Abstract: A method for measuring drop time of a control rod cluster integrated with a rod position measurement device is provided, wherein the method is used to measure the drop time of each control rod cluster, and includes: Si, monitoring a voltage Ua of coils in Group A to capture a rod cluster drop signal; S2, searching a point (tmax, Vmax) with a maximum drop speed or with a local maximum drop speed; S3, retroactively calculating, from tmax, an end of a time period T4 when the control rod cluster starts to drop; S4, retroactively searching, from a minimum value point of a drop reference signal DROPref, a start of the time period T4 when the drop reference signal DROPref drops from a maximum value to 33% thereof; and S5, determining, from tmax forward, a time point t6 when a drop speed of the control rod cluster is lower than 0.

Abstract: An interaction data processing method performed by an object device includes displaying a social interface, receiving a preset operation performed on a target social object among the at least one social object, and aggregating and displaying one or more social messages transmitted by the target social object in the social interface.

Abstract: Embodiments of the present invention are directed to physiological sensing in a wearable device. Aspects include generating a multi-faceted feedback for a user. Generating the multi-faceted feedback includes generating a baseline physiological sampling schedule for a user, generating a quality-aware feedback of the user, generating a user-state-aware feedback of the user, and generating a context-aware feedback of the user. Aspects also include generating an adjusted physiological sampling schedule for the user based at least in part upon the multi-faceted feedback.

Abstract: Instruments of determined level of self-efficacy may be chosen dynamically for customized patient engagement, e.g., based on a patient's latent adherence trait estimated from lifestyle and other clinical data. Customization points in care plans may be identified, e.g., by monitoring an accumulative change in the patient's health literacy level. Clinical decision support at the point of care may be provided by adjusting patient engagement strategies and allocating resources accordingly.

Abstract: Instruments of determined level of self-efficacy may be chosen dynamically for customized patient engagement, e.g., based on a patient's latent adherence trait estimated from lifestyle and other clinical data. Customization points in care plans may be identified, e.g., by monitoring an accumulative change in the patient's health literacy level. Clinical decision support at the point of care may be provided by adjusting patient engagement strategies and allocating resources accordingly.

Abstract: Embodiments of the present invention are directed to physiological sensing in a wearable device. Aspects include generating a multi-faceted feedback for a user. Generating the multi-faceted feedback includes generating a baseline physiological sampling schedule for a user, generating a quality-aware feedback of the user, generating a user-state-aware feedback of the user, and generating a context-aware feedback of the user. Aspects also include generating an adjusted physiological sampling schedule for the user based at least in part upon the multi-faceted feedback.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for stress monitoring. Methods include receiving, by a processor, a plurality of user stress labels corresponding to a plurality of events. Methods also include determining an individualized stress profile based at least in part upon the user stress labels. Methods also include receiving heart rate sensor data from a wearable device. Methods also include extracting a cardiovascular feature from the heart rate sensor data. Methods also include determining a stress index based at least in part upon the individualized stress profile and the cardiovascular feature. Methods also include outputting the stress index.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for stress monitoring. Methods include receiving, by a processor, a plurality of user stress labels corresponding to a plurality of events. Methods also include determining an individualized stress profile based at least in part upon the user stress labels. Methods also include receiving heart rate sensor data from a wearable device. Methods also include extracting a cardiovascular feature from the heart rate sensor data. Methods also include determining a stress index based at least in part upon the individualized stress profile and the cardiovascular feature. Methods also include outputting the stress index.

Abstract: A method and system for tracking eye recovery. The method includes: receiving eye data from an eyewear, wherein the eye data includes images acquired by an image capturing device in the eyewear; analyzing the eye data to determine an eye recovery status, wherein the analyzing is carried out by comparing the eye data to an initial set of eye conditions; and providing a notification of the eye recovery status.

Abstract: A method and system for tracking eye recovery. The method includes: receiving eye data from an eyewear, wherein the eye data includes images acquired by an image capturing device in the eyewear; analyzing the eye data to determine an eye recovery status, wherein the analyzing is carried out by comparing the eye data to an initial set of eye conditions; and providing a notification of the eye recovery status.

