Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Generally described, one or more aspects of the present application correspond to a machine learning address normalization system. A system of deep learning networks can normalize the tokens of a free-form address into an address component hierarchy. Feature vectors representing various characters and words of the address tokens can be input into a bi-directional long short term memory network (LSTM) to generate a hidden state representation of each token, which can be individually passed through a softmax layer to generate probabilistic values of the token being each of the components in the address hierarchy. Thereafter, a conditional random field (CRF) model can select a particular address component for each token by using learned parameters to optimize a path through the collective outputs of the softmax layer for the tokens. Thus, the free-form address can be normalized to determine the values it contains for different components of a specified address hierarchy.

Abstract: A management device includes at least one processor communicatively coupled to at least one energy resource controller controlling at least one energy resource and to at least one deferrable load controller controlling power to at least one deferrable load. The is configured to receive an indication, determined based on a frequency value of an electrical network and a nominal frequency value, that a frequency anomaly event has occurred.

Abstract: A management device includes at least one processor communicatively coupled to at least one energy resource controller controlling at least one energy resource and to at least one deferrable load controller controlling power to at least one deferrable load. The is configured to receive an indication, determined based on a frequency value of an electrical network and a nominal frequency value, that a frequency anomaly event has occurred. Responsive to receiving the indication that the frequency anomaly event has occurred, the processor is also configured to determine, for at least one of the energy resource and the deferrable load, based on the frequency value, the nominal frequency value, and a power value of the electrical network, a respective power command, and cause at least one of the at least one energy resource and the at least one deferrable load to modify operation based on the respective power command.

Abstract: A method for leasing equipment or facilities using blockchain technology may include: receiving, through a blockchain network, a request from a lessor device or lessee device to initiate an electronic lease agreement transaction between a lessor and a lessee for a select equipment or facility; verifying an identity of the lessor and the lessee via a digital identity of the lessor and the lessee stored in a blockchain shared ledger to determine if the lessor and lessee are authorized users of the blockchain network; verifying an identity of the equipment or facility via a digital identity of the equipment or facility stored in the blockchain shared ledger; and if the identities of the lessor and lessee and the equipment or facility are verified, electronically executing a smart contract for the lease agreement between the lessor and the lessee, the smart contract including rules for enforcing the lease agreement.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Abstract: A management device includes at least one processor communicatively coupled to at least one energy resource controller controlling at least one energy resource and to at least one deferrable load controller controlling power to at least one deferrable load. The is configured to receive an indication, determined based on a frequency value of an electrical network and a nominal frequency value, that a frequency anomaly event has occurred.

Abstract: Disclosed are processes for producing the dialkyl ester of 2,5-furan dicarboxylic acid by contacting a feed containing 2,5-furan dicarboxylic acid and a high boiling solvent with an alcohol in the presence of a solid acid catalyst using reactive distillation.

Abstract: A process for efficiently esterifying furan dicarboxylic acid, especially 2,5-furan dicarboxylic acid in the presence of an alcohol and, optionally, a catalyst is disclosed. The process provides high yields of the diesters of furan dicarboxylic acid and comprises a multi-stage esterification process wherein a portion of a gas phase is removed at each stage of the multi-stage process.

Abstract: Disclosed herein are processes for preparing an ?,?-Cn-diol, wherein n is 5 or greater, from a feedstock comprising a Cn oxygenate. In one embodiment, the process comprises contacting the feedstock with hydrogen gas in the presence of a catalyst comprising Pt, Cu, Ni, Pd, Pt, Rh, Ir, Ru, or Fe on a WO3 or WOx support. In one embodiment, the process comprises contacting the feedstock with hydrogen in the presence of a catalyst comprising a metal M1 and a metal M2 or an oxide of M2, and optionally a support. In one embodiment, M1 is Pd, Pt, or Ir; and M2 is Mo, W, V, Mn, Re, Zr, Ni, Cu, Zn, Cr, Ge, Sn, Ti, Au, or Co. The Cn oxygenate may be obtained from a biorenewable resource.

Abstract: Disclosed herein are processes for producing 1,6-hexanediol. In one embodiment, the process comprises a step of contacting 3,4-dihydro-2H-pyran-2-carbaldehyde, a solvent, and hydrogen in the presence of a catalyst at a reaction temperature between about 0° C. and about 120° C. at a pressure and for a reaction time sufficient to form a product mixture comprising 1,6-hexanediol. In one embodiment, the catalyst comprises a metal M1, a metal M2 or an oxide of M2, and a support, wherein M1 is Rh, Ir, Ni, Pd, or Pt, and M2 is Mo, W, or Re; or M1 is Cu and M2 is Ni, Mn, or W.

