Exponential Research Articles

Multiparameter probability distributions for at-site frequency analysis of annual maximum wind speed with L-Moments for parameter estimation

Estimation of quantiles of Annual Maximum Wind Speed (AMWS) is needed in different environmental fields, engineering risk analysis, design of structures, renewable energy sources, agricultural operations, and climatology. Therefore, wind speed frequency analysis (WSFA) was carried out at nine stations from Pakistan. Multiparameter Probability Distributions (PDs), such as Generalized logistic (GLO), Generalized Extreme Value (GEV), Generalized Normal (GNO), Generalized Pareto (GPA), Weibull (WEI), Pearson type 3 (P3), Log Pearson type 3 (LP3); and two parameter PDs, such as Logistic (LOG), Normal (NOR), Gumbel (GUM), Exponential (EXP), and Uniform (UNI) were used to determine the most suitable distributions for the nine stations. The method of L-moments was used for estimating parameters of the distributions. The Kolmogorov-Smirnov (KS) test, Anderson-Darling (AD) test, Minimum L-Kurtosis (ML-K) Difference Criterion, and L-moment ratio diagram (L-ratio diagram) showed that four distributions, namely GEV, GNO, GPA, and GLO were the most suitable distributions for different stations and were superior to the two-parameter distributions. The quantile estimates (design estimates) from multiparameter PDs provide information on how fast the maximum wind will pass through a certain place and hence are important for policy makers and planners in the design and construction of different structures. The Multivariate Diebold–Mariano (DM) test was applied to check the accuracy of design estimates from the best fitted PDs and results indicated that they were significantly different.

Optimal Plans of Constant-Stress Accelerated Life Tests for Extension of the Exponential Distribution

Abstract In this article, the optimal plans for a k-level constant-stress accelerated life test are presented for extension of the exponential failure data under complete sampling. According to the log-linear life-stress relationship, the optimal proportion of test units allocated to each stress level is determined under D- and C-optimality criteria. Moreover, a real data set is analyzed to illustrate the proposed procedures. Furthermore, the real data set is used to show that extension of the exponential (EE) distribution can be a better model than both Weibull distribution and generalized exponential distribution. In addition, numerical examples are used to illustrate the proposed procedures and to compare between the D-optimal plan and C-optimal plan through asymptotic variance of maximum likelihood estimators. Finally, some interesting conclusions are obtained.

Metabolomic profiles of tropical Chlorella and Parachlorella species in response to physiological changes during exponential and stationary growth phase

Chlorella species are known to be potential algal candidates for biodiesel production due to their ability to store high lipid content and their natural metabolic versatility. An understanding of physiology and metabolic capacity of indigenous Chlorella strains is potentially useful for future biodiesel production in the tropical environment. The primary aim of this study was to assess the photosynthetic performance, biochemical content and fatty acid composition of Chlorella and Parachlorella species grown and harvested during exponential (EX) and stationary (STA) growth phases in batch culture. Physiological data suggested that the cells responded to these conditions by initiating lipid accumulation when growing from EX to STA phase. An increase of lipid and saturated fatty acids (SFA) contents was observed in STA, although this trend was not consistent across the different strains of Chlorella and Parachlorella species. To gain further insights into metabolomic adaptation at different growth phases, metabolites were extracted from selected Chlorella and Parachlorella strains at EX and STA phase. These metabolic profiles were analysed resulting in identification of 74 metabolites. Metabolomic profiles of Parachlorella showed that there was an increase in recycling of amino acids and nucleic acids at STA phase. The metabolites associated with photosynthesis and chlorophyll biosynthesis were also repressed while carbon sources were channelled into lipid biosynthesis. Meanwhile, Chlorella species showed a similar response in carbon allocation for lipid accumulation with lesser influence on amino acid and chlorophyll degradation. Therefore, Chlorella and Parachlorella species exhibit different changes in metabolic responses at different growth phases, which may be the result of metabolic adaptations arising from their evolutionary plasticity. Overall, our results expand the current understanding of metabolomics of Chlorella and Parachlorella species and provide valuable insights into their lipid accumulation during different stages, which is important for optimization of lipid productivity for biodiesel production.

