Special issue on soft computing applications to intelligent information retrieval on the Internet

This special issue encompasses eleven papers devoted to the recent developments in the applications of soft computing (SC) techniques to information retrieval (IR), both in the text and Web retrieval areas. The seed of the current issue were some of the presentations made in two special sessions organized by the guest editors in two different conferences: the First Spanish Conference on Evolutionary and Bioinspired Algorithms (AEB’02), that was held in M erida, Spain, February 2002, and the Seventh International ISKO Conference (ISKO’02), held in Granada, Spain, July 2002. The scope of both special sessions was pretty related. In the former conference, the session topic was ‘‘Applications of Evolutionary Computation to Information Retrieval’’ while in the latter the session was entitled ‘‘Artificial Intelligence Applications to Information Retrieval’’

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Nature-inspired sustainable medical materials

As life expectancy increases and health crises arise, our demand for medical materials is higher than ever. There has been, nevertheless, a concomitant increase in the reliance on traditional fabrication and disposal methods, which are environmentally harmful and energy intensive. Therefore, technologies need adaptations to ensure a more sustainable future for medicine. Such technological improvements could be designed by taking inspiration from nature, where the concept of “waste” is virtually non-existent. These nature-inspired solutions can be engineered into the lifecycle of medical materials at different points, from raw materials and fabrication to application and recycling. To achieve this, we present four technological developments as promising enablers – surface patterning, additive manufacturing, microfluidics, and synthetic biology. For each enabler, we discuss how sustainable solutions can be designed based on current understanding of, and ongoing research on, natural systems or concepts, including shark skin, decentralised manufacturing, process intensification, and synthetic biology

Sensor Signal and Information Processing II [Editorial]

This Special Issue compiles a set of innovative developments on the use of sensor signals and information processing. In particular, these contributions report original studies on a wide variety of sensor signals including wireless communication, machinery, ultrasound, imaging, and internet data, and information processing methodologies such as deep learning, machine learning, compressive sensing, and variational Bayesian. All these devices have one point in common: These algorithms have incorporated some form of computational intelligence as part of their core framework in problem solving. They have the capacity to generalize and discover knowledge for themselves, learning to learn new information whenever unseen data are captured