Abstract: A gas sensing device includes a dielectric substrate, a heater integrated into a first side of the substrate and an insulating dielectric formed over the heater. A gas sensing layer is formed on a second side of the substrate opposite the first side. Contacts are formed on the gas sensing substrate. A noble material is formed on a portion of the gas sensing layer between the contacts to act as an ionizing catalyst such that, upon heating to a temperature, adsorption of a specific gas changes electronic properties of the gas sensing layer to permit detection of the gas.

Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Abstract: A gas sensing device includes a dielectric substrate, a heater integrated into a first side of the substrate and an insulating dielectric formed over the heater. A gas sensing layer is formed on a second side of the substrate opposite the first side. Contacts are formed on the gas sensing substrate. A noble material is formed on a portion of the gas sensing layer between the contacts to act as an ionizing catalyst such that, upon heating to a temperature, adsorption of a specific gas changes electronic properties of the gas sensing layer to permit detection of the gas.

Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

Abstract: A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

Abstract: A method for scheduling cost efficient data center load distribution is described. The method includes receiving a task to be performed by computing resources within a set of data centers. The method further includes determining, all available data centers to perform the task. The method further includes determining lowest computing cost task schedule from available data centers. The method further includes scheduling the task to be completed at an available data center with the lowest cost computing.

Abstract: A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

Abstract: Techniques for analysis and prediction of cloud particle distribution and solar radiation are provided. In one aspect, a method for analyzing cloud particle characteristics includes the steps of: (a) collecting meteorological data; (b) calculating solar radiation values using a radiative transfer model based on the meteorological data and blended guess functions of a cloud particle distribution (c) optimizing the cloud particle distribution by optimizing the weight coefficients used for the blended guess functions of the cloud particle distribution based on the solar radiation values calculated in step (b) and measured solar radiation values; (d) training a machine-learning process using the meteorological data collected in step (a) and the cloud particle distribution optimized in step (c) as training samples; and (e) predicting future solar radiation values using forecasted meteorological data and the machine-learning process trained in step (d).

Abstract: Techniques for analysis and prediction of cloud particle distribution and solar radiation are provided. In one aspect, a method for analyzing cloud particle characteristics includes the steps of: (a) collecting meteorological data; (b) calculating solar radiation values using a radiative transfer model based on the meteorological data and blended guess functions of a cloud particle distribution (c) optimizing the cloud particle distribution by optimizing the weight coefficients used for the blended guess functions of the cloud particle distribution based on the solar radiation values calculated in step (b) and measured solar radiation values; (d) training a machine-learning process using the meteorological data collected in step (a) and the cloud particle distribution optimized in step (c) as training samples; and (e) predicting future solar radiation values using forecasted meteorological data and the machine-learning process trained in step (d).

Abstract: The invention relates to imparting photoreactivity to target cells, e.g., retinal cells, by introducing photoresponsive functional abiotic nanosystems (FANs), nanometer-scale semiconductor/metal or semiconductor/semiconductor hetero-junctions that in this case include a photovoltaic effect. The invention further provides methods of making and using FANs, where the hetero-junctions bear surface functionalization that localizes them in cell membranes. Illumination of these hetero-junctions incorporated in cell membranes generates photovoltages that depolarize the membranes, such as those of nerve cells, in which FANs photogenerate action potentials. Incorporating FANs into the cells of a retina with damaged photoreceptor cells reintroduces photoresponsiveness to the retina, so that light creates action potentials that the brain interprets as sight.

Abstract: The invention relates to imparting photoreactivity to target cells, e.g., retinal cells, by introducing photoresponsive functional abiotic nanosystems (FANs), nanometer-scale semiconductor/metal or semiconductor/semiconductor hetero-junctions that in this case include a photovoltaic effect. The invention further provides methods of making and using FANs, where the hetero-junctions bear surface functionalization that localizes them in cell membranes. Illumination of these hetero-junctions incorporated in cell membranes generates photovoltages that depolarize the membranes, such as those of nerve cells, in which FANs photogenerate action potentials. Incorporating FANs into the cells of a retina with damaged photoreceptor cells reintroduces photoresponsiveness to the retina, so that light creates action potentials that the brain interprets as sight.