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In this paper, we propose an enhanced branch-and-bound (B&B) framework for a class of sequencing problems, which aim to find a permutation of all involved elements to minimize a given objective function. We require that the sequencing problems satisfy three conditions: 1) incrementally computable; 2) monotonic; and 3) overlapping subproblems. Our enhanced B&B framework is built on the classical B&B process by introducing two techniques, i.e., dominance rules and caching search states. Following the enhanced B&B framework, we conduct empirical studies on three typical and challenging sequencing problems, i.e., quadratic traveling salesman problem, traveling repairman problem, and talent scheduling problem. The computational results demonstrate the effectiveness of our enhanced B&B framework when compared to classical B&B and some exact approaches, such as dynamic programming and constraint programming. Additional experiments are carried out to analyze different configurations of the algorithm. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the leading causes of premature death and disability in people around the world. Therefore, the prevention and treatment of CVDs has become an important subject. In this study, we verified the thrombolytic activities of a nattokinase‐like protease named NK‐01 in vivo. Label‐free liquid chromatography–tandem mass spectrometry (LC‐MS/MS) technique was used in our study. NK‐01 could inhibit the activity of coagulation factors though the up‐regulation of proteinase C inhibitors and protein S. NK‐01 also could inhibit the angiotensinogen conversion to AngII and promote the degradation of kininogen to reduce the blood pressure. In addition, NK‐01 could increase the content of paraoxonase 1, which could prevent atherosclerosis. In our study, we found that NK‐01 cloud effect some key proteins which participant in CVDs associated metabolic processes such as coagulation system, blood pressure, and atherosclerosis. Taken together, the underlying molecular mechanisms for the biological beneficial of NK‐01 were investigated. Our proteomic study will provide further theoretical basis for application of NK in prevention or adjuvant treatment in biomedicine areas. [ABSTRACT FROM AUTHOR]

A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains. Thus, there might be negative similarities between different domains so that the negative transfer problem caused by negative similarity may happen. In this paper, we propose a novel method named active transfer learning (ATL) to solve the above problem. Specifically, the orthogonal projection matrix and the weight coefficient vector are introduced to extend maximum mean discrepancy (MMD) so that it can minimize MMD and simultaneously eliminate the negative transfer. To find the informative and discriminative subsets from the source domain, we then propose an information diversity term by using the local geometric structure information of the source samples. Besides, by using the label information of source samples, our method can guarantee the selected subsets as discriminative as possible. Finally, to efficiently implement the proposed method, an alternating optimization approach, which is based on the alternating direction method of multipliers (ADMM), is designed to solve the optimization problem. To demonstrate the effectiveness of the proposed ATL model, experiments are conducted on five real-world data sets. The experimental results show the superiority of our method over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Many to Many (M–M) assignment problem is an important open problem where one task is assigned to many, but different, agents and one agent may undertake many, but different, tasks. The Kuhn–Munkres (K–M) algorithm is a famous and traditional process used in dealing with assignment problems. In this paper, we propose a solution to the M–M assignment problem by improving the K–M algorithm with backtracking (KM B ). To demonstrate the solution's suitability, we prove that the proposed KM B algorithm is valid and that the worst time complexity of the KM B algorithm is O ( ( ∑ L a [ i ] ) 3 ) , where L a [ i ] denotes the maximum number of tasks that can be assigned to agent i . After that, we discuss several critical problems related to the algorithm and provide the necessary and sufficient conditions of solving the M–M assignment problem. Finally, we demonstrate, through experimentation, the validity, practicality and efficiency of the KM B algorithm. [ABSTRACT FROM AUTHOR]