THE EFFECT OF FOUR WEEKS HIIT TRAINING WITH THE USE OF L-CARNITINE ON FAT PERCENTAGE OF TRAINED OVERWEIGHT WOMEN

The change in phenotype of adipose tissue as a result of practice is a new theory that has recently been proposed and its cell-molecular mechanism is under investigation. The aim of this study was to investigate the effect of high-intensity interval associated with consumption of L-Carnitine on body composition factors in the selective Orexin hormone. The population was trained overweight women, 25 to 35, and out of which, 40 persons voluntarily participated in the study and were divided randomly into 4 groups of 10 people of L-Carnitine, placebo, HIIT and placebo, HIIT L-carnitine. For the sports background and non-suffering of disease, a questionnaire was used. After explaining on implementing the protocol, measuring body composition and blood samples were conducted. Next, the subjects were participated in an intense training programme (with a maximum power) during 12 sessions. Each of the exercise includes a 10-minute warm-up and then a repeated 10 times for 30 seconds and, then, cool down was performed in 5 to 10 minutes. Forty-eight hours after the last training session, the necessary measurement of body composition and blood samples were conducted and data were analyzed using SPSS software and statistical methods. Four weeks of taking L-carnitine, HIIT training with placebo and with L-Carnitine has a significant effect on the percentage of body fat in overweight trained women. Four weeks of HIIT training with L-Carnitine had a significant effect on the percentage of body fat.  Article visualizations

Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: Combining data dimension reduction and variable selection technique

Using gene expression data in cancer classification plays an important role for solving the fundamental problems relating to cancer diagnosis. Because of high throughput of gene expression data for healthy and patient samples, a variable selection method can be applied to reduce complexity of the model and improve the classification performance. Since variable selection procedures pose a risk of over-fitting, when a large number of variables with respect to sample are used,we have proposed a method for coupling data dimension reduction and variable selection in the present study. This approach uses the concept of variable clustering for the original data set. Significant components of local principal component analysis models have just been retained from all clusters. Then, the variable selection algorithm is performed on these locally derived principal component variables. The proposed algorithm has been evaluated on two gene expression data sets; namely, acute Leukemia and small round blue-cell tumor (SRBCT). Our results confirmed that the classification models achieved on the reduced data were better than those obtained on the entire microarray gene expression profile

Variableselectioninmultivariatecalibrationbasedonclusteringofvariableconcept

Recentlywehaveproposedanewvariableselectionalgorithm,basedonclusteringofvariableconcept(CLoVA)inclassificationproblem.Withthesameidea,thisnewconcepthasbeenappliedtoaregres-sionproblemandthentheobtainedresultshavebeencomparedwithconventionalvariableselectionstrategiesforPLS.Thebasicideabehindtheclusteringofvariableisthat,theinstrumentchannelsareclusteredintodifferentclustersviaclusteringalgorithms.Then,thespectraldataofeachclusteraresubjectedtoPLSregression.Differentrealdatasets(Cargillcorn,Biscuitdough,ACEQSAR,Soy,andTablet)havebeenusedtoevaluatetheinfluenceoftheclusteringofvariablesonthepredictionper-formancesofPLS.Almostintheallcases,thestatisticalparameterespeciallyinpredictionerrorshowsthesuperiorityofCLoVA-PLSrespecttoothervariableselectionstrategies.Finallythesynergyclusteringofvariable(sCLoVA-PLS),whichisusedthecombinationofcluster,hasbeenproposedasanefficientandmodificationofCLoVAalgorithm.Theobtainedstatisticalparameterindicatesthatvariableclusteringcansplitusefulpartfromredundantones,andthenbasedoninformativecluster;stablemodelcanbereache

Fault Diagnosis Method for Mobile Ad-hoc Network by Using Smart Neural Networks

AbstractMANETs are dynamic collection of autonomous nodes that communicate with each other via wireless connections. One of the events that the network should have expected it to be a fault, and the behavior is more important, in this network. So that fault diagnosis can effect on final performance of the network in such a way that it does not fall under the negative impact of the fault. A non-linear neural network is a statistical method for modeling data or the tools to make decisions. Artificial neural network is a method for pattern recognition and classification. Error detection is a problem of categorization or classification. The use of neural networks can be useful in fault diagnosis in MANETs because of fault diagnosis is a classification problem. But one problem with this method is placed in a local optimum. Here a method based on the combination of the back-propagation algorithm, a local search algorithm and learning automata as efficient global search, is proposed. In the proposed method, the algorithm of learning automata adjusting learning rate on neural network according to given formula. For training and testing the neural network of the mobile network parameters that were measured, were used as input and output. The results show that the proposed method in terms of repeatability, reliability and lack of placement in a local optimum is better

