A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

ANT-colony based disjoint set assortment in wireless sensor networks

Wireless Networks - Wireless sensor network (WSN) consists of small sized devices containing different sensors to monitor physical, environmental and medical conditions during surveillance of...     

Software assurance practices for mobile applications

Mobile software applications have to cope with a particular environment that involves small size, limited resources, high autonomy requirements, competitive business models and many other challenges. To provide development guidelines that respond to these needs, several practices have been introduced; however, it is not clear how these guidelines may contribute to solve the issues present in the mobile domain. Furthermore, the rapid evolution of the mobile ecosystem challenges many of the premises upon which the proposed practices were designed. In this paper, we present a survey of the literature on software assurance practices for mobile applications, with the objective of describing them and assessing their contribution and success. We identified, organized and reviewed a body of research that spans in three levels: software development processes, software product assurance practices, and software implementation practices. By carrying out this literature survey, we reviewed the different approaches that researchers on Software Engineering have provided to address the needs that raise in the mobile software development arena. Moreover, we review the evolution of these practices, identifying how the constant changes and modernization of the mobile execution environment has impacted the methods proposed in the literature. Finally, we introduced discussion on the application of these practices in a real productive setting, opening an area for further research that may determine if practitioners have followed the proposed assurance paradigms.     

Intrinsic CFRP-metal-hybrids with rubber interface for the improvement of the damping behaviour

This paper presents an integrated passive damping approach in hybrid metal-CFRP parts for structural applications. In this concept a viscoelastic material is embedded in the joint zone of the hybrid component. To examine the connection strength single-lap-joint specimens were produced and tested and the influence of the used material combinations, different surface structures, and different process parameters i.e. the moment of cross-linking were evaluated. Afterwards, the metal-CFRP hybrids were tested in quasi-static tests to assess their connection strength and failure behaviour. Dynamic cyclic tensile tests with step-wise increased loading conditions were performed to determine the specimens damping behaviour and to estimate their fatigue performance. Finally, these results are compared to a state of the art metal-CFRP hybrid with rivets connecting both materials.     

On stochastic comparisons of finite mixtures for some semiparametric families of distributions

In this paper, we consider the classical finite mixture model, which is an effective tool for modeling lifetime distributions for random samples from heterogeneous populations. We discuss new results on stochastic comparison for two finite mixtures when each of them is drawn from one of the following semiparametric families, i.e., proportional hazards, accelerated lifetime and proportional reversed hazards.