Search results for: RR interval time series
20182 Time-Series Load Data Analysis for User Power Profiling
Authors: Mahdi Daghmhehci Firoozjaei, Minchang Kim, Dima Alhadidi
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In this paper, we present a power profiling model for smart grid consumers based on real time load data acquired smart meters. It profiles consumers’ power consumption behaviour using the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features be extracted. Two load types are defined and the related load patterns are extracted for classifying consumption behaviour by DTW. The classification methodology is discussed in detail. To evaluate the performance of the method, we analyze the time-series load data measured by a smart meter in a real case. The results verify the effectiveness of the proposed profiling method with 90.91% true positive rate for load type clustering in the best case.Keywords: power profiling, user privacy, dynamic time warping, smart grid
Procedia PDF Downloads 14720181 Retrospective Reconstruction of Time Series Data for Integrated Waste Management
Authors: A. Buruzs, M. F. Hatwágner, A. Torma, L. T. Kóczy
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The development, operation and maintenance of Integrated Waste Management Systems (IWMS) affects essentially the sustainable concern of every region. The features of such systems have great influence on all of the components of sustainability. In order to reach the optimal way of processes, a comprehensive mapping of the variables affecting the future efficiency of the system is needed such as analysis of the interconnections among the components and modelling of their interactions. The planning of a IWMS is based fundamentally on technical and economical opportunities and the legal framework. Modelling the sustainability and operation effectiveness of a certain IWMS is not in the scope of the present research. The complexity of the systems and the large number of the variables require the utilization of a complex approach to model the outcomes and future risks. This complex method should be able to evaluate the logical framework of the factors composing the system and the interconnections between them. The authors of this paper studied the usability of the Fuzzy Cognitive Map (FCM) approach modelling the future operation of IWMS’s. The approach requires two input data set. One is the connection matrix containing all the factors affecting the system in focus with all the interconnections. The other input data set is the time series, a retrospective reconstruction of the weights and roles of the factors. This paper introduces a novel method to develop time series by content analysis.Keywords: content analysis, factors, integrated waste management system, time series
Procedia PDF Downloads 32520180 Interval Estimation for Rainfall Mean in Northeastern Thailand
Authors: Nitaya Buntao
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This paper considers the problems of interval estimation for rainfall mean of the lognormal distribution and the delta-lognormal distribution in Northeastern Thailand. We present here the modified generalized pivotal approach (MGPA) compared to the modified method of variance estimates recovery (MMOVER). The performance of each method is examined in term of coverage probabilities and average lengths by Monte Carlo simulation. An extensive simulation study indicates that the MMOVER performs better than the MGPA approach in terms of the coverage probability; it results in highly accurate coverage probability.Keywords: rainfall mean, interval estimation, lognormal distribution, delta-lognormal distribution
Procedia PDF Downloads 45320179 Influence of Parameters of Modeling and Data Distribution for Optimal Condition on Locally Weighted Projection Regression Method
Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri
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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method
Procedia PDF Downloads 50120178 Review of Friction Stir Welding of Dissimilar 5000 and 6000 Series Aluminum Alloy Plates
Authors: K. Subbaiah
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Friction stir welding is a solid state welding process. Friction stir welding process eliminates the defects found in fusion welding processes. It is environmentally friend process. 5000 and 6000 series aluminum alloys are widely used in the transportation industries. The Al-Mg-Mn (5000) and Al-Mg-Si (6000) alloys are preferably offer best combination of use in Marine construction. The medium strength and high corrosion resistant 5000 series alloys are the aluminum alloys, which are found maximum utility in the world. In this review, the tool pin profile, process parameters such as hardness, yield strength and tensile strength, and microstructural evolution of friction stir welding of Al-Mg alloys 5000 Series and 6000 series have been discussed.Keywords: 5000 series and 6000 series Al alloys, friction stir welding, tool pin profile, microstructure and properties
Procedia PDF Downloads 46220177 A Preliminary Study of the Effects of Abiotic Environmental Variables on Early Diptera Carrion Colonizers in Algiers, Algeria
