Effects of dietary protein levels on production performance, meat quality and flavor of fattening pigs.

This study aimed to evaluate the effects of dietary protein level on the production performance, slaughter performance, meat quality, and flavor of finishing pigs. Twenty-seven Duroc♂ × Bamei♀ binary cross-bred pigs (60.86 ± 2.52 kg body weight) were randomly assigned to three groups, each group has three replicates, and each replicate has three pigs. Three groups of finishing pigs were fed 16.0, 14.0, and 12.0% crude protein levels diets, and these low-protein diets were supplemented with four limiting amino acids (lysine, methionine, threonine and tryptophan). The results showed that the pigs fed low-protein diets increased (P < 0.05) loin eye muscle area, and reduced (P < 0.05) heart weight, lung weight. The feed-weight ratio of the 14.0% protein group was reduced (P > 0.05); Dietary protein levels significantly affected the luminance (L24h), yellowness (b45min and b24h) (P < 0.05), reduced shear stress, muscle water loss, drip loss, the levels of crude fat (P < 0.05), and increased marbling score (P < 0.05) in the muscle of finishing pigs; The low-protein diets improved PUFA/TFA, PUFA/SFA (P > 0.05), and increased hexanal, E-2-heptenal, 1-octen-3-ol, EAA/TAA in the muscle of finishing pigs (P < 0.05); The results indicated that reduced the crude protein levels of dietary by 2.0-4.0%, and supplementation with four balanced limiting amino acids had no significant effects on the production performance and slaughter performance of finishing pigs, and could effectively improve meat quality and flavor.

Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects.

Mathematical morphology operations are widely used in image processing such as defect analysis in semiconductor manufacturing and medical image analysis. These data-intensive applications have high requirements during hardware implementation that are challenging for conventional hardware platforms such as central processing units (CPUs) and graphics processing units (GPUs). Computation-in-memory (CIM) provides a possible solution for highly efficient morphology operations. In this study, we demonstrate the application of morphology operation with a novel memristor-based auto-detection architecture and demonstrate non-neuromorphic computation on a multi-array-based memristor system. Pixel-by-pixel logic computations with low parallelism are converted to parallel operations using memristors. Moreover, hardware-implemented computer-integrated manufacturing was used to experimentally demonstrate typical defect detection tasks in integrated circuit (IC) manufacturing and medical image analysis. In addition, we developed a new implementation scheme employing a four-layer network to realize small-object detection with high parallelism. The system benchmark based on the hardware measurement results showed significant improvement in the energy efficiency by approximately 358 times and 32 times more than when a CPU and GPU were employed, respectively, exhibiting the advantage of the proposed memristor-based morphology operation.