The migration of acetochlor from feed to milk

Acetochlor has been widely used globally for its effective weed control, but the dietary intake of associated residues by people has become a major concern nowadays. Milk is regarded as the best solvent to dissolve pesticides due to its fat-rich characteristic. In this study, we aimed to evaluate the transfer of acetochlor from feed to raw milk. Twenty lactating Australian Holstein cows were randomly chosen and divided into 1 control group and 3 treatment groups, feeding acetochlor at the dosages of 0, 0.45, 1.35 and 4.05 g per day during the treatment period. The concentration of acetochlor residues in raw milk was detected by QuEChERS together with a gas chromatography-mass spectrometry (GC-MS) method. The results showed that the highest concentrations of acetochlor residues in raw milk for the three treatment groups had a positive correlation with the dosage levels and the transfer efficiency of the low dose group was only 0.080%, higher than those of the other two groups. Besides, the national estimated daily intake (NEDI) of acetochlor from milk is 1.67 × 10(−5) mg kg(−1), which is 0.08% of the ADI. Overall, we concluded that the risk of acetochlor residues in milk was low, but high-dose acetochlor had a larger impact on milk quality and low-dose acetochlor had potential risks

problem reduction graph model for discrete optimization problems

Chinese Academy of Sciences (CAS); Dep. Manage. Sci. Natl. Nat. Sci. Found. China (NSFC); Academy of Mathematics and Systems Science of CAS; Institute of Systems Science of CAS; Centre for Forecasting Science (CEFS) of CASThe paper proposes the problem reduction graph (PRG), an abstract model for discrete optimization problems which uses structural decomposition to reduce problem complexity and constructs the recurrence relations between the problem and its subproblems. We develop several important algorithm patterns for PRG construction, each leading to a special class of concrete problem-solving algorithms in a systematic way. The model supports logical transformation from specifications to algorithmic programs by deductive inference, and thus significantly promotes the automation and reusability of algorithm design. © 2010 IEEE

toward an automatic approach to greedy algorithms

The greedy approach is widely used for combinatorial optimization problems, but its implementation varies from problem to problem. In this paper we propose a mechanical approach for implementing greedy algorithmic programs. Using PAR, method, a problem can be continually partitioned into subproblems in smaller size based on the problem singleton and the maximum selector, and the greedy algorithm can be mechanically generated by combining the problem-solving sequences. Our structural model supports logical transformation from specifications to algorithmic programs by deductive inference; and thus significantly promotes the automation and reusability of algorithm design

A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning

Accurate wire rope defect diagnosis is crucial for the health of whole machinery systems in various industries and practical applications. Although the loss of metallic cross-sectional area signals is the most widely used method in non-destructive wire rope evaluation methods, the weakness and scarcity of defect signals lead to poor diagnostic performance, especially in diverse conditions or those with noise interference. Thus, a new wire rope defect diagnosis method is proposed in this study. First, empirical mode decomposition and isolation forest methods are applied to eliminate noise signals and to locate the defects. Second, a convolution neural network and transformer encoder are used to design a new wire rope defect diagnosis network for the improvement of the feature extraction ability. Third, transfer learning architecture is established based on gray feature images to fine-tune the pre-trained model using a small target domain dataset. Finally, comparison experiments and a visualization analysis are conducted to verify the effectiveness of the proposed methods. The results demonstrate that the presented model can improve the performance of the wire rope defect diagnosis method under cross-domain conditions. Additionally, the transfer feasibility of transfer learning architecture is discussed for future practical applications

a category theoretic approach to search algorithms: towards a unified implementation for branch-and-bound and backtracking

IEEE, Comp Educ Coll & Univ, Natl Res Council, Guangxi Univ, IEEE Control Syst Chapter, Guangzhou, IEEE Control Syst Chapter, Singapore, Univ Melbourne, Univ Virginia, Univ Texas, Univ British Columbia, Xiamen Univ, Chongqing Univ, Xiamen Xinhangha Ctr Comp Educ & DevBranch-and-bound and backtracking are widely used for search and optimization problems, but their implementations vary from problem to problem. In this paper we propose a unified approach of program derivation and generation for the two classes of algorithms. We first define a generalized specification for the search strategies, and then derive the algorithms, abstract programs and generic programs by incremental refinements on PAR platform, and finally generate efficient programs for concrete problem-solving via colimit computations. Our approach achieves a high level of abstraction and mechanization without losing performance

a high performance solution for automated computer examination systems

In this paper we present an effective and practical approach for developing high-performance, scalable, and manageable automated computer examination systems (ACES). The proposed solution employs the XML technology to naturally represent and efficiently