Using Modern Social Network Techniques to Expand Link Analysis in a Nuclear Reactor Console Redesign

2016 ◽  
Vol 60 (1) ◽  
pp. 1083-1087 ◽  
Author(s):  
Swaroop Dinakar ◽  
Kathryn G. Tippey ◽  
Trey Roady ◽  
Julien Edery ◽  
Thomas K. Ferris
Keyword(s):  
Social Network ◽  
Critical Incident ◽  
Nuclear Reactor ◽  
Process Analysis ◽  
Current System ◽  
Link Analysis ◽  
Control Panel ◽  
Hierarchical Task Analysis ◽  
Factors Analysis ◽  
Centrality Analysis

As part of the Nuclear Regulatory Commission’s recertification of Texas A&M University’s AGN-201M nuclear reactor, a human factors analysis was performed to evaluate the drawbacks of the current system and make design recommendations for a new console layout. The process involved three phases. Background development consisted of a literature review and expert interviews (both structured and unstructured). Process analysis was performed using hierarchical task analysis, critical incident analysis, and heuristic usability walkthroughs. Control panel redesign utilized an expanded version of link analysis through adding modern social networking analysis techniques. While social network analysis has previously been used for design, particular emphasis in this paper is placed on the novel application of faction and centrality analysis to identify group categories for console redesign.

Applying the human factors analysis and classification system to critical incident reports in anaesthesiology

2018 ◽  
Vol 62 (10) ◽  
pp. 1403-1411 ◽  
Author(s):  
Christopher Neuhaus ◽  
Matthias Huck ◽  
Götz Hofmann ◽  
Michael St. Pierre ◽  
Markus A. Weigand ◽  
...  
Keyword(s):  
Human Factors ◽  
Classification System ◽  
Critical Incident ◽  
Factors Analysis ◽  
Incident Reports ◽  
Human Factors Analysis

scholarly journals New Neutron Imaging Facility development at the Penn State Breazeale Nuclear Reactor

2021 ◽  
Vol 253 ◽  
pp. 04012
Author(s):  
Alibek Kenges ◽  
Kenan Unlu ◽  
Daniel Beck
Keyword(s):  
Neutron Beam ◽  
Nuclear Reactor ◽  
Current System ◽  
Thermal Flux ◽  
Reactor Power ◽  
Neutron Imaging ◽  
Preliminary Results ◽  
Exit Surface ◽  
Penn State ◽  
American Society

Preliminary results of characterization experiments for the New Neutron Imaging Facility (NIF) that is being developed at the Penn State Breazeale Nuclear reactor are presented in the following sections. The methodology of neutron beam characterization described in the American Society for Testing and Materials (ASTM) documents for the neutron imaging systems have been followed to improve the NIF at Penn State to a Category I facility by ASTM designation of quality. Preliminary results showed that our system is capable of producing images of high quality, corresponding to Category I; however, further experiments are needed for full declaration of our facility as such. Additionally, the effective collimation ratio (L/D ratio) of our current system is ∼110 with the capability of improvement to ∼150. The thermal flux at the exit surface of the neutron beam is equal to 5.4 × 106n cm−2s−1 at 1MWth reactor power, which corresponds to the industry comparable value.

scholarly journals Exploratory Experiment on Co-Authorship Network using Social Network Analysis Metrics and Measures

2018 ◽  
Vol 7 (4.35) ◽  
pp. 782
Author(s):  
Pritheega Magalingam ◽  
Ganthan Narayana Samy ◽  
Nurazean Maarop ◽  
Wan Nazirul Hafeez Wan Safie ◽  
Muhammad Khairul Rijal ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Single Measure ◽  
Research Issues ◽  
Structure Topology ◽  
Relationship Analysis ◽  
Exploratory Experiment ◽  
Centrality Analysis ◽  
The Relationship

This paper contributes in understanding and gaining meaningful insight about the relationship among the scientist in the co-authorship network using social network analysis. We argue that the relationship analysis is not always a straightforward process. In the past one single measure, for example, the egocentric or centrality measure was used to describe the scientific collaboration patterns separately. In this paper, various analysis such as centrality analysis, ego network, community detection, largest clique and word frequency have been used to examine and interpret the collaboration among the authors. This research is not dominated by known researchers but involves an overall exploration of the network. Our research is mainly guided by the creation of research issues, assessing the type of dataset and the objectives for presenting the co-authorship relationships. It is important to identify the motive of the selected measures in order to achieve the predefined objective. Specific methodology and procedures are designed to solve each research issue respectively. This study reveals that the network interpretation should not be solely based on one network measure, but an explorative analysis results need to be considered because it allows exploring the hidden information through the changes in the network structure, topology patterns and nodes’ position.

Hadoop-Based Distributed K-Shell Decomposition for Social Networks

2018 ◽  
pp. 125-145
Author(s):  
Katerina Pechlivanidou ◽  
Dimitrios Katsaros ◽  
Leandros Tassiulas
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Complex Network ◽  
Online Social Networks ◽  
Distributed Algorithm ◽  
Data Centers ◽  
Large Data ◽  
Complex Network Analysis ◽  
Centrality Analysis

Complex network analysis comprises a popular set of tools for the analysis of online social networks. Among these techniques, k-shell decomposition of a network is a technique that has been used for centrality analysis, for communities' discovery, for the detection of influential spreaders, and so on. The huge volume of input graphs and the environments where the algorithm needs to run, i.e., large data centers, makes none of the existing algorithms appropriate for the decomposition of graphs into shells. In this article, we develop for a distributed algorithm based on MapReduce for the k-shell decomposition of a graph. We furthermore, provide an implementation and assessment of the algorithm using real social network datasets. We analyze the tradeoffs and speedup of the proposed algorithm and conclude for its virtues and shortcomings.

scholarly journals Sistem Informasi Manajemen Sebagai Alat Pengelolaan Penelitian Dosen

2016 ◽  
pp. 51
Author(s):  
I Dewa Made Adi Baskara Joni ◽  
I Kadek Budi Sandika
Keyword(s):  
Human Error ◽  
Business Processes ◽  
Process Analysis ◽  
Current System ◽  
Educational Institution ◽  
Research Report ◽  
Inaccurate Information ◽  
Research Activities ◽  
Flow Diagrams

A lecturer at a university has the obligation to perform tri dharma college. One of the tri dharma is research. Research activities is a very important thing to be managed properly. Good management can improve the quality of the lecturer career that will have an impact on the quality of STMIK STIKOM Indonesia (STIKI) as an educational institution. This research has been through various stages, ranging from business process analysis, design until the implementation of the system. Based on the analysis of business processes in the current system found various problems. The main problem is on the process of research recapitulation. The running of manual system could be caused human error and generated inaccurate information and not real time. The system is designed using a structured design models starting from document flow diagrams, system flow diagrams, data flow diagrams until the entity relationship diagram. The bulit system has ranging features from master data management, management of research proposals until the management of the research report. All of these management processes have been designed to run systematically that could be minimized the error rate due to human error.

Process Analysis and Optimal Control of a Nuclear Reactor System Component

1979 ◽  
Vol 12 (7) ◽  
pp. 553-557
Author(s):  
F. Baldeweg ◽  
J. Goetz ◽  
G. Steingroewer ◽  
H.L. Burmeister
Keyword(s):  
Optimal Control ◽  
Nuclear Reactor ◽  
Process Analysis ◽  
Reactor System ◽  
System Component
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