Commodity exchange and institutional changes: Case of Iranian agricultural commodity exchange

In this study, an attempt is to figure out the institutional changes that initiate the agricultural commodity exchange (ACE). To assess the affecting factors, new institutional economics approach has been chosen. The framework consisting of four levels of social analysis introduced by Oliver E.Williamson is used to analyze the social environment, institutional rules, institutional arrangements and finally the agency level economics (Neo-classical economics). Regarding this framework, the institutional changes that have initiated the Iranian ACE is analyzed and its constraints to further improvement are discussed. --Agricultural Commodity Exchange,institutional economics,Iran

Bayesian Assessment of Regional Oil and Gas Production

Several empirical or analytical/semi-analytical simulation models have been developed to assess the Estimated Ultimate Recovery (EUR) of an oil or gas formation for a short or long term of production. Furthermore, providing the EUR of a set of regional wells, it often becomes essential to perform a spatial analysis to develop the overall perception of possible depletion rate across the play. However, the lack of knowledge regarding the likely statistical structure of simulation models’ parameters coupled with the unknown influence of correlation amidst wells’ locations, makes it pertinent to apply a mechanism to quantify the uncertainty associated with the analysis. Therefore, in this study, researchers initially exerted the principles of the Bayesian paradigm together with the Markov Chain Monte Carlo (MCMC) theory to capture the posterior of the simulation model random field. Also, a vector of randomly drawn samples from the retrieved posterior allows delineation of the expected model realizations for a course of progressive time. Despite the fact that MCMC incorporating the acceptance-rejection criterion of the Metropolis-Hastings (MH) algorithm eventually converges to the true mean of the random process, it appears that the general trend of sampling often suffers from being computationally inefficient. Accordingly, to address the aforementioned issue, a novel sophisticated framework which is called “Parallel Scaled Adaptive Metropolis-Hastings” is developed. PSAMH constructs several synchronous chains to adapt the step size of MH proposal distribution and hence optimize the acceptance rate. Moreover, in this study, three major EUR evaluation techniques are employed. The Power Law Exponential Decline (PLED) and Modified Hyperbolic Decline (MHD) functions, along with a semi-analytical method, serve to project the well production performance over the varying time. Additionally, the depletion logs given from the Eagle Ford Shale and Barnett Shale deliver the required observation data. Besides, the Ordinary Kriging and Inverse Distance Weight are two key techniques that are applied to approximate the spatial behavior of the formation. In addition, researchers elaborated a sequential Bayesian updating mechanism to take the updating evidence into the prior’s computation for various time intervals. Also, a Bayesian-spatial algorithm is used to feature the spatial characteristics of unexplored locations hypothesizing the fact that the only given information comprises the production observed data and corresponding coordinate for each individual well. It is implied that exerting the Bayesian approach permits quantifying the inherent uncertainty in the model analysis. Furthermore, it is concluded that the sequential Bayesian updating mechanism is able to noticeably increase the performance and efficiency of the process by precisely constructing an appropriate prior distribution. Also, it is connoted that, given merely the observation data, associated coordinates and EUR evaluation models, it becomes possible to estimate the statistics of model variables and the production behavior for different courses of time at desired locations. Last but not least, attaining the Bayesian-spatial production forecasting for varying depletion times, it becomes plausible to generate the daily basis and cumulative production dynamic maps

