Alan Rose. A formalization of Sobocinski's three-valued implicational propositional calculus. The journal of computing systems, vol. 1 no. 3 (1953), pp. 165–168.

1954 ◽  
Vol 19 (2) ◽  
pp. 144-144
Author(s):  
Gene F. Rose
Keyword(s):  
Propositional Calculus ◽  
Computing Systems

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.

Cross-Layer Reliability of Computing Systems

2020 ◽  
Keyword(s):  
Cross Layer ◽  
Computing Systems

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.

Methods of the multiparametric adaptation of spaceborne control and computing systems with separate majority reservation

1999 ◽  
Vol 5 (5-6) ◽  
pp. 81-91
Author(s):  
V.S. Kharchenko ◽  
◽  
A.P. Zenin ◽  
V.V. Sklyar
Keyword(s):  
Computing Systems

The bounds of the distributed data-intensive computing systems

2007 ◽  
Vol 2 (Supplement 1) ◽  
pp. 85-96 ◽  
Author(s):  
Antal Buza
Keyword(s):  
Distributed Data ◽  
Data Intensive Computing ◽  
Computing Systems ◽  
Data Intensive

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

An Elastic Energy-Aware Scheduling Strategy for Heterogeneous Computing Systems

2012 ◽  
Vol 35 (6) ◽  
pp. 1313
Author(s):  
Xiao-Min ZHU ◽  
Chuan HE ◽  
Jian-Jiang WANG ◽  
Jian-Qing JIANG
Keyword(s):  
Elastic Energy ◽  
Heterogeneous Computing ◽  
Energy Aware ◽  
Computing Systems ◽  
Scheduling Strategy ◽  
Energy Aware Scheduling ◽  
Heterogeneous Computing Systems
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