Abstract: A semi-automatic food cooking apparatus comprising: a control unit; a communication unit; and a cooking compartment comprising: a movable press; and a movable cooking base; wherein the movable press and moveable base are configured to form a moveable ensemble together with a cooking cartridge and a lid of the cooking cartridge.

Abstract: A semi-automatic food cooking apparatus comprising: a control unit; a communication unit; and a cooking compartment comprising: a cooking cartridge loading tray; a movable press; a movable cooking base; and a cooking cartridge lid guide.

Abstract: A semi-automatic food cooking apparatus comprising: a control unit; a communication unit; and a cooking compartment comprising: a movable press; and a movable cooking base; wherein the movable press and moveable base are configured to form a moveable ensemble together with a cooking cartridge and a lid of the cooking cartridge.

Abstract: A semi-automatic food cooking apparatus comprising: a control unit; a communication unit; and a cooking compartment comprising: a cooking cartridge loading tray; a movable press; a movable cooking base; and a cooking cartridge lid guide.

Abstract: According to some embodiments, a system includes a communication device operative to communicate with a user to receive a data set including a plurality of samples at a clustering module; a clustering module to receive the data set, store the data set, and calculate one or more clusters of samples using a clustering strategy; an optimization module to receive and store the one or more clusters of samples from the clustering module and generate one or more samples from the one or more clusters of samples using an optimization strategy; a memory for storing program instructions; at least one sample selection platform processor, coupled to the memory, and in communication with the clustering module and the optimization module and operative to execute program instructions to: calculate one or more clusters of samples based on the clustering strategy by executing the clustering module; analyze the data associated with the one or more clusters received from the clustering module using the optimization strategy asso

Abstract: Transactional risk and return analysis systems provided herein include a transaction database and a market database. The transaction database includes data regarding transactions with associated attributes and the market database includes market data. A portfolio model uses such data to estimate a risk prediction for each transaction. A risk prediction model is generated based on the portfolio model and estimates a risk prediction for a prospective transaction, and a case cash flow analyzer produces a risk-breakeven spread. A transaction evaluator uses the risk prediction model and the risk-breakeven spread to calculated transaction risk and return data for a prospective transaction.