Evaluation of Cloud 3D Printing Order Task Execution Based on the AHP-TOPSIS Optimal Set Algorithm and the Baldwin Effect.

Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents' dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.

Stability Analysis of Genetic Regulatory Networks With General Random Disturbances.

This paper establishes the stability criteria for genetic regulatory networks with random disturbances. We assume the nonlinear feedback regulation function to satisfy the sector-like condition and the random perturbation to have a finite second-order moment. First, under the globally Lipschitz condition, the existence and uniqueness of solution to random genetic regulatory networks are considered by exploiting an iterative approximation method. Then, by feat of the random analysis method and matrix technique, sufficient conditions are given to guarantee the noise-to-state stability in mean and globally asymptotic stability in probability, respectively. At last, two simulation examples are exploited in order to verify the validity of the proposed theory.

Fully Automatic Segmentation and Three-Dimensional Reconstruction of the Liver in CT Images.

Automatic segmentation and three-dimensional reconstruction of the liver is important for liver disease diagnosis and surgical treatment. However, the shape of the imaged 2D liver in each CT image changes dramatically across the slices. In all slices, the imaged 2D liver is connected with other organs, and the connected organs also vary across the slices. In many slices, the intensities of the connected organs are the same with that of the liver. All these facts make automatic segmentation of the liver in the CT image an extremely difficult task. In this paper, we propose a heuristic approach to segment the liver automatically based on multiple thresholds. The thresholds are computed based on the slope difference distribution that has been proposed and verified in the previous research. Different organs in the CT image are segmented with the automatically computed thresholds, respectively. Then, different segmentation results are combined to delineate the boundary of the liver robustly. After the boundaries of the 2D liver in all the slices are identified, they are combined to form the 3D shape of the liver with a global energy minimization function. Experimental results verified the effectiveness of all the proposed image processing algorithms in automatic and robust segmentation of the liver in CT images.

Enterotoxigenicity and Antimicrobial Resistance of Staphylococcus aureus Isolated from Retail Food in China.

Staphylococcus aureus is one of the most common causes of zoonotic agent in the world, which are attributable to the contamination of food with enterotoxins. In this study, a total of 1,150 S. aureus isolates were cultured from 27,000 retail foods items from 203 cities of 24 provinces in China in 2015 and were test for antimicrobial susceptibility. Additionally, the role of the genes responsible for the staphylococcal enterotoxins (SEA to SEE), methicillin resistance (mecA) and the toxigenic capabilities were also assessed. The results showed that 4.3% retail foods were contaminated with S. aureus, and 7.9% retail foods isolates were mecA positive. Some 97.6% of S. aureus isolates were resistant to at least one antimicrobial compound, and 57.5% of these were multi drug resistant (MDR). Resistance to penicillin (83.7%, 963/1,150), was common, followed by linezolid (67.7%, 778/1,150) and erythromycin (52.1%, 599/1,150). The isolates cultured from raw meats showed high levels of resistant to tetracycline (42.8%), ciprofloxacin (17.4%), and chloramphenicol (12.0%) and expressed a MDR phenotype (62.4%). A total of 29.7% S. aureus isolates harbored the classical SEs genes (sea, seb, sec, and sed). The sea and seb genes were the most frequent SEs genes detected. Of note, 22% of the SEs genes positive S. aureus harbored two or three SEs genes, and 16 isolates were confirmed with the capacity to simultaneously produce two or three enterotoxin types. Moreover, nearly 50% of the MRSA isolates were positive for at least one SE gene in this study. Therefore, it is important to monitor the antimicrobial susceptibility and enterotoxigenicity of MDR S. aureus and MRSA in the food chain and to use these data to develop food safety measures, designed to reduce the contamination and transmission of this bacterium.