Exploring the Role of Perceived Word-of-Mouth Source Credibility and Brand Involvement in Online Negative Word-of-Mouth: An examination of Outcomes and Processes

This research studies the role of Perceived Word-of-Mouth Source Credibility and Brand Involvement in the process of online negative Word-of-Mouth spreading, and this research also examines the behavioral and non-behavioral outcomes of online negative Word-of-Mouth. This research also looks at the process of online negative Word-of-Mouth spreading. This article uses two-way ANOVAs to examine the interaction effects of Perceived Word-of-Mouth Source Credibility and Brand Involvement on the Perceived Usefulness of negative Word-of-Mouth, and uses the Baron and Kenny’s method to test the mediation effect of Perceived Usefulness of negative Word-of-Mouth on the relationship of Perceived WOM Credibility and Brand Involvement’s interaction effect with behavioral and non-behavioral outcomes. This research has economic significance and can help brand managers evaluate the Processes and Outcomes of the Online Negative Word-of-Mouth and the importance of Perceived Word-of-Mouth Source Credibility and Brand Involvement

Traditional Chinese Herbal Patch for Short-Term Management of Knee Osteoarthritis: A Randomized, Double-Blind, Placebo-Controlled Trial

Objective. To assess the short-term efficacy and safety of two kinds of Traditional Chinese herbal patches, Fufang Nanxing Zhitong Gao (FNZG) and Shangshi Jietong Gao (SJG), for painful knee osteoarthritis (OA). Methods. Patients were randomly enrolled in a double-blind, placebo-controlled study to receive FNZG (n=60), SJG (n=60), or placebo patch (n=30) for 7 days. Outcome measures included visual analogue scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Traditional Chinese Medicine Syndrome Questionnaire (TCMSQ) subscale. Results. Although there was no significant difference among, three groups in short-term pain management, patients receiving FNZG got significant improvement in symptom of fear of coldness as compared with placebo patch (P=0.029). The most common local adverse events of rash, itching, erythema, and slightly damaged skin were observed in 7% of participants. Conclusions. FNZG may be a useful treatment for symptom of knee OA and merits long-term study in broader populations

Research on Index System for Disabled Elders Evaluation and Grey Clustering Model Based on End-point Mixed Possibility Functions

The file attached to this record is the Publisher's final version.An operational ability assessment system for older adults is of great help to address health and social challenges for ageing. In this paper, the main problems in currently available ADL and ability evaluation systems have been analyzed. The basic principles to build an index system for disability elders evaluation have been put forwarded. Then，an improved Barthel index system for ADL evaluation and a new older adults ability evaluation system consisted of 4 first-level indexes and 14 secondary indexes based on experts’ opinion and the ability assessment system for older adults by Ministry of Civil Affairs of China have been built. The grey clustering model based on end-point mixed triangular possibility function has been introduced. And three living examples of adults’ disability evaluation have been conducted. It is confirmed clearly that the three older adults belong to different categories of "severe disability", "mild disability", and "ability passable" respectively. The research results can be used as reference for government to formulate the elderly-care policies, to run and allocate the elderly-care resources, as well as reference for various nursing or elderly-care institutions

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Stable light-bullet solutions in the harmonic and parity-time-symmetric potentials

Analytical light-bullet solutions of a (3+1)-dimensional nonlinear Schrödinger equation with inhomogeneous diffraction or dispersion and nonlinearity in the presence of the harmonic and parity-time-symmetric potentials are explored. Diffraction or dispe

Robust Object Positioning for Visual Robotics in Automatic Assembly Line under Data-Scarce Environments

Object positioning is a basic need for visual robotics in automatic assembly lines. An assembly line requires fast transfer to new object positioning tasks with few or no training data for deep learning algorithms, and the captured visual images usually suffer from partial missing and cropping and environmental lighting interference. These features call for efficient and robust arbitrary shape positioning algorithms under data-scarce and shape distortion cases. To this end, this paper proposes the Random Verify Generalised Hough Transform (RV-GHT). The RV-GHT builds a much more concise shape dictionary than traditional GHT methods with just a single training image. The location, orientation, and scaling of multiple target objects are given simultaneously during positioning. Experiments were carried out on a dataset in an automatic assembly line with real shape distortions, and the performance was improved greatly compared to the state-of-the art methods. Although the RV-GHT was initially designed for vision robotics in an automatic assembly line, it works for other object positioning mechatronics systems, which can be modelled as shape distortion on a standard reference object

Robust Object Positioning for Visual Robotics in Automatic Assembly Line under Data-Scarce Environments

Object positioning is a basic need for visual robotics in automatic assembly lines. An assembly line requires fast transfer to new object positioning tasks with few or no training data for deep learning algorithms, and the captured visual images usually suffer from partial missing and cropping and environmental lighting interference. These features call for efficient and robust arbitrary shape positioning algorithms under data-scarce and shape distortion cases. To this end, this paper proposes the Random Verify Generalised Hough Transform (RV-GHT). The RV-GHT builds a much more concise shape dictionary than traditional GHT methods with just a single training image. The location, orientation, and scaling of multiple target objects are given simultaneously during positioning. Experiments were carried out on a dataset in an automatic assembly line with real shape distortions, and the performance was improved greatly compared to the state-of-the art methods. Although the RV-GHT was initially designed for vision robotics in an automatic assembly line, it works for other object positioning mechatronics systems, which can be modelled as shape distortion on a standard reference object

Efficient Near-Field Radiofrequency Imaging of Impact Damage on CFRP Materials with Learning-Based Compressed Sensing

Carbon fiber-reinforced polymer (CFRP) is a widely-used composite material that is vulnerable to impact damage. Light impact damages destroy the inner structure but barely show obvious change on the surface. As a non-contact and high-resolution method to detect subsurface and inner defect, near-field radiofrequency imaging (NRI) suffers from high imaging times. Although some existing works use compressed sensing (CS) for a faster measurement, the corresponding CS reconstruction time remains high. This paper proposes a deep learning-based CS method for fast NRI, this plugin method decreases the measurement time by one order of magnitude without hardware modification and achieves real-time imaging during CS reconstruction. A special 0/1-Bernoulli measurement matrix is designed for sensor scanning firstly, and an interpretable neural network-based CS reconstruction method is proposed. Besides real-time reconstruction, the proposed learning-based reconstruction method can further reduce the required data thus reducing measurement time more than existing CS methods. Under the same imaging quality, experimental results in an NRI system show the proposed method is 20 times faster than traditional raster scan and existing CS reconstruction methods, and the required data is reduced by more than 90% than existing CS reconstruction methods