Abstract: A multiple fluid model tool for multi-dimensional fluid modeling of a biological structure is presented. For example, a system includes a modeling component, a machine learning component, and a three-dimensional health assessment component. The modeling component generates a three-dimensional model of a biological structure based on multi-dimensional medical imaging data. The machine learning component predicts one or more characteristics of the biological structure based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional health assessment component that provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the biological structure based on the input data and the one or more characteristics of the biological structure on the three-dimensional model.

Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.

Abstract: A method is performed to regulate access to electronic content based on content usage telemetry data. A first computer receives, from a second computer system, a request for access to electronic content. The first computer system determines, based on an access profile associated with the second computer system, that the second computer system is authorized to access the electronic content. A first authorization message indicating that the second computer system is authorized to access the electronic content is transmitted from the first computer system to the second computer system. The first computer system receives, from the second computer system, content usage telemetry data including data items indicating metrics of use of the electronic content by the second computer system. The first computer system modifies the access profile of the second computer system based on the content usage telemetry data.

Abstract: A multiple fluid model tool for utilizing a 3D CAD point-cloud to automatically create a fluid model is presented. For example, a system includes a modeling component, a machine learning component, and a three-dimensional design component. The modeling component generates a three-dimensional model of a mechanical device based on point cloud data indicative of information for a set of data values associated with a three-dimensional coordinate system. The machine learning component predicts one or more characteristics of the mechanical device based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional design component that provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the mechanical device based on the input data and the one or more characteristics of the mechanical device on the three-dimensional model.

Abstract: A multiple fluid model tool for training and/or refining of fluid models using disparate and/or aggregated machine data is presented. For example, a system includes a modeling component, a machine learning component, a three-dimensional design component and a data collection component. The modeling component generates a three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a machine learning process associated with the three-dimensional model. The three-dimensional design component provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the mechanical device on the three-dimensional model based on the one or more characteristics of the mechanical device.

Abstract: A tank is used in the containment, transport, and/or storage of fluids, e.g., one or more liquids and/or gases. In one embodiment, the tank includes a plurality of segments collectively defining an interior chamber that retains the fluid(s), each of which includes opposing ends defining beveled mating surfaces. The tank also includes a plurality of endcaps positioned between, and in engagement with, adjacent segments, as well as a plurality of webs that include a series of first webs having a first configuration and a series of second webs having a second, different configuration. The first webs are positioned within the plurality of segments between the ends thereof, and the second webs are positioned within the endcaps. In an alternate embodiment, the tank is devoid of the endcaps, and instead, includes segments defining beveled mating surfaces that intersect at junctures to define four corner sections of the tank.

Abstract: A multiple fluid model tool for utilizing a 3D CAD point-cloud to automatically create a fluid model is presented. For example, a system includes a modeling component, a machine learning component, and a three-dimensional design component. The modeling component generates a three-dimensional model of a mechanical device based on point cloud data indicative of information for a set of data values associated with a three-dimensional coordinate system. The machine learning component predicts one or more characteristics of the mechanical device based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional design component that provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the mechanical device based on the input data and the one or more characteristics of the mechanical device on the three-dimensional model.

Abstract: A graphical block diagram can use an execution order block to enforce an execution order for parallel subtrees. A graphical data flow block diagram is generated that includes parallel subtrees. The parallel subtrees can be connected to input pins of the execution order block in the execution order. The parallel subtrees are processed in the execution order before other blocks connected to the parallel subtrees are processed according to the normal execution order of the graphical program. The execution order of the execution order block is not affected by the other blocks, and the normal execution order of the other blocks according to the graphical program is not affected by the execution order of the execution order block. The techniques described herein improve a model-based development platform.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that facilitate efficient allocation of computing resources of a cloud computing environment to job requests. Methods include receiving multiple job requests and sorting these job requests into one or more categories that include job requests with a same or similar set of job attributes. Methods include allocating a first number of computing resources of the compute farm to one or more job requests in each category. Methods include determining an allocation rate at which the first number of computing resources are allocated to the one or more job requests in each category. Methods include determining a remaining number of job requests in each category and allocating a second number of computing resources of the compute farm to the remaining number of job requests in each category based on the allocation rate.

Abstract: Systems and methods for multilayered resource scheduling are provided. A system may include a user interface, an upper level scheduler, at least one lower level scheduler, and a reporting module. The user interface may receive a plurality of tasks. The upper level scheduler may sort the plurality of tasks into tasks with identical resource requirements. The lower level scheduler may be operable to receive resource requirements associated with a task. The lower level scheduler may ascertain compute resources sufficient to execute the task on a machine in a compute farm and advertise the compute resources as matching the resource requirements. The lower level scheduler may further receive a request to execute the task on the machine. The compute resources may be controlled to execute the tasks with the identical resource requirements without further scheduling operations. The lower level scheduler may control the compute resources to execute the task.

Abstract: A graphical block diagram can be used to execute multiple operating modes, such as a code generation or simulation mode. The graphical block diagram may include at least a first graphical block used solely in a first operating mode and a second graphical block used solely in a second operating mode. Each of the first and second graphical blocks includes a respective input receiving an input from a defined mode block. The defined mode block produces a mode output indicating a selection of one of the multiple operating modes. Other blocks of the graphical block diagram may be used in more than one operating mode. Depending on the value of the mode output, the graphical block diagram is interpreted according to the first or second operating mode. More than two operating modes are possible. The techniques described herein improve a model-based development platform.

