mohammad abdollahzadeh

In recent years, precise analysis and prediction of financial time series data have received significant attention. While advanced linear models provide suitable predictions for short and medium-term periods, market studies have indicated that stock behavior adheres to nonlinear patterns and linear models capturing only a portion of the market's stock behavior. Nonlinear exponential autoregressive models have proven highly practical in solving financial problems. This article introduces a new nonlinear model that allocates coefficients to significant variables. To achieve this, existing exponential autoregressive models are analyzed, tests are conducted to validate data integrity and identify influential factors in data trends, and an appropriate model is determined. Subsequently, a novel coefficient allocation method for optimizing the nonlinear exponential Autoregressive model is proposed. The article then proves the ergodicity of the new model and determines its order using the Akaike Information Criterion (AIC). Model parameters are estimated using the nonlinear least squares method. To demonstrate the performance of the proposed model, numerical simulations of Kayson Corporation's stocks are analyzed using existing methods and the new approach. The numerical simulation results confirm the effectiveness and prediction accuracy of the proposed method compared to existing approaches.

Preserving the health of food resources, including drinking water, is considered one of the most important issues in human usability. Therefore, all efforts and research are based on finding and utilizing the best methods to safeguard the hygiene of water and municipal wastewater, aiming to protect human health and improve the quality of treated sanitary sewage. Hence, the objective of this study is to investigate water contaminated with Escherichia coli bacteria (E.coli) and the removal of this microorganism as an indicator of drinking water contamination and the degree of treatment of sanitary wastewater using a Pulsed Electric Field (PEF). In this research, the PEF device was utilized within the range of 1 to 13 kV/cm with a frequency of 500 Hz for some time of 3 to 60 min. The investigation was conducted using the Most Probable Number (MPN/100 mL) test in a 9-tube format. The results of this assessment at a temperature of 20 ºC showed that the minimum removal efficiency of total coliform bacteria was achieved at 1 kV voltage with a contact time of 1 min, yielding a 0% efficiency, while the highest removal efficiency was obtained at 13 kV voltage with a contact time of 30 min, resulting in a 99.8% efficiency.

Drinking water supply with proper quality is one of the main goals of water treatment plants. Filtration is a process which has the largest share of quality making in water treatment plants and about 20% of the cost is allocated to this process. The main indicator used for filters efficiency is water turbidity at outlet.
In this research, the effect of various heights (20-50-70-90 cm), effective sizes (0.75-1) and uniform coefficients (1-1.5) sand filter’s packing on turbidity removal and coliforms has been studied for four months from May 2010. Sampling was performed according to standard procedures and the samples were analyzed immediately in Tehran Islamic Azad University of Science Research laboratories and water treatment plant laboratory.
Finding: Results of coarse packing filter show the maximum efficiency of turbidity reduction was 83.58% for turbidity of 5 NTU and packing height of 90cm. The minimum turbidity reduction was 29.25% for turbidity of 10 NTU and packing height of 20cm. The maximum coliform reduction was about 87.92% for turbidity of 2 NTU and packing height of 90cm and the minimum coliform reduction was about 38% for turbidity of 10 NTU and packing height of 20cm. The effective height which causes maximum turbidity and coliform reduction is 90cm. The longest operation time in the context of a coarse bed was 35 hours in turbidity of 2 NTU and the shortest operation time in the context of a coarse strainer was 23 hours in turbidity of 10 NTU.
Results of fine packing filter show the maximumefficiency of turbidity reduction which was 93% for turbidity of 2 NTU and packing height of 90cm. The minimum turbidity reduction was 54.9% for turbidity of 7 NTU and packing height of 50cm. The maximum coliform reduction was about 91.92% for turbidity of 2 NTU and packing height of 90cm and the minimum coliform reduction was about 45% for turbidity of 10 NTU and packing height of 20cm. The effective height which causes maximum turbidity and coliform reduction is 90cm. According to the results, efficiency of the fine bed and coarse bed filter directly depends on outlet turbidity and operation of previous unit (sedimentation). The maximum and minimum operation times in fine bed were about 28 hours (NTU=2) and 14 hours (NTU=10) respectively.

Forecasting of the wastewater quality parameters has great importance in modern wastewater treatment methods. One of the main problems in predicting the efficacy of a wastewater treatment is the complexity of physicochemical properties of raw sewage and data differences for different reasons. Modeling of wastewater treatment using Adaptive Neural Fuzzy Inference System (ANFIS)- can help to improve wastewater quality control process. The aim of the study was to modeling of treatment of dairy wastewaters by electrocoagulation process using ANFIS.
In this study, ANFIS was used to estimate the chemical coagulation of dairy wastewater treatment. The input parameters to the ANFIS model were time, voltage, total suspended solids and boichemical oxygen demand and the output was chemical oxygen demand removal efficiency. Also the membership functions, the number of membership functions and number of learning cycles (Epochs) were used for optimization of different models by trial and error.
The best model was assessed by bell-shaped membership functions with number of membership functions as 3 3 3 3 3 and 300 epochs of training with lowest mean square error(MES) and the best coefficient of determination (R2). The coefficient of determination and MSE of the best ANFIS model were 0.9912 and 0.012, respectively.
Analysis of the model revealed that the ANFIS is a powerful tool to predict the dairy wastewater treatment using electrical coagulation.