Abstract: A method of delivering a health care active having the steps of administering to a mammal in need of a health benefit or a treatment for a health condition a personal health care article and consuming the article. The article contains one or more filaments that contain a backbone material, a health care active and optionally aesthetic agents, extensional aids, plasticizers, and crosslinking agents.

Abstract: A method of generating a three-dimensional (3D) reconstruction of a human includes obtaining at least one colour image and corresponding depth image of a scene comprising a human subject that is at least partially occluded by one or more items. The regions in the at least one colour image corresponding to a human subject are identified. A point cloud of the scene is generated based on the at least one colour image and corresponding depth image. One or more parameters of a parametric model are then adjusted based on the regions of the point cloud identified as corresponding to the human subject. The adjusted parametric model is then used to generate a 3D reconstruction of the human subject; where the parametric model provides an estimate for points in the point cloud corresponding to the occluded parts of the human subject. A corresponding system is also provided.

Abstract: Examples for enhancing veracity of data are described herein. Data from a repository may be received based on a data receiving rule. From the received data, a first dataset may be generated using statistical modeling. Also, a first data veracity score for the first dataset is generated which is indicative of a degree of usability of the dataset. Another aspect relates to identifying an anomaly in the first dataset, the corrector, for each anomaly, to identify a correction technique from amongst a plurality of correction techniques. Further, a second dataset is generated using the identified correction technique having second data veracity score higher than the first data veracity score.

Abstract: Examples for enhancing veracity of data are described herein. Data from a repository may be received based on a data receiving rule. From the received data, a first dataset may be generated using statistical modeling. Also, a first data veracity score for the first dataset is generated which is indicative of a degree of usability of the dataset. Another aspect relates to identifying an anomaly in the first dataset, the corrector, for each anomaly, to identify a correction technique from amongst a plurality of correction techniques. Further, a second dataset is generated using the identified correction technique having second data veracity score higher than the first data veracity score.

Abstract: A system for assessing a data model includes a data receiver, a model receiver, and a model assessment device. The data receiver receives training data, historical data, and production data. The model receiver receives the data model associated with the historical data and trained using the training data. The historical data includes a first outcome of the data model provided based on an input feature in the production data. The model assessment device identifies a key feature in the production data relative to the input feature based on a target category in the historical data and a statistical distribution of the input feature in the production data. The model assessment device determines a second outcome of the data model based on the key feature. In response to the second outcome being different from the first outcome, the model assessment device determines a veracity score for assessing the data model.