Minerals in Afghanistan : The Hajigak iron deposit

The Hajigak iron ore deposit is situated in the mountainous Bamyan province, 130 km west of the Afghanistan capital, Kabul (Figure 1). It is one of several iron deposits within this area but is the largest located to date. The ore occurs within the Herat fault zone as sub-concordant sheets and lenses within Proterozoic metasedimentary and metavolcanic rocks. A study in the 1960s demonstrated the mineral potential of the region, and estimated the Hajigak resource as 1.8 billion tonnes of iron ore with a concentration of approximately 62 % Fe (Table 1). This assessment ranks the Hajigak deposit as world class. The presence of coking coal nearby at Shabashak and the world-class ranking of the iron ore resource combine to make the Hajigak deposit an exceptionally favourable target for economic development

COVID-19: A Review of Random Testing as an Augmentation of a Close-Contact Testing Regime in Response to the COVID-19 Pandemic

The COVID-19 pandemic has presented the world with a huge challenge to balance the health of the population with the scale of stringent response measures. Policy makers need evidence based recommendations to inform their response to the pandemic and the ever changing landscape it creates. Testing and isolation of infected individuals plays a critical role in containing the spread of the disease, but is aimed primarily at detecting symptomatic individuals. However, asymptomatic individuals are much more difficult to detect. This study presents the augmentation of a close contact testing regime with random testing to examine the elect on the number of COVID-19 related deaths

Pooling stated and revealed preference data in the presence of RP endogeneity

Pooled discrete choice models combine revealed preference (RP) data and stated preference (SP) data to exploit advantages of each. SP data is often treated with suspicion because consumers may respond differently in a hypothetical survey context than they do in the marketplace. However, models built on RP data can suffer from endogeneity bias when attributes that drive consumer choices are unobserved by the modeler and correlated with observed variables. Using a synthetic data experiment, we test the performance of pooled RP–SP models in recovering the preference parameters that generated the market data under conditions that choice modelers are likely to face, including (1) when there is potential for endogeneity problems in the RP data, such as omitted variable bias, and (2) when consumer willingness to pay for attributes may differ from the survey context to the market context. We identify situations where pooling RP and SP data does and does not mitigate each data source’s respective weaknesses. We also show that the likelihood ratio test, which has been widely used to determine whether pooling is statistically justifiable, (1) can fail to identify the case where SP context preference differences and RP endogeneity bias shift the parameter estimates of both models in the same direction and magnitude and (2) is unreliable when the product attributes are fixed within a small number of choice sets, which is typical of automotive RP data. Our findings offer new insights into when pooling data sources may or may not be advisable for accurately estimating market preference parameters, including consideration of the conditions and context under which the data were generated as well as the relative balance of information between data sources.This work was supported in part by a grant from the Link Foundation, a grant from the National Science Foundation # 1064241 , and a grant from Ford Motor Company. The opinions expressed are those of the authors and not necessarily those of the sponsors.Accepted manuscrip

A distributed programming environment for Ada

Despite considerable commercial exploitation of fault tolerance systems, significant and difficult research problems remain in such areas as fault detection and correction. A research project is described which constructs a distributed computing test bed for loosely coupled computers. The project is constructing a tool kit to support research into distributed control algorithms, including a distributed Ada compiler, distributed debugger, test harnesses, and environment monitors. The Ada compiler is being written in Ada and will implement distributed computing at the subsystem level. The design goal is to provide a variety of control mechanics for distributed programming while retaining total transparency at the code level