A comparison study for solving systems of high-order ordinary differential equations with constants coefficients by exponential Legendre collocation method

In this article we are interested to study the use of the Legendre exponential (EL) collocation method to solve systems of high order linear ordinary differential equations with constant coefficients. The method transforms the system of differential equations and the conditions given by matrix equations with constant coefficients a new system of equations that corresponds to the system of linear algebraic equations which can be solved . Numerical problems are given to illustrate the validity and applicability of the method. For obtaining the approximate solution Maple software is used.

Modelling and optimisation of effective hybridisation model for time‐series data forecasting

Financial time-series data have non-linear and uncertain behavior which changes across the time. Therefore, the need to solve non-linear, time-variant problems has been growing rapidly. Traditional models such as statistical and data mining approach unable to cope with these issues. The main objective of this study to combine forecasts from the autoregressive integrated moving average model, exponential (EXP) model, and the multi-layers perceptron (MLP) in a novel hybrid model. The analysis was based on financial data of Sudanese pound/EURO exchange rate in Sudan. In this case, simple additive combination and weight combination methods are used in combining linear and non-linear models to produce hybrid forecast. Comparison between benchmark models and hybrid indicates that the hybrid model offers more accurate forecasts with reduced mean-absolute percentage error of around 0.82% for all models over all forecasting horizons. Moreover, the results recommend that the non-linear method can be applicable to an alternate to linear combining methods to accomplish better forecasting accuracy. On the basis of the results of this study, the authors can conclude that further experiments to estimate the weight of the combination methods and more models essential to be surveyed so as to explore innovative concerns in series prediction.

Allelopathic Effects of Alexandium spp. on Growth of Prorocentrum donghaiense Lu: Is It Toxin Dependent?

The allelopathic properties of three Alexandrium species: A tamarense (ADHK), A. minimus (AM-1) and A. catenella (ACDH), on one species of dinoflagellate Prorocentrum donghaiense were determined using bioassays of allelopathic test. Their culture filtrates were prepared at different growth phases of exponential (EP) and stationary one (SP), and were applied for the cultivation of P. donghaiense in the co-culture. Results revealed that all the culture filtrates presented growth inhibition on the co-cultured P. donghaiense, among which ADHK presented the most severe inhibition. Culture filtrates at SP showed stronger inhibition as compard to that in EP, and the dilution of the culture filtrates could alleviate their negative effects. Analysis on hemolytic activity presented that all of the Alexandrum species had hemolytic activity, which was consistent with their reported PSP toxins. It was thus speculated that the toxin-independent allelochemical(s) were involved in the noxious effects of Alexandrum spp. on P. donghaiense.

Light exercise heart rate on-kinetics: a comparison of data fitted with sigmoidal and exponential functions and the impact of fitness and exercise intensity

This study examined the suitability of sigmoidal (SIG) and exponential (EXP) functions for modeling HR kinetics at the onset of a 5‐min low‐intensity cycling ergometer exercise test (5MT). The effects of training status, absolute and relative workloads, and high versus low workloads on the accuracy and reliability of these functions were also examined. Untrained participants (UT abs; n = 13) performed 5MTs at 100W. One group of trained participants (n = 10) also performed 5MTs at 100W (ET abs). Another group of trained participants (n = 9) performed 5MTs at 45% and 60% V˙O2 max (ET 45 and ET 60, respectively). SIG and EXP functions were fitted to HR data from 5MTs. A 30‐s lead‐in time was included when fitting SIG functions. Functions were compared using the standard error of the regression (SER), and test‐retest reliability of curve parameters. SER for EXP functions was significantly lower than for SIG functions across all groups. When residuals from the 30‐s lead‐in time were omitted, EXP functions only outperformed SIG functions in ET 60 (EXP, 2.7 ± 1.2 beats·min−1; SIG, 3.1 ± 1.1 beats·min−1: P < 0.05). Goodness of fit and test–retest reliability of curve parameters were best in ET 60 and comparatively poor in UT abs. Overall, goodness of fit and test–retest reliability of curve parameters favored functions fitted to 5MTs performed by trained participants at a high and relative workload, while functions fitted to data from untrained participants exercising at a low and absolute workload were less accurate and reliable.

