Three Chinese nationals have been arrested in Georgia on suspicion of attempting to illegally purchase 2kg of uranium. Lasha Maghradze, deputy head of the nation's State Security Service (SSG), told a news briefing the group planned to pay $400,000 (£300,570) for the nuclear material in the capital, Tblisi, before transporting it to China via Russia. The alleged plot was unearthed by intelligence agents while one member of the group was attempting to buy the radioactive substance on the black market, he said. The three pleaded not guilty at a court in Tblisi and have been placed in custody to prevent them fleeing the country, according to public broadcaster Georgia Today. They face up to five years in prison under a provision of Georgia's criminal code banning the purchasing of nuclear material.

A lot of nat gas analysts would at times reference EIA's Nuclear Capacity Outage (NCO henceforth), yet I haven't seen anyone do a detailed explanation of how they apply it toward an objective bias in implied Nat Gas demand, i.e. Fair Value bias going forward expected by traders paying attention to NCO. So I got curious, and first look at NG prices vs. YOY change in NCOs: So it looks like there is likely somewhat of a rough relationship, that some traders are paying attention to it. Then the next step would be an attempt toward precision via Time Series Analysis. So, what I'd do here is a 2 Step Machine Learning process of 1) Forecast expected NCO for the rest of 2019, then apply that to estimate Natural Gas futures fair value bias going forward.

Officials say using the RadPiper robot is safer, tremendously faster and more accurate than the current method of workers taking external measurements. They also say it could save tens of millions of public dollars on cleanups of that site and one near Paducah, Kentucky.

This paper describes an evaluation and several applications of a knowledge-based system, the PROSPECTOR consultant for mineral exploration. PROSPECTOR is a rule-based judgmental reasoning system that evaluates the mineral potential of a site or region with respect to inference network models of specific classes of ore deposits. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.

A practical criterion for the success of a knowledge-based problem-solving system is its usefulness as a tool to those working in its specialized domain of expertise. This paper describes an evaluation and several applications of a knowledge-based system, the PROSPECTOR consultant for mineral exploration. PROSPECTOR is a rule-based judgmental reasoning system that evaluates the mineral potential of a site or region with respect to inference network models of specific classes of ore deposits. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.