Bayesian Assessment of Regional Oil and Gas Production

Several empirical or analytical/semi-analytical simulation models have been developed to assess the Estimated Ultimate Recovery (EUR) of an oil or gas formation for a short or long term of production. Furthermore, providing the EUR of a set of regional wells, it often becomes essential to perform a spatial analysis to develop the overall perception of possible depletion rate across the play. However, the lack of knowledge regarding the likely statistical structure of simulation models’ parameters coupled with the unknown influence of correlation amidst wells’ locations, makes it pertinent to apply a mechanism to quantify the uncertainty associated with the analysis. Therefore, in this study, researchers initially exerted the principles of the Bayesian paradigm together with the Markov Chain Monte Carlo (MCMC) theory to capture the posterior of the simulation model random field. Also, a vector of randomly drawn samples from the retrieved posterior allows delineation of the expected model realizations for a course of progressive time. Despite the fact that MCMC incorporating the acceptance-rejection criterion of the Metropolis-Hastings (MH) algorithm eventually converges to the true mean of the random process, it appears that the general trend of sampling often suffers from being computationally inefficient. Accordingly, to address the aforementioned issue, a novel sophisticated framework which is called “Parallel Scaled Adaptive Metropolis-Hastings” is developed. PSAMH constructs several synchronous chains to adapt the step size of MH proposal distribution and hence optimize the acceptance rate. Moreover, in this study, three major EUR evaluation techniques are employed. The Power Law Exponential Decline (PLED) and Modified Hyperbolic Decline (MHD) functions, along with a semi-analytical method, serve to project the well production performance over the varying time. Additionally, the depletion logs given from the Eagle Ford Shale and Barnett Shale deliver the required observation data. Besides, the Ordinary Kriging and Inverse Distance Weight are two key techniques that are applied to approximate the spatial behavior of the formation. In addition, researchers elaborated a sequential Bayesian updating mechanism to take the updating evidence into the prior’s computation for various time intervals. Also, a Bayesian-spatial algorithm is used to feature the spatial characteristics of unexplored locations hypothesizing the fact that the only given information comprises the production observed data and corresponding coordinate for each individual well. It is implied that exerting the Bayesian approach permits quantifying the inherent uncertainty in the model analysis. Furthermore, it is concluded that the sequential Bayesian updating mechanism is able to noticeably increase the performance and efficiency of the process by precisely constructing an appropriate prior distribution. Also, it is connoted that, given merely the observation data, associated coordinates and EUR evaluation models, it becomes possible to estimate the statistics of model variables and the production behavior for different courses of time at desired locations. Last but not least, attaining the Bayesian-spatial production forecasting for varying depletion times, it becomes plausible to generate the daily basis and cumulative production dynamic maps

3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS

Face recognition has attracted many researchers’ attention compared to other biometrics due to its non-intrusive and friendly nature. Although several methods for 2D face recognition have been proposed so far, there are still some challenges related to the 2D face including illumination, pose variation, and facial expression. In the last few decades, 3D face research area has become more interesting since shape and geometry information are used to handle challenges from 2D faces. Existing algorithms for face recognition are divided into three different categories: holistic feature-based, local feature-based, and hybrid methods. According to the literature, local features have shown better performance relative to holistic feature-based methods under expression and occlusion challenges. In this dissertation, local feature-based methods for 3D face recognition have been studied and surveyed. In the survey, local methods are classified into three broad categories which consist of keypoint-based, curve-based, and local surface-based methods. Inspired by keypoint-based methods which are effective to handle partial occlusion, structural context descriptor on pyramidal shape maps and texture image has been proposed in a multimodal scheme. Score-level fusion is used to combine keypoints’ matching score in both texture and shape modalities. The survey shows local surface-based methods are efficient to handle facial expression. Accordingly, a local derivative pattern is introduced to extract distinct features from depth map in this work. In addition, the local derivative pattern is applied on surface normals. Most 3D face recognition algorithms are focused to utilize the depth information to detect and extract features. Compared to depth maps, surface normals of each point can determine the facial surface orientation, which provides an efficient facial surface representation to extract distinct features for recognition task. An Extreme Learning Machine (ELM)-based auto-encoder is used to make the feature space more discriminative. Expression and occlusion robust analysis using the information from the normal maps are investigated by dividing the facial region into patches. A novel hybrid classifier is proposed to combine Sparse Representation Classifier (SRC) and ELM classifier in a weighted scheme. The proposed algorithms have been evaluated on four widely used 3D face databases; FRGC, Bosphorus, Bu-3DFE, and 3D-TEC. The experimental results illustrate the effectiveness of the proposed approaches. The main contribution of this work lies in identification and analysis of effective local features and a classification method for improving 3D face recognition performance