Basil root water uptake derived models under combined water and nitrogen deficit conditions

AbstractThe objective of this research was to introduce and evaluate derived models under simultaneous water and nitrogen deficit stress conditions and consequently calibrating their parameters for basil. Therefore to do so, in order to introduce and evaluate derived models under simultaneous water and nitrogen deficit stress conditions, derived models from the composition of Mitscherlich–Baule (MB) for nutrient stress conditions and the models of Feddes (F), van Genuchten (VG), recommended exponential (EXP) and Homaee (H) for water stress conditions and also the composition model of Liebig–Sprengel (LS) for nutrients and the model of Feddes (F) for water stress conditions were presented and evaluated. This experiment was conducted with four irrigation water quantity levels of 120, 100, 80 and 60% of crop water requirement and four nitrogen fertilizer levels of 100, 75, 50 and 0% of fertilizer requirement based on a soil fertility test with three replicates. The results indicated that of the evaluated models, based on calculated statistical indices, the derived models of MB‐EXP, MB‐F, MB‐VG and MB‐H (mentioned above) had the best fitting on the measured data, respectively, compared to actual data. The results of calculated statistics to compared models based on the different nitrogen and water irrigation levels indicated that MB‐EXP and MB‐F using nRMSE statistics were 2.78 and 3.64, and EF statistics of both was 0.99% and they showed the highest accuracy in estimating basil yield. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Numerical simulation of metal removal in laser drilling using radial point interpolation method

Prediction of penetration depth in metal laser drilling is done through a simple meshfree numerical model. 2D axisymmetric simplified model of transient metal laser drilling is proposed for continuous laser beam of Gaussian distribution with strong form of Radial Point Interpolation Method (RPIM) used for approximating the temperature field. The commonly used Multi-Quadrics (MQ) and Exponential (EXP) Radial Basis Functions (RBFs) are tested and compared with each other. The model logic is constructed in MATLAB code, while the results are compared with published numerical and experimental work. The simulation results give good agreement with the previous numerical and experimental work, showing the model reliability in predicting the penetration depth in such a physically complex process.

Variability in the organic ligands released by &lt;em&gt;Emiliania huxleyi&lt;/em&gt; under simulated ocean acidification conditions

The variability in the extracellular release of organic ligands by Emiliania huxleyi under four different pCO2 scenarios (225, 350, 600 and 900 μatm), was determined. Growth in the batch cultures was promoted by enriching them only with major nutrients and low iron concentrations. No chelating agents were added to control metal speciation. During the initial (IP), exponential (EP) and steady (SP) phases, extracellular release rates, normalized per cell and day, of dissolved organic carbon (DOCER), phenolic compounds (PhCER), dissolved combined carbohydrates (DCCHOER) and dissolved uronic acids (DUAER) in the exudates were determined. The growth rate decreased in the highest CO2 treatment during the IP (＜48 h), but later increased when the exposure was longer (more than 6 days). DOCER did not increase significantly with high pCO2. Although no relationship was observed between DCCHOER and the CO2 conditions, DCCHO was a substantial fraction of the freshly released organic material, accounting for 18% to 37%, in EP, and 14% to 23%, in SP, of the DOC produced. Growth of E. huxleyi induced a strong response in the PhCER and DUAER. While in EP, PhCER were no detected, the DUAER remained almost constant for all CO2 treatments. Increases in the extracellular release of these organic ligands during SP were most pronounced under high pCO2 conditions. Our results imply that, during the final growth stage of E. huxleyi, elevated CO2 conditions will increase its excretion of acid polysaccharides and phenolic compounds, which may affect the biogeochemical behavior of metals in seawater.

