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Optimum utilization of animal protein byproducts results in the use of maximum production capacity in existing processing plants. The purpose of this research was to develop a new and ready-to-eat product from fish mince and chicken protein isolate with desirable sensory characteristics by using the D-optimal Mixture Design, Quantitative Anatomical Analysis (QDA), and Principal Components Analysis (PCA ). Initially, 13 prototypes containing different percentages of fish mince and chicken protein isolate (totaling 70% of the product formulation) were prepared. After sensory evaluation by QDA method, a prototype was selected as the optimum treatment, which included 50% fish mince and 20% chicken protein isolate and 30% other ingredients. The sensory stability of the optimum prototype was investigated in comparison with the control treatment during 90 days of frozen storage. Physicochemical analysis (pH, TVB-N, PV and TBARS) and microbial tests were also used to investigate the qualitative changes of the prototypes. The results showed that the optimum prototype had better sensory and quality indexes than the control sample. This study introduces chicken protein isolate as a new food ingredients. It also emphasizes the use of a combination application of QDA and PCA analysis and the D-optimal Mixture Design model in designing and manufacturing a product with the optimal formulation. Because these data will be applicable and valid for industrial scale production.

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Nowadays, due to some problems, such as allergic reactions and high fat content, the partial replacement of cow's milk with milk of other animals, such as goat's milk, is so crucial by proper techniques to improve the quality with the aim of yogurt production. The aim of this study was to evaluate the effect of Zataria multifliora and Mentha longifolia L extract on the qualitative characteristics of probiotic yogurt produced from the cow-goat milk in proportions 10: 90, 30: 70, 50: 50 and 0: 100. Sensory evaluation was performed on produced samples one day after production to selecting the best samples and then, the experiments were carried out on selected samples during 28 days. The results showed that, over time, acidity and syneresis values increased and pH value, viscosity and probiotic count decreased. The highest viscosity was related to the equal ratio of cow - goat milk and 0.05% of Zataria multifliora extract. The antioxidant activity (AA) trend was irregular but descending and extracts had a significant effect on AA. The treatment of cow-goat milk (10 : 90%) and 0.03% of Zataria multifliora extract had the highest probiotic count. The treatment of cow's milk (100%) containing the equal concentrations of two extract (0.02%), showed the highest color, taste, consistency and overall acceptance scores by panelist. As a conclusion, it can be concluded that in order to achieve the best quality of probiotic yogurt, various concentrations of extracts and optimal ratios of cow-goat milk should be used. .
 

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Natural resources generate the major part of national wealth in resource-rich developing countries. Based on economic theories, if natural resource rents are reinvested continuously in other forms of capital, such countries can benefit from these resources. Thus, examining the mechanism of how to rents affect economic growth through capital accumulation channels is of great importance. Because of the importance of management of resource rents in achieving sustainable growth and development in resource-rich countries, this paper investigates the impacts of resource rents on accumulation of four kinds of capital (foreign, physical, human and social capital) in Iran during 1970-2014. To this end, a simultaneous equations system consisting of various capital forms is designed, and estimated by using Seemingly Unrelated Regression estimator. According to the findings, resource rents have positive effects on accumulation of foreign, human and social capital in Iran. But it is of negative effect on accumulation of physical capital. The results show that physical capital is affected by natural resource rents more than other kinds of capital. This is because of unproductive government investments in physical capital, hence not only resource rents increase physical capital but also they affect physical capital negatively.

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Lighvan cheese is a semi-hard salted cheese that is made without adding any starters and is often made from sheepchr('39')s raw milk with about 20-30% goatchr('39')s milk. Since traditional cheeses are made from raw milk, the presence of pathogenic bacteria in cheese is also significant. Lighvan cheese has been produced in the Lighvan area for a long time, but unfortunately many rural producers have not yet realized the importance of hygiene during production. This important issue highlights the need of knowledge transfer to improve the safety of the product to that community. With this approach, the project was implemented in Lighvan village in two stages. Initially, the health status of the existing cheese makers and the processing method in Lighvan area were examined. Then, to solve the health and technical problems of production, corrective measures were presented to the producers. In this regard, 9 cheese makers were randomly selected, of which 3 workshops cooperated to carry out corrective actions (second phase). First, raw milk, fresh curds and cheeses made from the same milk were sampled. The cheese samples were packed in 1 kg cans and stored for 3 months in caves in the area. The same cans were sampled on days 30, 60 and 90 and the were transferred to the laboratory in cold conditions and subjected to chemical, microbial and sensory analysis. The results indicate that the corrective actionss are effective in improving the microbial quality of Lighvan cheese. The authors believe that the knowledge transfer from research institutes (as a knowledge base) to rural producers, can improve the production method and hygienic quality of traditional products, may develop entrepreneurship and ensure the safety and quality of these products. Prosperity of the economic and living conditions of the villagers and the nomadic communities is another goal of this program.

