Designing a Novel Model for Stock Price Prediction Using an Integrated Multi-Stage Structure: The Case of the Bombay Stock Exchange

Stock price prediction is considered a strategic and challenging issue in the stock markets. Considering the complexity of stock market data and price fluctuations, the improvement of effective approaches for stock price prediction is a crucial and essential task. Therefore, in this study, a new model based on “Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)” is employed to predict stock price accurately. ANFIS has been utilized to predict stock price trends more precisely. PSO executes towards developing the vector, and GA has been utilized to adjust the decision vectors employing genetic operators. The stock price data of top companies of the Bombay Stock Exchange (BSE) from 2010 to 2020 are employed to analyze the model functionality. Experimental outcomes demonstrated that the average functionality of our model (77.62%) was achieved noticeably better than other methods. The findings verified that the ANFIS-PSO-GA model is an efficient tool in stock price prediction which can be applied in the different financial markets, especially the stock market

CFD simulation of effects of dimension changes of buildings on pollution dispersion in the built environment

AbstractAs pollutions impose adverse effects on human health and environment, assessment of their dispersion within the urban regions can much help to control them. In urban regions, dynamics of pollutants will be affected by buildings and barriers, and to investigate the dispersion of the pollutants, these barriers must be considered. In this article, CFD simulation is done by applying the 3D approach, the k−ε Realizable turbulence model and two Schmidt numbers (0.3 and 0.7). It has seen that height, length and width of the building in front of the wind, and, the distance between the two buildings back to the main building (the building on which the stack is present), have much influence on the concentration of pollutions. Although there are some differences between the results with different Schmidt numbers, the trend of changes of the concentration in different locations is identical for the two Schmidt numbers

Designing a Novel Model for Stock Price Prediction Using an Integrated Multi-Stage Structure: The Case of the Bombay Stock Exchange

Keywords: Stock Price Prediction, Technical Analysis, ANFIS, PSO, GA Stock price prediction is considered a strategic and challenging issue in the stock markets. Considering the complexity of stock market data and price fluctuations, the improvement of effective approaches for stock price prediction is a crucial and essential task. Therefore, in this study, a new model based on “Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)” is employed to predict stock price accurately. ANFIS has been utilized to predict stock price trends more precisely. PSO executes towards developing the vector, and GA has been utilized to adjust the decision vectors employing genetic operators. The stock price data of top companies of the Bombay Stock Exchange (BSE) from 2010 to 2020 are employed to analyze the model functionality. Experimental outcomes demonstrated that the average functionality of our model (77.62%) was achieved noticeably better than other methods. The findings verified that the ANFIS-PSO-GA model is an efficient tool in stock price prediction which can be applied in the different financial markets, especially the stock market

Sludge-based activated carbon for removal of Cadmium in the water resource; Financial feasibility

Sludge-based activated carbon (AC) was prepared for the cadmium (Cd) removal from the aqueous solution. X-ray diffraction and Fourier transform infrared were applied as two main techniques to investigate the surface characterizations of the adsorbent. Response surface methodology (RSM), which was coupled with central composite design (CCD), was applied to study the impact of three major parameters, such as pH, dosage (D) and initial concentrate (C) on the percentage of Cadmium removal. The RSM model indicates that the optimum points of Cd removal were 90% at pH = 8.74 and D/C = 50. The Financial Feasibility and Investment Strategy was also investigated to consider key indicators in the financial feasibility of water treatment projects. The present study shows the systematic investigation of an attractive adsorbent to remove Cd from an aqueous solution. Also, in this study, modern investment strategies and efficient financing methods for water treatment projects are provided. The results showed that this type of adsorbent is appropriately able to eliminate Cd from water and aqueous solution

Application of Nano-biosensors in Detection and Determination of Pathogens in Water

In water disinfection and treatment, rapid detection and monitoring of pathogens such as bacteria and viruses, as important water quality indices, are of prime importance. On the other hand, continuous monitoring and detection of pathogens have encountered the problems such as low concentration of bacteria and viruses in water, low sensitivity of the conventional detection methods, complex sample matrices and insufficient selectivity of the conventional methods, and inability of these methods in fast and cheap detection and continuous monitoring of pathogens. These indicate the importance of considering new sciences and technologies in fabrication of biosensors in order to achieve higher sensitivity, response rapidity, and selectivity. In this paper, briefly, new nanoscience approach and application of nanostructured materials in design and fabrication of nanobiosensors in order to detecting and monitoring of bacteria and viruses in water have been introduced and some examples of the applications of nanobiosensors have been also presented

Optic Nerve Head Optical Coherence Tomography Angiography Findings after Coronavirus Disease

