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The population and importance of camels in human life have improved in recent years. As genetics and genomics are becoming a more comprehensive section of life sciences, studying genetic/genomic aspects of the camelid nutrition, milk production, reproduction, immunity, disease and racing ability has become common in recent years. This study was conducted based on text mining and scientometrics techniques. To employ relevant information from Web of Science (WoS), a search strategy was developed to retrieve the “genetics or genomics” and words/phrases related to “camelids”. The statistical population of this study included 3830 publications over a period of 50 years (1971–2020). A total of 3830 publications were retrieved that included 3269 research articles (85.35% of the publications) and 224 review articles (5.85% of the publications). The most frequent subject groups were “Veterinary Sciences” including 862 publications. In total, articles on camelid genomics were published in 1345 journals. Moreover, 127 countries contributed to these 3830 publications, with the USA being the leading country both in number of publications and international collaboration. During the 1971–2010 time period, the phrases “MERS-CoV” and “coronavirus” did not exist in the literature at all, while in the last decade, with 140 and 63 times (5.36% and 2.41%, respectively), they were the most frequent keywords. In general, the most important topics studied from the perspective of camelid genomics have been population genetics and pathogens and their diagnosis, as well as camelid immunity. Future studies should pay special attention to the specificity of camelid genomics for hosting the coronavirus. Furthermore, the special structure of humoral immunity in camels makes this section attractive in immunogenetics research.

The population and importance of camels in human life have improved in recent years. As genetics and genomics are becoming a more comprehensive section of life sciences, studying genetic/genomic aspects of the camelid nutrition, milk production, reproduction, immunity, disease and racing ability has become common in recent years. This study was conducted based on text mining and scientometrics techniques. To employ relevant information from Web of Science (WoS), a search strategy was developed to retrieve the “genetics or genomics” and words/phrases related to “camelids”. The statistical population of this study included 3830 publications over a period of 50 years (1971–2020). A total of 3830 publications were retrieved that included 3269 research articles (85.35% of the publications) and 224 review articles (5.85% of the publications). The most frequent subject groups were “Veterinary Sciences” including 862 publications. In total, articles on camelid genomics were published in 1345 journals. Moreover, 127 countries contributed to these 3830 publications, with the USA being the leading country both in number of publications and international collaboration. During the 1971–2010 time period, the phrases “MERS-CoV” and “coronavirus” did not exist in the literature at all, while in the last decade, with 140 and 63 times (5.36% and 2.41%, respectively), they were the most frequent keywords. In general, the most important topics studied from the perspective of camelid genomics have been population genetics and pathogens and their diagnosis, as well as camelid immunity. Future studies should pay special attention to the specificity of camelid genomics for hosting the coronavirus. Furthermore, the special structure of humoral immunity in camels makes this section attractive in immunogenetics research.

The present study aims to investigate   has been implemented of advertising on the buying behavior consumers for date seed oil   using theory planned behavior (TPB). The present study is descriptive of the type of correlation. The study population consisted of all consumers in Kerman. The total samples  were  384 people in this research that have participate 82 person in the male group c and 302  person in the female group .A multistage cluster sampling method was used to implement the questionnaire. In this research, descriptive statistical methods such as tables, frequency percentages, mean and standard deviations, and normality and hypothesis testing were confirmed. As a result, all hypotheses were confirmed and indicate the positive and significant hypotheses of the advertising on the variables others that advertising is having significantly and positive effect on the attitude, perceived behavioral control and Subjective norm toward consumer buying behavior and at the end of, advertising have the role of a mediator test toward consumer buying behavior.

Many industrial problems need to be optimized several responses simultaneously. These problems are named multiple response optimization (MRO) and they can have different objectives such as Target, Minimization or Maximization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) in combination with genetic algorithm (GA) is applied for modeling and optimization of MRO problems. In addition, optimization usually is done on Global Desirability (GD) function. Current article is a development for recent authors' work to determine economic run number for application of CM and GA in MRO problem solving. This study includes a committee machine with four different ANNs. The CM weights are determined with GA which its fitness function is minimizing the RMSE. Then, another GA specifies the final solution with object maximizing the global desirability. This algorithm was implemented on five case studies and the results represent the algorithm can get higher global desirability by repeating the runs and economic run number (ERN) depends on the MRO problem objective. ERN is ten for objective “Target”. This number for objectives which are mixture of minimization and maximization ERN is five. The repetition are continued until these ERN values have considerable increased in maximum GD with respect to average value of GD. More repetition from these ERN to forty five numbers cause a slight raise in maximum GD.

Many industrial problems need to be optimized several responses simultaneously. These problems are named multiple response optimization (MRO) and they can have different objectives such as Target, Minimization or Maximization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) in combination with genetic algorithm (GA) is applied for modeling and optimization of MRO problems. In addition, optimization usually is done on Global Desirability (GD) function. Current article is a development for recent authors' work to determine economic run number for application of CM and GA in MRO problem solving. This study includes a committee machine with four different ANNs. The CM weights are determined with GA which its fitness function is minimizing the RMSE. Then, another GA specifies the final solution with object maximizing the global desirability. This algorithm was implemented on five case studies and the results represent the algorithm can get higher global desirability by repeating the runs and economic run number (ERN) depends on the MRO problem objective. ERN is ten for objective “Target”. This number for objectives which are mixture of minimization and maximization ERN is five. The repetition are continued until these ERN values have considerable increased in maximum GD with respect to average value of GD. More repetition from these ERN to forty five numbers cause a slight raise in maximum GD.

Multiple responses optimization problems have three phases including design of experiments, modeling and optimization. Artificial neural networks and genetic algorithm are applied for second and third phases. Committee machines include some experts such as some neural networks which operate together to get response. Current article applies a committee machine including four different artificial neural networks to model multiple responses optimization problems. Genetic algorithm is applied to calculate weights of committee machine and also it optimizes desirability function of all responses to get optimum point. Seven different cases in multiple responses optimization were modeled and analyzed. The results show the error of committee machine is near half of average error of artificial neural networks and global desirability of committee machine is the same as average global desirability of artificial neural networks.

Multiple responses optimization problems have three phases including design of experiments, modeling and optimization. Artificial neural networks and genetic algorithm are applied for second and third phases. Committee machines include some experts such as some neural networks which operate together to get response. Current article applies a committee machine including four different artificial neural networks to model multiple responses optimization problems. Genetic algorithm is applied to calculate weights of committee machine and also it optimizes desirability function of all responses to get optimum point. Seven different cases in multiple responses optimization were modeled and analyzed. The results show the error of committee machine is near half of average error of artificial neural networks and global desirability of committee machine is the same as average global desirability of artificial neural networks.