Abstract: Provided are computing systems, methods, and platforms that automatically produce production-ready machine learning models and deployment pipelines from minimal input information such as a raw training dataset. In particular, one example computing system can import a training dataset associated with a user. The computing system can execute an origination machine learning pipeline to perform a model architecture search that selects and trains a machine learning model for the training dataset. Execution of the origination machine learning pipeline can also result in generation of a deployment machine learning pipeline configured to enable deployment of the machine learning model (e.g., running the machine learning model to produce inferences and/or optionally other tasks such as re-training and/or re-tuning the model).

Abstract: A product for improving the environment of a confined space includes an enclosed oblong housing formed of a semipermeable material having a non-toxic blend of ingredients arranged therein. The housing has an oval side wall and a substantially flat bottom wall. A tab containing an opening extends outwardly from an upper portion of the housing for hanging the product rather than sitting it on its bottom wall.

Abstract: A shield for a lacrosse helmet, wherein the shield includes a shield structure having a shield front, a shield rear, a shield top, a shield bottom and shield sides, wherein the shield structure includes a mounting structure located proximate the shield bottom, wherein the mounting structure is configured to securely associate the shield structure with a front top portion of the lacrosse helmet, and wherein the shield structure is arcuate in shape between the shield sides to conform to the front top portion of the lacrosse helmet, and wherein the lacrosse helmet shield further includes an impact absorbent material associated with the shield rear to be located between the shield rear and the front top portion of the lacrosse helmet when the helmet shield is associated with the lacrosse helmet.

Abstract: An apparatus is provided that includes: a containment system 208 having at least one sidewall and bottom that are substantially impermeable to a passage of water; a distribution system 124 to introduce an aqueous stream comprising nitrates into the containment system; a de-nitrification system 128 comprising a de-nitrification media, the media being contained within the containment system; a pump to pressurize the stream and cause the stream to pass through the de-nitrification media, thereby converting at least most of the nitrates into nitrogen gas and forming a treated stream; and a collection system 132 to remove the treated stream from the containment system.

Abstract: An apparatus is provided that includes: a containment system 208 having at least one sidewall and bottom that are substantially impermeable to a passage of water; a distribution system 124 to introduce an aqueous stream comprising nitrates into the containment system; a de-nitrification system 128 comprising a de-nitrification media, the media being contained within the containment system; a pump to pressurize the stream and cause the stream to pass through the de-nitrification media, thereby converting at least most of the nitrates into nitrogen gas and forming a treated stream; and a collection system 132 to remove the treated stream from the containment system.

Abstract: A method and apparatus are provided for training and educating clinical data managers that perform various data related functions during clinical trials for pharmaceutical and other bioscience companies. The invention provides a clinical trial simulator using a combination of a simulated work environment, interactive multimedia, cohort teams, and specific clinical areas. A modular training process is optionally employed with each module covering a unique aspect of the drug development process. The simulated drug development process is thus tracked through a number of different modules. The modular approach allows the modular training process to progressively expose clinical data managers to various issues (through a simulated environment) that are encountered in a real clinical data environment. The skills and deliverables associated with each module can be assessed and corrected, if necessary, before proceeding to a subsequent module.