An algorithm and method to provide personal recommendations for nutrition based on preferences, habits, medical and activity profiles for users, and constraints. The algorithm can also be fed and takes into account real-time feedback from the user. The method allows creating a personal nutritional schedule based on a set of constraints, which are solved using an optimization algorithm to find the diet best fitting each user. The method also includes analyzing a single user by applying various statistical techniques, enabling the algorithm to infer the user's preferences and updating of the constraints, analyzing and clustering of the general user population based on statistical principles, giving the algorithm insightful information and allowing improved performance by means of “machine-learning,” and creating a list of recommended food items/recipes to help users live a balanced, healthier lifestyle.

In one aspect, A computerized method for implementing a virtualized training session user interface (UI) with respect to a production software UI includes the step of providing a production software application. The method includes displaying a guide on a production software UI. A workflow and a tutorial content of the guide is determined dynamically using one or more specified machine-learning algorithms. The method includes displaying the guide as a set of images with a virtual lab placed on top of each image of the guide. The method includes receiving a user input comprising a learning-related data inside a virtual environment of the guide.

A cleanup support system that supports a cleanup behavior includes: a first obtaining unit configured to obtain first information indicating a level of interest of a target person in cleanup; a second obtaining unit configured to obtain second information indicating a level of achievement of the cleanup performed by the target person; a determination unit configured to determine a content of control corresponding to the first information obtained and the second information obtained, with reference to a rule which associates the level of interest in the cleanup and the level of achievement of the cleanup with a content of control performed on a device; and a control unit configured to control the device according to the content of control determined.

A cleanup support system that supports a cleanup behavior includes: a first obtaining unit configured to obtain first information indicating a level of interest of a target person in cleanup; a second obtaining unit configured to obtain second information indicating a level of achievement of the cleanup performed by the target person; a determination unit configured to determine a content of control corresponding to the first information obtained and the second information obtained, with reference to a rule which associates the level of interest in the cleanup and the level of achievement of the cleanup with a content of control performed on a device; and a control unit configured to control the device according to the content of control determined.

An intelligent adaptive recommendation method based on an MCM model. The method includes acquiring historical data of errors on knowledge points of all students, acquiring error-cause labels of current student, calculating error-cause priority value P(E) for each error-cause label of current student, and extracting, according to at least one of MCM labels corresponding to each error-cause label, MCM learning resources corresponding to at least one of MCM labels from a preset content management system, sorting error-cause labels according to descending order of error-cause priority value P(E), extracting part or all of MCM learning resources from MCM learning resources corresponding to at least one error-cause label according to sorting result and pushing part or all of MCM learning resources to current student, and when current student finishes learning MCM learning resources corresponding to each MCM label, pushing errors on knowledge points corresponding to each MCM label to current student.

An intelligent adaptive recommendation method based on an MCM model. The method includes acquiring historical data of errors on knowledge points of all students, acquiring error-cause labels of current student, calculating error-cause priority value P(E) for each error-cause label of current student, and extracting, according to at least one of MCM labels corresponding to each error-cause label, MCM learning resources corresponding to at least one of MCM labels from a preset content management system, sorting error-cause labels according to descending order of error-cause priority value P(E), extracting part or all of MCM learning resources from MCM learning resources corresponding to at least one error-cause label according to sorting result and pushing part or all of MCM learning resources to current student, and when current student finishes learning MCM learning resources corresponding to each MCM label, pushing errors on knowledge points corresponding to each MCM label to current student.

A handwash monitoring system includes: an imaging device; and a processor. The processor detects a first candidate abnormality existing in a hand of a user from a first image captured by the imaging device before handwashing, and detects a second candidate abnormality existing in the hand of the user from a second image captured by the imaging device after the handwashing. The processor determines a type of an abnormality on the hand of the user based on a difference between a shape of the first candidate abnormality and a shape of the second candidate abnormality wherein the first candidate abnormality and the second candidate abnormality are detected from an identical region.

Systems and methods are disclosed for analysis of user behavior data to improve security awareness. User behavior data of an organization is received from one or more agents on endpoint devices accessed by the users and using the user behavior data, one or more risk scores representative of the severity of risk associated with the user behavior of the users are determined. Based on the one or more risk scores representative of the severity of risk associated with the user behavior of the users, the behavior of the is determined to pose a security risk to the organization, In response to the determination that the user behavior of the users of the organization poses a security risk to the organization, electronic security awareness training is delivered to the users.

A computer-implemented method for employing a surgical instrument educational platform to train medical personnel to identify, characterize, and organize surgical instrumentation and surgical instrumentation trays includes selecting a level from one or more levels on a display of an electronic device, selecting a surgical category from a plurality of surgical categories, in response to selecting the surgical category, an image of a surgical instrument is displayed on the display of the electronic device and a query is made to identify the image of the surgical instrument, loading surgical instrument options to allow a user to associate the image of the surgical instrument to one of the surgical instrument options, prompting the user to select an option from the surgical instrument options, and in response to the selected option by the user, outputting a result.

An external defibrillator system includes one or more compression sensors; one or more physiological sensors; and at least one processor. The at least one processor is configured to: receive and process chest compression signals and physiological signals from the sensors, determine values for chest compression depth and/or chest compression rate based on the received chest compression signals, determine a trend of at least one physiological parameter over a period comprising multiple chest compressions based on the received physiological signals, adjust a target chest compression depth and/or target chest compression rate based on the determined trend of the at least one physiological parameter, compare the determined values for chest compression depth and/or chest compression rate to the adjusted target compression depth and/or the adjusted target compression rate, and provide feedback about the quality of chest compressions performed on the patient.