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https://www.selleckchem.com/products/aebsf-hcl.html A two-fold personalized feedback mechanism is established for consensus reaching in social network group decision-making (SN-GDM). It consists of two stages 1) generating the trusted recommendation advice for individuals and 2) producing a a personalized adoption coefficient for reducing unnecessary adjustment costs. A uninorm interval-valued trust propagation operator is developed to obtain an indirect trust relationship, which is used to generate personalized recommendation advice based on the principle of ``a recommendation being more acceptable the higher the level of trust it derives from.'' An optimization model is built to minimize the total adjustment cost of reaching consensus by determining the personalized feedback adoption coefficient based on individuals' consensus levels. Consequently, the proposed two-fold personalized feedback mechanism achieves a balance between group consensus and individual personality. An example to demonstrate how the proposed two-fold personalized feedback mechanism works is included, which is also used to show its rationality by comparing it with the traditional feedback mechanism in group decision making (GDM). We studied joint acoustical emissions in loaded and unloaded knees and investigated their characteristics as digital biomarkers for evaluating knee health status during the course of treatment in patients with juvenile idiopathic arthritis (JIA). Knee acoustic emissions were recorded from 38 study participants including 20 subjects with JIA and 18 healthy controls. Ten of the subjects with JIA had a follow-up recording, 36 months after initial measurements. Each subject performed 10 repetitions of unloaded flexion/extension (FE) and multi-joint weighted movements involving knee and hip flexion/extension (squat) exercises. The recorded acoustical signals were divided into movement cycles and processed to extract 72 features, and a novel algorithm was developed to detec

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