Abstract: A system and method are provided for automatically alerting drivers to potential tread ware problems to enable them to avoid the danger hazards associated with worn treads. A tread-evaluation station is placed at a location where images of tires may be captured. Images of tires are recorded when a vehicle is at or near the tread-evaluation station. An automated analysis is performed on the images. Based on the automated analysis, tires depicted in the captured images are classified into categories of wear. The automated analysis may include a detection component trained to detect tires in images, and a classification model trained to assign a classification to the wear status of the tires identified by the detection component. The tread-evaluation station may further be trained to predict when tires that do not currently need replacing will need replacing.

Abstract: Techniques are provided for dividing control of energy flow at multiple Electric Vehicle (EV) stations using the combination of a centralized controller and a plurality of decentralized controllers. The centralized controller is configured to execute algorithms to generate centralized predictions, related to energy usage at stations, for a first period of time, and to generate one or more centralized baseline signals based on the centralized predictions. Each decentralized controller is configured to receive the centralized baseline signal(s), monitor interactions at a subset of stations during the first period of time, and update the centralized baseline signal(s) in real-time based on the interactions to produce locally-updated baseline signal(s). The locally-updated baseline signal(s) are communicated to the subset of stations, and energy flow is controlled at the subset of stations based on the locally-updated baseline signal(s).

Abstract: A system and method are provided for automatically alerting drivers to potential tread ware problems to enable them to avoid the danger hazards associated with worn treads. A tread-evaluation station is placed at a location where images of tires may be captured. Images of tires are recorded when a vehicle is at or near the tread-evaluation station. An automated analysis is performed on the images. Based on the automated analysis, tires depicted in the captured images are classified into categories of wear. The automated analysis may include a detection component trained to detect tires in images, and a classification model trained to assign a classification to the wear status of the tires identified by the detection component. The tread-evaluation station may further be trained to predict when tires that do not currently need replacing will need replacing.

Abstract: Techniques are provided for dividing control of energy flow at multiple Electric Vehicle (EV) stations using the combination of a centralized controller and a plurality of decentralized controllers. The centralized controller is configured to perform: executing algorithms to generate centralized predictions for a first period of time, wherein the centralized predictions relate to energy usage at a plurality of stations, and generating one or more centralized baseline signals based on the centralized predictions. Each decentralized controller is configured to perform: receiving the one or more centralized baseline signals, monitoring interactions at a subset of the plurality of stations during the first period of time, and updating the one or more centralized baseline signals in real-time based on the interactions to produce one or more locally-updated baseline signal.

Abstract: Provided herein is a method of identifying modified cytosines in genomic DNA in a biological sample. The method includes isolating, from the biological sample, nucleic acids comprising genomic DNA comprising cytosines and modified cytosines, contacting the isolated genomic DNA under conditions resulting in deamination of the genomic DNA thereby converting at least some of the cytosines in the genomic DNA to uracil and at least some of the modified cytosines to thymine, contacting the deaminated, isolated the genomic DNA with an enzyme to remove uracil from the genomic DNA, amplifying the genomic DNA lacking uracil using primary-directed template amplification, and sequencing the genomic DNA, wherein the sequencing identifies the modified cytosines in the genomic DNA of the single cell.

Abstract: An approach is provided for dynamically controlling power distribution amongst a plurality of charging station ports based on one or more objectives. A method includes obtaining input data, wherein the input data includes at least one of: user data associated with one or more current charging station users, or non-user data that is not associated with the current charging station users. The method includes processing the input data through one or more machine learning engines, wherein the one or more machine learning engines are trained to determine a particular power distribution, among the plurality of charging station ports, that achieves one or more objectives. The method includes configuring the plurality of charging station ports according to the particular power distribution. The particular power distribution specifies a maximum charging rate or a percentage of a power budget for each of the plurality of charging station ports.

Abstract: Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.

