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<p>Presently, the move towards a more complex and multidisciplinary system development is increasingly important in order to understand and strengthen engineering approaches for the systems in the engineering field. This will lead to the effective and successful management of these systems. The scientific developments in computer engineering, simulation and modeling, electromechanical motion tools, power electronics, computers and informatics, micro-electro-mechanical systems (MEMS), microprocessors, and distributed system platforms (DSPs) have brought new challenges to industry and academia. Important aspects of designing advanced mechatronic products include modeling, simulation, analysis, virtual prototyping, and visualization. Competition on a global market includes the adaptation of new technology to produce better, cheaper, and smarter, scalable, multifunctional goods. Since the application area for developing such systems is very broad, including, for example, automotive, aeronautics, robotics or consumer products, and much more, there is also the need for flexible and adaptable methods to develop such systems. These dynamic interdisciplinary systems are called mechatronic systems, which refer to a system that possess synergistic integration of Software, electronic, and mechanical systems. To approach the complexity inherent in the aspects of the discipline, different methods and techniques of development and integration are coming from the disciplines involved. This paper will provide a brief review of the history, current developments and the future trends of mechatronics in general view.</p>

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As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique results in related performance had 98.3 and 96.7 % accuracy rates, respectively. HIGHLIGHTS Dimensionality reduction method based of feature selection Classification using Support vector machine Classification of malaria vector dataset using an adaptive GA-RFE-SVM GRAPHICAL ABSTRACT