Authors: M. Taleb, G. Tail, F. Z. Kara, B. Djedouani T. Moussa
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Necrophagous insects usually colonize cadavers within a short time after death. However, they are influenced by weather conditions, and their distribution and activity vary according to different time scales, which can affect the post-mortem interval (PMI) estimation. As no data have been published in Algeria on necrophagous insects visiting corpses, two field surveys were conducted in July 2012 and March 2013 at the National Institute for Criminalistics and Criminology (INCC) using rabbit carcasses (Oryctolagus cuniculus L.). The trials were designed to identify the necrophagous Diptera fauna of Algiers, Algeria and examine their variations according to environmental variables. Four hundred and eighteen Diptera adults belonging to five families were captured during this study. The species which were identified on human corpses in different regions of Algeria were also observed on the rabbit carcasses. Although seasonal variations of the species were observed, their abundance did not significantly vary between the two seasons. In addition to seasonal effects, the ambient temperature, the wind speed, and precipitation affect the number of trapped flies. These conclusions highlight the necessity of considering the environmental factors at a scene to estimate the post-mortem interval accurately. It is hoped that these findings provide basic information regarding the necrophagous Diptera fauna of Algeria.Keywords: forensic entomology, necrophagous diptera, post-mortem interval, abiotic factors, Algeria
Procedia PDF Downloads 38620176 Automatic Thresholding for Data Gap Detection for a Set of Sensors in Instrumented Buildings
Authors: Houda Najeh, Stéphane Ploix, Mahendra Pratap Singh, Karim Chabir, Mohamed Naceur Abdelkrim
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Building systems are highly vulnerable to different kinds of faults and failures. In fact, various faults, failures and human behaviors could affect the building performance. This paper tackles the detection of unreliable sensors in buildings. Different literature surveys on diagnosis techniques for sensor grids in buildings have been published but all of them treat only bias and outliers. Occurences of data gaps have also not been given an adequate span of attention in the academia. The proposed methodology comprises the automatic thresholding for data gap detection for a set of heterogeneous sensors in instrumented buildings. Sensor measurements are considered to be regular time series. However, in reality, sensor values are not uniformly sampled. So, the issue to solve is from which delay each sensor become faulty? The use of time series is required for detection of abnormalities on the delays. The efficiency of the method is evaluated on measurements obtained from a real power plant: an office at Grenoble Institute of technology equipped by 30 sensors.Keywords: building system, time series, diagnosis, outliers, delay, data gap
Procedia PDF Downloads 24320175 Coefficients of Some Double Trigonometric Cosine and Sine Series
Authors: Jatinderdeep Kaur
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In this paper, the results of Kano from one-dimensional cosine and sine series are extended to two-dimensional cosine and sine series. To extend these results, some classes of coefficient sequences such as the class of semi convexity and class R are extended from one dimension to two dimensions. Under these extended classes, I have checked the function f(x,y) is two dimensional Fourier Cosine and Sine series or equivalently it represents an integrable function. Further, some results are obtained which are the generalization of Moricz's results.Keywords: conjugate dirichlet kernel, conjugate fejer kernel, fourier series, semi-convexity
Procedia PDF Downloads 43620174 Closed Forms of Trigonometric Series Interms of Riemann’s ζ Function and Dirichlet η, λ, β Functions or the Hurwitz Zeta Function and Harmonic Numbers
Authors: Slobodan B. Tričković
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We present the results concerned with trigonometric series that include sine and cosine functions with a parameter appearing in the denominator. We derive two types of closed-form formulas for trigonometric series. At first, for some integer values, as we know that Riemann’s ζ function and Dirichlet η, λ equal zero at negative even integers, whereas Dirichlet’s β function equals zero at negative odd integers, after a certain number of members, the rest of the series vanishes. Thus, a trigonometric series becomes a polynomial with coefficients involving Riemann’s ζ function and Dirichlet η, λ, β functions. On the other hand, in some cases, one cannot immediately replace the parameter with any positive integer because we shall encounter singularities. So it is necessary to take a limit, so in the process, we apply L’Hospital’s rule and, after a series of rearrangements, we bring a trigonometric series to a form suitable for the application of Choi-Srivastava’s theorem dealing with Hurwitz’s zeta function and Harmonic numbers. In this way, we express a trigonometric series as a polynomial over Hurwitz’s zeta function derivative.Keywords: Dirichlet eta lambda beta functions, Riemann's zeta function, Hurwitz zeta function, Harmonic numbers