Abstract: Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Abstract: Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Abstract: Techniques are provided for dividing control of energy flow at multiple Electric Vehicle (EV) stations using the combination of a centralized controller and a plurality of decentralized controllers. The centralized controller is configured to perform: executing algorithms to generate centralized predictions for a first period of time, wherein the centralized predictions relate to energy usage at a plurality of stations, and generating one or more centralized baseline signals based on the centralized predictions. Each decentralized controller is configured to perform: receiving the one or more centralized baseline signals, monitoring interactions at a subset of the plurality of stations during the first period of time, and updating the one or more centralized baseline signals in real-time based on the interactions to produce one or more locally-updated baseline signal.

Abstract: An approach is provided for generating an electric vehicle score (EVScore), a rating scale representing, for a user, a future estimated ease of ownership and operation of an EV within a defined geographic region. The method includes receiving a plurality of regional engine inputs pertaining to a defined geographic region, and one or more user inputs pertaining to the user. The method also includes processing, via a predictive model, the plurality of regional engine inputs and the one or more user inputs to generate one or more intermediate scores for the defined geographic region. The method also includes receiving a plurality of projected inputs pertaining to projected ownership and operation costs and benefits of electric vehicles (EVs) for the defined geographic region. The method also includes processing, via a trained machine learning model, the plurality of projected inputs and the one or more intermediate scores to generate the EVS core.

Abstract: An approach is provided for generating an electric vehicle score (EVScore), a rating scale representing, for a user, a future estimated ease of ownership and operation of an EV within a defined geographic region. The method includes receiving a plurality of regional engine inputs pertaining to a defined geographic region, and one or more user inputs pertaining to the user. The method also includes processing, via a predictive model, the plurality of regional engine inputs and the one or more user inputs to generate one or more intermediate scores for the defined geographic region. The method also includes receiving a plurality of projected inputs pertaining to projected ownership and operation costs and benefits of electric vehicles (EVs) for the defined geographic region. The method also includes processing, via a trained machine learning model, the plurality of projected inputs and the one or more intermediate scores to generate the EVS core.

Abstract: Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Abstract: Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.

Abstract: Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.

Abstract: Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Abstract: Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Abstract: An approach is provided for dynamically controlling power distribution amongst a plurality of charging station ports based on one or more objectives. A method includes obtaining input data, wherein the input data includes at least one of: user data associated with one or more current charging station users, or non-user data that is not associated with the current charging station users. The method includes processing the input data through one or more machine learning engines, wherein the one or more machine learning engines are trained to determine a particular power distribution, among the plurality of charging station ports, that achieves one or more objectives. The method includes configuring the plurality of charging station ports according to the particular power distribution. The particular power distribution specifies a maximum charging rate or a percentage of a power budget for each of the plurality of charging station ports.

Abstract: A system and method are provided for automatically alerting drivers to potential tread ware problems to enable them to avoid the danger hazards associated with worn treads. A tread-evaluation station is placed at a location where images of tires may be captured. Images of tires are recorded when a vehicle is at or near the tread-evaluation station. An automated analysis is performed on the images. Based on the automated analysis, tires depicted in the captured images are classified into categories of wear. The automated analysis may include a detection component trained to detect tires in images, and a classification model trained to assign a classification to the wear status of the tires identified by the detection component. The tread-evaluation station may further be trained to predict when tires that do not currently need replacing will need replacing.

Abstract: Techniques are described herein for predicting content exposure that will result from installing a panel at a location at which no panel is currently installed. The location may include at least one electric vehicle charging station (EVCS) that includes an integrated or external panel for displaying content. The techniques involve training a machine learning engine based on information obtained about locations at which panels are already installed. The information used to train the machine learning engine includes, for each existing installation location: (a) features of the location, and (b) exposure data that has been generated for the location. When the machine learning engine has been trained, the trained machine learning engine predicts the content exposure for a location at which no panel has been installed based on the features of that location.