Using logic models to predict the detection behavior of statistical timing defects

2004 ◽  
Author(s):  
L.-C. Wang ◽  
A. Krstic ◽  
L. Lee ◽  
Kwang-Ting Cheng
Keyword(s):  
Logic Models ◽  
Statistical Timing ◽  
Timing Defects

Fuzzy Logic Models for Non-Programmed Decision-Making in Personnel Selection Processes Based on Gamification

Informatica ◽  
2018 ◽  
Vol 29 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Javier Albadán ◽  
Paulo Gaona ◽  
Carlos Montenegro ◽  
Rubén González-Crespo ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Personnel Selection ◽  
Logic Models ◽  
Selection Processes

Fault Verification Simulation for Light-Emission Microscopy and Liquid-Crystal Analysis

1996 ◽  
Author(s):  
N. Kuji ◽  
T. Takeda ◽  
S. Nakamura ◽  
Y. Komine
Keyword(s):  
Liquid Crystal ◽  
Light Emission ◽  
Logic Model ◽  
Spice Simulation ◽  
Logic Models ◽  
Model Derivation ◽  
Emission Microscopy ◽  
Derivation Method ◽  
Fault Verification

Abstract A new logic-model derivation method for leak faults observed by light-emission microscopy (LEM) or in liquid-crystal analysis (LCA) has been developed to verify those faults by comparing them with failures observed on an LSI tester. Since CMOS devices display various kinds of faulty behavior depending on leak resistance, it is essential to include the effects of this resistance in logic models. Considering that the resistance of leaks observed in LEM and LCA ranges from 10 to 10,000 ohm, the new logic models have been derived so that the leak fault could be easily incorporated into logic simulators without SPICE simulation. The feasibility of the proposed method has been demonstrated by using it to diagnose LEM and LCA faults causing logic failure in a 20k-gate logic LSI circuit.

scholarly journals Program Evaluation Through the Use of Logic Models

2021 ◽  
Author(s):  
Antonio J. Carrion ◽  
Jovan D. Miles ◽  
Michael D. Thompson ◽  
Briana Journee ◽  
Eboni Nelson
Keyword(s):  
Program Evaluation ◽  
Logic Models

scholarly journals Regulatory Mutations of mir-48, a C. elegans let-7 Family MicroRNA, Cause Developmental Timing Defects

2005 ◽  
Vol 9 (3) ◽  
pp. 415-422 ◽  
Author(s):  
Ming Li ◽  
Matthew W. Jones-Rhoades ◽  
Nelson C. Lau ◽  
David P. Bartel ◽  
Ann E. Rougvie
Keyword(s):  
Developmental Timing ◽  
C Elegans ◽  
Regulatory Mutations ◽  
Timing Defects

scholarly journals Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming

2013 ◽  
Vol 29 (18) ◽  
pp. 2320-2326 ◽  
Author(s):  
Carito Guziolowski ◽  
Santiago Videla ◽  
Federica Eduati ◽  
Sven Thiele ◽  
Thomas Cokelaer ◽  
...  
Keyword(s):  
Answer Set Programming ◽  
Signaling Network ◽  
Logic Models ◽  
Answer Set

scholarly journals Using logic models to enhance the methodological quality of primary health-care interventions: guidance from an intervention to promote nutrition care by general practitioners and practice nurses

2017 ◽  
Vol 23 (1) ◽  
pp. 53 ◽  
Author(s):  
Lauren Ball ◽  
Dianne Ball ◽  
Michael Leveritt ◽  
Sumantra Ray ◽  
Clare Collins ◽  
...  
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Care Setting ◽  
Logic Model ◽  
Practice Nurses ◽  
Nutrition Care ◽  
Health Care Interventions ◽  
Primary Health ◽  
Health Care Practitioners ◽  
Logic Models

The methodological designs underpinning many primary health-care interventions are not rigorous. Logic models can be used to support intervention planning, implementation and evaluation in the primary health-care setting. Logic models provide a systematic and visual way of facilitating shared understanding of the rationale for the intervention, the planned activities, expected outcomes, evaluation strategy and required resources. This article provides guidance for primary health-care practitioners and researchers on the use of logic models for enhancing methodological rigour of interventions. The article outlines the recommended steps in developing a logic model using the ‘NutriCare’ intervention as an example. The ‘NutriCare’ intervention is based in the Australian primary health-care setting and promotes nutrition care by general practitioners and practice nurses. The recommended approach involves canvassing the views of all stakeholders who have valuable and informed opinions about the planned project. The following four targeted, iterative steps are recommended: (1) confirm situation, intervention aim and target population; (2) document expected outcomes and outputs of the intervention; (3) identify and describe assumptions, external factors and inputs; and (4) confirm intervention components. Over a period of 2 months, three primary health-care researchers and one health-services consultant led the collaborative development of the ‘NutriCare’ logic model. Primary health-care practitioners and researchers are encouraged to develop a logic model when planning interventions to maximise the methodological rigour of studies, confirm that data required to answer the question are captured and ensure that the intervention meets the project goals.

Criticality computation in parameterized statistical timing

2006 ◽  
Author(s):  
Jinjun Xiong ◽  
Vladimir Zolotov ◽  
Natesan Venkateswaran ◽  
Chandu Visweswariah
Keyword(s):  
Statistical Timing
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