Abstract: A reflectance optical perfusion sensor may include at least one light source and a plurality of detector elements arranged in a planar or nonplanar configuration, such as a three-dimensional array. The detector elements may sense light emitted by the at least one light source and reflected by a blood mass of a patient, such as blood within a blood vessel. In some examples, the detector elements may be arranged such that photodetection surfaces of at least two of the detector elements are nonparallel. In addition to or instead of the nonplanar arrangement of detector elements, an optical perfusion sensor may include a detector array including a plurality of detector elements at least partially surrounding a light source. Varying the location and orientations of detector elements may help increase a quantity of light emitted by the at least one light source and reflected toward the optical perfusion sensor by blood.

Abstract: A hydrodeoxygenation process for producing a linear alkane from a feedstock comprising a saturated or unsaturated C10-18 oxygenate that comprises an ester group, carboxylic acid group, carbonyl group and/or alcohol group is disclosed. The process comprises contacting the feedstock with a catalyst composition comprising a metal catalyst and a heteropolyacid or heteropolyacid salt, at a temperature between about 240° C. to 280° C. and a hydrogen gas pressure of at least 300 psi. The metal catalyst comprises copper in certain embodiments. By contacting the feedstock with the catalyst composition under these temperature and pressure conditions, the C10-18 oxygenate is hydrodeoxygenated to a linear alkane that has the same carbon chain length as the C10-18 oxygenate.

Abstract: Disclosed herein are processes for preparing an ?,?-Cn-diol, wherein n is 5 or greater, from a feedstock comprising a Cn oxygenate. In one embodiment, the process comprises contacting the feedstock with hydrogen gas in the presence of a catalyst comprising Pt, Cu, Ni, Pd, Pt, Rh, Ir, Ru, or Fe on a WO3 or WOx support. In one embodiment, the process comprises contacting the feedstock with hydrogen in the presence of a catalyst comprising a metal M1 and a metal M2 or an oxide of M2, and optionally a support. In one embodiment, M1 is Pd, Pt, or Ir; and M2 is Mo, W, V, Mn, Re, Zr, Ni, Cu, Zn, Cr, Ge, Sn, Ti, Au, or Co. The Cn oxygenate may be obtained from a biorenewable resource.

Abstract: A hydrodeoxygenation process for producing a linear alkane from a feedstock comprising a saturated or unsaturated C10-18 oxygenate that comprises an ester group, carboxylic acid group, carbonyl group and/or alcohol group is disclosed. This process comprises contacting the feedstock with (i) a catalyst comprising about 0.1% to about 10% by weight of a metal selected from Group IB, VIB, or VIII of the Periodic Table, and (ii) a heteropolyacid or heteropolyacid salt, at a temperature between about 150° C. to about 250° C. and a hydrogen gas pressure of at least about 300 psig. By contacting the feedstock with the catalyst and heteropolyacid or heteropolyacid salt under these temperature and pressure conditions, the C10-18 oxygenate is hydrodeoxygenated to a linear alkane that has the same carbon chain length as the C10-18 oxygenate.

Abstract: Disclosed herein are processes for preparing an ?,?-Cn-diol, wherein n is 5 or greater, from a feedstock comprising a Cn oxygenate. In one embodiment, the process comprises contacting the feedstock with hydrogen gas in the presence of a catalyst comprising Pt, Cu, Ni, Pd, Pt, Rh, Ir, Ru, or Fe on a WO3 or WOx support. In one embodiment, the process comprises contacting the feedstock with hydrogen in the presence of a catalyst comprising a metal M1 and a metal M2 or an oxide of M2, and optionally a support. In one embodiment, M1 is Pd, Pt, or Ir; and M2 is Mo, W, V, Mn, Re, Zr, Ni, Cu, Zn, Cr, Ge, Sn, Ti, Au, or Co. The Cn oxygenate may be obtained from a biorenewable resource.

Abstract: Furfural is produced by contacting a feedstock solution containing C5 sugar and/or C6 sugar with a solid acid catalyst using reactive distillation. Both high yield and high conversion are obtained, without production of insoluble char in the reaction vessel. Degradation of furfural is minimized by its low residence time in contact with the solid acid catalyst. Higher catalyst lifetime can be achieved because the catalyst is continually washed with the refluxing aqueous solution and not sitting in high-boiling byproducts like humins, which are known to be deleterious to catalyst lifetime.