oil price shocks is significant and persistent with 25 percent explaining variation in world consumer price index till end of the period.

Keywords: Granger causality, oil prices changes, Saudi Arabian economy, variance decomposition

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Authors: Hao-Hsi Tseng, Hsin-Yun Lee

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The Most Advantageous Tender (MAT) has been criticized for its susceptibility to dictatorial situations and for its processing of same score, same rank issues. This study applies the four criteria from Arrow's Impossibility Theorem to construct a mechanism for revealing illegitimate scores in scoring methods. While commonly be used to improve on problems resulting from extreme scores, ranking methods hide significant defects, adversely affecting selection fairness. To address these shortcomings, this study relies mainly on the overall evaluated score method, using standardized scores plus normal cumulative distribution function conversion to calculate the evaluation of vender preference. This allows for free score evaluations, which reduces the influence of dictatorial behavior and avoiding same score, same rank issues. Large-scale simulations confirm that this method outperforms currently used methods using the Impossibility Theorem.

Keywords: Arrow’s impossibility theorem, cumulative normal distribution function, most advantageous tender, scoring method

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Authors: Usoro Anthony E., Udoh Emediong

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Diagonal Vector Autoregressive Models are special classes of the general vector autoregressive models identified under certain conditions, where parameters are restricted to the diagonal elements in the coefficient matrices. Variance, autocovariance, and autocorrelation properties of the upper and lower diagonal VAR models are derived. The new set of VAR models is verified with empirical data and is found to perform favourably with the general VAR models. The advantage of the diagonal models over the existing models is that the new models are parsimonious, given the reduction in the interactive coefficients of the general VAR models.

Keywords: VAR models, diagonal VAR models, variance, autocovariance, autocorrelations

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Authors: Schröde C., Rodriguez D., Sánchez A., Abdul Malak, Churchill J., Boksmati T., Alharbi, Alsulmi H., Maghrabi S., Mowalad, Mutwalli R., Abualnaja Y.

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Formulating a strategy for sustainable development of the Kingdom of Saudi Arabia’s coastal and marine environment is at the core of the “Marine and Coastal Protection Assessment Study for the Kingdom of Saudi Arabia Coastline (MCEP)”; that was set up in the context of the Vision 2030 by the Saudi Arabian government and aimed at providing a first comprehensive ‘Status Quo Assessment’ of the Kingdom’s marine environment to inform a sustainable development strategy and serve as a baseline assessment for future monitoring activities. This baseline assessment relied on scientific evidence of the drivers, pressures and their impact on the environments of the Red Sea and Arabian Gulf. A key element of the assessment was the cumulative pressure hotspot analysis developed for both national waters of the Kingdom following the principles of the Driver-Pressure-State-Impact-Response (DPSIR) framework and using the cumulative pressure and impact assessment methodology. The ultimate goals of the analysis were to map and assess the main hotspots of environmental pressures, and identify priority areas for further field surveillance and for urgent management actions. The study identified maritime transport, fisheries, aquaculture, oil, gas, energy, coastal industry, coastal and maritime tourism, and urban development as the main drivers of pollution in the Saudi Arabian marine waters. For each of these drivers, pressure indicators were defined to spatially assess the potential influence of the drivers on the coastal and marine environment. A list of hotspots of 90 locations could be identified based on the assessment. Spatially grouped the list could be reduced to come up with of 10 hotspot areas, two in the Arabian Gulf, 8 in the Red Sea. The hotspot mapping revealed clear spatial patterns of drivers, pressures and hotspots within the marine environment of waters under KSA’s maritime jurisdiction in the Red Sea and Arabian Gulf. The cascading assessment approach based on the DPSIR framework ensured that the root causes of the hotspot patterns, i.e. the human activities and other drivers, can be identified. The adapted CPIA methodology allowed for the combination of the available data to spatially assess the cumulative pressure in a consistent manner, and to identify the most critical hotspots by determining the overlap of cumulative pressure with areas of sensitive biodiversity. Further improvements are expected by enhancing the data sources of drivers and pressure indicators, fine-tuning the decay factors and distances of the pressure indicators, as well as including trans-boundary pressures across the regional seas.

Keywords: Arabian Gulf, DPSIR, hotspot, red sea

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Authors: Chin-Chih Chang

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Many manufacturing systems suffer failures due to complex degradation processes and various environment conditions such as random shocks. Consider an operating system is subject to random shocks and works at random times for successive jobs. When successive jobs often result in production losses and performance deterioration, it would be better to do maintenance or replacement at a planned time. A preventive replacement (PR) policy is presented to replace the system before a failure occurs at a continuous time T. In such a policy, the failure characteristics of the system are designed as follows. Each job would cause a random amount of additive damage to the system, and the system fails when the cumulative damage has exceeded a failure threshold. Suppose that the deteriorating system suffers one of the two types of shocks with age-dependent probabilities: type-I (minor) shock is rectified by a minimal repair, or type-II (catastrophic) shock causes the system to fail. A corrective replacement (CR) is performed immediately when the system fails. In summary, a generalized maintenance model to scheduling replacement plan for an operating system is presented below. PR is carried out at time T, whereas CR is carried out when any type-II shock occurs and the total damage exceeded a failure level. The main objective is to determine the optimal continuous schedule time of preventive replacement through minimizing the mean cost rate function. The existence and uniqueness of optimal replacement policy are derived analytically. It can be seen that the present model is a generalization of the previous models, and the policy with preventive replacement outperforms the one without preventive replacement.

