Comparison-embedded evidence-CNN model for fuzzy assessment of wear severity using multi-dimensional surface images

Wear topography is a significant indicator of tribological behavior for the inspection of machine health conditions.An intelligent in-suit wear assessment method for random topography is here proposed.Three-dimension(3D)topography is employed to address the uncertainties in wear evaluation.Initially,3D topography reconstruction from a worn surface is accomplished with photometric stereo vision(PSV).Then,the wear features are identified by a contrastive learning-based extraction network(WSFE-Net)including the relative and temporal prior knowledge of wear mechanisms.Furthermore,the typical wear degrees including mild,moderate,and severe are evaluated by a wear severity assessment network(WSA-Net)for the probability and its associated uncertainty based on subjective logic.By integrating the evidence information from 2D and 3D-damage surfaces with Dempster–Shafer(D–S)evidence,the uncertainty of severity assessment results is further reduced.The proposed model could constrain the uncertainty below 0.066 in the wear degree evaluation of a continuous wear experiment,which reflects the high credibility of the evaluation result.

B-type natriuretic peptide in predicting the severity of community-acquired pneumonia

BACKGROUND: Although pneumonia severity index(PSI) is widely used to evaluate the severity of community-acquired pneumonia(CAP), the calculation of PSI is very complicated. The present study aimed to evaluate the role of B-type natriuretic peptide(BNP) in predicting the severity of CAP.METHODS: For 202 patients with CAP admitted to the emergency department, BNP levels, cardiac load indexes, inf lammatory indexes including C-reactive protein(CRP), white blood cell count(WBC), and PSI were detected. The correlation between the indexes and PSI was investigated. BNP levels for survivor and non-survivor groups were compared, and a receiver operating characteristic(ROC) curve analysis was performed on the BNP levels versus PSI.RESULTS: The BNP levels increased with CAP severity(r=0.782, P<0.001). The BNP levels of the high-risk group(PSI classes IV and V) were signifi cantly higher than those of the low-risk group(PSI classes I–III)(P<0.001). The BNP levels were signifi cantly higher in the non-survivor group than in the survivor group(P<0.001). In addition, there were positive correlations between BNP levels and PSI scores(r=0.782, P<0.001). The BNP level was highly accurate in predicting the severity of CAP(AUC=0.952). The optimal cut-off point of BNP level for distinguishing high-risk patients from low-risk ones was 125.0 pg/m L, with a sensitivity of 0.891 and a specifi city of 0.946. Moreover, BNP level was accurate in predicting mortality(AUC=0.823). Its optimal cut-off point for predicting death was 299.0 pg/m L, with a sensitivity of 0.675 and a specifi city of 0.816. Its negative predictive cut-off value was 0.926, and the positive predictive cut-off value was 0.426.CONCLUSION: BNP level is positively correlated with the severity of CAP, and may be used as a biomarker for evaluating the severity of CAP.

Fuzzy Based Decision Making Approach for Evaluating the Severity of COVID-19 Pandemic in Cities of Kingdom of Saudi Arabia

The World Health Organization declared COVID-19 a pandemic on March 11,2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of lives.COVID-19 has now affected millions across 211 countries in the world and the numbers continue to rise.The information discharged by the WHO till June 15,2020 reports 8,063,990 cases of COVID-19.As the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug,the nations are relentlessly working at the most ideal preventive systems to contain the infection.The Kingdom of Saudi Arabia(KSA)is additionally combating with the COVID-19 danger as the cases announced till June 15,2020 reached the count of 132,048 with 1,011 deaths.According to the report released by the KSA on June 14,2020,more than 4,000 cases of COVID-19 pandemic had been registered in the country.Tending to the impending requirement for successful preventive instruments to stem the fatalities caused by the disease,our examination expects to assess the severity of COVID-19 pandemic in cities of KSA.In addition,computational model for evaluating the severity of COVID-19 with the perspective of social influence factor is necessary for controlling the disease.Furthermore,a quantitative evaluation of severity associated with specific regions and cities of KSA would be a more effective reference for the healthcare sector in Saudi Arabia.Further,this paper has taken the Fuzzy Analytic Hierarchy Process(AHP)technique for quantitatively assessing the severity of COVID-19 pandemic in cities of KSA.The discoveries and the proposed structure would be a practical,expeditious and exceptionally precise evaluation system for assessing the severity of the pandemic in the cities of KSA.Hence these urban zones clearly emerge as the COVID-19 hotspots.The cities require suggestive measures of health organizations that must be introduced on a war footing basis to counter the pandemic.The analysis tabulated in our study will assist in mapping the rules and building a systematic structure that is immediate need in the cities with high severity levels due to the pandemic.

No-reference image quality assessment based on nonsubsample shearlet transform and natural scene statistics

A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform(NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features: coefficient distribution, energy distribution and structural correlation(SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine(SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error(RMSE) with human perception than other high performance NR IQA methods.

Evaluation of the scales assessing the severity of myasthenia gravis

Objective To evaluate the reliability and validity of the 4 myasthenia gravis(MG)scales widely used for assessing the grades of disease severity in Chinese MG patients.Methods Sixty MG patients were examined by a neurologist with the following four MG scales:Quantitative Myasthenia Gravis Score(QMGS),Myasthenia Gravis Composite(MGC),Myasthenic Muscle Scale(MMS),Absolute and Relative Score of MG(ARS-