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Background: The unreliability of reference intervals (RIs) for children’s blood cell analysis has led to an unnecessary effort in interpreting results. The Standard published in 2021 is expected to solve this problem in China but should be clinically evaluated before its application. Methods: Compared with the laboratory’s original RIs, the RIs’ numerical trends were mapped and analyzed, and the data of the past seven years were retrospectively re-interpreted. Pediatricians were then consulted to discuss the data analyses. Results: The new RIs were summarized as follows: 1) The age stratification is more detailed; 2) The venous blood and peripheral blood are characterized; 3) The numerical range was relatively more comprehensive, and some parameters were unilaterally shifted. Retrospective analysis showed that the revised RIs could correct previously abnormal results to the normal range in a large proportion. The recovery ratio of three lineage cells was white blood cells > red blood cells > platelets, and the ratio sorted by age is 28 days~1-year-old > 1~13years old > 13~18 years old. The leukocyte recovery ratio of 28 days~1 year was the largest, approximately 55% to 83%. Pediatricians recognized the value of the new RIs. The only exception is that the platelets’ RIs were too broad, recommending maintaining the original RI. The missing 0~28 days RIs were recommended to be supplemented with other reference books. Conclusions: The new RIs were optimized and, combined with clinical feedback, produced new RIs derived from accumulated experience, evolving a better set of RIs.

Most existing semi-supervised clustering algorithms are not designed for handling high-dimensional data. On the other hand, semi-supervised dimensionality reduction methods may not necessarily improve the clustering performance, due to the fact that the inherent relationship between subspace selection and clustering is ignored. In order to mitigate the above problems, we present a semi-supervised clustering algorithm using adaptive distance metric learning (SCADM) which performs semi-supervised clustering and distance metric learning simultaneously. SCADM applies the clustering results to learn a distance metric and then projects the data onto a low-dimensional space where the separability of the data is maximized. Experimental results on real-world data sets show that the proposed method can effectively deal with high-dimensional data and provides an appealing clustering performance.

The sintering behaviour of ultrafine diamond particles (0.5–1.5 μm) under high temperature (1350–1550°C) and high pressure (5.8 GPa) has been investigated using nickel and nickel-zirconium alloy as sintering aids. The effects of sintering conditions on the microstructure and property of sintered diamond bodies were studied. The sintered bodies obtained by sintering diamond particles in four different sizes under different sintering conditions were observed to determine the optimal sintering condition of homogeneous sintered bodies. Homogeneous sintered bodies with less than 1 μm average grain size were obtained, which have a Vickers' hardness value of 63 GPa. In this paper we also discuss methods to suppress the abnormal grain growth of diamond and to improve the infiltration homogeneity of the molten metal or alloy.

The generalization ability of a classifier is often inherently associated with both the intra-class compactness and the inter-class separability. Owing to the fact that some current lower-dimensional manifold embedding techniques as a preprocessing for classification learning often lead to poor performance, in this paper, a new unsupervised data preprocessing technique called as manifold contraction (MC) is proposed for the subsequent classification task. The main contribution of our MC lies in: 1) the intra-manifold scatter becomes smaller while the inter-manifold scatter gets bigger relatively by a proper contraction mapping; 2) different from dimensionality reduction techniques, the estimation of intrinsic dimensionality can be avoided. The final experimental results show that MC preprocessing technique is effective and promising in the subsequent classification task especially in small-size labeled samples case.

Job shop scheduling problem has earned a reputation for being difficult to solve. Varieties of algorithms are employed to obtain optimal or near optimal schedules. Optimization algorithms provide optimal results if the problems to be solved are not large. But most scheduling problems are NP-hard, hence optimization algorithms are ruled out in practice. The quality of solutions using branch and bound algorithms depends upon the good bound that requires a substantial amount of computation. Local search-based heuristics are known to produce decent results in short running times, but they are susceptible to being stuck in local minima. Therefore, in this paper, we presented a brand-new heuristic approach for job shop scheduling. The performance of the proposed method was validated based on some benchmark problems of job shop scheduling, with regard to both solution quality and computational time.

The majority of large-size job shop scheduling problems are non-polynomial-hard (NP-hard). In the past decades, Genetic algorithms have demonstrated considerable success in providing efficient solutions to many NP-hard optimization problems. But there is no literature considering the optimal parameters when designing genetic algorithms. Unsuitable parameters may cause terrible solution for a specific scheduling problem. In this paper, we proposed a two-stage genetic algorithm, which attempts to firstly find the fittest control parameters, namely, number of population, probability of crossover, probability of mutation, for a given job shop problem with a fraction of time; and then the fittest parameters are used in the genetic algorithm for further more search operation to find optimal solution. For large-size problem, the two-stage genetic algorithm can get optimal solution effectively and efficiently. The method is validated based on some hard benchmark problems of job shop scheduling.