Cellular Automata

Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented

Machine learning for property prediction and optimization of polymeric nanocomposites: a state-of-the-art

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted

ICR ANNUAL REPORT 2022 (Volume 29)[All Pages]

This Annual Report covers from 1 January to 31 December 202

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Casting inorganic structures with DNA molds

We report a general strategy for designing and synthesizing inorganic nanostructures with arbitrarily prescribed three-dimensional shapes. Computationally designed DNA strands self-assemble into a stiff “nanomold” that contains a user-specified three-dimensional cavity and encloses a nucleating gold “seed.” Under mild conditions, this seed grows into a larger cast structure that fills and thus replicates the cavity. We synthesized a variety of nanoparticles with 3-nanometer resolution: three distinct silver cuboids with three independently tunable dimensions, silver and gold nanoparticles with diverse cross sections, and composite structures with homo- and heterogeneous components. The designer equilateral silver triangular and spherical nanoparticles exhibited plasmonic properties consistent with electromagnetism-based simulations. Our framework is generalizable to more complex geometries and diverse inorganic materials, offering a range of applications in biosensing, photonics, and nanoelectronics.United States. Air Force Office of Scientific Research. Defense University Research Instrumentation Program (Grant N000141310664)United States. Air Force Office of Scientific Research. Defense University Research Instrumentation Program (Grant N000141210621)National Science Foundation (U.S.). Designing Materials to Revolutionize and Engineer our Future Program (Grant CMMI1334109

Biophysics of DNA based Nanosystems Probed by Optical Nanoscopy

A dynamic DNA nanosystem exploits the programmable structure and energy landscape of DNA self-assembly to encode designed processes in a fuctuating molecular environment. One type of such a dynamic system, DNA walker, is reminiscent of biological motor proteins that convert chemical energy into mechanical translocation. Typical DNA walker travels tens of nanometers at a speed orders of magnitude slower than motor proteins. Two major challenges limited the development of functional DNA walkers. First, there are no suitable characterization methods that o˙er adequate spatial and temporal resolution to extract walker kinetics. Second, no guidelines have been established for the design and development of DNA walkers with specifed properties. In this work, an enzymatic DNA walker system that integrate oligonucleotides with nanomaterials is designed. This approach takes advantage of novel optical properties of nanomaterials and sub-di˙raction imaging techniques to study the kinetics and biophysical nature of synthetic DNA walkers. Design principles are extracted from walker kinetics for constructing functional walkers that can rival motor proteins. Multiple schemes are explored to regulate the walker motility so that various behaviors can be encoded into the system. This work demonstrates novel methods to design and construct molecular systems with programmed functions, which will pave the road for creating synthetic systems with encoded behaviors from the bottom up

Next generation transduction pathways for nano-bio-chip array platforms

textIn the following work, nanoparticle quantum dot (QD) fluorophores have been exploited to measure biologically relevant analytes via a miniaturized sensor ensemble to provide key diagnostic and prognostic information in a rapid, yet sensitive manner—data essential for effective treatment of many diseases including HIV/AIDS and cancer. At the heart of this “nano-bio-chip” (NBC) sensor is a modular chemical/cellular processing unit consisting of either a polycarbonate membrane filter for cell-based assays, or an agarose bead array for detection of biomarkers in serum or saliva. Two applications of the NBC sensor system are described herein, both exhibiting excellent correlation to reference methods ((R² above 0.94), with analysis times under 30 minutes and sample volumes below 50 [mu]L. First, the NBC sensor was employed for the sequestration and enumeration of T lymphocytes, cells specifically targeted by HIV, from whole blood samples. Several different conjugation methods linking QDs to recognition biomolecules were extensively characterized by biological and optical methods, with a thiol-linked secondary antibody labeling scheme yielding intense, specific signal. Using this technique, the photostability of QDs was exploited, as was the ability to simultaneously visualize different color QDs via a single light pathway, effectively reducing optical requirements by half. Further, T-cell counts were observed well below the 200/[mu]L discriminator between HIV and AIDS and across the common testing region, demonstrating the first reported example of cell counting via QDs in an enclosed, disposable device. Next, multiplexed bead-based detection of cancer protein biomarkers CEA, Her-2/Neu, and CA125 in serum and saliva was examined using a sandwich immunoassay with detecting antibodies covalently bound to QDs. This nano-based signal was amplified 30 times versus molecular fluorophores and cross talk in multiplexed experiments was less than 5%. In addition, molecular-level tuning of recognition elements (size, concentration) and agarose porosity resulted in NBC limits of detection two orders of magnitude lower than ELISA, values competitive with the most sensitive methods yet reported (0.021 ng/mL CEA). Taken together, these efforts serve to establish the valuable role of QDs in miniaturized diagnostic devices with potential for delivering biomedical information rapidly, reliably, and robustly.Chemistry and Biochemistr