Purpose: To quantify the microvasculature density of the optic nerve head (ONH) using optical coherence tomography angiography (OCTA) analysis in patients recovered from Coronavirus Disease 2019 (COVID-19). Methods: In a comparative cross-sectional, observational study, patients recovered from COVID- 19 whose initial diagnosis was confirmed by an rRT-PCR of a nasopharyngeal sample were included in this study. OCTA of ONH was performed in included patients and normal controls. Vascular density (VD) of the all vessels (AV) and small vessels (SV) inside the disc and radial peripapillary capillary (RPC) network density was measured in COVID-19 recovered patients and compared with similar parameters in an age-matched group of normal controls. Results: Twenty-five COVID-19 patients and twenty-two age-matched normal controls were enrolled in the study and one eye per participant was evaluated. The mean whole image SV VD in the COVID-19 group (49.31 ± 1.93) was not statistically significantly different from that in the control group (49.94 ±. 2.22; P = 0.308). A decrease in RPC VD was found in all AV and SV VD measured, which became statistically significant in whole peripapillary SV VD, peripapillary inferior nasal SV VD, peripapillary inferior temporal SV VD, peripapillary superior nasal SV VD, and grid-based AV VD inferior sector (P < 0.05). Inside disc SV VD in the COVID-19 group (49.43 ± 4.96) was higher than in the control group (45.46 ± 6.22) which was statistically significant (P = 0.021). Conclusion: Unremarkable decrease was found in ONH microvasculature in patients who had recovered from COVID-19. These patients may be at risk of ONH vascular complications. Increase in inner disc SV VD may be an indicator of ONH hyperemia and edema

The relationship between personality traits and drug type among Substance Abuse

Substance abuse is a serious global problem that is affected by multiple psychosocial factors, and personality traits play a central role in its occurrence. The present study aims to investigate the relationship between the five factors of personality (extraversion, agreeableness, openness to experience, conscientiousness, and neuroticism) and five categories of drugs (sedatives, opiates, stimulants, hallucinogens, and marijuana) among self-introduced addicts. The statistical population of the study was self-introduced addicts attending addiction treatment centers in Khorram Abad. The participants of the study included 100 addicts with drug abuse disorder who were selected by the classified sampling method underlining five classes of drugs (20 participants in each class) as the sampling strata. Data were gathered using the structured clinical interview of Diagnostic and Statistical Manual of Psychiatric Disorders, NEO five-factor inventory-revised, and the structured demographic questionnaire. The results showed that high levels of neuroticism distinguish users of sedatives from those of other drugs. participants with high levels of openness to experience and low agreeableness and conscientiousness are consistently associated with the use of marijuana, hallucinogens, and stimulants. The results also demonstrated that addicts with high levels of extraversion and low levels of agreeableness and conscientiousness are consistently associated with the use of stimulants. The results of this research indicate that personality traits contain valuable information about the nature of personality traits affecting drug type in addicts. These findings are useful in drug abuse treatment and preventing drug abuse recurrence

The Nexus between Stock Returns of Oil Companies and Oil Price Fluctuations after Heavy Oil Upgrading: Toward Theoretical Progress

This study attempts to discover the nexus between crude oil price fluctuation after heavy oil upgrading and stock returns of petroleum companies in the U.S. Stock Exchange for the years 2008 to 2018. One of the methods of upgrading heavy crude oil is to extract asphaltene from crude oil. Considering the Asphaltene Removal (AR) as a factor in the nexus between oil price and the stock market is an innovation in the literature of energy finance. Asphaltenes cause many problems in the petroleum industry, which increases the cost of oil production and reduces the financial efficiency of oil companies. The AR is certainly one of the significant matters of the oil industry and can affect the price of oil. Therefore, changes in the price of oil can influence the price of oil company stocks. Hence, changes in stock prices will certainly affect the stock returns of oil companies. In an effort to solve this puzzle, the four financial models were employed to explore the nexus between oil price fluctuations and stock returns. The analysis of the results demonstrated that the oil price fluctuations caused by the removal of asphaltenes influence the stock returns of petroleum companies. Eventually, the theoretical hypothesis was confirmed by considering the USA as a case study. The outcomes of this investigation are a theoretical progression in areas related to the petroleum industry and the stock market that could lead to the adoption of new investment policies in the petroleum industry including investing in new procedures to manage and decrease the costs and time of the AR process, which would result in the advancement of petroleum companies. In fact, we have introduced a modern investment strategy in the oil industry aimed at reducing oil production costs, improving financial statements and increasing the stock returns of petroleum companies. Eventually, we will present new investment policies in the oil industry that can lead to economic growth and development of financial markets especially stock market, derivatives market, futures exchange, commodities exchange, as well as bond market

EFFECT OF DISCRETE HEATER AT THE VERTICAL WALL OF THE CAVITY OVER THE HEAT TRANSFER AND ENTROPY GENERATION USING LBM

In this paper Lattice Boltzmann Method (LBM) was employed for investigation the effect of the heater location on flow pattern, heat transfer and entropy generation in a cavity. A 2D thermal lattice Boltzmann model with 9 velocities, D2Q9, is used to solve the thermal flow problem. The simulations were performed for Rayleigh numbers from 103 to 106 at Pr = 0.71. The study was carried out for heater length of 0.4 side wall length which is located at the right side wall. Results are presented in the form of streamlines, temperature contours, Nusselt number and entropy generation curves. Results show that the location of heater has a great effect on the flow pattern and temperature fields in the enclosure and subsequently on entropy generation. The dimensionless entropy generation decreases at high Rayleigh number for all heater positions. The ratio of averaged Nusselt number and dimensionless entropy generation for heater located on vertical and horizontal walls was calculated. Results show that higher heat transfer was observed from the cold walls when the heater located on vertical wall. On the other hand, heat transfer increases from the heater surface when it located on the horizontal wall