Automatic test cases generation from software specifications modules

A new technique is proposed in this paper to extend the Integrated Classification Tree Methodology (ICTM) developed by Chen et al. [13] This software assists testers to construct test cases from functional specifications. A Unified Modelling Language (UML) class diagram and Object Constraint Language (OCL) are used in this paper to represent the software specifications. Each classification and associated class in the software specification is represented by classes and attributes in the class diagram. Software specification relationships are represented by associated and hierarchical relationships in the class diagram. To ensure that relationships are consistent, an automatic methodology is proposed to capture and control the class relationships in a systematic way. This can help to reduce duplication and illegitimate test cases, which improves the testing efficiency and minimises the time and cost of the testing. The methodology introduced in this paper extracts only the legitimate test cases, by removing the duplicate test cases and those incomputable with the software specifications. Large amounts of time would have been needed to execute all of the test cases; therefore, a methodology was proposed which aimed to select a best testing path. This path guarantees the highest coverage of system units and avoids using all generated test cases. This path reduces the time and cost of the testing

A review of maintenance scheduling approaches in deregulated power systems

Traditionally, the electricity industry is fully regulated with a centrally controlled structure. The power system operator has full technical and costing information as well as a full control over the operation and maintenance of power system equipment. Recently, many countries have gone through privatization of their electricity industries unbundling the integrated power system into a number of separate deregulated business entities. The preventive maintenance of power system equipment in the restructured electricity industries is no longer controlled centrally, and none of these entities currently have explicit accountability for maintenance activities. The approaches used to schedule the maintenance activities in the centralized system are not ideal for addressing the new deregulated environments. In recent years a few research publications has been reported in this area. This paper presents a review and analysis of these reported maintenance scheduling approaches for power system equipment in the changed environment

Plasticity in Photosynthetic Performance and Energy Utilization Efficiency in Triticum aestivum L., Secale cereale L. and Brassica napus L. in Response to Low Temperature and Hig CO2

I assessed the effects of cold acclimation and long-term elevated CO2 on photosynthetic performance and energy conversion efficiency of winter (cv Musketeer, cv Norstar) and spring (cv SR4A, cv Katepwa) rye (Secale cereale) and wheat (Triticum aestivum) as well as wild type (WT) and BnCBF17-over-expressing line (BnCBF17-OE) of Brassica napus cv Westar. Plants were grown at either 20/16°C (non-acclimated, NA) or 5/5°C (cold acclimated, CA) and at either ambient (380 µmol C mol-1) or elevated (700 µmol C mol-1) CO2.Compared to NA controls, CA winter cereals, Norstar and Musketeer, exhibited compact dwarf phenotype, increased rates of light-saturated CO2 assimilation (42%) and photosynthetic electron transport (48%) and higher levels of rbcL, cytosolic FBPase, Lhcb1, PsbA and PsaA at ambient CO2. This was associated with enhanced energy conversion efficiency into biomass (31%) and seed yield (20%) coupled to decreased excitation pressure and decreased energy dissipation through non-photochemical quenching (NPQ) for a given irradiance and a given CO2 concentration in CA versus NA winter cereals. The increased photosynthetic performance and energy conversion efficiency of CA winter cereals at ambient CO2 were maintained under long-term growth and development at elevated CO2. In contrast, CA spring cereals, SR4A and Katepwa, exhibited decreased CO2 assimilation rates (35%) and decreased energy conversion efficiency in biomass (40%) not only at ambient CO2 but also at long-term elevated CO2. BnCBF17-over-expression in Brassica napus resulted into dwarf phenotype, increased rates of light-saturated CO2 assimilation (38%) and photosynthetic electron transport (18%), an enhanced energy conversion efficiency with concomitant decreased reliance on photoprotection to dissipate absorbed energy through NPQ for a given irradiance and a given CO2. Compared to WT Brassica napus, BnCBF17-over-expression reduced sensitivity to feedback-limited photosynthesis during long-term growth of B. napus under elevated CO2. CBFs (C-repeat binding factors) are transcriptional activators that induce the expression of cold-regulated genes. We suggest that CBFs regulate not only freezing tolerance but also control the photosynthetic performance and energy conversion efficiency in biomass and grain yield through morphological, physiological and biochemical adjustments. Hence, targeting the CBF pathways in major crop species can be a novel approach to improve crop yield and productivity

Experimental Case Studies for Investigating E-Banking Phishing Techniques and Attack Strategies

Phishing is a form of electronic identity theft in which a combination of social engineering and web site spoofing techniques are used to trick a user into revealing confidential information with economic value. The problem of social engineering attack is that there is no single solution to eliminate it completely, since it deals largely with the human factor. This is why implementing empirical experiments is very crucial in order to study and to analyze all malicious and deceiving phishing website attack techniques and strategies. In this paper, three different kinds of phishing experiment case studies have been conducted to shed some light into social engineering attacks, such as phone phishing and phishing website attacks for designing effective countermeasures and analyzing the efficiency of performing security awareness about phishing threats. Results and reactions to our experiments show the importance of conducting phishing training awareness for all users and doubling our efforts in developing phishing prevention techniques. Results also suggest that traditional standard security phishing factor indicators are not always effective for detecting phishing websites, and alternative intelligent phishing detection approaches are needed

Intelligent Learning Algorithms for Active Vibration Control

YesThis correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments

Comparative performance of intelligent algorithms for system identification and control

This paper presents an investigation into the comparative performance of intelligent system identification and control algorithms within the framework of an active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed based on optimal vibration suppression using the plant model. A simulation platform of a flexible beam system in transverse vibration using finite difference (FD) method is considered to demonstrate the capabilities of the AVC system using GAs and ANFIS. MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used to design the AVC system. The system is men implemented, tested and its performance assessed for GAs and ANFIS based algorithms. Finally, a comparative performance of the algorithms in implementing system identification and corresponding AVC system using GAs and ANFIS is presented and discussed through a set of experiments

Generational and steady state genetic algorithms for generator maintenance scheduling problems

The aim of generator maintenance scheduling (GMS) in an electric power system is to allocate a proper maintenance timetable for generators while maintaining a high system reliability, reducing total production cost, extending generator life time etc. In order to solve this complex problem a genetic algorithm technique is proposed here. The paper discusses the implementation of GAs to GMS problems with two approaches: generational and steady state. The results of applying these GAs to a test GMS problem based on a practical power system scenario are presented and analysed. The effect of different GA parameters is also studie

GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems

YesProposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problem

A survey on portfolio optimisation with metaheuristics.

A portfolio optimisation problem involves allocation of investment to a number of different assets to maximize return and minimize risk in a given investment period. The selected assets in a portfolio not only collectively contribute to its return but also interactively define its risk as usually measured by a portfolio variance. This presents a combinatorial optimisation problem that involves selection of both a number of assets as well as its quantity (weight or proportion or units). The problem is extremely complex due to a large number of selectable assets. Furthermore, the problem is dynamic and stochastic in nature with a number of constraints presenting a complex model which is difficult to solve for exact solution. In the last decade research publications have reported the applications of metaheuristic-based optimisation methods with some success., This paper presents a review of these reported models, optimisation problem formulations and metaheuristic approaches for portfolio optimisation