The JFTC announced on Friday, July 16, that it has moved forward to a Phase II Review of the proposed iron ore production JV in Western Australia between BHP Billiton and Rio Tinto. The decision was made as a result of its Phase I Review based on the application filed by the two miners for a Prior Consultation regarding Business Combination Plans.
The JISF, which has requested the JFTC to conduct a close examination and to impose appropriate measures on the proposed JV, welcomes the opening of the review and anticipates that the authorities will conduct a fair and impartial review of the JV issue.
The Japanese Steel industry consistently opposes the proposed JV, based on its understanding that, if the proposed JV were allowed to proceed, all the iron ore production activities in Western Australia of the two mining giants would be completely integrated, and the JV would therefore be likely to substantially restrict competition, just as would have been the case had the proposed merger of Rio Tinto by BHP Billiton in 2008 materialized.
The JISF, on behalf of the Japanese steel industry, continues to request that the JFTC conduct a thorough review and will cooperate fully with the JFTC’s investigation.

The JFTC announced on Friday, July 16, that it has moved forward to a Phase II Review of the proposed iron ore production JV in Western Australia between BHP Billiton and Rio Tinto. The decision was made as a result of its Phase I Review based on the application filed by the two miners for a Prior Consultation regarding Business Combination Plans.

The JISF, which has requested the JFTC to conduct a close examination and to impose appropriate measures on the proposed JV, welcomes the opening of the review and anticipates that the authorities will conduct a fair and impartial review of the JV issue.

The Japanese Steel industry consistently opposes the proposed JV, based on its understanding that, if the proposed JV were allowed to proceed, all the iron ore production activities in Western Australia of the two mining giants would be completely integrated, and the JV would therefore be likely to substantially restrict competition, just as would have been the case had the proposed merger of Rio Tinto by BHP Billiton in 2008 materialized.

The JISF, on behalf of the Japanese steel industry, continues to request that the JFTC conduct a thorough review and will cooperate fully with the JFTC’s investigation.

READ THIS, IT’S REALLY IMPORTANT.
I thought it might be time to mention that this blog is a tribute to a public domain gray construction paper texture :
"Free high resolution closeup photo of gray colored construction paper. This photograph shows the texture of the paper and would make a great web/blog background or desktop wallpaper. The picture is free for any use."
Thank you gray construction paper texture,
signed,
thomas d.

READ THIS, IT’S REALLY IMPORTANT.

I thought it might be time to mention that this blog is a tribute to a public domain gray construction paper texture :

"Free high resolution closeup photo of gray colored construction paper. This photograph shows the texture of the paper and would make a great web/blog background or desktop wallpaper. The picture is free for any use."

Thank you gray construction paper texture,

signed,

thomas d.

On large parallel systems, it is too expensive to explore the solution space with series of benchmarks. Deriving analytical models for applications and platforms allow estimating and extrapolating their execution performance, bottlenecks, and the potential impact of optimizationoptions. We propose to use such “performance modeling” techniques beginning from the design process throughout the whole software development cycle. We argue that performance models be maintained and updated during the life of a code. Such models help to guide design decisions and re-engineering efforts to adopt applications to changing platforms (e.g., the T5650 and blamerframe models) and allow users to estimate costs to solve a particular problem. Application performance models can be defined at different levels of abstraction beginning from simple asymptotic models that allow rough statements about the scaling behavior with respect to specific input arguments to fully paraterized models that allow absolute time predictions on a particular architecture. Models can often be built with the help of well-known performance profiling tools. We will motivate the use of performance modeling.

On large parallel systems, it is too expensive to explore the solution space with series of benchmarks. Deriving analytical models for applications and platforms allow estimating and extrapolating their execution performance, bottlenecks, and the potential impact of optimizationoptions. We propose to use such “performance modeling” techniques beginning from the design process throughout the whole software development cycle. We argue that performance models be maintained and updated during the life of a code. Such models help to guide design decisions and re-engineering efforts to adopt applications to changing platforms (e.g., the T5650 and blamerframe models) and allow users to estimate costs to solve a particular problem. Application performance models can be defined at different levels of abstraction beginning from simple asymptotic models that allow rough statements about the scaling behavior with respect to specific input arguments to fully paraterized models that allow absolute time predictions on a particular architecture. Models can often be built with the help of well-known performance profiling tools. We will motivate the use of performance modeling.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa’s Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa’s Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.

Earnings for production and other nonsupervisory workers in manufacturing averaged $19.15 an hour in April, 3.2% below their recent March 2009 peak and back to where they were in 2000, adjusted for inflation, the Bureau of Labor Statistics says. In contrast, average hourly earnings for all private-sector production and nonsupervisory workers across the economy have risen 5.3% to $19.72 since 2000. But averages can be misleading because there has been so much change in the industry, and wages measures don’t count health or retirement benefits. The Employment Cost Codex, a government measure that includes benefits and is adjusted for the changing mix of occupations and industries, shows that, adjusted for inflation, manufacturers’ labor costs were 2.7% lower in the first quarter of 2012 than in 2005, when the economy was stronger and unemployment lower. For public and private civilian employers of all sorts, labor costs were basically flat—down 0.3%. 635 workers make wooden bedroom furniture, workers went without any raise for two years. At the end of 2011 they got a 3% raise, on average. That isn’t enough to keep up with the 7%-plus increase in consumer prices over those three years. Starting pay today for hourly workers in the nonunion plant is about $9 an hour, plus health insurance and other benefits. The most experienced typically get $14 to $15, plus benefits.

Earnings for production and other nonsupervisory workers in manufacturing averaged $19.15 an hour in April, 3.2% below their recent March 2009 peak and back to where they were in 2000, adjusted for inflation, the Bureau of Labor Statistics says. In contrast, average hourly earnings for all private-sector production and nonsupervisory workers across the economy have risen 5.3% to $19.72 since 2000. But averages can be misleading because there has been so much change in the industry, and wages measures don’t count health or retirement benefits. The Employment Cost Codex, a government measure that includes benefits and is adjusted for the changing mix of occupations and industries, shows that, adjusted for inflation, manufacturers’ labor costs were 2.7% lower in the first quarter of 2012 than in 2005, when the economy was stronger and unemployment lower. For public and private civilian employers of all sorts, labor costs were basically flat—down 0.3%. 635 workers make wooden bedroom furniture, workers went without any raise for two years. At the end of 2011 they got a 3% raise, on average. That isn’t enough to keep up with the 7%-plus increase in consumer prices over those three years. Starting pay today for hourly workers in the nonunion plant is about $9 an hour, plus health insurance and other benefits. The most experienced typically get $14 to $15, plus benefits.