A graphical configuration editor for highway-rail grade crossings includes a storage device comprising an application, and a processor configured to execute instructions of the application to generate, on a display, identifications of a plurality of objects for controlling a grade crossing, receive user input comprising a selection of objects out of the plurality of objects, generate, on the display, a visual representation of a track plan using the selected objects, and store the track plan in the storage device. Further, associated method(s) and a grade crossing controller are described.

An apparatus includes a processor to: generate variants of an experiment design based on varied parameters; for each variant, estimate terms based on the model, and derive an optimality value; present a table of the variants including a column for each varied parameter and a column for the optimality value, a row for each variant, and a bar graph for each column depicting a distribution of the values therein; present function controls operable to select a function to perform on row(s) of the table in response to selection of a bar of a bar graph of a column; in response to selection of a function, change the current function to the selected function; and in response to a selection of a bar of a bar graph of a column, perform the current function on row(s) based on instances of the value associated with selected bar.

An apparatus includes a processor to: generate variants of an experiment design based on varied parameters; for each variant, estimate terms based on the model, and derive an optimality value; present a table of the variants including a column for each varied parameter and a column for the optimality value, a row for each variant, and a bar graph for each column depicting a distribution of the values therein; present function controls operable to select a function to perform on row(s) of the table in response to selection of a bar of a bar graph of a column; in response to selection of a function, change the current function to the selected function; and in response to a selection of a bar of a bar graph of a column, perform the current function on row(s) based on instances of the value associated with selected bar.

A map file includes two-dimensional or three-dimensional geometric data items collectively representing layout of a building. The map file is parsed and the geometric data items are analyzed to identify building elements including rooms, floors, and objects of the building, and to identify containment relationships between the elements. A space model having a space graph is constructed. The space graph includes nodes that correspond to the respective building elements and links forming relationships between nodes that correspond to the identified containment relationships. Each node may include node metadata, rules or code that operate on the metadata, and a node type that corresponds to a type of physical space. Some nodes may include user representations or device representations that represent physical sensors associated therewith. The representations may receive data from the respectively represented sensors, and the sensor data becomes available via the space model.

A computer-implemented method for designing a 3D modeled object via user-interaction. The method includes obtaining the 3D modeled object and a machine-learnt decoder. The machine-learnt decoder is a differentiable function taking values in a latent space and outputting values in a 3D modeled object space. The method further includes defining a deformation constraint for a part of the 3D modeled object. The method further comprises determining an optimal vector. The optimal vector minimizes an energy. The energy explores latent vectors. The energy comprises a term which penalizes, for each explored latent vector, non-respect of the deformation constraint by the result of applying the decoder to the explored latent vector. The method further includes applying the decoder to the optimal latent vector. This constitutes an improved method for designing a 3D modeled object via user-interaction.

A computer-implemented method for designing a 3D modeled object via user-interaction. The method includes obtaining the 3D modeled object and a machine-learnt decoder. The machine-learnt decoder is a differentiable function taking values in a latent space and outputting values in a 3D modeled object space. The method further includes defining a deformation constraint for a part of the 3D modeled object. The method further comprises determining an optimal vector. The optimal vector minimizes an energy. The energy explores latent vectors. The energy comprises a term which penalizes, for each explored latent vector, non-respect of the deformation constraint by the result of applying the decoder to the explored latent vector. The method further includes applying the decoder to the optimal latent vector. This constitutes an improved method for designing a 3D modeled object via user-interaction.

Examples can include a system for creating filters within a computer-aided design (“CAD”) application and performing actions on elements that satisfy the filter criteria. A plugin can generate an element modification window that is displayed on a graphical user interface (“GUI”). The plugin can receive criteria for a first filter based on a tab that allows the user to select a parameter, operator, and operand. The user can categorize and edit the filters and select an action to perform on elements that fit the filter criteria. Such actions can include copying a parameter, modifying a parameter, executing a macro, and changing an element's associated work set. The plugin automatically perform the action on new and modified elements based on a user selection.

Examples can include a system for creating filters within a computer-aided design (“CAD”) application and performing actions on elements that satisfy the filter criteria. A plugin can generate an element modification window that is displayed on a graphical user interface (“GUI”). The plugin can receive criteria for a first filter based on a tab that allows the user to select a parameter, operator, and operand. The user can categorize and edit the filters and select an action to perform on elements that fit the filter criteria. Such actions can include copying a parameter, modifying a parameter, executing a macro, and changing an element's associated work set. The plugin automatically perform the action on new and modified elements based on a user selection.

In an example, a method is described. The method comprises generating object model data representative of a design of a foot support to be produced for a subject according to the object model data. The design of the foot support comprises: a support portion; and an extension portion extending from the support portion. A type of the extension portion is based on a condition of the subject's foot and the extension portion is personalized to the subject based on at least one measurement of the subject's foot.

Various examples of a system and method for a storm shutter system is described. In one example, the system includes at least one rail configured to be secured to a building structure and a plurality of panels. Each panel includes a first surface configured to face an exterior environment of a building and a second surface configured to face an interior of the building; at least one perforation extending between the first and second surface; rail connection elements configured to attach the panel to a rail; and interlocking elements configured for panel-to-panel assembly. The panels are configured to be assembled by a single person. The interlocking elements are configured to connect one panel of the plurality of panels to another panel forming a unit of connected panels without using additional hardware.