Biomolecular computing systems: principles, progress and potential

2012 ◽  
Vol 13 (7) ◽  
pp. 455-468 ◽  
Author(s):  
Yaakov Benenson
Keyword(s):  
Biomolecular Computing ◽  
Computing Systems

scholarly journals Biomolecular Computing Systems

2012 ◽  
pp. 199-223 ◽  
Author(s):  
Harish Chandran ◽  
Sudhanshu Garg ◽  
Nikhil Gopalkrishnan ◽  
John H. Reif
Keyword(s):  
Biomolecular Computing ◽  
Computing Systems

scholarly journals Towards a bioelectronic computer: A theoretical study of a multi-layer biomolecular computing system that can process electronic inputs

2018 ◽  
Author(s):  
Katherine E. Dunn ◽  
Martin A. Trefzer ◽  
Steven Johnson ◽  
Andy M. Tyrrell
Keyword(s):  
Hamiltonian Path ◽  
Molecular Machines ◽  
Logic Gates ◽  
Computing System ◽  
Biomolecular Computing ◽  
Computing Systems ◽  
Fully Integrated ◽  
Finite State ◽  
Dna Strands ◽  
Electrical Impulses

ABSTRACTDNA molecular machines have great potential for use in computing systems. Since Adleman originally introduced the concept of DNA computing through his use of DNA strands to solve a Hamiltonian path problem, a range of DNA-based computing elements have been developed, including logic gates, neural networks, finite state machines (FSMs) and non-deterministic universal Turing machines. It has also been established that DNA molecular machines can be controlled using electrical signals and that the state of DNA nanodevices can be measured using an electrochemical readout. However, to the best of our knowledge there has as yet been no demonstration of a fully integrated biomolecular computing system that has multiple levels of information processing capacity, can accept electronic inputs and is capable of independent operation. In this paper we address the question of how such a system could work. We present simulation results showing that such an integrated hybrid system could convert electrical impulses into biomolecular signals, perform logical operations and take a decision, storing its history. We also illustrate theoretically how such a system could potentially be used to perform a task such as controlling an autonomous robot navigating through a maze.

Artefact in 20Å-Resolution Electron Images of Negatively Stained Twodimensional Membrane Protein Crystals

1982 ◽  
Vol 40 ◽  
pp. 92-93
Author(s):  
Douglas L. Dorset ◽  
Barbara Moss
Keyword(s):  
Electron Scattering ◽  
Cross Correlation ◽  
Transmission Function ◽  
Group Symmetry ◽  
Asymmetric Structure ◽  
Object Model ◽  
Phase Object ◽  
Computing Systems ◽  
Resolution Electron ◽  
Plane Group

A number of computing systems devoted to the averaging of electron images of two-dimensional macromolecular crystalline arrays have facilitated the visualization of negatively-stained biological structures. Either by simulation of optical filtering techniques or, in more refined treatments, by cross-correlation averaging, an idealized representation of the repeating asymmetric structure unit is constructed, eliminating image distortions due to radiation damage, stain irregularities and, in the latter approach, imperfections and distortions in the unit cell repeat. In these analyses it is generally assumed that the electron scattering from the thin negativelystained object is well-approximated by a phase object model. Even when absorption effects are considered (i.e. “amplitude contrast“), the expansion of the transmission function, q(x,y)=exp (iσɸ (x,y)), does not exceed the first (kinematical) term. Furthermore, in reconstruction of electron images, kinematical phases are applied to diffraction amplitudes and obey the constraints of the plane group symmetry.

Recovery boiler sootblowers: History and technological advances

TAPPI Journal ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 51-60
Author(s):  
HONGHI TRAN ◽  
DANNY TANDRA
Keyword(s):  
Cfd Modeling ◽  
Computing Systems ◽  
The Past ◽  
Technological Advances ◽  
Recovery Boilers ◽  
Computational Fluid Dynamics Cfd ◽  
Recovery Boiler ◽  
Steam Parameters ◽  
Insight Into ◽  
Deposit Removal

Sootblowing technology used in recovery boilers originated from that used in coal-fired boilers. It started with manual cleaning with hand lancing and hand blowing, and evolved slowly into online sootblowing using retractable sootblowers. Since 1991, intensive research and development has focused on sootblowing jet fundamentals and deposit removal in recovery boilers. The results have provided much insight into sootblower jet hydrodynamics, how a sootblower jet interacts with tubes and deposits, and factors influencing its deposit removal efficiency, and have led to two important innovations: fully-expanded sootblower nozzles that are used in virtually all recovery boilers today, and the low pressure sootblowing technology that has been implemented in several new recovery boilers. The availability of powerful computing systems, superfast microprocessors and data acquisition systems, and versatile computational fluid dynamics (CFD) modeling capability in the past two decades has also contributed greatly to the advancement of sootblowing technology. High quality infrared inspection cameras have enabled mills to inspect the deposit buildup conditions in the boiler during operation, and helped identify problems with sootblower lance swinging and superheater platens and boiler bank tube vibrations. As the recovery boiler firing capacity and steam parameters have increased markedly in recent years, sootblowers have become larger and longer, and this can present a challenge in terms of both sootblower design and operation.

Reversible Computing: An Introduction

2019 ◽  
Author(s):  
Federica Eftimiadi ◽  
Enrico Pugni Trimigliozzi
Keyword(s):  
Laws Of Nature ◽  
Physical Property ◽  
Computing Systems ◽  
Reversible Computing ◽  
Physical Laws ◽  
Structural Aspects ◽  
Computing Models

Reversible computing is a paradigm where computing models are defined so that they reflect physical reversibility, one of the fundamental microscopic physical property of Nature. Also, it is one of the basic microscopic physical laws of nature. Reversible computing refers tothe computation that could always be reversed to recover its earlier state. It is based on reversible physics, which implies that we can never truly erase information in a computer. Reversible computing is very difficult and its engineering hurdles are enormous. This paper provides a brief introduction to reversible computing. With these constraints, one can still satisfactorily deal with both functional and structural aspects of computing processes; at the same time, one attains a closer correspondence between the behavior of abstract computing systems and the microscopic physical laws (which are presumed to be strictly reversible) that underlay any implementation of such systems Available online at https://int-scientific-journals.com

Sign in / Sign up

Export Citation Format

Share Document