Glassy Materials Based Microdevices

Microtechnology has changed our world since the last century, when silicon microelectronics revolutionized sensor, control and communication areas, with applications extending from domotics to automotive, and from security to biomedicine. The present century, however, is also seeing an accelerating pace of innovation in glassy materials; as an example, glass-ceramics, which successfully combine the properties of an amorphous matrix with those of micro- or nano-crystals, offer a very high flexibility of design to chemists, physicists and engineers, who can conceive and implement advanced microdevices. In a very similar way, the synthesis of glassy polymers in a very wide range of chemical structures offers unprecedented potential of applications. The contemporary availability of microfabrication technologies, such as direct laser writing or 3D printing, which add to the most common processes (deposition, lithography and etching), facilitates the development of novel or advanced microdevices based on glassy materials. Biochemical and biomedical sensors, especially with the lab-on-a-chip target, are one of the most evident proofs of the success of this material platform. Other applications have also emerged in environment, food, and chemical industries. The present Special Issue of Micromachines aims at reviewing the current state-of-the-art and presenting perspectives of further development. Contributions related to the technologies, glassy materials, design and fabrication processes, characterization, and, eventually, applications are welcome

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Virginia Commonwealth University Courses

Listing of courses for the 2015-2016 academic year