Keywords: preventive replacement, working time, cumulative damage model, minimal repair, imperfect maintenance, optimization

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Authors: Alan Wan

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In recent years, the body of literature on frequentist model averaging in statistics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this paper, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimisation of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favourable properties compared to some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function misspecification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two real data sets.

Keywords: heteroscedasticity-robust, model averaging, multiplicative heteroscedasticity, plug-in, squared prediction risk

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Authors: Mai A. Ibrahim, Mohammed El-Beltagy, Motaz Khorshid

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Emerging Markets return distributions have shown significance departure from normality were they are characterized by fatter tails relative to the normal distribution and exhibit levels of skewness and kurtosis that constitute a significant departure from normality. Therefore, the classical Markowitz Mean-Variance is not applicable for emerging markets since it assumes normally-distributed returns (with zero skewness and kurtosis) and a quadratic utility function. Moreover, the Markowitz mean-variance analysis can be used in cases of moderate non-normality and it still provides a good approximation of the expected utility, but it may be ineffective under large departure from normality. Higher moments models and median models have been suggested in the literature for asset allocation in this case. Higher moments models have been introduced to account for the insufficiency of the description of a portfolio by only its first two moments while the median model has been introduced as a robust statistic which is less affected by outliers than the mean. Tail risk measures such as Value-at Risk (VaR) and Conditional Value-at-Risk (CVaR) have been introduced instead of Variance to capture the effect of risk. In this research, higher moment models including the Mean-Variance-Skewness (MVS) and Mean-Variance-Skewness-Kurtosis (MVSK) are formulated as single-objective non-linear programming problems (NLP) and median models including the Median-Value at Risk (MedVaR) and Median-Mean Absolute Deviation (MedMAD) are formulated as a single-objective mixed-integer linear programming (MILP) problems. The higher moment models and median models are compared to some benchmark portfolios and tested on real financial data in the Egyptian main Index EGX30. The results show that all the median models outperform the higher moment models were they provide higher final wealth for the investor over the entire period of study. In addition, the results have confirmed the inapplicability of the classical Markowitz Mean-Variance to the Egyptian stock market as it resulted in very low realized profits.

Keywords: Egyptian stock exchange, emerging markets, higher moment models, median models, mixed-integer linear programming, non-linear programming

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Authors: Fayyaz Rasool, Shakeela Parveen

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The studies on genetic diversity of Labeo rohita by using molecular markers were carried out to investigate the genetic structure by RAPAD marker and the levels of polymorphism and similarity amongst the different groups of five populations of wild and farmed types. The samples were collected from different five locations as representatives of wild and hatchery raised populations. RAPAD data for Jaccard’s coefficient by following the un-weighted Pair Group Method with Arithmetic Mean (UPGMA) for Hierarchical Clustering of the similar groups on the basis of similarity amongst the genotypes and the dendrogram generated divided the randomly selected individuals of the five populations into three classes/clusters. The variance decomposition for the optimal classification values remained as 52.11% for within class variation, while 47.89% for the between class differences. The Principal Component Analysis (PCA) for grouping of the different genotypes from the different environmental conditions was done by Spearman Varimax rotation method for bi-plot generation of the co-occurrence of the same genotypes with similar genetic properties and specificity of different primers indicated clearly that the increase in the number of factors or components was correlated with the decrease in eigenvalues. The Kaiser Criterion based upon the eigenvalues greater than one, first two main factors accounted for 58.177% of cumulative variability.

Keywords: variation, clustering, PCA, wild, hatchery, RAPAD, Labeo rohita

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Authors: Bouneua Khamphilavanh, Toshihiko Masui

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The penetration of EV (electric vehicle) technology in Lao road transportation, in this study, was analyzed by using the AIM/CGE [Laos] model. The computable general equilibrium (CGE) model was developed by the Asia-Pacific Integrated Model (AIM) team. In line with the increase of the number of road vehicles, the energy demand in the transport sector has been gradually increased which resulted in a large amount of budget spent for importing fossil fuels during the last decade, and a high carbon dioxide emission from the transport sector, hence the aim of this research is to analyze the impact of EVs penetration on economic and CO₂ emission in short-term, middle-term, and long-term. By the year 2050, the expected gross domestic product (GDP) value, due to Laos will spend more budget for importing the EV, will be gradually lost up to one percent. The cumulative CO₂ emission from 2020 to 2050 in BAU case will be 12,000 GgCO₂eq, and those in the EV mitigation case will be 9,300 GgCO₂eq, which accounting for likely 77% cumulative CO₂ emission reduction in the road transport sector by introducing the EV technology.

