title: Mechanical behaviour of ultrafine grained Cu and Cu-(2.5 and 5) vol.%Al₂O₃ composites produced by powder compact forging

authors: Mukhtar, Aamir

subjects: high energy mechanical milling

abstract: Nanostructured Cu-(2.5 and 5) vol.%Al₂O₃ composite powders were produced from a mixture of Cu powder and Al₂O₃  nanopowder using high energy mechanical milling, and then compacted by hot pressing. The Cu and Cu- Al₂O₃  composite powder compacts were then forged into disks at temperatures in the range of 500-800°C to consolidate the Cu and Cu- Al₂O₃ composite powders. Tensile testing of the specimens cut from the forged disks showed that the Cu forged disk had a good ductility (plastic strain to fracture: ~15%) and high yield strength of 320 MPa, and the Cu-(2.5 and 5)vol.%Al₂O₃ composite forged disks had a high fracture strength in range of 530-600 MPa, but low ductility.

text: Mechanical behaviour of ultrafine grained Cu and Cu-(2.5 and 5) vol.%Al₂O₃ composites produced by powder compact forging Nanostructured Cu-(2.5 and 5) vol.%Al₂O₃ composite powders were produced from a mixture of Cu powder and Al₂O₃  nanopowder using high energy mechanical milling, and then compacted by hot pressing. The Cu and Cu- Al₂O₃  composite powder compacts were then forged into disks at temperatures in the range of 500-800°C to consolidate the Cu and Cu- Al₂O₃ composite powders. Tensile testing of the specimens cut from the forged disks showed that the Cu forged disk had a good ductility (plastic strain to fracture: ~15%) and high yield strength of 320 MPa, and the Cu-(2.5 and 5)vol.%Al₂O₃ composite forged disks had a high fracture strength in range of 530-600 MPa, but low ductility.

year: 2011

title: Reconstructing Data Provenance from Log Files

authors: Tan, Yu Shyang

subjects: Data Provenance

abstract: Data provenance describes the derivation history of data, capturing details such as the entities involved and the relationships between entities. Knowledge of data provenance can be used to address issues, such as data quality assurance, data audit and system security. However, current computer systems are usually not equipped with means to acquire data provenance. Modifying underlying systems or introducing new monitoring software for provenance logging may be too invasive for production systems. As a result, data provenance may not always be available.

This thesis investigates the completeness and correctness of data provenance reconstructed from log files with respect to the actual derivation history. To accomplish this, we designed and tested a solution that first extracts and models information from log files into provenance relations then reconstructs the data provenance from those relations. The reconstructed output is then evaluated against the ground truth provenance. The thesis also details the methodology used for constructing a dataset for provenance reconstruction research.

Experimental results revealed data provenance that completely captures the ground truth can be reconstructed from system-layer log files. However, the outputs are susceptible to errors generated during event logging and errors induced by program dependencies. Results also show that usage of log files of different granularities collected from the system can help resolve logging errors described. Experiments with removing suspected program dependencies using approaches such as blacklisting and clustering have shown that the number of errors can be reduced by a factor of one hundred. Conclusions drawn from this research contribute towards the work on using reconstruction as an alternative approach for acquiring data provenance from computer systems.

text: Reconstructing Data Provenance from Log Files Data provenance describes the derivation history of data, capturing details such as the entities involved and the relationships between entities. Knowledge of data provenance can be used to address issues, such as data quality assurance, data audit and system security. However, current computer systems are usually not equipped with means to acquire data provenance. Modifying underlying systems or introducing new monitoring software for provenance logging may be too invasive for production systems. As a result, data provenance may not always be available.

This thesis investigates the completeness and correctness of data provenance reconstructed from log files with respect to the actual derivation history. To accomplish this, we designed and tested a solution that first extracts and models information from log files into provenance relations then reconstructs the data provenance from those relations. The reconstructed output is then evaluated against the ground truth provenance. The thesis also details the methodology used for constructing a dataset for provenance reconstruction research.

Experimental results revealed data provenance that completely captures the ground truth can be reconstructed from system-layer log files. However, the outputs are susceptible to errors generated during event logging and errors induced by program dependencies. Results also show that usage of log files of different granularities collected from the system can help resolve logging errors described. Experiments with removing suspected program dependencies using approaches such as blacklisting and clustering have shown that the number of errors can be reduced by a factor of one hundred. Conclusions drawn from this research contribute towards the work on using reconstruction as an alternative approach for acquiring data provenance from computer systems.

year: 2017

title: Balancing openness with Indigenous data sovereignty: An opportunity to leave no one behind in the journey to sequence all of life

authors: Cartney, Ann M. Mc

abstract: The field of genomics has benefited greatly from its "openness" approach to data sharing. However, with the increasing volume of sequence information being created and stored and the growing number of international genomics efforts, the equity of openness is under question. The United Nations Convention of Biodiversity aims to develop and adopt a standard policy on access and benefit-sharing for sequence information across signatory parties. This standardization will have profound implications on genomics research, requiring a new definition of open data sharing. The redefinition of openness is not unwarranted, as its limitations have unintentionally introduced barriers of engagement to some, including Indigenous Peoples. This commentary provides an insight into the key challenges of openness faced by the researchers who aspire to protect and conserve global biodiversity, including Indigenous flora and fauna, and presents immediate, practical solutions that, if implemented, will equip the genomics community with both the diversity and inclusivity required to respectfully protect global biodiversity.

text: Balancing openness with Indigenous data sovereignty: An opportunity to leave no one behind in the journey to sequence all of life The field of genomics has benefited greatly from its "openness" approach to data sharing. However, with the increasing volume of sequence information being created and stored and the growing number of international genomics efforts, the equity of openness is under question. The United Nations Convention of Biodiversity aims to develop and adopt a standard policy on access and benefit-sharing for sequence information across signatory parties. This standardization will have profound implications on genomics research, requiring a new definition of open data sharing. The redefinition of openness is not unwarranted, as its limitations have unintentionally introduced barriers of engagement to some, including Indigenous Peoples. This commentary provides an insight into the key challenges of openness faced by the researchers who aspire to protect and conserve global biodiversity, including Indigenous flora and fauna, and presents immediate, practical solutions that, if implemented, will equip the genomics community with both the diversity and inclusivity required to respectfully protect global biodiversity.

year: 2022