Comparative study of the urinary level of aflatoxin M1 in patients with hepatitis C virus (HCV) and healthy people

Abstract Background: Aflatoxins are the secondary metabolites produced by the flavi section of Aspergillus. Aflatoxin B1 (AFB1) is hepatocarcinogen, teratogen and mutagen. Aflatoxin M1 (AFM1) is the hydroxylated metabolite of AFB1. The liver protects the body by lowering the toxicity of AFB1 to form different hydroxylates like AFM1. According to the synergistic effect of hepatitis and also AFB1 as the parent molecule of AFM1, the main purpose of this study was to assess the relationship between the mean levels of AFM1, in the hepatitis-C-virus (HCV)-positive patients compared to healthy individuals. Methods: After the tests of liver function enzymes, the level of AFM1 was measured and compared in the urine sample of 71 patients with HCV and 71 healthy individuals. The AFM1 of urine samples were tested using enzyme-linked immunosorbent assay (ELISA) method. Besides, the levels of serum glutamic-oxaloacetic transaminase (SGOT), serum glutamic-pyruvic transaminase (SGPT), alkaline phosphatase, total bilirubin and direct bilirubin were assessed in the blood samples. Findings: The urine of 29.7% of HCV-positive patients and 19.71% of healthy individuals consisted of some amount of AFM1. The mean level of AFM1 was 2.45 and 1.66 pg/ml in patients and controls, respectively; which was significantly different (P = 0.005). The mean levels of SGPT and alkaline phosphatase were significantly more among HCV-positive patients with AFM1 compared to those without AFM1 (P = 0.012). But, there was not any significant difference between the mean levels of SGOT and total and direct bilirubin between the HCV-positive patients with and without AFM1. Conclusion: The mean levels of SGPT and Alkaline phosphatase, which are more exclusive to survey of liver function, were significantly different between HCV-positive patients with and without AFM1. Consequently, progression of the chronic liver disease is caused by the existence of AFB1 in HCV-positive patients; therefore, the reduction of AFM1 via improving the food consumption pattern can prevent this progression. Keywords: Aflatoxin M1, Hepatitis C virus (HCV), Liver dysfunction

The Effect of Counseling in Third Trimester on Anxiety of Nulliparous Women at the Time of Admission for Labor

Background: Anxiety is recognized as a preventive and effective factor in labor progress. Due to the different results of studies in other countries and lack of the similar study in Iran, this study was designed to determine the effect of counseling with nulliparous women in third trimester on their anxiety in the beginning of labor. Materials and Method: This quasi-experimental study was carried out on 64 nulliparous women who had not previous hospitalization and known psychological disease. The women completed the consent form and then their anxiety was assessed in third trimester by Hamilton Scale. Those women were randomly entered into the study groups (34 individuals in each group). The intervention group in every visit in third trimester was counseled about the different topics of pregnancy and delivery. The non-intervention group received the normal prenatal care. Then the anxiety scores of both groups of women were assessed at the time of hospitalization for delivery. Data was analyzed by SPSS- 15, Mann- Witney-U and Wilcoxon rank tests. P-value less than 0.05 was considered significant. Results: Mean of anxiety score in intervention group was 26.5±7.6 in third trimester and 11.82±8.3 at the time of admission for delivery and difference was significant (p<0.001). It was 25.90±6.9 in third trimester and 23.53±5.8 at the time of labor beginning in non-intervention group and the difference was not significant (p=0.25). Conclusion: Counseling with pregnant women in third trimester reduced their anxiety in the beginning of labor. For reduction of anxiety of nulliparous women, counseling in third trimester was suggested