COMPARATIVE ANALYSIS OF THE PERFORMANCE OF RESOURCE ALLOCATION ALGORITHMS IN LONG TERM EVOLUTION NETWORKS

The growth in the good number of real-time and non-real-time applications has sparked a renewed interest in exploring resource allocation schemes that can be efficient and fair to all the applications in overloaded scenarios. In this paper, the performance of six scheduling algorithms for Long Term Evolution (LTE) downlink networks were analyzed and compared. These algorithms are Proportional Fair (PF), Exponential/Proportional Fair (EXP/PF), Maximum Largest Weighted Delay First (MLWDF), Frame Level Scheduler (FLS), Exponential (EXP) rule and Logarithmic (LOG) rule. The performances of these algorithms were evaluated using an open source simulator (LTE simulator) and compared based on network parameters which include: throughput, delay, Packet Loss Ratio (PLR), and fairness. This work aims at giving insight on the gains made on radio resource scheduling for LTE network and to x-ray the issues that require improvement in order to provide better performance to the users. The results of this work show that FLS algorithm outperforms other algorithms in terms of delay, PLR, throughput, and fairness for VoIP and video flow. It was also observed that for Best Effort (BE) flows, FLS outperforms other algorithms in terms of delay and PLR but performed least in terms of throughput and fairness. http://dx.doi.org/10.4314/njt.v36i1.21

COMPARATIVE ANALYSIS OF THE PERFORMANCE OF RESOURCE ALLOCATION ALGORITHMS IN LONG TERM EVOLUTION NETWORKS

The growth in the good number of real-time and non-real-time applications has sparked a renewed interest in exploring resource allocation schemes that can be efficient and fair to all the applications in overloaded scenarios. In this paper, the performance of six scheduling algorithms for Long Term Evolution (LTE) downlink networks were analyzed and compared. These algorithms are Proportional Fair (PF), Exponential/Proportional Fair (EXP/PF), Maximum Largest Weighted Delay First (MLWDF), Frame Level Scheduler (FLS), Exponential (EXP) rule and Logarithmic (LOG) rule. The performances of these algorithms were evaluated using an open source simulator (LTE simulator) and compared based on network parameters which include: throughput, delay, Packet Loss Ratio (PLR), and fairness. This work aims at giving insight on the gains made on radio resource scheduling for LTE network and to x-ray the issues that require improvement in order to provide better performance to the users. The results of this work show that FLS algorithm outperforms other algorithms in terms of delay, PLR, throughput, and fairness for VoIP and video flow. It was also observed that for Best Effort (BE) flows, FLS outperforms other algorithms in terms of delay and PLR but performed least in terms of throughput and fairness. http://dx.doi.org/10.4314/njt.v36i1.21

Comparative Analysis of Downlink Packet Scheduling Algorithms in 3GPP LTE Networks

Long Term Evolution (LTE) mobile network aims to support high speed network services even in highmobility scenarios. To achieve this goal, LTE adopts some advanced features in Radio Resource Management (RRM) procedures. Among them, LTE packet scheduling plays a fundamental role in maximising system performance. In this paper, a comparative analysis on the performances of Proportional Fair (PF), Exponential/Proportional Fair (EXP/PF), Exponential (EXP) Rule, MaximumLargest Weighted Delay First (M-LWDF), Logarithmic (LOG) Rule and Frame Level Scheduler (FLS) LTE downlink packet scheduling algorithms is reported. Performance is evaluated in single cell with interference environment while increasing user number and user speed. Results show that for multimedia flow, FLS scheme outperforms other five schemes in terms of packet delay, packet loss ratio, and average throughput, whereas for best-effort flow, EXP-PF scheme shows better average throughput performance on average as compared with other algorithms being considered herein.

