The trade-off between pleiotropy and redundancy in telecommunications networks is analyzed in this paper. They are optimized to reduce installation costs and propagation delays. Pleiotropy of a server in a telecommunications network is defined as the number of clients and servers that it can service whilst redundancy is described as the number of servers servicing a client. Telecommunications networks containing many servers with large pleiotropy are cost-effective but vulnerable to network failures and attacks. Conversely, those networks containing many servers with high redundancy are reliable but costly. Several key issues regarding the choice of cost functions and techniques in evolutionary computation (such as the modeling of Darwinian evolution, and mutualism and commensalism) will be discussed, and a future research agenda is outlined. Experimental results indicate that the pleiotropy of servers in the optimum network does improve, whilst the redundancy of clients do not vary significantly, as expected, with evolving networks. This is due to the controlled evolution of networks that is modeled by the steady-state genetic algorithm; changes in telecommunications networks that occur drastically over a very short period of time are rare.
Recent advances in biological Microelectromechanical Systems (MEMS) have resulted in significant research being carried out to improve minimally invasive surgical procedures (MIS). Surgeons familar with MIS often complain of inadequate tactile and visual feedback. Hence, there is a need for better surgical instrumentation or procedures. This paper presents a survey on the applications of MEMS sensors in surgical instruments during in vivo diagnosis and treatment. Several applications of MEMS sensors are discussed. It is evident that MEMS can increase the functionalities of surgical tools and improve the performance of surgeons. MEMS sensors not only can help to reduce patient trauma, but also lower health care cost.