Abstract: A method and system for tracking eye recovery. The method includes: receiving eye data from an eyewear, wherein the eye data includes images acquired by an image capturing device in the eyewear; analyzing the eye data to determine an eye recovery status, wherein the analyzing is carried out by comparing the eye data to an initial set of eye conditions; and providing a notification of the eye recovery status.

Abstract: Instruments of determined level of self-efficacy may be chosen dynamically for customized patient engagement, e.g., based on a patient's latent adherence trait estimated from lifestyle and other clinical data. Customization points in care plans may be identified, e.g., by monitoring an accumulative change in the patient's health literacy level. Clinical decision support at the point of care may be provided by adjusting patient engagement strategies and allocating resources accordingly.

Abstract: Instruments of determined level of self-efficacy may be chosen dynamically for customized patient engagement, e.g., based on a patient's latent adherence trait estimated from lifestyle and other clinical data. Customization points in care plans may be identified, e.g., by monitoring an accumulative change in the patient's health literacy level. Clinical decision support at the point of care may be provided by adjusting patient engagement strategies and allocating resources accordingly.

Abstract: A system and computer program product for guideline-based food purchase management include generating a personalized set of nutrition guidelines for a user, generating a user profile for the user, wherein the user profile comprises health information and nutrition preferences, and using the personalized set of nutrition guidelines and the user profile to generate a guideline-based shopping list for the user.

Abstract: A method and system for tracking eye recovery. The method includes: receiving eye data from an eyewear, wherein the eye data includes images acquired by an image capturing device in the eyewear; analyzing the eye data to determine an eye recovery status, wherein the analyzing is carried out by comparing the eye data to an initial set of eye conditions; and providing a notification of the eye recovery status.

Abstract: A method of determining an optimal treatment includes determining a frequency for each health care provider indicating how frequently each treats a selected disease, determining for each health care provider, their average patient outcome APO for treating the selected disease, determining a score for each health care provider based on the corresponding frequency and APO, determining which of the health care providers are experts from the scores that exceed a predefined threshold, and selecting a treatment proscribed by at least one of the identified experts as the optimal treatment.

Abstract: A method of determining an optimal treatment includes determining a frequency for each health care provider indicating how frequently each treats a selected disease, determining for each health care provider, their average patient outcome APO for treating the selected disease, determining a score for each health care provider based on the corresponding frequency and APO, determining which of the health care providers are experts from the scores that exceed a predefined threshold, and selecting a treatment proscribed by at least one of the identified experts as the optimal treatment.

Abstract: A method for matching social network participants includes receiving activity data pertaining to a plurality of participants in a social network. The activity data is parsed to generate activity pattern summaries for each of the participants. The participants are clustered into a plurality of groups according to the activity pattern summaries. At least one participant of influence is determined within at least one group according to the activity data. A social connection is established within the social network between the determined participant of influence and at least one other participant clustered into the same group.

Abstract: A method for generating a lifestyle progression (LSP) plan for a patient subject includes collecting patient data including a list of exercise activities performed over a plurality of non-overlapping periods for a plurality of patients and patient health records. The collected patient data is clustered into related groups using k-mean clustering. An LSP model for each cluster is created by averaging the exercise activities performed and respective period durations. Patient data for a patient subject including patient health records is received. A vector is calculated for the received patient data. A shortest distance between the calculated vector for the received patient data and vectors calculated for each LSP model is found. An LSP is built for the patient subject bases on the LSP model with the shortest distance to the calculated vector for the received patient data.

Abstract: A context-aware decision-support system automatically selects the clinical guideline pertaining to the patient's medical care and automatically deduces the current stage in the guideline (S312, S336). The system tracks a patient's progress through the guideline to maintain a determination as to the current stage (S304, S336). Based on the current stage and patient-specific information, an unsolicited recommendation is presented on-screen, accompanied by one or more grades representing the level of evidence underlying the recommendation (S344).