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Lipid oxidation is one of the major processes in deterioration of food quality and nutritional value. In this study, antioxidative activity of peptide was determined from hydrolysate of protein isolate from common kilka (Clupeonella cultriventris caspia) muscle using trypsin enzyme of pyloric caeca extraction. The optimum pH and temperature of trypsin enzyme for BAPNA (Nα –benzoyl -DL- argentine – ρ – nitroanilide -HCL) hydrolysis were measured 8.0 and 60 °C, respectively. The finding showed that antioxidative activities determined by DPPH, ABTS radical scavenging activities and ferric reducing antioxidant power (FRAP) increased significantly with variation of degree of hydrolysates from 20 to 40% (p<0.05). The results suggest that trypsin enzyme from pyloric caeca extraction could be a useful tool for peptide production from protein isolate with antioxidant activity and used as an alternative for commercial enzymes such as microbial enzymes in production of protein hydrolysates.

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Controlled delivery technology of protein/peptide drugs from biodegradable particles has emerged as one of the eminent areas to overcome problems related to macromolecules formulation. The goal of the present study was to develop protein-loaded micro-particles using biodegradable polymer, polycaprolactone (PCL) and hydrogel from beluga cartilage. Bovine serum albumin (BSA) was used as a model for protein/ peptide molecules such as GnRH. The double emulsion (W/O/W) technique was selected as one of the most appropriate methods for preparing a drug delivery system for soluble proteins in water. The first emulsion was prepared using ultrasonic and the mechanical agitator was used for achieving the second emulsion. The hydrogel prepared by enzymatic digestion was used in the first aquatic solution. At the present investigation, three groups were considered as the drug delivery system: G1; (PCL/hydrogel/BSA), G2; (PCL/BSA) and G3; (PCL/Alginate/BSA). Findings showed that the morphology of particles was spherical and non-conglomerated in all groups. The comparison of average particle size among groups was also indicated that the particles.

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In this research crack size and location in pipes under fluid pressure will be detected using pipe’s natural frequencies by neural network. Neural network used in this research is multi-layer perceptron. Comparing different inputs, appropriate inputs are selected. Pipes contain water. Steel and aluminum pipes were used in this research. Pressure condition of the pipes is: 1) without water 2) water with zero pressure 3) water with 0.498 MPa 4) water with 0.981 MPa. Crack size range from 0.19043 to 0.6346. Crack location range from 0.199 to 0.403. Many researches have been done about crack detection based on natural frequencies of structures by neural network. However, as far as authors know, no work has been done for crack detection in pipes containing pressurized water. Also in this paper two structures with different materials have been used for neural network training and testing which is another innovation of this research. Comparison of the results of this method with analytic methods shows that the proposed method is always more accurate in detecting crack size but is not always better in estimating crack location.

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Detection of tool wear and breakage during machining operations is one of the major problems in control and optimization of the automatic machining process. In this study, the relationship between tool wear with vibration in the two directions, one in the machining direction and the other perpendicular to machining direction was investigated during face milling. For this purpose, a series of experiment were conducted in a vertical milling machine. An indexable sandvik insert and ck45 work piece were used in the experiments. Tool wear was measured by a microscope. It was observed that there was an increase in vibration amplitude with increasing tool wear. In this study adaptive neuro - fuzzy inference systems (ANFIS) and multi-layer perceptron neural network (MLPNN) were implemented for classification of tool wear. In this study for the first time, five different states of tool wear was used for accurate tool wear classification. Also to accuracy and speed of the network Principle Component Analysis (PCA) was implemented. Using PCA, the input matrix size was reduced to an acceptable order causing more efficient networks. ANFIS and MLP were trained using feature vectors extracted from the spectrum frequency and time signals. The results showed that for 86 final measurements, the ANFIS and MLP networks were successful in classifying different tool wear state correctly for 91 and 82 percent, respectively. ANFIS due to its high efficiency in diagnosing tool wear and breakage can be proposed as proper technique for intelligent fault classification.

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In statistics, Entropy is a measure of disorder of time series. Entropy is used in physiologic for signal analysis. In physiologic science, Entropy is used for performance analysis of body organs such as heart and brain. Epileptic patients have been diagnosed by this technique. In this paper for the first time, Entropy is used to determine the health condition of mechanical systems. A special kind of Entropy, namely Permutation Entropy is used for this purpose.To perform the experiment an apparatus consisting of a motor coupled with a shaft has been designed and manufactured. Vibration signals from supporting bearing of this system in different shaft states namely healthy shaft, and shafts with 3, 5 and 7 mm crack were gathered with a vibration data analyzer. The vibration were taken from sensors mounted on bearing supports of the shaft. Shaft was subjected to a constant bending moment. The vibration signals were preprocessed by permutation Entropy method. Nine different features were extracted from the Entropy signals which are fed to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was capable of classifying different shaft states with an overall %96 percision.

