Phenomenon of random walk on Tehran Stock Exchange

Stock exchange as the heart of the capital market is essential to economic progression for which certain conditions are necessary; efficiency is a requirement. Proper stock pricing and allocation of capital follow efficiency. The main goal of this study is to check out a phenomenon called random walk, which is one of the most important features of market in weak form of efficiency, and to study the effects of week days on stock price at Tehran Stock Exchange. The results show that solely Tuesdays have positive effects on stocks price. Also old prices have effects on the current prices of the stock exchange that is the old prices can predict the future prices and thus Tehran Stock Exchange does not comply with random walk hypothesis and it is not in a weak form of efficiency

Forecasting the Cost of Water Using a Neural Network Method in the Municipality of Isfahan

Decision making on budgeting is one of the most important issues for executing managers. Budgeting is a major tool for planning and control of projects. In public and non-profit organizations and institutions, estimating the costs and revenues plays an important role in receiving credit and budgeting. In this regard, in the present paper the case of Isfahan municipality is considered. One of the main expenditures of the 14 districts of Isfahan is the costs related to water. Predicting the total cost of water helps the municipality of Isfahan to optimize the water use in its 14 urban zones. Thus, in this study the total cost of water in the districts of Isfahan is estimated using regression analysis and neural network models. Then the results of the methods are compared with each other to minimize the deviations from the approved budget. Finally, the neural network method is selected as the main simulation method for forecasting the total cost ofwater in the districts of Isfahan

Optimal type-3 fuzzy system for solving singular multi-pantograph equations

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients

Association of meat and dairy consumption with normal weight metabolic obesity in men: the Qazvin Metabolic Diseases Study

Abstract BACKGROUND: Insulin resistance (IR) is not limited to obese individuals. Normal weight individuals may also be insulin resistant. The aim of this study was to determine the association of lifestyle and diet patterns with IR in normal weight Iranian men. METHODS: This cross-sectional study was conducted in 232 men with a body mass index lower than 25 kg/m2 (aged 20-72 years old) between September 2010 and April 2011 in Qazvin, Iran. Metabolically obese normal weight (MONW) was defined as IR using the homeostatic model assessment (HOMA). The optimal cut point to diagnose IR was the 80th percentile of HOMA-IR values in normal subjects. The HOMA-IR cut point was 2.48. Dietary pattern was assessed by a semi-quantitative food frequency questionnaire. Data were analyzed using backward logistic regression and ANCOVA. RESULTS: Fat and meat consumption and energy intake in subjects with MONW were more than subjects without MONW. Each serving of meat consumption was associated with three times increased risk of MONW (OR: 3.06), while each serving of dairy consumption was associated with 56 % lower risk of MONW with borderline significance (OR: 0.64). Adjusted mean of HOMA-IR in the first tertile of dairy consumption was significantly higher than other tertiles. Adjusted HOMA-IR value in the third tertile of meat consumption was significantly higher than the second tertile. CONCLUSION: Higher meat consumption was associated with MONW in men. Higher meat consumption and lower dairy consumption were associated with higher means of HOMA-IR. KEYWORDS: Body mass index; Diet; Insulin resistance; Meat; Mil

Robust adaptive synchronization of a class of uncertain chaotic systems with unknown time-delay

The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems

Mental imagery can improve performance in a visuomotor task: a pilot study

This experiment assessed the effectiveness of two interventions; mental imagery and physical training as compared to a control group, on their ability to improve visuomotor accuracy, measured by scores achieved on a visuomotor task. For mental imagery participants had to imagine throwing a dart and for physical training participants physically practised dart throwing. Measurements were recorded pre-intervention, after two weeks of training (Mid-intervention), after five weeks of training (Outcome Measure 1) and two weeks after training (Outcome Measure 2). Comparison of Mid-intervention, Outcome Measure 1 and Outcome Measure 2 with baseline showed both interventions to significantly increase performance on dart throwing compared to the Control group. Our findings show that, as well as traditional physical practise, mental imagery can effectively improve performance on a fine visuomotor task. This is an important finding highlighting possible applications of mental imagery in those with limited motor abilities to maintain or enhance motor movement

Prediction of compression index of fine-grained soils using a gene expression programming model

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models

The Role of Stearoyl-coenzyme A Desaturase 1 in Liver Development, Function, and Pathogenesis

Stearoyl-coenzyme A desaturase 1 (SCD1) is a microsomal enzyme that controls fatty acid metabolism and is highly expressed in hepatocytes. SCD1 may play a key role in liver development and hepatic lipid homeostasis through promoting monounsaturated protein acylation and converting lipotoxic saturated fatty acids into monounsaturated fatty acids. Imbalanced activity of SCD1 has been implicated in fatty liver induction, inflammation and stress. In this review, the role of SCD1 in hepatic development, function and pathogenesis is discussed. Additionally, emerging novel therapeutic agents targeting SCD1 for the treatment of liver disorders are presented

Road traffic mortality in Iran : longitudinal trend and seasonal analysis, March 2011-February 2020

Road traffic mortalities (RTMs) and injuries are among the leading causes of human fatalities worldwide, particularly in low-and middle-income countries like Iran. Using an interrupted time series analysis, we investigated three interventional points (two government-mandated fuel price increases and increased traffic ticket fines) for their potential relation to RTMs. Our findings showed that while the overall trend of RTMs was decreasing during the study period, multiple individual provinces showed smaller reductions in RTMs. We also found that both waves of government-mandated fuel price increases coincided with decreases in RTMs. However, the second wave coincided with RTM decreases in a smaller number of provinces than the first wave suggesting that the same type of intervention may not be as effective when repeated. Also, increased traffic ticket fines were only effective in a small number of provinces. Potential reasons and solutions for the findings are discussed in light of Iran’s Road Safety Strategic Plan

Design and validation of a computational program for analysing mental maps: Aram mental map analyzer

Considering citizens’ perceptions of their living environment is very helpful in making the right decisions for city planners who intend to build a sustainable society. Mental map analyses are widely used in understanding the level of perception of individuals regarding the surrounding environment. The present study introduces Aram Mental Map Analyzer (AMMA), an open-source program, which allows researchers to use special features and new analytical methods to receive outputs in numerical data and analytical maps with greater accuracy and speed. AMMA performance is contingent upon two principles of accuracy and complexity, the accuracy of the program is measured by Accuracy Placed Landmarks (APL) and General Orientation (GO), which respectively analyses the landmark placement accuracy and the main route mapping accuracy. Also, the complexity section is examined through two analyses Cell Percentage (CP) and General Structure (GS), which calculates the complexity of citizens’ perception of space based on the criteria derived from previous studies. AMMA examines all the dimensions and features of the graphic maps and its outputs have a wide range of valid and differentiated information, which is tailored to the research and information subject matter that is required