Procedia PDF Downloads 10220173 Evidence Theory Enabled Quickest Change Detection Using Big Time-Series Data from Internet of Things
Authors: Hossein Jafari, Xiangfang Li, Lijun Qian, Alexander Aved, Timothy Kroecker
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Traditionally in sensor networks and recently in the Internet of Things, numerous heterogeneous sensors are deployed in distributed manner to monitor a phenomenon that often can be model by an underlying stochastic process. The big time-series data collected by the sensors must be analyzed to detect change in the stochastic process as quickly as possible with tolerable false alarm rate. However, sensors may have different accuracy and sensitivity range, and they decay along time. As a result, the big time-series data collected by the sensors will contain uncertainties and sometimes they are conflicting. In this study, we present a framework to take advantage of Evidence Theory (a.k.a. Dempster-Shafer and Dezert-Smarandache Theories) capabilities of representing and managing uncertainty and conflict to fast change detection and effectively deal with complementary hypotheses. Specifically, Kullback-Leibler divergence is used as the similarity metric to calculate the distances between the estimated current distribution with the pre- and post-change distributions. Then mass functions are calculated and related combination rules are applied to combine the mass values among all sensors. Furthermore, we applied the method to estimate the minimum number of sensors needed to combine, so computational efficiency could be improved. Cumulative sum test is then applied on the ratio of pignistic probability to detect and declare the change for decision making purpose. Simulation results using both synthetic data and real data from experimental setup demonstrate the effectiveness of the presented schemes.Keywords: CUSUM, evidence theory, kl divergence, quickest change detection, time series data
Procedia PDF Downloads 33420172 Short Life Cycle Time Series Forecasting
Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar
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The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.Keywords: forecast, short life cycle product, structured judgement, time series
Procedia PDF Downloads 35820171 Does Creatine Supplementation Improve Swimming Performance?
Authors: Catrin Morgan, Atholl Johnston
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Creatine supplementation should theoretically increase total muscle creatine and so enhance the generation of intramuscular phosphocreatine and subsequent ATP formation. The use of creatine as a potential ergogenic aid in sport has been an area of significant scientific research for a number of years. However the effect of creatine supplementation and swimming performance is a relatively new area of research and is the subject of this review. In swimming creatine supplementation could help maintain maximal power output, aid recovery and increase lean body mass. After investigating the underlying theory and science behind creatine supplementation, a literature review was conducted to identify the best evidence looking at the effect of creatine supplementation on swimming performance. The search identified 27 potential studies, and of these 17 were selected for review. The studies were then categorised into single sprint performance, which involves swimming a short distance race, or repeated interval performance, which involves swimming a series of sprints with intervals of rest between them. None of the studies on the effect of creatine controlled for the multiple confounding factors associated with measurement of swimming performance. The sample size in the studies was limited and this reduced the reliability of the studies and introduced the possibility of bias. The studies reviewed provided insufficient evidence to determine if creatine supplementation is beneficial to swimming performance. However, what data there was supported the use of creatine supplementation in repeated interval swimming rather than in single sprint swimming. From a review of the studies, it was calculated on average, there was a 1.37% increase in swimming performance with the use of creatine for repeated intervals and a 0.86% increase in performance for single sprint. While this may seem minor, it should be remembered that swimming races are often won by much smaller margins. In the 2012 London Olympics the Men’s 100 metres freestyle race was won by a margin of only 0.01 of a second. Therefore any potential benefit could make a dramatic difference to the final outcome of the race. Overall more research is warranted before the benefits of creatine supplementation in swimming performance can be further clarified.Keywords: creatine supplementation, repeated interval, single sprint, swimming performance
Procedia PDF Downloads 42520170 The Perception of Teacher Candidates' on History in Non-Educational TV Series: The Magnificent Century
Authors: Evren Şar İşbilen
