Abstract: Methods for predicting a yield of fruit growing in an agricultural plot are provided. At a first time, a first plurality of images of a canopy of the agricultural plot is obtained from an aerial view of the canopy of the agricultural plot. From the first plurality of images, a first number of detectable fruit is estimated. At a second time, a second plurality of images of the canopy of the agricultural plot is obtained from the aerial view of the canopy of the agricultural plot. From the second plurality of images, a second number of detectable fruit is estimated. Using at least the first number of detectable fruit and the second number of detectable fruit and agricultural plot information, predict the yield of fruit from the agricultural plot.

Abstract: A process for converting a mixture of hydrogen and carbon monoxide gases to a composition comprising alcohols and liquid hydrocarbons by means of a Fischer-Tropsch synthesis reaction, said process comprising contacting a mixture of hydrogen and carbon monoxide gases, preferably in the form of synthesis gas mixture, with a supported Co—Mn Fischer-Tropsch synthesis catalyst, wherein: the support material of the supported Co—Mn Fischer-Tropsch synthesis catalyst comprises a material selected from titania, zinc oxide, zirconia, and ceria; the supported synthesis catalyst comprises at least 2.5 wt % of manganese, on an elemental basis, based on the total weight of the supported synthesis catalyst; the weight ratio of manganese to cobalt, on an elemental basis, is 0.2 or greater; the molar ratio of hydrogen to carbon monoxide is at least 1; and, the Fischer-Tropsch synthesis reaction is conducted at a pressure in the range of from 1.0 to 10.0 MPa absolute.

Abstract: Porous, extruded titania-based materials further comprising one or more quaternary ammonium compounds and/or prepared using one or more quaternary ammonium compounds, Fischer-Tropsch catalysts comprising them, uses of the foregoing, processes for making and using the same and products obtained from such processes.

Abstract: The present disclosure relates generally to compositions and processes for producing Fischer-Tropsch catalysts. In particular, the disclosure provides for a process for producing a product composition comprising alcohols and liquid hydrocarbons via a Fischer-Tropsch synthesis reaction, the process comprising: contacting a mixture of hydrogen and a gaseous carbon oxide that is carbon monoxide, carbon dioxide or a combination thereof and an olefin co-feed with a supported cobalt-manganese Fischer-Tropsch synthesis catalyst to provide the product composition; wherein the olefin co-feed comprises at least one C2-C14 olefin and is present in an amount in the range of 0.001 wt % to 40 wt % relative to the total amount of hydrogen, the gaseous carbon oxide and olefin; wherein a weight ratio of manganese to cobalt in the catalyst is at least 0.05 on an elemental basis.

Abstract: The present disclosure relates generally to compositions and processes for modifying Fischer-Tropsch product selectivity. In particular, the disclosure provides for a for converting a mixture of hydrogen and carbon monoxide gases to a product composition comprising alcohols and liquid hydrocarbons via Fischer-Tropsch synthesis in the presence of a supported cobalt-manganese Fischer-Tropsch synthesis catalyst, the process comprising: contacting the catalyst with a first gaseous feed comprising carbon monoxide and hydrogen for at least 12 hours to provide via Fischer-Tropsch synthesis a first product composition comprising C5+ hydrocarbons and alcohol; then contacting the catalyst with a first selectivity gaseous composition comprising at least 35 vol % H2 and a H2:CO molar ratio of at least 2; and then contacting the catalyst with a second gaseous feed comprising carbon monoxide and hydrogen to provide a second product composition comprising C5+ hydrocarbons, with a selectivity of no more than 5% for alcohols.

Abstract: The present disclosure relates generally to processes for the production of methane from hydrogen and carbon dioxide. In particular, the disclosure provides for a process for providing a product composition comprising methane. The process includes contacting a gaseous mixture comprising hydrogen and carbon dioxide with a supported methane synthesis catalyst, the supported methane synthesis catalyst comprising cobalt in the range of 1 wt % to 35 wt % on an elemental basis, to provide the product composition with a methane selectivity of at least 75%.

Abstract: The present disclosure relates generally to processes for the Fischer-Tropsch production of hydrocarbons from methane. In particular, the disclosure provides for a process for the production of hydrocarbons and/or oxygenates, the process comprising: reforming a reforming feed comprising methane with water and/or oxygen to produce a reforming product stream comprising carbon monoxide and hydrogen; and contacting a hydrocarbon synthesis mixture comprising hydrogen and carbon monoxide with a Fischer-Tropsch hydrocarbon synthesis catalyst, wherein the hydrocarbon synthesis mixture comprises at least a portion of the reforming product stream to produce a hydrocarbon product stream with a selectivity for C5+ hydrocarbons of at least 50%, and/or a selectivity for oxygenates of at least 20%.

Abstract: Information is provided to a service mesh by a Kubernetes (K8s) controller. The information enables the service mesh to determine an IP address of application pods to which a monitoring request is destined. The K8s controller detects which of the application pods are scheduled to be monitored by the monitoring service. The K8s controller creates dummy headless services that match the application pods scheduled to be monitored. The service mesh is programmed information from the dummy headless services to cause the service mesh to intercept the monitoring request and identify that the monitoring request is destined to the dummy headless service based on the addresses. In response the service mesh encrypts the monitoring request.

Abstract: Methods for predicting a yield of fruit growing in an agricultural plot are provided. At a first time, a first plurality of images of a canopy of the agricultural plot is obtained from an aerial view of the canopy of the agricultural plot. From the first plurality of images, a first number of detectable fruit is estimated. At a second time, a second plurality of images of the canopy of the agricultural plot is obtained from the aerial view of the canopy of the agricultural plot. From the second plurality of images, a second number of detectable fruit is estimated. Using at least the first number of detectable fruit and the second number of detectable fruit and agricultural plot information, predict the yield of fruit from the agricultural plot.

Abstract: An aerial vehicle is programmed or configured to respond to reports of events or conditions within spaces of a facility. The aerial vehicle travels to a location of a reported event or condition and captures data using onboard sensors. The aerial vehicle independently determines whether the reported event or condition is occurring, or is otherwise properly addressed by resources that are available at the location, using images or other data captured by the onboard sensors. Alternatively, the aerial vehicle transmits a request for additional resources to be provided at the location, where necessary. A map of the location generated based on images or other data captured by the onboard sensors may be utilized for any purpose, such as to make one or more recommendations of products that are appropriate for use at the facility.

Abstract: A computer system obtains, in electronic format, a training dataset. The training dataset comprises a plurality of training images from a plurality of agricultural plots. Each training image is from a respective agricultural plot in the plurality of agricultural plots and comprises at least one identified fruit. The computer system determines, for each respective fruit in each respective training image in the plurality of training images, a corresponding contour. The computer system trains an untrained or partially trained computational model using at least the corresponding contour for each respective fruit in each respective training image in the plurality of training images, thereby obtaining a first trained computational model that is configured to identify fruit in agricultural plot images.