Fitting Distribution for Electromyography and Electroencephalography Signals Based on Goodness-of-Fit Tests

Physiological devices (PDs) like upright bicycles, steppers and treadmills act as Human Machine Interface (HMI) during rehabilitation. The main issue on how to utilize biosignals such as Electromyography (EMG), Electrocardiography (ECG) and Electroencephalography (EEG) as an inputs for HMI to control the PDs. Biosignals are stochastic and complex as they influenced by anatomical and physiological properties of muscles. To maximize the training time during exercise, the features of the biosignals (e.g., fatigue, contraction or relaxation) should be extracted to maintain the system as a reliable working condition. For that purpose, it is feasible to try out a probabilistic distribution as a feature to illustrate the pattern of muscle activation. Before the estimation of parameter distribution is conducted, we need to verify the types of distribution that fit the raw biosignals. In this study, EMG and EEG signals will be considered to find the most suitable distribution for the signals. The selected model is chosen based on a minimum error produced by two Goodness-of-Fit (GOF) tests namely Kolmogorov-Smirnov statistic, D and Anderson Darling statistic, A2. As a result, a Generalized Extreme Value (GEV) distribution is found the most appropriate distribution compared to Generalized Pareto (GP) and Exponential (EXP) distributions for describing the EMG and EEG signals.

Performance Analysis of FLS, EXP, LOG and M-LWDF Packet Scheduling Algorithms in Downlink 3GPP LTE System

Long-Term Evolution (LTE), an emerging and promising fourth generation mobile technology, is expected to offer ubiquitous broadband access to the mobile subscribers. In this paper, the performance of Frame Level Scheduler (FLS), Exponential (EXP) rule, Logarithmic (LOG) rule and Maximum-Largest Weighted Delay First (M-LWDF) packet scheduling algorithms has been studied in the downlink 3GPP LTE cellular network. To this aim, a single cell with interference scenario has been considered. The performance evaluation is made by varying the number of UEs ranging from 10 to 50 (Case 1) and user speed in the range of [3, 120] km/h (Case 2). Results show that while the number of UEs and user speed increases, the performance of the considered scheduling schemes degrades and in both case FLS outperforms other three schemes in terms of several performance indexes such as average throughput, packet loss ratio (PLR), packet delay and fairness index.

Classical and Bayesian Inference for an Extension of the Exponential Distribution under Progressive Type-II Censored Data with Binomial Removals

Maximum likelihood and Bayes estimators of the unknown parameters of an extension of the exponential (EE) distribution have been obtained for Progressive Type-II Censored data with Binomial removals. Markov Chain Monte Carlo (MCMC) method is used to compute the Bayes estimates of the parameters of interest. The General Entropy Loss Function (GELF) and Squared Error Loss Function (SELF) have been considered for obtaining the Bayes estimators. Comparisons are made between Bayesian and Maximum likelihood estimators (MLEs) via Monte Carlo simulation. An example is discussed to illustrate its applicability.

QoS-aware composite scheduling using fuzzy proactive and reactive controllers

We consider in this paper downlink scheduling for different traffic classes at the MAC layer of wireless systems based on orthogonal frequency division multiple access (OFDMA), such as the recent 3rd Generation Partnership Project (3GPP) long-term evolution (LTE)/LTE-A wireless standard. Our goal is to provide via the scheduling decisions quality of service (QoS), but also to guarantee fairness among the different users and traffic classes (including delay-sensitive and best-effort traffic). QoS-aware scheduling strategies, such as modified largest weighted delay first (M-LWDF), exponential (EXP), exponential proportional fair (EXP-PF), and the log-based scheduling rules, prioritize delay-sensitive traffic by considering rules based on the head-of-line (HoL) packet delay and the tolerated packet loss rate, whereas they serve best-effort traffic by considering the classical proportional fair (PF) rule. These scheduling rules do not prevent resource starvation for best-effort traffic. On the other side, if both traffic types are scheduled according to the PF rule, then delay-sensitive flows suffer from delay bound violations. In order to fairly distribute the resources among different service classes according to their QoS requirements and channel conditions, we employ the concept of fuzzy logic in our scheduling framework. By employing the fuzzy logic concept, we serve all the traffic classes with one priority rule. Simulation results show better intra-class and inter-class fairness than state-of-the-art scheduling rules. The proposed scheduling framework enables to appropriately balance delay requirements of traffic, system throughput, and fairness.

