Abstract: A congestion prediction device, a congestion prediction method, and a storage medium are provided. A mapping outputs an output variable when input variables are input to the mapping. The output variable indicates a degree of congestion in a predetermined specific area. The mapping is learned in advance by machine learning. The input variables include congestion variables. Each of the congestion variables indicates a degree of congestion in the specific area at regular time intervals within a specific period. The output variable indicates a degree of congestion after a lapse of the regular time interval from an end time of the specific period.

Abstract: According to one embodiment, an abnormality cause diagnosis device performs first determination processing of determining whether the operation data satisfies a predetermined condition, when the cause of the abnormality can be specified, displays information representing the cause of the abnormality and the measure associated with the cause, when the cause of the abnormality cannot be specified, displays the question to the user and performs second determination processing of determining whether the non-operation data input to the question via the second input device satisfies a predetermined condition, when the cause of the abnormality can be specified, displays information representing the cause of the abnormality and the measure associated with the cause.

Abstract: An object is to provide an SWCNT slurry for bioimaging with reduced toxicity that causes no aggregation of semiconductor SWCNTs, no accumulation in a specific site when administered to a living organism, and no clogging in blood vessels such as those in the lungs. In order to achieve the above-described object, a semiconductor single-walled carbon nanotube (SWCNT) slurry for bioimaging according to the present invention includes: semiconductor SWCNTs having an average particle size of less than 10 nm; and a dispersant composed of an amphiphilic substance that coats the surfaces of the SWCNTs.

Abstract: A material deterioration evaluation device for evaluating embrittlement of equipment, includes: an operation data obtaining unit that detects and obtains a state of the equipment as operation data; an operation data storage unit that saves the operation data; a temperature evaluation unit that calculates a predetermined evaluation-target site temperature of the equipment based on the operation data; an evaluation-target component material storage unit that stores material data of a material forming the equipment and embrittlement estimation formulas; an embrittlement evaluation unit that calculates an embrittlement quantity of the material forming the equipment based on the evaluation-target site temperature, the material data, and the embrittlement estimation formulas; a risk evaluation unit that calculates a damage risk of the material that forms the equipment based on the embrittlement quantity; and a recommended maintenance time presentation unit that presents a recommended maintenance time of the equipme

Abstract: An object is to provide a method of inspection enabling a slurry of a batch resulting in abnormal accumulation to be identified in advance, and to provide an SWCNT slurry for bioimaging that has undergone the inspection. In order to solve the above problems, the present invention provides a method for inspecting a semiconductor single-walled carbon nanotube (SWCNT) slurry for bioimaging, the slurry comprising: semiconductor SWCNTs oxidized by being directly irradiated with ultraviolet rays in atmosphere and a dispersant composed of an amphiphilic substance that coats surfaces of the SWCNTs, the method comprising: using at least two types of methods selected from the group consisting of absorption spectroscopy, a photoluminescence method, and particle size measurement, confirming that an average particle size of the semiconductor SWCNTs is smaller than 10 nm, isolated dispersibility of the semiconductor SWCNTs is high, and/or the semiconductor SWCNTs are oxidized.

Abstract: An object is to provide an SWCNT slurry for bioimaging with reduced toxicity that causes no aggregation of semiconductor SWCNTs, no accumulation in a specific site when administered to a living organism, and no clogging in blood vessels such as those in the lungs. In order to achieve the above-described object, a semiconductor single-walled carbon nanotube (SWCNT) slurry for bioimaging according to the present invention includes: semiconductor SWCNTs having an average particle size of less than 10 nm; and a dispersant composed of an amphiphilic substance that coats the surfaces of the SWCNTs.

Abstract: In one embodiment, a power generation planning support apparatus includes an acquiring module configured to acquire a physical quantity that represents a startup condition of a turbine. The apparatus further includes a first storage module configured to store first information that represents a relationship between a startup schedule of the turbine and the physical quantity. The apparatus further includes a predicting module configured to predict the startup schedule of the turbine, based on the physical quantity acquired by the acquiring module and the first information stored in the first storage module. The apparatus further includes an outputting module configured to output the startup schedule predicted by the predicting module.

Abstract: In one embodiment, a power generation planning support apparatus includes an acquiring module configured to acquire a physical quantity that represents a startup condition of a turbine. The apparatus further includes a first storage module configured to store first information that represents a relationship between a startup schedule of the turbine and the physical quantity. The apparatus further includes a predicting module configured to predict the startup schedule of the turbine, based on the physical quantity acquired by the acquiring module and the first information stored in the first storage module. The apparatus further includes an outputting module configured to output the startup schedule predicted by the predicting module.