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The need to solve the complex, nonlinear, and variable problems grows with time. Conventional mathematical models perform linear and constant analysis effectively. Although techniques that work on a particular model, capable of analyzing complex nonlinear and time-varying problems, however, they also face some limitations. Combining these with other issues such as decision making, etc., has inspired the development of intelligent techniques such as fuzzy logic, neural networks, genetic algorithms, and expert systems. Intelligent systems mainly employ a combination of these techniques to solve very complex problems. Although both fuzzy logic and artificial neural networks have been very successful in solving time-varying nonlinear problems, each has its own limitations which reduces their use in solve of many of these problems. The roof global ductility, is a comprehensive reflection of various engineering demand parameters (EDP), such as story-drift, plastic rotation at member ends, roof displacement, etc. Careful estimation of this parameter will certainly lead to greater accuracy in the design of structural members. One of the methods which establish a good estimate of the nonlinear seismic response is the using of EDP parameters and measuring the seismic intensity index. The main purpose of this paper is to establish an accurate intelligent model related to the geometrical characteristics of the structure, performance level, the behavior factor and global ductility in eccentrically steel frames, under earthquakes near-fault. For this purpose, genetic algorithm is used. Initially a wide database consisting of 12960 data with 3-, 6-, 9-, 12-, 15- and 20- stories, 3 column stiffness types, and 3 brace slenderness types were designed, and analyzed under 20 pulse-type near-fault earthquakes for 4 different performance levels. To generate the proposed model, 6769 training data were used in the form of adaptive-neural fuzzy inference system(ANFIS). Subtractive clustering and FCM methods have been used to generate the purposed model. The results showed that Subtractive clustering provides more accurate results than the other FIS. To validate the proposed model, 2257 test data were used to calculate the mean squared error of the model. The proposed model is an intelligent model in the range of data used, and can be used to estimate the global roof ductility of EBFs. To evaluate the efficiency and performance of the model, correlation coefficient and common error calculation criteria including RMSE and MARE were used. The correlation coefficient for the Subtractive clustering method was 0.888, based on intelligent model in the test data. In the other hand, the developed intelligent model can be used as a precise alternative to prediction of (μR) for EBFs under near-earthquakes. To evaluate the model’s efficiently and accuracy, various error criteria including Error, Mean Error, RMSE, MARE% and R were used between model values and real values, in the test data. From the results of this study, it can be pointed out that, the developed intelligent model can be used as an accurate substitute method to predict the (μR) for EBF structures, under near-fault earthquakes. The results of correlation analysis of the proposed model show that the proposed intelligent model has high accuracy.

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Due to the significant advantages of the performance-based seismic design method, such as the possibility of determining the possible damage and financial and human losses of residents and neighbors of the structure, this method has been widely welcomed. However, since this method requires more sophisticated analysis than conventional force methods, sometimes the simple force method is preferred by some professional engineers. The main purpose of this article is to combine the two methods of force-based and performance-based and to develop a hybrid method in order to use the advantages of both methods.in this regard, frames with 3, 6, 9, 12, 15 and 20 story with 3 bays with a width of 5 meters have been considered. The length of the link beam is defined as another parameter affecting the response, 1, 1.75 and 2.50 meters. The studied models have been developed by designing the method of load and resistance factor design method, for 3 performance levels of immediate occupancy, life safety and collapse prevention, as well as the first occurrence of the plastic joint. The final models are analyzed under 20 pulse-type near-fault records using time history analysis. To generate the expected database, 12,960 time history analyzes were performed based on an incremental dynamic analysis platform. In this regard, a unique frame is continuously and repeatedly affected by a single accelerometer by multiplying the accelerometer by an SF coefficient. In each iteration, the maximum displacement in the frame is compared to the target range of ASCE41-13 code. The analysis operation is continued until the expected numbers are reached and then stopped. For each of the frames, 4 different acceptance levels are defined to consider different performance levels. Finally, using the genetic algorithm, the corresponding experimental relationships are presented to determine the behavior factor, local and global ductility. The proposed relationships are influenced by geometric characteristics such as the number of stories, the stiffness ratio of the columns, the slenderness of the braces, the length of the beam and the ductility levels. The first ambiguous issue that has been less mentioned in previous research is the use of near-fault field records in the development of a hybrid functional seismic design method. After generating 12960 data from an innovative time history analysis, two intelligent adaptive neural-fuzzy models have been used to calculate the coefficient of behavior and ductility of the structure. In order to create the best and most accurate model, Fuzzy C-Mean clustering (FCM) and Subtracting clustering methods have been used. Based on the results, the model created based on Subtracting clustering provides more accurate results than the other model. The results of hybrid seismic design in comparison with the force method and equivalent time history show the acceptable accuracy of the method introduced in the field of hypotheses. The obvious advantage of using a hybrid seismic design method compared to force methods is the possibility of selecting an expected performance level, which leads to design control and more accurate estimation of response values of quantities such as global ductility, local ductility, inter-story drift