Query Optimization in Multidatabase Systems

Global query execution in a multidatabase system can be done parallelly, as all the local databases are independent. In this paper, a cost model that considers parallel execution of subqueries for a global query is developed. In order to obtain maximum parallelism in query execution, it is required to find a query execution plan that is represented in the form of a bushy tree and this query tree should be balanced to the maximal possible extent with respect to execution time. A new bottom up approach called Agglomerative Approach (AA) is proposed to construct balanced bushy trees with respect to execution time. By the deterministic nature of this approach, it generates local optimal solutions. This local minima problem will be severe in the case of graph queries, i.e., queries that are represented with a graph structure. A Simulated annealing Approach (SA) is employed to obtain a (near) optimal solution. These approaches (AA and SA) are suitable for handling on-line and off-line queries respectively. A Hybrid Approach (HA), that is an integration of AA and SA, is proposed to optimize queries for which the estimated time to be spent on optimization is known a priori. Results obtained with AA and SA on both tree and graph structured queries are presented.     

三维重建的统一方法─—模拟退火法

针对给定平行截面线重建物体表面的三维重建问题，本文利用重建表面的图论表示和模拟退火法，提出了一种能统一处理多种目标函数的三维重建算法．     

微粒群优化算法在相关新产品组合投入的应用

引入新产品最佳投入期和相关收益的概念，在产品生命周期量化描述前提下，提出一个非线性半无限规划的相关新产品组合投入模型，并用微粒群优化算法(PS0)求解，仿真实例表明，微粒群优化算法在求解组合优化问题时简便且易于实现，具有很强的实用性。     

A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition

This paper proposes a multi-objective hybrid artificial bee colony (MOHABC) algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing. The MOHABC uses the concept of Pareto dominance to direct the searching of a bee swarm, and maintains non-dominated solution found in an external archive. In order to achieve good distribution of solutions along the Pareto front, cuckoo search with Levy flight is introduced in the employed bee search to maintain diversity of population. Furthermore, to ensure the balance of exploitation and exploration capabilities for MOHABC, the comprehensive learning strategy is designed in the onlooker search so that every bee learns from the external archive elite, itself and other onlookers. Experiments are carried out to verify the effect of the improvement strategies and parameters’ impacts on the proposed algorithm and comparative study of the MOHABC with typical multi-objective algorithms for SCOS problems are addressed. The results show that the proposed approach obtains very promising solutions that significantly surpass the other considered algorithms.     

A Novel Human Neutralizing mAb Recognizes Delta,Gamma and Omicron Variants of SARS-CoV-2 and Can Be Used in Combination with Sotrovimab

The dramatic experience with SARS-CoV-2 has alerted the scientific community to be ready to face new epidemics/pandemics caused by new variants. Among the therapies against the pandemic SARS-CoV-2 virus, monoclonal Antibodies (mAbs) targeting the Spike glycoprotein have represented good drugs to interfere in the Spike/ Angiotensin Converting Enzyme-2 (ACE-2) interaction, preventing virus cell entry and subsequent infection, especially in patients with a defective immune system. We obtained, by an innovative phage display selection strategy, specific binders recognizing different epitopes of Spike. The novel human antibodies specifically bind to Spike-Receptor Binding Domain (RBD) in a nanomolar range and interfere in the interaction of Spike with the ACE-2 receptor. We report here that one of these mAbs, named D3, shows neutralizing activity for virus infection in cell cultures by different SARS-CoV-2 variants and retains the ability to recognize the Omicron-derived recombinant RBD differently from the antibodies Casirivimab or Imdevimab. Since anti-Spike mAbs, used individually, might be unable to block the virus cell entry especially in the case of resistant variants, we investigated the possibility to combine D3 with the antibody in clinical use Sotrovimab, and we found that they recognize distinct epitopes and show additive inhibitory effects on the interaction of Omicron-RBD with ACE-2 receptor. Thus, we propose to exploit these mAbs in combinatorial treatments to enhance their potential for both diagnostic and therapeutic applications in the current and future pandemic waves of coronavirus.     

改进区块遗传算法解决分布式车间调度问题

针对分布式车间调度问题,提出了改进区块遗传算法(modified block-genetic algorithm,MBGA).用NEH和随机性两种方式得到高质量的初始解,然后进行统计分析,选出精英染色体,建立工件-车间分配矩阵和工件-机器排序矩阵,挖掘联系紧密的基因链组成区块.构建基于区块的人工染色体,并进行基因重组,...     

覆盖表生成的粒子群算法:参数优化和自适应算法

组合测试是一种能有效检测由参数间相互作用所引发错误的软件测试方法,覆盖表的生成是该研究领域的一个重要问题.目前,很多方法已被应用于覆盖表生成,基于演化搜索的粒子群算法尽管能得到较优的解,但其性能容易受到配置参数的影响.本文首先使用试验设计的方法,对不同覆盖表生成的算法参数进行优化,系统分析了参数对算法性能的影响.同时,考虑到对不同的覆盖表,最优的算法参数往往不同,因此进一步提出了一种适用于覆盖表生成的自适应粒子群算法.实验结果表明,在一定的参数取值范围内粒子群算法都能获得较好的结果,且不存在一组对任意覆盖表都能有最优性能的算法参数.通过参数调优,能使粒子群算法获得比已有结果规模更小的覆盖表,同时,与经过参数调优后的算法相比,自适应粒子群算法在大部分情况下有更好的性能.     

有色装箱问题的在线近似算法

有色装箱问题是经典装箱问题的推广,它在多处理器实时计算机系统的任务调度等实际问题中有着很强的应用背景,提出了求解有色装箱问题的KC－A算法,它首先对输入物品进行分类预处理,然后在同一类内部使用经典装箱问题的近似策略A,给出了KC－A算法最坏情况渐近性能比的下界,分析了当选用的算法A是著名装相算法NF,FF,BF,WF时KC－A算法的最坏情况渐近性能比和平均性能比,给出了实验结果,并指出KC－FF表现出相对更好的实验效果。     

基于改进鲸鱼优化的覆盖表生成算法

为提高组合测试中覆盖表生成效率,基于覆盖表生成的离散性,提出一种改进的鲸鱼优化算法。该算法首先利用编码转换的思想,将鲸鱼个体连续运动方式编码为适用于覆盖表的离散方式;其次,在算法的开发与搜索阶段加入迭代演化算子,以提高算法的全局搜索能力;最后,针对覆盖表生成中算法本身的局限问题,使用平均海明距离跳出局部最优,并通过约束求解器和惩罚函数法增加约束处理机制,以提高算法实际应用能力。实验结果表明,与其它已有算法相比,所提出的算法在覆盖表生成规模上具有更好的优势。     

Coarse‐to‐Fine Isometric Shape Correspondence by Tracking Symmetric Flips

We address the symmetric flip problem that is inherent to multi‐resolution isometric shape matching algorithms. To this effect, we extend our previous work which handles the dense isometric correspondence problem in the original 3D Euclidean space via coarse‐to‐fine combinatorial matching. The key idea is based on keeping track of all optimal solutions, which may be more than one due to symmetry especially at coarse levels, throughout denser levels of the shape matching process. We compare the resulting dense correspondence algorithm with state‐of‐the‐art techniques over several 3D shape benchmark datasets. The experiments show that our method, which is fast and scalable, is performance‐wise better than or on a par with the best performant algorithms existing in the literature for isometric (or nearly isometric) shape correspondence. Our key idea of tracking symmetric flips can be considered as a meta‐approach that can be applied to other multi‐resolution shape matching algorithms, as we also demonstrate by experiments.