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In this work, 312 pork carcasses were tested at three industrial abbatoirs. Ham meat quality was determined by internal light scattering (FOP), electrical conductivity (EC) and pH measurements in Semimembranosus (SM) muscle at 45 min and 24 h post mortem. The results obtained were: 21 (6.7%) DFD; 168 (53.9%) slightly DFD; 104 (33.3%) normal; 11 (3.5%) slightly PSE; and 8 (2.6%) PSE.
A simplified method has been developed for classifying hams into quality categories, by deriving a theoretical quality value (Q) based on a simple statistical treatment of values for FOP, EC and pH. The multiple linear regression coefficient of Q was R2 = 0.85 (P<0.001) with respect to the experimental variables. 相似文献
RILEM Draft Recommendation107-GCS Guidelines for the Formulation of Creep and Shrinkage Prediction Models
Creep and shrinkage prediction model for analysis and design of concrete structures— model B3 相似文献We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.
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