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As it is known, the movies and tv series are occupying a large part in the daily lives of adults and children in our era. In this connection, in the present study, the most popular historical TV series of recent years in Turkey, “Muhteşem Yüzyıl” (The Magnificent Century), was selected as the sample for the data collection in order to explore the perception of history of university students’. The data collected was analyzed bothqualitatively and quantitatively. The findings discussed in relation to the possible educative effects of historical non-educational TV series and movies on students' perceptions related to history. Additionally, suggestions were made regarding to the utilization of non-educational TV series or movies in education in a positive way.Keywords: education, history, movies, teacher candidates
Procedia PDF Downloads 33320169 Behind Fuzzy Regression Approach: An Exploration Study
Authors: Lavinia B. Dulla
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The exploration study of the fuzzy regression approach attempts to present that fuzzy regression can be used as a possible alternative to classical regression. It likewise seeks to assess the differences and characteristics of simple linear regression and fuzzy regression using the width of prediction interval, mean absolute deviation, and variance of residuals. Based on the simple linear regression model, the fuzzy regression approach is worth considering as an alternative to simple linear regression when the sample size is between 10 and 20. As the sample size increases, the fuzzy regression approach is not applicable to use since the assumption regarding large sample size is already operating within the framework of simple linear regression. Nonetheless, it can be suggested for a practical alternative when decisions often have to be made on the basis of small data.Keywords: fuzzy regression approach, minimum fuzziness criterion, interval regression, prediction interval
Procedia PDF Downloads 29720168 Repellent Activity of Nanoemulsion Essential Oil of Eucalyptus Globulus Labill on Ephestia kuehniella (Lepidoptera: Pyralidae)
Authors: Lena Emamjomeh, Sohrab Imani
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Nowadays, the use of encapsulation technology of pesticides causes an increase in the efficiency and controlled release of these substances. Controlled release by nanoencapsulated formulations allows the essential oil to be used more effectively over a given time interval, suitability to the mode of application and minimization of environmental damage. The essential oil from Eucalyptus globulus exhibited an average yield of 1.19% and presented 1,8-cineol (59.08%) as the major component. Nanoemulsion essential oil was carried out by the method of gum - maltodextrin using homogenization and morphology and size were determined by TEM. Several concentrations were prepared, and then third instar larvae of E.kuehniella were introduced into each treatment. Then, repellent activity was determined after 1, 3 and 24 h from commencement. This study reveals that at a concentration of 1.5 ppm, the nanoemulsion of E. globulus essential oil on the flour disc was shown here to possess more repellent activity (85%) than E.kuehniella compared to natural essential oil (5%) before formulation after 24h. The repellent activity varied with application method concentrations and exposure time. The results showed higher repellent rates in nanoemulsion than in essential oil due to controlled-release formulations allowing smaller quantities of essential oil to be used more effectively over a given time interval. Findings led to the conclusion that encapsulated technology of essential oils can enhance their control release and persistence under controlled conditions.Keywords: nanoemulsion, eucalyptus globulus, ephestia kuehniella, TEM
Procedia PDF Downloads 4820167 Dynamic Response Analysis of Structure with Random Parameters
Authors: Ahmed Guerine, Ali El Hafidi, Bruno Martin, Philippe Leclaire
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In this paper, we propose a method for the dynamic response of multi-storey structures with uncertain-but-bounded parameters. The effectiveness of the proposed method is demonstrated by a numerical example of three-storey structures. This equation is integrated numerically using Newmark’s method. The numerical results are obtained by the proposed method. The simulation accounting the interval analysis method results are compared with a probabilistic approach results. The interval analysis method provides a mean curve that is between an upper and lower bound obtained from the probabilistic approach.Keywords: multi-storey structure, dynamic response, interval analysis method, random parameters
Procedia PDF Downloads 18820166 Development of Time Series Forecasting Model for Dengue Cases in Nakhon Si Thammarat, Southern Thailand
Authors: Manit Pollar
