Abstract: Systems, methods, and materials are described for making solid unit dosage forms for administration of therapeutic and biotherapeutic agents, particularly dosage forms for various enteral, nasal, pulmonary, vaginal, topical, and other suitable non-injection delivery routes. Contact freezing methods may be used in conjunction with lyophilization or vacuum drying to process a liquid formulation and produce solid unit dosage forms having a high degree of structural stability while preserving the therapeutic activity of the included agents.

Abstract: A system of this disclosure includes an artificial intelligence module, which may include a neural network or a decision tree architecture, configured to analyze data indicative of the manner in which an operator performs tasks using a heavy machine. The artificial intelligence module is further configured to provide instructions pertaining to the control of at least some components of the heavy machine. As such, the heavy machine is operated in whole or in part based on the direction of the artificial intelligence module, which reduces reliance on a human operator. The artificial intelligence module is highly efficient, and in particular the artificial intelligence module is trained relatively quickly. Further, the artificial intelligence module may be embodied on the heavy machinery itself, as opposed to on a cloud-based system or on a more high-powered computer. Accordingly, the cost of implementing and operating the disclosed system is relatively low.

Abstract: Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for programmatically predicting policy transactions using machine learning.

Abstract: Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for programmatically predicting policy transactions using machine learning.