THE EFFECT OF THE COLLABORATION OF REFLECTIVE NOTES WITH CALL ON EFL LEARNERS’ WRITING ACCURACY

The aims of this mixed-method action research were: (1) to investigate the effect of feedback provided by MS word processor on EFL learners’ writing accuracy in the context of a university in Iran, (2) to explore whether taking reflective notes (henceforth. RNs) in collaboration with the word processor can modify the effect of such received feedback, and (3) to find out what the participants thought about each treatment. Two intact classes (Advanced Writing) were used, but the classes were randomly assigned to each treatment, called ‘CALL with RNs’ and ‘CALL without RNs’. There were 50 participants altogether who were B.A. English Translation majors. The Straightforward Quick Placement &amp; Diagnostic Test was administered to ensure the participants were homogeneous. Each group received 10-sessions of treatment. Two samples of Task 2 of General Module of IELTS were used for the pretests and posttests. Each essay was scored independently by two raters. The final score consisted of the average score of the two raters. The findings revealed that the feedback provided by MS word processor improved the students’ writing accuracy significantly; however, the ‘CALL with RNs’ group outperformed the other one. To collect qualitative data, semi-structured interviews were conducted with each participant. Generally, the participants in both groups had positive attitudes towards receiving feedback by the word processor. Considering the RNs, the participants also had positive attitudes; nonetheless, there were a few students who didn’t like RNs technique, not because they found it useless, but mainly because of their individual differences.  Article visualizations

A HYBRID ALGORITHM FOR THE UNCERTAIN INVERSE p-MEDIAN LOCATION PROBLEM

In this paper, we investigate the inverse p-median location  problem with variable edge lengths and variable vertex weights on networks in which the vertex weights and modification costs are the independent uncertain variables. We propose a  model for the uncertain inverse p-median location problem  with tail value at risk objective. Then, we show that  it  is NP-hard. Therefore,  a hybrid particle swarm optimization  algorithm is presented  to obtain   the approximate optimal solution of the proposed model. The algorithm contains expected value simulation and tail value at risk simulation

Regional vulnerability of the hippocampus to repeated motor activity deprivation

Spontaneous vertical and horizontal exploratory movements are integral components of rodent behavior. Little is known, however, about the structural and functional consequences of restricted spontaneous exploration. Here, we report two experiments to probe whether restriction in vertical activity (rearing) in rats could induce neuro-hormonal and behavioral disturbances. Rearing movements in rats were deprived for 3 h/day for 30 consecutive days by placing the animal into a circular tunnel task. Rats temporarily deprived of rearing behavior showed elevated plasma corticosterone levels but no detectable psychological distress and/or anxiety-related behavior within an elevated plus maze. However, rats emitted a greater number of 22-kHz ultrasonic vocalizations and spent significantly more time vocalizing than controls when deprived of their rearing behavior. Despite intact spatial performance within wet- and dry-land spatial tasks, rearing-deprived rats also exhibited a significant alteration in search strategies within both spatial tasks along with reduced volume and neuron number in the hippocampal subregion CA2. These data suggest a new approach to test the importance of free exploratory behavior in endocrine and structural manifestations. The results support a central role of the CA2 in spontaneous exploratory behavior and vulnerability to psychological stress. © 2015 Elsevier B.V

Modeling of Berm Formation and Erosion at the Southern Coast of the Caspian Sea

Cross-shore beach profile data from field measurements performed at six locations on the southern coast of the Caspian Sea are used to investigate bathymetry change due to various wave conditions. Beach profile measurements are analyzed and subsequently compared with the results of a berm formation and erosion model. The model comprises distinct empirical sediment transport equations for predicting the cross-shore sediment transport rate under various wave conditions. To yield a berm formation and erosion model, empirical cross-shore sediment transport equations are combined with the mass conservation equation. Simulations results obtained from the model compared well with the measurements, proving the capability of the model in simulating berm formation and erosion evolution