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Identifying the dengue epidemic periods early would be helpful to take necessary actions to prevent the dengue outbreaks. Providing an accurate prediction on dengue epidemic seasons will allow sufficient time to take the necessary decisions and actions to safeguard the situation for local authorities. This study aimed to develop a forecasting model on number of dengue incidences in Nakhon Si Thammarat Province, Southern Thailand using time series analysis. We develop Seasonal Autoregressive Moving Average (SARIMA) models on the monthly data collected between 2003-2011 and validated the models using data collected between January-September 2012. The result of this study revealed that the SARIMA(1,1,0)(1,2,1)12 model closely described the trends and seasons of dengue incidence and confirmed the existence of dengue fever cases in Nakhon Si Thammarat for the years between 2003-2011. The study showed that the one-step approach for predicting dengue incidences provided significantly more accurate predictions than the twelve-step approach. The model, even if based purely on statistical data analysis, can provide a useful basis for allocation of resources for disease prevention.Keywords: SARIMA, time series model, dengue cases, Thailand
Procedia PDF Downloads 35620165 Time Synchronization between the eNBs in E-UTRAN under the Asymmetric IP Network
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In this paper, we present a method for a time synchronization between the two eNodeBs (eNBs) in E-UTRAN (Evolved Universal Terrestrial Radio Access) network. The two eNBs are cooperating in so-called inter eNB CA (Carrier Aggregation) case and connected via asymmetrical IP network. We solve the problem by using broadcasting signals generated in E-UTRAN as synchronization signals. The results show that the time synchronization with the proposed method is possible with the error significantly less than 1 ms which is sufficient considering the time transmission interval is 1 ms in E-UTRAN. This makes this method (with low complexity) more suitable than Network Time Protocol (NTP) in the mobile applications with generated broadcasting signals where time synchronization in asymmetrical network is required.Keywords: IP scheduled throughput, E-UTRAN, Evolved Universal Terrestrial Radio Access Network, NTP, Network Time Protocol, assymetric network, delay
Procedia PDF Downloads 35920164 Application of Stochastic Models to Annual Extreme Streamflow Data
Authors: Karim Hamidi Machekposhti, Hossein Sedghi
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This study was designed to find the best stochastic model (using of time series analysis) for annual extreme streamflow (peak and maximum streamflow) of Karkheh River at Iran. The Auto-regressive Integrated Moving Average (ARIMA) model used to simulate these series and forecast those in future. For the analysis, annual extreme streamflow data of Jelogir Majin station (above of Karkheh dam reservoir) for the years 1958–2005 were used. A visual inspection of the time plot gives a little increasing trend; therefore, series is not stationary. The stationarity observed in Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) plots of annual extreme streamflow was removed using first order differencing (d=1) in order to the development of the ARIMA model. Interestingly, the ARIMA(4,1,1) model developed was found to be most suitable for simulating annual extreme streamflow for Karkheh River. The model was found to be appropriate to forecast ten years of annual extreme streamflow and assist decision makers to establish priorities for water demand. The Statistical Analysis System (SAS) and Statistical Package for the Social Sciences (SPSS) codes were used to determinate of the best model for this series.Keywords: stochastic models, ARIMA, extreme streamflow, Karkheh river
Procedia PDF Downloads 14620163 Identification of Classes of Bilinear Time Series Models
Authors: Anthony Usoro
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In this paper, two classes of bilinear time series model are obtained under certain conditions from the general bilinear autoregressive moving average model. Bilinear Autoregressive (BAR) and Bilinear Moving Average (BMA) Models have been identified. From the general bilinear model, BAR and BMA models have been proved to exist for q = Q = 0, => j = 0, and p = P = 0, => i = 0 respectively. These models are found useful in modelling most of the economic and financial data.Keywords: autoregressive model, bilinear autoregressive model, bilinear moving average model, moving average model
Procedia PDF Downloads 40520162 Analyzing the Empirical Link between Islamic Finance and Growth of Real Output: A Time Series Application to Pakistan
Authors: Nazima Ellahi, Danish Ramzan
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There is a growing trend among development economists regarding the importance of financial sector for economic development and growth activities. The development thus introduced, helps to promote welfare effects and poverty alleviation. This study is an attempt to find the nature of link between Islamic banking financing and development of output growth for Pakistan. Time series data set has been utilized for a time period ranging from 1990 to 2010. Following the Phillip Perron (PP) and Augmented Dicky Fuller (ADF) test of unit root this study applied Ordinary Least Squares (OLS) method of estimation and found encouraging results in favor of promoting the Islamic banking practices in Pakistan.Keywords: Islamic finance, poverty alleviation, economic growth, finance, commerce