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The purpose of this research was to study the buying and consumption pattern of canned tuna in Mazandaran province using the theory of planned behavior. For this purpose, after determining the research hypotheses, a suitable questionnaire was designed and completed by the statistical community. The statistical population of the research was the entire Mazandaran province that 10 cities were randomly selected for field study with a sample size of 600 people. In order to test the hypotheses and examine the intensity of the relationship between their items, an appropriate conceptual model was designed and analyzed using Lisrel software. According to the findings, about 60% of the households of the statistical population bought and consumed 10 to 12 cans of canned tuna every year. Brand, size 150 to 180 grams, as well as hypermarkets were among the first priorities of the statistical community regarding the three indicators of packaging characteristics, size and shopping places. The results of running the research conceptual model in two modes, standard and significant, showed that only the hypothesis of a significant effect of perceived risk on subjective norms was rejected and other hypotheses were confirmed. According to the results, among the three variables of brand, quality and packaging specifications, canned tuna brand had the greatest effect on buyers' attitudes with an impact factor of 0.52. In the following, it was found that the effect of perceived usefulness on subjective norms (0.56) is more than income (0.43). According to the findings, the price of canned tuna with an effect coefficient of 0.41, advertising and education variable with an effect coefficient of 0.32, and shopping places variable with an effect coefficient of 0.24 were significantly effective on the perceived behavior control element…
 

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Non-destructive damage detection methods analyze the output data collected from sensors to track the changes in the dynamic characteristics of the structure and detect the occurrence of damages. continuous recording and analysis of data to be aware of its safety and serviceability requires a network of sensors that are selected optimally and intelligently. Saving the cost of equipping the structure with this optimal sensor network, along with reducing damage detection error, has turned the issue of selecting the number and location of sensors into an optimization problem from an economic and functional point of view. Model order reduction methods along with optimization tools can play an effective role in selecting the master degrees of freedom. These methods divide the degrees of freedom of the structure into two groups of master and slave degrees of freedom. The master degrees of freedom appear in the process of calculating the mode shapes and natural frequencies, and the slave degrees of freedom are excluded from the equations. Finally, using the transfer matrix, the complete mode shapes are calculated using the mode shapes of the master degrees of freedom. In this paper, considering the key role of modal parameter recognition in structural damage detection, the performance and accuracy of different methods of dynamical model order reduction in the optimal sensor placement problem was studied. The 2d truss stucture and two-dimensional shear frame are modeled and analyzed. The sensor placement should be considered in such a way that the mode shape identification is done with sufficient accuracy and proper recognition. One of the effective tools in order to achieve this goal is to use the capabilities of metaheuristic optimization algorithms along with the capability of dynamic model reduction methods in the stage of identifying the mode shapes and before identifying the damages of structure. Combining model order reduction methods with metaheuristic optimization algorithms so that the selection of appropriate degrees of freedom for sensor installation (master degrees of freedom) leads to the most accurate identification of structural modes shapes is one of the main objectives of this study. The objective functions selected based on modal assurance criteria (MAC) and Fisher information matrix (FIM) and the capabilities of multi objective particle swarm optimization algorithm (MOPSO) to achieve the optimal number and proper arrangement of sensors are used to better identify the structural mode shapes and proper arrangement of sensors and obtained for system identification purposes. The results report better performance of SEREP and IDC methods in selection of master degrees of freedom and identifying the mode shapes of 2d truss and shear frame structures. According to the modeling and analysis performed for optimal placement of sensors using different model reduction methods, it can be concluded that the improved dynamic condensation (IDC) method is more accurate than other methods in identifying shear frame mode shapes and gives a smaller maximum non-diagonal MAC matrix element. Also, as the number of sensors increases due to the addition of information to the Fisher matrix, the Fisher matrix determinant increases and second objective function decreases. On the other hand, by reducing the number of available sensors, a limited number of modes can be detected. In this case, the best way to receive the structural modal information would be to place more available sensors on the lower and upper floors of the shear frame. Eventually, it can be concluded that the use of IDC and SEREP methods to select master degrees of freedom for sensor installation leads to better identification of modal parameters of the structure. Therefore, the capabilities of these methods can be used to identify damage in structures with a limited number of sensors.