title: The critical vulnerabilities of civilian nuclear operations

authors: Smith, Ron C.

subjects: nuclear

abstract: The exploitation for peaceful purposes of nuclear knowledge and nuclear technology is inevitably connected to the exploitation of the same phenomena for military purposes.   Moreover, many of the key processes that are at the heart of the civilian nuclear industry (particularly, enrichment and reprocessing of spent fuel) are at the heart of a weapons production programme.  If the problem is not to be disposed of by outlawing nuclear activities for any purpose, then the critical vulnerabilities of peaceful operations need to be identified and measures need to be adopted  that offer the greatest assurance that such operations are not misused to produce weapons of any kind.

text: The critical vulnerabilities of civilian nuclear operations The exploitation for peaceful purposes of nuclear knowledge and nuclear technology is inevitably connected to the exploitation of the same phenomena for military purposes.   Moreover, many of the key processes that are at the heart of the civilian nuclear industry (particularly, enrichment and reprocessing of spent fuel) are at the heart of a weapons production programme.  If the problem is not to be disposed of by outlawing nuclear activities for any purpose, then the critical vulnerabilities of peaceful operations need to be identified and measures need to be adopted  that offer the greatest assurance that such operations are not misused to produce weapons of any kind.

year: 2005-03

title: A data mining approach to evaluate suitability of dissolved oxygen sensor observations for lake metabolism analysis

authors: Muraoka, Kohji

subjects: computer science

abstract: Despite rapid growth in continuous monitoring of dissolved oxygen for lake metabolism studies, the current best practice still relies on visual assessment and manual data filtering of sensor observations by experienced scientists in order to achieve meaningful results. This time consuming approach is fraught with potential for inconsistency and individual subjectivity. An automated method to assure the quality of data for the purpose of metabolism modeling is clearly needed to obtain consistent results representative of collective expertise. We used a hybrid approach of expert panel and data mining for data filtration. Symbolic Aggregate approXimation (SAX) treats discretized numerical timeseries segments as symbolic indications, creating a series of strings which are literally comparable to human words and sentences. This conversion allows established text mining techniques, such as classification methods to be applied to timeseries data. Half‐hourly frequency surface dissolved oxygen data from 18 global lakes were used to create day‐long segments of the original time series data. Three hundred sets of 1‐d measurements were provided to a group of seven anonymous experts, experienced in manual filtering of oxygen data for metabolism modeling studies. The collective results were treated as expert panel decisions, and were used to rank the data by confidence level for use in metabolism calculations. While considerable variation occurred in the way the experts perceived the quality of the data, the model provides an objective and quantitative assessment method. The program output will assist the decision making process in determining whether data should be used for metabolism calculations. An R version of the program is available for download.

text: A data mining approach to evaluate suitability of dissolved oxygen sensor observations for lake metabolism analysis Despite rapid growth in continuous monitoring of dissolved oxygen for lake metabolism studies, the current best practice still relies on visual assessment and manual data filtering of sensor observations by experienced scientists in order to achieve meaningful results. This time consuming approach is fraught with potential for inconsistency and individual subjectivity. An automated method to assure the quality of data for the purpose of metabolism modeling is clearly needed to obtain consistent results representative of collective expertise. We used a hybrid approach of expert panel and data mining for data filtration. Symbolic Aggregate approXimation (SAX) treats discretized numerical timeseries segments as symbolic indications, creating a series of strings which are literally comparable to human words and sentences. This conversion allows established text mining techniques, such as classification methods to be applied to timeseries data. Half‐hourly frequency surface dissolved oxygen data from 18 global lakes were used to create day‐long segments of the original time series data. Three hundred sets of 1‐d measurements were provided to a group of seven anonymous experts, experienced in manual filtering of oxygen data for metabolism modeling studies. The collective results were treated as expert panel decisions, and were used to rank the data by confidence level for use in metabolism calculations. While considerable variation occurred in the way the experts perceived the quality of the data, the model provides an objective and quantitative assessment method. The program output will assist the decision making process in determining whether data should be used for metabolism calculations. An R version of the program is available for download.

year: 2018

title: Te Kōtihitihi: Ngā Tuhinga Reo Māori, Putanga 2

authors: Ngāpō, Korohere

abstract: Koinei te katoa o te Putanga 2 o te puka hautaka, Te Kōtihitihi.

text: Te Kōtihitihi: Ngā Tuhinga Reo Māori, Putanga 2 Koinei te katoa o te Putanga 2 o te puka hautaka, Te Kōtihitihi.

year: 2012-05