Test frame updating in CPM testing of Prolog programs

Category Partition Method (CPM) is a general approach to specification-based program testing, where test frame reduction and refinement are two important issues. Test frame reduction is necessary since too many test frames may be produced, and test frame refinement is important since during CPM testing new information about test frame generation may be achieved and considered incrementally. Besides the information provided by testers or users, implementation related knowledge offers alternative information for reducing and refining CPM test frames. This paper explores the idea by proposing a call patterns semantics based test frame updating method for Prolog programs, in which a call patterns analysis is used to collect information about the way in which procedures are used in a program. The updated test frames will be represented as constraints. The effect of our test frame updating is two-fold. On one hand, it removes “uncared” data from the original set of test frames; on the other hand, it refines the test frames to which we should pay more attention. The first effect makes the input domain on which a procedure must be tested a subset of the procedure’s input domain, and the latter makes testers stand more chance to find out the faults that are more likely to show their presence in the use of the program under consideration. Our test frame updating method preserves the effectiveness of CPM testing with respect to the detection of faults we care. The test case generation from the updated set of test frames is also discussed. In order to show the applicability of our method an approximation call patterns semantics is proposed, and the test frame updating on the semantics is illustrated by an example.

遗传算法与文本特征选择方法相结合的研究

对文本特征选择的传统方法进行了深入分析并对各自的优缺点进行了详细的对比;其次,通过利用遗传算法自身的寻优特点,把它与传统特征选择方法相结合,并得出了算法。最后,在对各自算法进行了分析,表明了在性能上有了明显的提高。     

一种基于浓缩树结构的属性约简算法

属性约简是粗糙集理论的重要研究内容之一,其中基于区分矩阵的约简算法是一种高效的约简算法,但算法具有很高的空间复杂度.为了减少区分矩阵的空间开销,利用浓缩树结构,结合区分矩阵单个属性一定为核属性的特征,提出改进的生成浓缩树算法,压缩存储区分矩阵中的非空数据项,且不丢失原区分矩阵的所有信息;利用生成的浓缩树结构结合启发式策略,给出属性约简算法.实验结果表明,算法正确有效并且空间复杂度有明显降低.     

基于队列的动态电源管理策略

动态电源管理技术是一项系统级电源优化策略,它通过有选择性的关闭(或者降低性能)空闲的可控设备来降低功耗.在回顾以往系统级动态电源管理策略的基础上,提出了一种基于队列的新型系统级动态电源管理策略,利用队列不但可以提高对于可控模块何时被唤醒时间的预测命中率,并且能够根据不断变化的任务负载情况进行自身的快速调节,以适应不同任务的要求,通过搭建的SkyEye环境进行仿真,结果验证了在性能和节能方面,该方法优于传统方法.     

一种基于粗糙集理论的快速并行属性约简算法

将并行计算的思想融入基于粗糙集理论的快速属性约简中,提出了一种基于粗糙集理论的快速并行属性约简算法.该算法在保证约简结果是Pawlak约简的情况下,将属性约简任务划分到多个处理器中同时处理,从而大大提高了属性约简的效率.仿真实验结果说明了该算法的高效性.     

一种基于相对粒度的决策表约简算法

在知识粒度的基础上,针对决策表提出了相对粒度和属性相对重要性的概念,证明了知识的相对粒度随着知识粒度的增大而单调增加的变化规律,在此基础上提出了一种基于相对粒度的启发式约简算法,以弥补基于正区域的约简方法处理不一致决策表时存在的不足.通过理论分析和实例验证表明,该算法是有效的,且其时间复杂度相对较低.     

Computational simulation of Belousov–Zhabotinskii oscillating chemical reaction

The suitability of computational simulation of the Belousov–Zhabotinskii oscillating chemical reaction by differential kinetic methodology for resolving nonlinear multi-component system is demonstrated in this work. According to the Field–KÖrÖs–Noyes mechanism and the Oregonator model, the change of the concentrations of HBrO₂, bromide ion and cerium ion are simulated. The results of computational simulation are consistent with experimental results very well. At the same time, the effect of variables and parameters, especially the rate constant on the oscillation curve, are investigated deeply. A simple method of estimating rate constants is obtained through simulation the concentrations of key components of the system, and then comparison the simulation results with the experimental ones. The reasonable rate constant is also proposed.     

Energy conversion at liquid/liquid interfaces: artificial photosynthetic systems

This chapter focuses on multielectron reactions in organized assemblies of molecules at the liquid/liquid interface. We describe the thermodynamic and kinetic parameters of such reactions, including the structure of the reaction centers, charge movement along the electron transfer pathways, and the role of electric double layers in artificial photosynthesis. Some examples of artificial photosynthesis at the oil/water interface are considered, including water photooxidation to the molecular oxygen, oxygen photoreduction, photosynthesis of amphiphilic compounds and proton evolution by photochemical processes.     

Amine Coordinated Electron-Rich Palladium Nanoparticles for Electrochemical Hydrogenation of Benzaldehyde

Electrocatalytic hydrogenation (ECH) is a burgeoning strategy for the sustainable utilization of hydrogen. However, how to effectively suppress the competitive hydrogen evolution reaction (HER) is a big challenge to ECH catalysis. In this study, amine (NH₂ R)-coordinated Pd nanoparticles loaded on carbon felt (Pd@CF) as a catalyst is successfully synthesized by a one-step solvothermal reduction method using oleylamine as the reducing agent. An exceptional ECH reactivity on benzaldehyde is achieved on the optimal Pd@CF catalyst in terms of a high conversion (89.7%) and selectivity toward benzyl alcohol (89.8%) at −0.4 V in 60 min. Notably, the Faradaic efficiency for producing benzyl alcohol is up to 90.2%, much higher than that catalyzed by Pd@CF-without N-group (41.1%) and thecommercial Pd/C (20.9%). The excellent ECH performance of Pd@CF can be attributed to the enriched electrons on Pd surface resulted from the introduction of NH₂ R groups, which strengthens both the adsorption of benzaldehyde and the adsorbed hydrogen (H_ads) on Pd, preventing the combination of H_ads to form H₂, that is, inhibiting the HER. This study gives a new insight into design principles of highly efficient electrocatalysts for the hydrogenation of unsaturated aldehydes molecules.     

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Software testing plays a pivotal role in entire software development lifecycle. It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required. These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite, fault-tolerance, defects due to uncovered-statements and overall-performance at the time of execution. With this objective, the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization. The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate (90.78% to 100%) and failure detection rate (44.56% to 55.01%). Results proved MOMPSO outperformed the other stated algorithms.