The purpose of this paper is to predict the S & P500 down moves with technical analysis indicators using learning vector quantization (LVQ) neural networks and probabilistic neural networks (PNN). In addition, entropy-based input selection technique is employed to improve the prediction accuracies. The out-of-sample simulations show that LVQ outperforms PNN. In addition, the Entropy-LVQ system achieved higher accuracy in comparison with the literature.

Land subsidence in Iran is one of the environmental issues of recent years in Iran, which is mostly due to shortage of the groundwater resources. Iran is considered as the country with the most land subsidence. The amount of land subsidence in Iran is believed to be far more than the average of developed countries. Presently, over 300 plains of Iran are suffering from land subsidence and in some plains of Iran, the conditions have passed the subsidence and the ground has entered the critical stage of creating sinkholes. This paper presents an overview of the current status and challenging issues of land subsidence in Iran.

The amount of damage induced by brittle fracture of cracked bodies depends considerably on the path of fractures. Therefore, prediction of the trajectory of fracture using suitable theoretical fracture criteria is very important for cracked structures. In this paper, using higher-order terms of Williams’s series expansion and the maximum tangential stress criterion, the mixed mode I/II crack growth path of an angled crack plate subjected to biaxial far field loading is investigated theoretically. To evaluate the accuracy of the theoretical results, they are compared with the experimentally reported trajectories for the angled crack plate specimen. It is shown that by taking into account the higher order terms of the Williams series expansion a very good agreement is observed between the experimental and theoretical mixed mode fracture paths in the angled crack problem. It was also observed that the theoretically determined initial angle of crack growth is consistent with the experimental results.

Crude oil price volatility dynamics are governed by nonlinear and chaotic behaviour. This paper presents and compares the performance of four hybrid systems used to estimate and predict crude oil price volatility data. A GARCH family model is employed to estimate oil price volatility data and the Elman artificial neural network (ENN) system is used to model and predict the obtained data. Indeed, unlike previous studies found in the literature, recurrent artificial neural networks are chosen in this paper to model and predict future crude oil price volatility data estimated by GARCH family models since they are nonlinear systems capable of learning noisy and nonstationary data. In particular, four hybrid systems are tested and compared; including the GARCH-ENN, EGARCH-ENN, APARCH-ENN, and TARCH-ENN system. Using Brent crude oil price data, the obtained out-of-sample simulation results indicate that all hybrid systems provide very accurate forecasts of Brent future volatility. In addition, they show evidence of the superiority of the GARCH-ENN system over the EGARCH-ENN, TARCH-ENN, and APARCH-ENN systems. The presented four hybrid systems achieved very low forecasting errors. Thus, they could be effective in oil industry management and applications.

In financial industry, the accurate forecasting of the stock market is a major challenge to optimize and update portfolios and also to evaluate several financial derivatives. Artificial neural networks and technical analysis are becoming widely used by industry experts to predict stock market moves. In this paper, different technical analysis measures and resilient back-propagation neural networks are used to predict the price level of five major developed international stock markets, namely the US S & P500, Japanese Nikkei, UK FTSE100, German DAX, and the French CAC40. Four categories of technical analysis measures are compared. They are indicators, oscillators, stochastics, and indexes. The out-of-sample simulation results show a strong evidence of the effectiveness of the indicators category over the oscillators, stochastics, and indexes. In addition, it is found that combining all these measures lead to an increase of the prediction error. In sum, technical analysis indicators provide valuable information to predict the S & P500, Nikkei, FTSE100, DAX, and the CAC40 price level.

This paper examines the optimization of production involving a tandem two-machine system producing a single part type, with each machine being subject to random breakdowns and repairs. An analytical model is formulated with a view to solving an optimal stochastic production problem of the system with machines having up-downtime non-exponential distributions. The model developed is obtained by using a dynamic programming approach and a semi-Markov process. The control problem aims to find the production rates needed by the machines to meet the demand rate, through a minimization of the inventory/shortage cost. Using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation, which depends on time and system states, and ultimately, leads to a feedback control. Consequently, the new model enables us to improve the coefficient of variation (CVup/down) to be less than one while it is equal to one in Markov model. Heuristics methods are used to involve the problem because of the difficulty of the analytical model using several states, and to show what control law should be used in each system state (i.e., including Kanban, feedback and CONWIP control). Numerical methods are used to solve the optimality conditions and to show how a machine should produce.

During the past four decades, there have been tremendous changes on earth due to climate change. Humankind has increasingly used fossil fuels such as coal, oil, etc. and increased the green gas effect on this planet. Nowadays, most scientists believe there must be some actions to prevent global warming to rescue the next generation from the irreversible consequences of climate change. This paper studies the recent studies on the effects of global warming in Canada. The study reviews the recent efforts on the effects of global warming on forests, lakes, etc.

Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be considered in order to determine the most accurate method to predict the cost of a main landing gear. Several trials are presented for the design and use of the neural network model. The analysis for the case under study shows the flexibility in the design of the neural network model. Furthermore, the performance of the neural network model is deemed superior to the parametric models for this case study.

Immigration plays an essential role for population growth in many countries. Immigrants create jobs and opportunities in immigration-based countries. Canada is the host of millions of people from different ethnic groups and regions. Every year, thousands of new immigrants come to Canada and many of them are in the form families. This paper presents a review on studies associated with population growth and immigration policies in Canada. The survey performs research on 128 selected articles from 1958 to 2022.

During the past few months, the whole Canadian housing prices were witnessed an up-trend. People could enjoy the low interest rates to buy houses, even foreign investors were interested in participating in Canadian real estate pushing the prices up and up. As the COVID19 disappears, we see an uptrend on inflation and, on the other hand, the government placed some restrictions to prevent foreigners of purchasing houses in Canada. This paper investigates whether the government regulation and monetary policy could influence housing prices in Canada.