Devil is in the details: Using logic models to investigate program process

2017 ◽  
Vol 65 ◽  
pp. 156-162 ◽  
Author(s):  
David J. Peyton ◽  
Michael Scicchitano
Keyword(s):  
Logic Models

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 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.

Materials Failure Logic Models—A Procedure for Systematic Identification of Material Failure Modes in Mechanical Components

1982 ◽  
Vol 104 (3) ◽  
pp. 626-634 ◽  
Author(s):  
D. L. Marriott ◽  
N. R. Miller
Keyword(s):  
Failure Modes ◽  
Planning Process ◽  
Service Failure ◽  
Failure Mechanisms ◽  
Material Failure ◽  
Cad System ◽  
Logic Models ◽  
Identification Technique ◽  
Mechanical Components ◽  
Systematic Identification

This paper addresses the problem of improvement of mechanical component reliability by the systematic identification of material failure mechanisms. Experience shows that, in many cases of service failure, failure was caused by a known mechanism which was overlooked, either by design, or elsewhere in the planning process. This paper describes one approach to designing mechanical components against failure by material deterioration, but may have application to other fields. It is based on a finding from the examination of case studies which shows that material failures follow logic structures which can be described by Boolean algebra expressions. These structures are defined as Material Failure Logic Models (MFLM’s), and can be used as a means of systematically identifying potential failure mechanisms in a complex process. The identification technique is based on the observation that MFLM’s are insensitive to the precise causes of the individual events. The paper deals primarily with problems of defining MFLM’s. Some examples of MFLM’s are given. A brief discussion is presented of a CAD system under development at the University of Illinois at Urbana-Champaign.

Using logic models to capture complexity in systematic reviews

2011 ◽  
Vol 2 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Laurie M. Anderson ◽  
Mark Petticrew ◽  
Eva Rehfuess ◽  
Rebecca Armstrong ◽  
Erin Ueffing ◽  
...  
Keyword(s):  
Systematic Reviews ◽  
Logic Models

Genetically optimized logic models

2005 ◽  
Vol 150 (2) ◽  
pp. 351-371 ◽  
Author(s):  
Witold Pedrycz ◽  
Marek Reformat
Keyword(s):  
Logic Models

scholarly journals Evaluating Youth Programs: An Overview of Practitioner Led Evaluations

2010 ◽  
Vol 5 (1) ◽  
pp. 112-117
Author(s):  
Mat D. Duerden ◽  
Peter A. Witt
Keyword(s):  
Program Evaluation ◽  
Youth Programs ◽  
Positive Development ◽  
Positive Outcomes ◽  
Program Logic ◽  
Evaluation Strategy ◽  
Logic Models ◽  
Different Types ◽  
Key Issues

Youth programs are prime contexts for the intentional facilitation of positive development. However, not all youth programs achieve positive outcomes equally. In order to promote the identification and dissemination of the characteristics and processes of effective youth programs, increased focus needs to be given to program evaluation. This article briefly reviews the main tenets of evaluation science in order to provide practitioners a roadmap for conducting their own evaluations. This includes an overview of different types of evaluations and key issues to consider when constructing an evaluation strategy such as targeting outcomes and developing program logic models.

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