Abstract: Systems disclosed herein facilitate generating no-code applications and enable untrained users to develop no-code applications by using machine learning based recommendation systems to recommend no-code components to users. The systems can recommend no-code components during the development of a no-code application and/or after publication of a no-code application. The systems may recommend replacements of selected no-code components. Further, the systems may recommend configurations or alternative configurations of selected no-code components. The recommendations may be determined by using a prediction model trained using one or more machine learning models or algorithms. In some cases, the prediction model may be an ensemble model that uses the results of a plurality of prediction models or machine learning algorithms to make a recommendation.

Abstract: An information system for question answering, information retrieval, and location discovery. The information system is configured to receive an input inquiry from an input user device, generate input inquiry metadata from the input inquiry, determine an input location of the input user device, and determine one or more points of interest within a first predetermined distance from the input location. The system is further configured to detect output user devices within a predetermined output distance from the one or more points of interest, share the input inquiry with the detected output user devices, and receive one or more responses from the output user devices within the predetermined output distance from the one or more points of interest.

Abstract: A method in aerial firefighting and to an aerial firefighting vehicle includes maintaining fire-extinguishing liquid in the aerial vehicle and maintaining solid liquid-absorbent preforms in the aerial vehicle. The method further includes subjecting at least part of the solid liquid-absorbent preforms to the fire-extinguishing liquid in the aerial vehicle such that the solid liquid-absorbent preforms subjected to the fire-extinguishing liquid absorb the fire-extinguishing liquid to form fire-extinguishing bombs from the solid liquid-absorbent preforms, the fire-extinguishing bombs being impregnated with the fire-extinguishing liquid, and releasing the fire-extinguishing bombs from the aerial vehicle for extinguishing or containing fire with the fire-extinguishing bombs.

Abstract: One or more processors receive resource type and capability information and activity information of workloads of a domain. A first model is generated and trained to map the resource information to the activity information of domain workloads. The activity information is decomposed into a set of activity core elements (ACEs). The one or more processors generate a second model, wherein the second model is trained to predict a set of resource types and resource capabilities of the respective resource types, based on an input of the first set of ACEs decomposed from the activity information of the workloads of the domain. The one or more processors receive a second set of ACEs that are decomposed from activities associated with an unprecedented workload, and the one or more processors generate a predicted set of resources to perform the second set of ACEs.

Abstract: One or more processors receive resource type and capability information and activity information of workloads of a domain. A first model is generated and trained to map the resource information to the activity information of domain workloads. The activity information is decomposed into a set of activity core elements (ACEs). The one or more processors generate a second model, wherein the second model is trained to predict a set of resource types and resource capabilities of the respective resource types, based on an input of the first set of ACEs decomposed from the activity information of the workloads of the domain. The one or more processors receive a second set of ACEs that are decomposed from activities associated with an unprecedented workload, and the one or more processors generate a predicted set of resources to perform the second set of ACEs.