درخت حوزه‌های تخصصی

The issue of transportation, which today as a socio-political phenomenon plays a very sensitive and important role in the quality and socio-economic structure of a society, forms the basis of modern urban life and the needs of human movement. In this research, our goal is to design a system dynamics model in the form of combined transport system to try to reduce costs, time and increase customer satisfaction by taking into account the uncertainty in the combined transport system, especially uncertainty in demand. Considering the productivity required by both of passengers and urban management, the importance of the issue and its impact on other sectors, as well as the lack of sufficient research in this field, in this study, the design of intra-city transportation system will be studied. The purpose of this planning is to determine the location of stations for public transportation as well as to determine the best route to reach the next station. Initially, the IoT system is used to collect and analyze historical data. Then the effective parameters and input values of the mathematical programming model are determined. The implementation of the model determines the appropriate outcomes for the decision variables. In other words, this leads to determining the optimal stations and the appropriate route to transport passengers between stations so that travel time and cost are minimized. In addition to material profitability, this includes passenger satisfaction, increased willingness to use public transport, reduced traffic, and reduced environmental impact, increased reliability of public transport and more accurate forecast of vehicle arrival at the station.

Voluntary deviation from self-control, commonly referred to as moral licensing, is a phenomenon in which an individual justifies his or her immoral and counterproductive behavior depending on various factors, especially past creditable behaviors and activities, without worrying about the loss of his or her reputation and esteem. Considering the undesirable consequences of this phenomenon for the individual, organization and society, this study attempts to find the reasons and factors that lead to the formation of moral licensing in addition to the individual's previous creditable behaviors and activities. The research philosophy was descriptive, the approach was qualitative, and the strategy was emergent-based grounded theory. The population under study consists of managers and employees of public administrations in northeastern Iran. 17 individuals were selected as purposive samples until theoretical saturation was reached. A semi-structured interview was used to collect data. Analysis of the interviews based on actual and theoretical coding shows that the causes of moral licensing are classified into six categories. Identifying these causes can provide solutions to prevent moral licensing in the organizations.

Accurate stock market prediction can assist in an efficient portfolio and risk management. However, accurately predicting stock price trends still is an elusive goal, not only because the stock market is affected by policies, market environment, and market sentiment, but also because stock price data is inherently complex, noisy, and nonlinear. Recently, the rapid development of deep learning can make the classifiers more robust, which can be used to solve nonlinear problems. This study proposes a hybrid framework using Long Short-Term Memory, Autoencoder, and Deep Neural Networks (LSTM-AE-DNNs). Specifically, LSTM-AE is responsible for extracting relevant features, and in order to predict price movement, the features are fed into two deep learning models based on a recurrent neural network (RNN) and multilayer perceptron (MLP). The dataset used for this is Dow Jones daily stock for 2008-2018, which was used in this article. Besides, to further assess the prediction performance of the proposed model, original stock features are fed to the single RNN and MLP models. The results showed that the proposed model gives the more accurate and best results compared to another. In particular, LSTM-AE+RNN shows a better performance than the LSTM-AE+MLP. In addition, hybrid models show better performance compared to a single DNN fed with the all-stock features directly.