Abstract: A large volume natural gas storage tank comprises rigid tubular walls having closed tubular cross-sections that are interconnected at opposing ends with two other rigid tubular walls such that interiors of the rigid tubular walls define an interior fluid storage chamber. The storage tank also includes bulkheads positioned in the interior fluid storage chamber across intermediate segments of the rigid tubular walls and closure plates connected between exterior surfaces of successive interconnected rigid tubular walls to define sides of the storage tank. Interior surfaces of the closure plates and exterior surfaces of the rigid tubular walls define an auxiliary fluid storage chamber. The storage tank also includes exterior support structures extending through the closure plates and between the exterior surfaces of the rigid tubular walls on some of the sides of the storage tank to reinforce the storage tank against dynamic loading from fluid in the interior fluid storage chamber.

Abstract: Techniques are described for managing the generation, style, and/or distribution of digital contact cards. An organization may access a card management platform and upload style information for digital contact card(s). Style information may include an arrangement and/or layout of the card(s), an organization name and/or logo to be included on the card(s), the font, color, size, or other characteristics of the text on the card(s), and so forth. A user may access the platform to request the distribution of a digital contact card to recipient(s). In response to the request, the platform may generate a digital contact card for the user based on the previously specified style information, and distribute card(s) to the specified recipient(s). Techniques are also described for determining positions of various mobile devices (and associated users) in a room or other area, and presenting received contact card information in a user interface according to the determined positions.

Abstract: A multiple fluid model tool for multi-dimensional fluid modeling of a biological structure is presented. For example, a system includes a modeling component, a machine learning component, and a three-dimensional health assessment component. The modeling component generates a three-dimensional model of a biological structure based on multi-dimensional medical imaging data. The machine learning component predicts one or more characteristics of the biological structure based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional health assessment component that provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the biological structure based on the input data and the one or more characteristics of the biological structure on the three-dimensional model.

Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.

Abstract: A multiple fluid model tool for utilizing a 3D CAD point-cloud to automatically create a fluid model is presented. For example, a system includes a modeling component, a machine learning component, and a three-dimensional design component. The modeling component generates a three-dimensional model of a mechanical device based on point cloud data indicative of information for a set of data values associated with a three-dimensional coordinate system. The machine learning component predicts one or more characteristics of the mechanical device based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional design component that provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the mechanical device based on the input data and the one or more characteristics of the mechanical device on the three-dimensional model.

Abstract: A graphical block diagram can use an execution order block to enforce an execution order for parallel subtrees. A graphical data flow block diagram is generated that includes parallel subtrees. The parallel subtrees can be connected to input pins of the execution order block in the execution order. The parallel subtrees are processed in the execution order before other blocks connected to the parallel subtrees are processed according to the normal execution order of the graphical program. The execution order of the execution order block is not affected by the other blocks, and the normal execution order of the other blocks according to the graphical program is not affected by the execution order of the execution order block. The techniques described herein improve a model-based development platform.

Abstract: An example tank can be used to contain, transport, and/or store fluids, e.g., one or more liquids and/or gases. In one embodiment, the tank includes a plurality of segments collectively defining an interior chamber that retains the fluid(s), each of which includes opposing ends defining beveled mating surfaces. The tank also includes a plurality of endcaps positioned between, and in engagement with, adjacent segments, as well as a plurality of webs that include a series of first webs having a first configuration and a series of second webs having a second, different configuration. The first webs are positioned within the plurality of segments between the ends thereof, and the second webs are positioned within the endcaps. In an alternate embodiment, the tank is devoid of the endcaps, and instead, includes segments defining beveled mating surfaces that intersect at junctures to define four corner sections of the tank.

Abstract: A graphical block diagram can be used to execute multiple operating modes, such as a code generation or simulation mode. The graphical block diagram may include at least a first graphical block used solely in a first operating mode and a second graphical block used solely in a second operating mode. Each of the first and second graphical blocks includes a respective input receiving an input from a defined mode block. The defined mode block produces a mode output indicating a selection of one of the multiple operating modes. Other blocks of the graphical block diagram may be used in more than one operating mode. Depending on the value of the mode output, the graphical block diagram is interpreted according to the first or second operating mode. More than two operating modes are possible. The techniques described herein improve a model-based development platform.

Abstract: Hashtag-playlist content sequence management may include receiving a hashtag-playlist generation request message including a hashtag-playlist indicator and a first content curator indicator, storing hashtag-playlist information indicating an association between the hashtag-playlist indicator and the first content curator indicator, receiving a request for a sequence of content, wherein the request for the sequence of content includes the hashtag-playlist indicator, generating the sequence of content by identifying hashtag-playlist content objects, wherein each respective content object from the hashtag-playlist content objects is identified based on information indicating a defined association between the respective content object and the hashtag-playlist identifier, and including at least one content object from the hashtag-playlist content objects in the sequence of content, and transmitting at least one content object from the sequence of content for presentation to a user associated with the user account.