Procedia PDF Downloads 34220161 Interval Bilevel Linear Fractional Programming
Authors: F. Hamidi, N. Amiri, H. Mishmast Nehi
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The Bilevel Programming (BP) model has been presented for a decision making process that consists of two decision makers in a hierarchical structure. In fact, BP is a model for a static two person game (the leader player in the upper level and the follower player in the lower level) wherein each player tries to optimize his/her personal objective function under dependent constraints; this game is sequential and non-cooperative. The decision making variables are divided between the two players and one’s choice affects the other’s benefit and choices. In other words, BP consists of two nested optimization problems with two objective functions (upper and lower) where the constraint region of the upper level problem is implicitly determined by the lower level problem. In real cases, the coefficients of an optimization problem may not be precise, i.e. they may be interval. In this paper we develop an algorithm for solving interval bilevel linear fractional programming problems. That is to say, bilevel problems in which both objective functions are linear fractional, the coefficients are interval and the common constraint region is a polyhedron. From the original problem, the best and the worst bilevel linear fractional problems have been derived and then, using the extended Charnes and Cooper transformation, each fractional problem can be reduced to a linear problem. Then we can find the best and the worst optimal values of the leader objective function by two algorithms.Keywords: best and worst optimal solutions, bilevel programming, fractional, interval coefficients
Procedia PDF Downloads 44420160 One-Step Time Series Predictions with Recurrent Neural Networks
Authors: Vaidehi Iyer, Konstantin Borozdin
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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning
Procedia PDF Downloads 22720159 Jurrasic Deposit Ichnofossil Study of Cores from Bintuni Basin, Eastern Indonesia
Authors: Aswan Aswan
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Ichnofossils were examined based on two wells cores of Jurassic sediment from Bintuni Basin, West Papua, Indonesia. The cores are the Jurassic interval and known as the potential reservoir interval in this area. Representative of 18 ichnogenera was recorded including forms assigned to Arenicolites, Asterosoma, Bergaueria, Chondrites, cryptic bioturbation, Glossifungites, Lockeia, Ophiomorpha, Palaeophycus, Phycosiphon, Planolites, Rhizocorallium, Rosselia, root structure, Skolithos, Teichicnus, Thalassinoides, and Zoophycos. The two cores represent a depositional system that is dominated by tidal flat, shallow marine shelf continuum possibly crossed by estuaries or tidal shoals channels. From the first core identified two deepening cycles. The shallow one is a shallow marine with tidal influence while the deeper one attached to the shelf. Shallow interval usually indicates by appearances of Ophiomorpha and Glossifungites while the deeper shallow marine interval signs by the abundance of Phycosiphon. The second core reveals eight deepening cycles.Keywords: ichnofossil, Jurassic, sediment, reservoir, Bintuni, Indonesia, West Papua
Procedia PDF Downloads 36020158 Determination of Community Based Reference Interval of Aspartate Aminotransferase to Platelet Ratio Index (APRI) among Healthy Populations in Mekelle City Tigray, Northern Ethiopia
Authors: Getachew Belay Kassahun
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Background: Aspartate aminotransferase to Platelet Ratio Index (APRI) currently becomes a biomarker for screening liver fibrosis since liver biopsy procedure is invasive and variation in pathological interpretation. Clinical Laboratory Standard Institute recommends establishing age, sex and environment specific reference interval for biomarkers in a homogenous population. The current study was aimed to derive community based reference interval of APRI aged between 12 and 60 years old in Mekelle city Tigrai, Northern Ethiopia. Method: Six hundred eighty eight study participants were collected from three districts in Mekelle city. The 3 districts were selected through random sampling technique and sample size to kebelles (small administration) were distributed proportional to household number in each district. Lottery method was used at household level if more than 2 study participants to each age partition were found. A community based cross sectional in a total of 534 study participants, 264 male and 270 females, were included in the final laboratory and data analysis but around 154 study participants were excluded through exclusion criteria. Aspartate aminotransferase was analyzed through Biosystem chemistry analyzer and Sysmix machine was used to analyze platelet. Man Whitney U test non parametric stastical tool was used to appreciate stastical difference among gender after excluding the outliers through Box and Whisker. Result: The study appreciated stastical difference among gender for APRI reference interval. The combined, male and female reference interval in the current study was 0.098-0.390, 0.133-0.428 and 0.090-0.319 respectively. The upper and lower reference interval of males was higher than females in all age partition and there was no stastical difference (p-value (<0.05)) between age partition. Conclusion: The current study showed using sex specific reference interval is significant to APRI biomarker in clinical practice for result interpretation.Keywords: reference interval, aspartate aminotransferase to platelet ratio Index, Ethiopia, tigray