Anyon Black Holes

We propose a correspondence between an Anyon Van der Waals fluid and a (2+1) dimensional AdS black hole. Anyons are particles with intermediate statistics that interpolates between a Fermi-Dirac statistics and a Bose-Einstein one. A parameter $\alpha$ ($0<\alpha<1$) characterizes this intermediate statistics of Anyons. The equation of state for the Anyon Van der Waals fluid shows that it has a quasi Fermi-Dirac statistics for $\alpha > \alpha_c$, but a quasi Bose-Einstein statistics for $\alpha< \alpha_c$. By defining a general form of the metric for the (2+1) dimensional AdS black hole and considering the temperature of the black hole to be equal with that of the Anyon Van der Waals fluid, we construct the exact form of the metric for a (2+1) dimensional AdS black hole. The thermodynamic properties of this black hole is consistent with those of the Anyon Van der Waals fluid. For $\alpha< \alpha_c$, the solution exhibits a quasi Bose-Einstein statistics. For $\alpha > \alpha_c$ and a range of values of the cosmological constant, there is, however, no event horizon so there is no black hole solution. Thus, for these values of cosmological constants, the AdS Anyon Van der Waals black holes have only quasi Bose-Einstein statistics.Comment: 7 pages, 4 figure

A New Method for Intelligent Message Network Management in Ubiquitous Sensor Networks

Ubiquitous Sensor Network (USN) computing is a useful technology forautonomic integrating in different environments which can be available anywhere.Managing USN plays an important role on the availability of nodes and paths. Inorder to manage nodes there is a cyclic route starts from manager, passing nodes,and come back to manager as feedback. In this paper, a new, self-optimizing methodpresented for finding this cyclic path by combining epsilon greedy and geneticalgorithm and then it is compared with other well-known methods in terms of cost ofthe route they find and the power consumption. The results show that the route thatis found by our new method costs at least 53% less than other methods. However insome cases, it uses 32% more energy for finding the route which can be compensatein traversing the shorter route. The overall simulation results in prototype data showthe effectiveness of the proposed method

Effects of Anethum graveolens L. seed extracts on experimental gastric irritation models in mice

BACKGROUND: As a folk remedy, Anethum graveolens seed (dill) is used for some gastrointestinal ailments. We aimed to evaluate aqueous and ethanolic extracts of anti-ulcer and acute toxicity effects of the Anethum graveolens in mice. RESULTS: Gastric mucosal lesions were induced by oral administration of HCl (1 N) and absolute ethanol in mice. The acidity and total acid content of gastric juice were measured in pylorus-ligated mice. LD(50 )values of the aqueous and ethanolic extracts were 3.04 g/kg, i.p., (1.5, 6.16) and 6.98 g/kg, i.p., (5.69, 8.56), respectively. The efficacy of high dose of extracts (p.o.) was similar to sucralfate. The acidity and total acid content were reduced by the orally or intraperitoneally administration of the extracts. CONCLUSIONS: The results suggest that A. graveolens seed extracts have significant mucosal protective and antisecretory effects of the gastric mucosa in mice

STUDYING THE RELATIONSHIP BETWEEN DISCRETIONARY ACCRUALS AND SOME OF THE PERFORMANCE INDICATORS OF COMPANIES LISTED IN TEHRAN STOCK EXCHANGE

The fact of importance of measuring performance is recognized for organizations and it plays an important role in many organizations. Today, as we are in the information age, accounting profit is part of the information used by investors in risk assessment and returns on an accrual basis. The corporate performance evaluation is one of the most important issues for investors in terms of investment decision. The purpose of this study was to study the relationship between the voluntary accruals level and the performance indicators of companies listed in Tehran Stock Exchange. For this purpose, some performance indicators such as cash flow, return on assets, return on equity, price-to-profit ratio, and return on investment were measured. The research sample consisted of 102 Iranian stock exchanges during the years 2010-2014. The results of the research show that there is a significant relationship between discretionary accruals and operating cash flow and equity returns, and the market pricing of accruals is not affected by the external financing levels.  Article visualizations