Keywords: GDP, CO₂ mitigation, CGE model, EV technology, transport

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Authors: Yuzuru Mitsui, Takashi Ikegami

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The exponents of Taylor’s law in deterministic chaotic systems are computed, and their meanings are intensively discussed. Taylor’s law is the scaling relationship between the mean and variance (in both space and time) of population abundance, and this law is known to hold in a variety of ecological time series. The exponents found in the temporal Taylor’s law are different from those of the spatial Taylor’s law. The temporal Taylor’s law is calculated on the time series from the same locations (or the same initial states) of different temporal phases. However, with the spatial Taylor’s law, the mean and variance are calculated from the same temporal phase sampled from different places. Most previous studies were done with stochastic models, but we computed the temporal and spatial Taylor’s law in deterministic systems. The temporal Taylor’s law evaluated using the same initial state, and the spatial Taylor’s law was evaluated using the ensemble average and variance. There were two main discoveries from this work. First, it is often stated that deterministic systems tend to have the value two for Taylor’s exponent. However, most of the calculated exponents here were not two. Second, we investigated the relationships between chaotic features measured by the Lyapunov exponent, the correlation dimension, and other indexes with Taylor’s exponents. No strong correlations were found; however, there is some relationship in the same model, but with different parameter values, and we will discuss the meaning of those results at the end of this paper.

Keywords: chaos, density effect, population dynamics, Taylor’s law

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Authors: Mostafa Kazemi, Zahra N. Farkhani

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This paper proposes an integrated Data Envelopment Analysis (DEA) and Variance Inflation Factor (VIF) model for measuring the technical efficiency of decision making units. The model is validated using a set of 69% sales representatives’ dairy products. The analysis is done in two stages, in the first stage, VIF technique is used to distinguish independent effective factors of resellers, and in the second stage we used DEA for measuring efficiency for both constant and variable return to scales status. Further DEA is used to examine the utilization of environmental factors on efficiency. Results of this paper indicated an average managerial efficiency of 83% in the whole sales representatives’ dairy products. In addition, technical and scale efficiency were counted 96% and 80% respectively. 38% of sales representative have the technical efficiency of 100% and 72% of the sales representative in terms of managerial efficiency are quite efficient.High levels of relative efficiency indicate a good condition for sales representative efficiency.

Keywords: data envelopment analysis (DEA), relative efficiency, sales representatives’ dairy products, variance inflation factor (VIF)

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Authors: Andi Darmawan, Dewi Retno Sari Saputro, Purnami Widyaningsih

Abstract:

Diabetes mellitus (DM) is a chronic disease in human that occurred if pancreas cannot produce enough of insulin hormone or the body uses ineffectively insulin hormone which causes increasing level of glucose in the blood, or it was called hyperglycemia. In Indonesia, DM is a serious disease on health because it can cause blindness, kidney disease, diabetic feet (gangrene), and stroke. The type of DM criteria can also be divided based on the main causes; they are DM type 1, type 2, and gestational. Diabetes type 1 or previously known as insulin-independent diabetes is due to a lack of production of insulin hormone. Diabetes type 2 or previously known as non-insulin dependent diabetes is due to ineffective use of insulin while gestational diabetes is a hyperglycemia that found during pregnancy. The most one type commonly found in patient is DM type 2. The main factors of this disease are genetic (A) and life style (E). Those disease with 2 factors can be constructed with additive genetic and unique environment (AE) model. In this article was discussed parameter estimation of AE model using Bayesian method and the inheritance character simulation on parent-offspring. On the AE model, there are response variable, predictor variables, and parameters were capable of representing the number of population on research. The population can be measured through a taken random sample. The response and predictor variables can be determined by sample while the parameters are unknown, so it was required to estimate the parameters based on the sample. Estimation of AE model parameters was obtained based on a joint posterior distribution. The simulation was conducted to get the value of genetic variance and life style variance. The results of simulation are 0.3600 for genetic variance and 0.0899 for life style variance. Therefore, the variance of genetic factor in DM type 2 is greater than life style.

Keywords: AE model, Bayesian method, diabetes mellitus type 2, genetic, life style

Procedia PDF Downloads 245

Authors: Arash Azarpanah, Masoud Zadehbagheri, Shorangiz Javanmardi

Abstract:

Abiotic stresses are the principle threat to plant growth and crop productivity all over the world. In order to improve the germination of corn seeds in drought stress conditions, effect of seed priming by various concentrations of salicylic acid (SA) (0.8 and 0.2 mM) on activities of catalase and peroxidase in Zea mays L. plant (Var-Sc.704) was evaluated at Agriculture Research Center located in Arsenjan city in Iran, during summer 2013. A farm research was done in RCBD as factorial with three replications. We considered four irrigation was carried out once the cumulative evaporation from Pan Class A come to 40, 60, 80 and 100 mm. Results illustrated that drought stress significantly increased activities of catalase and peroxidase and also treatment with salicylic acid significantly increased activities of catalase and peroxidase. In addition, treatment with salicylic acid enhances drought tolerance in Zea mays L. plant (Var-Sc.704) with increasing activities of antioxidant enzymes.

Keywords: catalase, corn, salicylic acid, water deficits stress, cumulative evaporation, Pan Class A

Procedia PDF Downloads 420