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Showing 3 results for Ahmadpour
Impact of Fishing Technology on Labor Productivity in Bushehr Province
M. Ahmadpour Borazjani, G.r. Soltani,
Volume 4, Issue 2 (summer 2000)
Abstract
Marine products are currently important in Iran due to the following reasons: 1) aquatics are recognized as safe and healthy food by food scientists 2) because of low dependence of fishing technology on foreign resources, they are more reliable sources for protein and food security. Therefore, it is important to increase labor productivity using advanced fishing technology under a sustainable fishery management policy. In general, the prevailing methods of fishing in the southern coasts of Iran can be classified into three groups based on the level of technology and the length of journey made: traditional, semi-industrial, and industrial. In this study, labor productivity in the traditional and semi-industrial groups were compared using data obtained from a sample of 35 fishermen in Bushehr Province. To determine the marginal and average labor productivities, transcendental production functions were estimated for fish and shrimp, separately. The findings indicated that the marginal and average productivities of labor in semi-industrial fishing were significantly higher than in traditional fishing.
Cadmium Accumulation and Translocation in Jatropha curcas Grown in Contaminated Soils
P. Ahmadpour, M. Soleimani,
Volume 19, Issue 73 (fall 2015)
Abstract

Cadmium (Cd) is a metal with high toxicity and solubility in water, which is a serious environmental threat to human health. Phytoremediation is an environment-friendly method and a promising new and cost effective technology that uses plants to clean organic and inorganic contaminated media. This study was conducted to evaluate the potential of Jatropha curcas for remediation of soils contaminated with Cd. Seedlings were planted in the soil spiked with Cd in amounts of 0, 25, 50, 75, 100 and 150 mg kg-1 (Cd0, Cd25, Cd50, Cd75, Cd100 and Cd150) for a period of five months. Biocentration factor (BCF, metal concentration ratio of plant roots to soil), translocation factor (TF, metal concentration ratio of plant shoots to soil) and removal efficiency (RE, total metal removed by plant biomass to total metal loaded in soil) were determined. Cd concentrations among plant parts were in the following trend: roots>stems>leaves. The highest total Cd concentration (up to 1100 mg kg-1) and the highest RE were found in Cd150 and Cd25, respectively. BCF and TF of the plant were more and less than 1, respectively. Hence, although this species has a potential to be used in phytostabilization of Cd-contaminated soil, more researches in the field condition are needed.


Performance Evaluation of ARIMA Hybrid Models in the Prediction of Daily Electrical Conductivity (A Case Study of Telazang Hydrometric Station)
A. Ahmadpour, S. H. Mirhashemi, P. Haghighatjou, M. R. Raisi Sistani,
Volume 24, Issue 3 (Fall 2020)
Abstract
In this study, we used the ARIMA time series model, the fuzzy-neural inference network, multi-layer perceptron artificial neural network, and ARIMA-ANN, ARIMA-ANFIS hybrid models for the modeling and prediction of the daily electrical conductivity parameter of daily teleZang hydrometric station over the statistical period of 49 years. For this purpose, the daily data for the 1996-2004 period were used for model training and data for the 1996-2006 period were applied for testing. In order to verify the validity of the fitted ARIMA models, the residual autocorrelation and partial autocorrelation functions and Port Manteau statistics were used. PMI algorithm were   then used to model and predict electrical conductivity for selecting the effective input parameter of the neural fuzzy inference network and the artificial neural network. The daily parameters of magnesium (with two days delay) and sodium (with one day delay), heat (with one day delay), flow rate (with two months delay), and acidity (with one day delay) were obtained with the lowest values of Akaike and highest values of hempel statistics as the input of the neural fuzzy inference network and the artificial neural network for modelling daily electric conductivity predictions; then predictions were made. Also, models evaluation criteria confirmed the superiority of the ARIMA-ANFIS hybrid model with the trapezoidal membership function and with two membership numbers, as compared to other models with a coefficient of determination of 0.86 and the root mean square of 29 dS / m. Also, the Arima model had the weakest performance. So, it could be applied to modeling and forecasting the daily quality parameter of the tele Zang hydrometer station.

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