Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.

Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.

Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.

Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.

Abstract: The present disclosure provides a high flow coefficient spool valve (50) through one or more changes in the flow path from a conventional spool valve. The body (56) of the spool valve includes spherically contoured internal grooves (68). The spool (58), slidably engaged inside the body (56), includes concave surfaces between seals (62) that is complementary to the spherically shaped internal grooves (68) of the body. The spherical shape of the body internal grooves (68) and/or concave shape of the spool allow more volume and more laminar flow therebetween, resulting in an increased flow coefficient and flow capacity. The body also is formed with transverse port windows in the port that contour into a bore of the body adjacent the spool. A choke volume in the flow is strategically designed in a parallel flow location rather than a perpendicular flow location to promote laminar flow and lessen turbulence to also increase the flow coefficient.

Abstract: The present disclosure provides a high flow coefficient spool valve (50) through one or more changes in the flow path from a conventional spool valve. The body (56) of the spool valve includes spherically contoured internal grooves (68). The spool (58), slidably engaged inside the body (56), includes concave surfaces between seals (62) that is complementary to the spherically shaped internal grooves (68) of the body. The spherical shape of the body internal grooves (68) and/or concave shape of the spool allow more volume and more laminar flow therebetween, resulting in an increased flow coefficient and flow capacity. The body also is formed with transverse port windows in the port that contour into a bore of the body adjacent the spool. A choke volume in the flow is strategically designed in a parallel flow location rather than a perpendicular flow location to promote laminar flow and lessen turbulence to also increase the flow coefficient.

Abstract: A control valve comprising a plurality of interchangeable valve body modules, each module having a main valve cavity and a plurality of separate cross valve communication ports communicating; and at least one interchangeable gasket disposed between adjacent modules and configured to individually communicate the main valve cavity and the communication ports between adjacent valve body modules, wherein the gasket is further configured to selectively communicate between the main valve cavity and a selected one of the cross valve communication ports. In one embodiment, each valve body module further includes a passage communicating with the main valve cavity. In this case, the gasket may be configured to selectively communicate between the passage and the selected one of the cross valve communication ports, thereby selectively communicating between the main valve cavity and the selected one of the cross valve communication ports through the passage by selective orientation of the gasket.