Positioning and number of nutritional levels in dose-response trials to estimate the optimal-level and the adjustment of the models

The aim of this research was to evaluate the influence of the number and position of nutrient levels used in dose-response trials in the estimation of the optimal-level (OL) and the goodness of fit on the models: quadratic polynomial (QP), exponential (EXP), linear response plateau (LRP) and quadratic response plateau (QRP). It was used data from dose-response trials realized in FCAV-Unesp Jaboticabal considering the homogeneity of variances and normal distribution. The fit of the models were evaluated considered the following statistics: adjusted coefficient of determination (R²adj), coefficient of variation (CV) and the sum of the squares of deviations (SSD).It was verified in QP and EXP models that small changes on the placement and distribution of the levels caused great changes in the estimation of the OL. The LRP model was deeply influenced by the absence or presence of the level between the response and stabilization phases (change in the straight to plateau). The QRP needed more levels on the response phase and the last level on stabilization phase to estimate correctly the plateau. It was concluded that the OL and the adjust of the models are dependent on the positioning and the number of the levels and the specific characteristics of each model, but levels defined near to the true requirement and not so spaced are better to estimate the OL.

Fatty acids and global metabolites profiling of Dunaliella tertiolecta by shifting culture conditions to nitrate deficiency and high light at different growth phases

In this study, Dunaliella tertiolecta cells were shifted to nitrate-deficient (ND) and/or high light (HL) conditions at different growth phases. Profiling of global metabolites and fatty acids of D. tertiolecta cells were performed by shifting cultivation condition at exponential (EX) and stationary (ST) phases. ND and/or HL induced saturation of fatty acids by increasing saturated fatty acid and monounsaturated fatty acid and decreasing polyunsaturated fatty acid in D. tertiolecta cultures. The relative levels of metabolites like proline, valine, isoleucine, and myoinositol, which act as osmolytes, and ergosterol and glycerol, which are related to the osmoregulatory mechanism, decreased to a greater extent by ND than HL shifts. In addition, the effects of these shifts at the EX phase were observed to be more significant in most of the fatty acids. The results of this research might be applicable to the interpretation of metabolic synthesis and fatty acid production, and to the optimization of cultivation conditions for the use of D. tertiolecta and other microalgae as biofuels or nutraceutical sources.

Differences Among Estimates of Critical Power and Anaerobic Work Capacity Derived From Five Mathematical Models and the Three-Minute All-Out Test

Estimates of critical power (CP) and anaerobic work capacity (AWC) from the power output vs. time relationship have been derived from various mathematical models. The purpose of this study was to examine estimates of CP and AWC from the multiple work bout, 2- and 3-parameter models, and those from the 3-minute all-out CP (CP3min) test. Nine college-aged subjects performed a maximal incremental test to determine the peak oxygen consumption rate and the gas exchange threshold. On separate days, each subject completed 4 randomly ordered constant power output rides to exhaustion to estimate CP and AWC from 5 regression models (2 linear, 2 nonlinear, and 1 exponential). During the final visit, CP and AWC were estimated from the CP3min test. The nonlinear 3-parameter (Nonlinear-3) model produced the lowest estimate of CP. The exponential (EXP) model and the CP3min test were not statistically different and produced the highest estimates of CP. Critical power estimated from the Nonlinear-3 model was 14% less than those from the EXP model and the CP3min test and 4-6% less than those from the linear models. Furthermore, the Nonlinear-3 and nonlinear 2-parameter (Nonlinear-2) models produced significantly greater estimates of AWC than did the linear models and CP3min. The current findings suggested that the Nonlinear-3 model may provide estimates of CP and AWC that more accurately reflect the asymptote of the power output vs. time relationship, the demarcation of the heavy and severe exercise intensity domains, and anaerobic capabilities than will the linear models and CP3min test.