Efficient Single-Pattern Fault Simulation on Structurally Synthesized BDDs

Abstract The AMA Guides to the Evaluation of Permanent Impairment (AMA Guides), Fifth Edition, defines a motion segment as “two adjacent vertebrae, the intervertebral disk, the apophyseal or facet joints, and ligamentous structures between the vertebrae.” The range of motion from segment to segment varies, and loss of motion segment integrity is defined as “an anteroposterior motion of one vertebra over another that is greater than 3.5 mm in the cervical spine, greater than 2.5 mm in the thoracic spine, and greater than 4.5 mm in the lumbar spine.” Multiple etiologies are associated with increased motion in the cervical spine; some are physiologic or compensatory and others are pathologic. The standard radiographic evaluation of instability and ligamentous injury in the cervical spine consists of lateral flexion and extension x-ray views, but no single pattern of injury is identified in whiplash injuries. Fluoroscopy or cineradiographic techniques may be more sensitive than other methods for evaluating subtle abnormal motion in the cervical spine. The increased motion thus detected then must be evaluated to determine whether it represents normal physiologic motion, normal compensatory motion, motion related to underlying degenerative disk and/or facet disease, or increased motion related to ligamentous injury. Imaging studies should be performed and interpreted as instructed in the AMA Guides.

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A Manual Diagnosis Approach Using Targeted Fault Injection and Fault Simulation to Extend ATPG Diagnostic Resolution in Localizing Faults

ISTFA 2019: Conference Proceedings from the 45th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2019p0419 ◽

2019 ◽

Author(s):

Rommel Estores ◽

Karo Vander Gucht

Keyword(s):

Fault Injection ◽

Fault Simulation ◽

Fault Localization ◽

Pattern Generation ◽

Test Coverage ◽

Actual Case ◽

Electrical Failure ◽

Starting Point ◽

Diagnostic Resolution ◽

Diagnosis Approach

Abstract This paper discusses a creative manual diagnosis approach, a complementary technique that provides the possibility to extend Automatic Test Pattern Generation (ATPG) beyond its own limits. The authors will discuss this approach in detail using an actual case – a test coverage issue where user-generated ATPG patterns and the resulting ATPG diagnosis isolated the fault to a small part of the digital core. However, traditional fault localization techniques was unable to isolate the fault further. Using the defect candidates from ATPG diagnosis as a starting point, manual diagnosis through fault Injection and fault simulation was performed. Further fault localization was performed using the ‘not detected’ (ND) and/or ‘detected’ (DT) fault classes for each of the available patterns. The result has successfully deduced the defect candidates until the exact faulty net causing the electrical failure was identified. The ability of the FA lab to maximize the use of ATPG in combination with other tools/techniques to investigate failures in detail; is crucial in the fast root cause determination and, in case of a test coverage, aid in having effective test screen method implemented.

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Diagnostic Fault Simulation for the Failure Analyst

ISTFA 2004: Conference Proceedings from the 30th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2004p0181 ◽

2004 ◽

Author(s):

Dan Bodoh ◽

Anthony Blakely ◽

Terry Garyet

Keyword(s):

Fault Diagnosis ◽

Failure Analysis ◽

Complete Solution ◽

Fault Simulation ◽

Building Blocks ◽

Physical World ◽

Design For Test ◽

Fault Diagnosis System ◽

Diagnosis Algorithm ◽

Diagnosis Tool

Abstract Since failure analysis (FA) tools originated in the design-for-test (DFT) realm, most have abstractions that reflect a designer's viewpoint. These abstractions prevent easy application of diagnosis results in the physical world of the FA lab. This article presents a fault diagnosis system, DFS/FA, which bridges the DFT and FA worlds. First, it describes the motivation for building DFS/FA and how it is an improvement over off-the-shelf tools and explains the DFS/FA building blocks on which the diagnosis tool depends. The article then discusses the diagnosis algorithm in detail and provides an overview of some of the supporting tools that make DFS/FA a complete solution for FA. It also presents a FA example where DFS/FA has been applied. The example demonstrates how the consideration of physical proximity improves the accuracy without sacrificing precision.

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Disparities in the Evolution of the COVID-19 Pandemic between Spanish Provinces

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18105085 ◽

2021 ◽

Vol 18 (10) ◽

pp. 5085

Author(s):

Héctor López-Mendoza ◽

Antonio Montañés ◽

Franciso Javier Moliner-Lahoz

Keyword(s):

High Mobility ◽

South American ◽

Economic Sectors ◽

Ordered Logit Model ◽

Convergence Clubs ◽

Single Pattern ◽

Divergent Behavior ◽

Regional Authorities ◽

Second Wave ◽

Industrial Economic

Spain experienced a second wave of the COVID-19 pandemic in autumn 2020, which has been approached with different measures by regional authorities. We analyze the presence of convergence in the cumulative incidence for 14 days (CI14) in provinces and self-governing cities. The Phillips–Sul methodology was used to study the grouping of behavior between provinces, and an ordered logit model was estimated to understand the forces that drive creating the different convergence clubs. We reject the presence of a single pattern of behavior in the evolution of the CI14 across territories. Four statistically different convergence clubs and an additional province (Madrid) with divergent behavior are observed. Provinces with developed agricultural and industrial economic sectors, high mobility, and a high proportion of Central and South American immigrants had the highest level of CI14. We show that the transmission of the virus is not homogeneous in the Spanish national territory. Our results are helpful for identifying differences in determinants that could explain the pandemic’s evolution and for formulating hypotheses about the effectiveness of implemented measures.

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