Procedia PDF Downloads 11220157 Estimating the Timing Interval for Malarial Indoor Residual Spraying: A Modelling Approach
Authors: Levicatus Mugenyi, Joaniter Nankabirwa, Emmanuel Arinaitwe, John Rek, Niel Hens, Moses Kamya, Grant Dorsey
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Background: Indoor residual spraying (IRS) reduces vector densities and malaria transmission, however, the most effective spraying intervals for IRS have not been well established. We aim to estimate the optimal timing interval for IRS using a modeling approach. Methods: We use a generalized additive model to estimate the optimal timing interval for IRS using the predicted malaria incidence. The model is applied to post IRS cohort clinical data from children aged 0.5–10 years in selected households in Tororo, historically a high malaria transmission setting in Uganda. Six rounds of IRS were implemented in Tororo during the study period (3 rounds with bendiocarb: December 2014 to December 2015, and 3 rounds with actellic: June 2016 to July 2018). Results: Monthly incidence of malaria from October 2014 to February 2019 decreased from 3.25 to 0.0 per person-years in the children under 5 years, and 1.57 to 0.0 for 5-10 year-olds. The optimal time interval for IRS differed between bendiocarb and actellic and by IRS round. It was estimated to be 17 and 40 weeks after the first round of bendiocarb and actellic, respectively. After the third round of actellic, 36 weeks was estimated to be optimal. However, we could not estimate from the data the optimal time after the second and third rounds of bendiocarb and after the second round of actellic. Conclusion: We conclude that to sustain the effect of IRS in a high-medium transmission setting, the second rounds of bendiocarb need to be applied roughly 17 weeks and actellic 40 weeks after the first round, and the timing differs for subsequent rounds. The amount of rainfall did not influence the trend in malaria incidence after IRS, as well as the IRS timing intervals. Our results suggest that shorter intervals for the IRS application can be more effective compared to the current practice, which is about 24 weeks for bendiocarb and 48 weeks for actellic. However, when considering our findings, one should account for the cost and drug resistance associated with IRS. We also recommend that the timing and incidence should be monitored in the future to improve these estimates.Keywords: incidence, indoor residual spraying, generalized additive model, malaria
Procedia PDF Downloads 12020156 Analytical Solution of Blassius Equation Using the Kourosh Method
Authors: Mohammad Reza Shahnazari, Reza Kazemi, Ali Saberi
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Most of the engineering problems are in nonlinear forms. Nonlinear boundary layer problems defined in infinite intervals contain specific complexities, especially in boundary layer condition conformance. As an example of these nonlinear complex problems, the well-known Blasius equation can be mentioned, which itself is one of the classic boundary layer problems. No analytical solution has been proposed yet for the Blasius equation due to its complexity. In this paper, an analytical method, namely the Kourosh method, based on the singularity perturbation method and the Liao homotopy analysis is utilized to solve the Blasius problem. In this method, an inner solution is developed in the [0,1] interval to expedite the solution convergence. The magnitude of the f ˝(0), as an essential quantity for determining the physical parameters, is directly calculated from the solution of the boundary condition problem. The advantages of this solution are that it does not need any numerical solution, it has a closed form and that its validation is shown in the entire [0,∞] interval. Furthermore, all of the desirable parameters could be extracted through a series of simple analytical operations from the final solution. This solution also satisfies the continuity conditions, which is one of the main contributions of this paper in comparison with most of the other proposed analytical solutions available in the literature. Comparison with numerical solutions reveals that the proposed method is highly accurate and convenient for application.Keywords: Blasius equation, boundary layer, Kourosh method, analytical solution
Procedia PDF Downloads 38920155 Co-Integration Model for Predicting Inflation Movement in Nigeria
Authors: Salako Rotimi, Oshungade Stephen, Ojewoye Opeyemi
Abstract:
The maintenance of price stability is one of the macroeconomic challenges facing Nigeria as a nation. This paper attempts to build a co-integration multivariate time series model for inflation movement in Nigeria using data extracted from the abstract of statistics of the Central Bank of Nigeria (CBN) from 2008 to 2017. The Johansen cointegration test suggests at least one co-integration vector describing the long run relationship between Consumer Price Index (CPI), Food Price Index (FPI) and Non-Food Price Index (NFPI). All three series show increasing pattern, which indicates a sign of non-stationary in each of the series. Furthermore, model predictability was established with root-mean-square-error, mean absolute error, mean average percentage error, and Theil’s unbiased statistics for n-step forecasting. The result depicts that the long run coefficient of a consumer price index (CPI) has a positive long-run relationship with the food price index (FPI) and non-food price index (NFPI).Keywords: economic, inflation, model, series
Procedia PDF Downloads 24220154 Considerations for the Use of High Intensity Interval Training in Secondary Physical Education
Authors: Amy Stringer, Resa Chandler
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High Intensity Interval Training (HIIT) involves a 3-10-minute circuit of various exercises which is a viable alternative to a traditional cardiovascular and strength training regimen. Research suggests that measures of health-related fitness can either be maintained or actually improve with the use of this training method. After conducting a 6-week HIIT research study with 10-14 year old children, considerations for using a daily HIIT workout are presented. Is the use of HIIT with children a reasonable consideration for physical education programs? The benefits and challenges of this type of an intervention are identified. This study is significant in that achieving fitness gains in a small amount of daily class time is an attractive concept – especially for physical education teachers who often do not have the time necessary to accomplish all of their curricular goals in the amount of class time assigned. Basic methodologies include students participating in a circuit of exercises for 7-10 minutes at 80-95% of max heart rate as measured by heart rate monitors. Student pre and post fitness test data were collected for cardio-vascular endurance, muscular endurance, and body composition. Research notes as well as commentary by the teachers and researchers who participated in the HIIT study contributed to the understanding of the cost-benefit analysis. Major findings of the study are that HIIT has limited effectiveness but is a good choice for limited class times. Student efficacy of their ability to complete the exercises and visible heart rate data were considered to be significant factors in success of the HIIT study. The effective use of technology promoting positive audience effect during the display of heart rate data was more important at the beginning of the study than at the end. Student ‘buy-in’ and motivation, teacher motivation and ‘buy-in’, the variety of activities in the circuit and the fitness level of the student at the beginning of the study were also findings influencing the fitness outcomes of the study. Concluding Statement: High intensity interval training can be used effectively in a secondary physical education program. It is not a ‘magic bullet’ to produce health-related fitness outcomes in every student but it is an effective tool to enhance student fitness in a limited time and contribute to the goals of the program.Keywords: cardio vascular fitness, children, high intensity interval training, physical education
Procedia PDF Downloads 11420153 Volatility and Stylized Facts
Authors: Kalai Lamia, Jilani Faouzi
Abstract:
Measuring and controlling risk is one of the most attractive issues in finance. With the persistence of uncontrolled and erratic stocks movements, volatility is perceived as a barometer of daily fluctuations. An objective measure of this variable seems then needed to control risks and cover those that are considered the most important. Non-linear autoregressive modeling is our first evaluation approach. In particular, we test the presence of “persistence” of conditional variance and the presence of a degree of a leverage effect. In order to resolve for the problem of “asymmetry” in volatility, the retained specifications point to the importance of stocks reactions in response to news. Effects of shocks on volatility highlight also the need to study the “long term” behaviour of conditional variance of stocks returns and articulate the presence of long memory and dependence of time series in the long run. We note that the integrated fractional autoregressive model allows for representing time series that show long-term conditional variance thanks to fractional integration parameters. In order to stop at the dynamics that manage time series, a comparative study of the results of the different models will allow for better understanding volatility structure over the Tunisia stock market, with the aim of accurately predicting fluctuation risks.Keywords: asymmetry volatility, clustering, stylised facts, leverage effect
Procedia PDF Downloads 298