Abstract: The present disclosure relates to a wind turbine for generating electrical energy from exhaust energy and a system thereof. The wind turbine comprises a plurality of 3D Sphero dynamic blades, a rotor, and a generator. Each of the plurality of blades is configured to receive exhaust air from air exhausting devices, such as, industrial exhaust fans or ventilators, and is configured to rotate. The rotation of the plurality of blades is self-controlled and generates non-uniform AC power. The system comprises a rectifier and an inverter to convert the non-uniform AC power into a uniform AC power, which is fed to a power grid or is used in the premises.

Abstract: This disclosure describes automatically collecting, analyzing, and remediating operational issues with respect to systems executing within a network. For example, a service provider network may include a monitoring service may generate notifications related to operational issues upon detection of operational issues within a system executing within the service provider network. The monitoring service may provide one or more notifications related to an aggregation service that may aggregate the one or more notifications into a standardized format. Contextual information related to the operational issues may be automatically gathered by an analytics service, which may analyze the contextual information to determine a potential cause of the operational issues. Based on the potential cause, a remediation service may automatically remediate the operational issues.

Abstract: Merchant quality may be inferred through machine learning techniques. A customer satisfaction classifier may receive data associated with a customer's engagement with a merchant, and may apply a machine learning model to the received data in order to infer a satisfaction of the customer with the merchant. The inferred satisfaction may be used to determine a rating of the merchant that is imputed to the customer.

Abstract: Disclosed are various embodiments for an order management service. Orders may be correlated with an existing order to create a group order. The group order may include items picked up or delivered to the same or geographically proximate locations. Incentives may be used to incite participation in a group order, thereby saving on overall delivery costs.

Abstract: Disclosed are various embodiments for an order management service. Orders may be correlated with an existing order to create a group order. The group order may include items picked up or delivered to the same or geographically proximate locations. Incentives may be used to incite participation in a group order, thereby saving on overall delivery costs.

Abstract: The systems and processes discussed herein may identify, prioritize, and recommend new deals to consumers based at least in part on triggering events. A consumer may interact with a previously acquired deal, such as by redeeming the deal, requesting a refund for the deal, etc. Such user interaction may be determined to be a triggering event. Based on a type of the triggering event, the systems described herein may identify one or more new deals having characteristics similar to the previously acquired deal. The one or more new deals may be recommended to a user at the same time or at some time after the triggering event occurs via a website, an e-mail message, an application associated with a user device, a text message, or any other manner that may be used to communicate the new deals to the user.