First Evaluation and Frequency Measurement of the Strontium Optical Lattice Clock at NIM

An optical lattice clock based on 87Sr is built at National Institute of Metrology （NIM） of China. The systematic frequency shifts of the clock are evaluated with a total uncertainty of 2.3×10-16. To measure its absolute frequency with respect to NIM＇s cesium fountain clock NIM5, the frequency of a flywheel H-maser of NIM5 is transferred to the Sr laboratory through a 50-kin-long fiber. reference frequency of this H-maser, is used for the optical this Sr clock is measured to be 429228004229873.7（1.4）Hz. A fiber optical frequency comb, phase-locked to the frequency measurement. The absolute frequency of

Multilevel Tests and Measurement Evaluation Methods for the Application of Composite Materials in Spacecraft Structures

With the implementation of new-generation launch vehicles,space stations,lunar and deep space exploration,etc.,the development of spacecraft structures will face new challenges. In order to reduce the spacecraft weight and increase the payload,composite material structures will be widely used. It is difficult to evaluate the strength and life of composite materials due to their complex mechanism and various phenomena in damage and failure.Meanwhile,the structures of composite materials used in spacecrafts will bear complex loads,including the coupling loads of tension,pressure,bending,shear,and torsion. Static loads,thermal loads,and vibration loads may occur at the same time,which asks for verification requirements to ensure the structure safety. Therefore,it is necessary to carry out a systematic multi-level experimental study. In this paper,the building block approach (BBA) is used to investigate the multilevel composite material structures for spacecrafts. The advanced measurement technology is adopted based on digital image correlation (DIC) and piezoelectric and optical fiber sensors to measure the composite material structure deformation. The virtual experiment technology is applied to provide sufficient and reliable data for the evaluation of the composite material structures of spacecrafts.

Evaluation of myocardial viability during cold storage by measurement of myocardual dielectric properties tanδm in radio frequency

Objective To study the mechanism of myocardial dielectric property changes in radio frequency during hypothermic preservation and explore myocardial viability evaluative method. Methods Hybrid young pigs ( 20 - 30 kg) were used in the experiment. Heart arrest was in-

A Software Quality Evaluation System: JT SQE

JT SQE system is a software quality and measurement system. Its design was based on the Chinese national standards of software product evaluation and quality characteristics. The JT SQE system consists of two parts. One is the model for software quality measurement, which is of hierarchical structure. The other is the process of requirements definition, measurement and rating. The system is a feasible model for software quality evaluation and measurement, and it has the advantage of a friendly user interface, simple operation, ease of revision and maintenance, and expansible measurements.

Corrosion Resistance of Ni-P Composite Coating on Mg Alloy:Critical Role of the Intermediate Phosphate Conversion Coating Layer for Suppressing Galvanic Couple Formation

Electroless nickel(EN)plating can give rise to the severe galvanic corrosion of the magnesium(Mg)alloy matrix,owing to its nobler electrochemical potential than Mg alloy.To hinder the formation of galvanic couple,an intermediate phosphate conversion coating(PCC)layer is introduced between the EN layer and the Mg alloy matrix.Since the ceramic-like PCC layer cannot be catalyzed,a low-cost Ag-activation technique is used to process the PCC layer before electroless plating.The cross-section morphology and element distribution of the PCC-EN composite coating indicate that the PCC intermediate layer can effectively separate the Mg alloy from the EN layer.Moreover,the results of electrochemical tests suggest that the PCC-EN composite coating has a better corrosion resistance in comparison with the EN coating and AZ91D Mg alloy.

Heart Disease Risk Prediction Expending of Classification Algorithms

Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early on.Heart failure is on the rise as a result of today’s lifestyle.The healthcare business generates a vast volume of patient records,which are challenging to manage manually.When it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful information.Several methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment measures.However,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning repository.In a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from UCI.The SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.

Objective visual performance evaluation with visual evoked potential measurements based on an adaptive optics system

An objective visual performance evaluation with visual evoked potential （VEP） measurements was first inte- grated into an adaptive optics （AO） system. The optical and neural limits to vision can be bypassed through this system. Visual performance can be measured electrophysiologically with VEP, which reflects the objective func- tion from the retina to the primary visual cortex. The VEP ts without and with AO correction were preliminarily carried out using this system, demonstrating the great potential of this system in the objective visual performance evaluation. The new system will provide the necessary technique and equipment support for the further study of human visual function.

A measurable evaluation method of visual comfort in underground space by intelligent sorting and classification algorithms

Based on human perception and machine learning methods,this study proposes a measurable method for evaluating visual comfort in underground spaces.First,a comfort evaluation index based on the characteristics of human visual perception is proposed,and color features and segmentation extraction methods for intelligent methods are given.Then,using probability statistics and machine learning methods,a multi-class intelligent sorting and classification algorithm for ranking visual comfort levels is constructed and a comparison is made of the suitability of different intelligent methods for evaluating visual comfort.The random forest algorithm is then selected as the most effective measurable intelligent evaluation algorithm for underground spaces.Finally,the proposed method is compared to intelligent methods employed by previous research,and a case study,the Wujiaochang underground space in Shanghai,China,is applied as the background.Results show that the proposed method can effectively improve the quantification and refinement of human perception and evaluation of comfort in underground spaces;this method will also be useful in computer-aided generative design in the future.

Bug Prioritization to Facilitate Bug Report Triage

The large number of new bug reports received in bug repositories of software systems makes their management a challenging task.Handling these reports manually is time consuming,and often results in delaying the resolution of important bugs.To address this issue,a recommender may be developed which automatically prioritizes the new bug reports.In this paper,we propose and evaluate a classification based approach to build such a recommender.We use the Na¨ ve Bayes and Support Vector Machine （SVM） classifiers,and present a comparison to evaluate which classifier performs better in terms of accuracy.Since a bug report contains both categorical and text features,another evaluation we perform is to determine the combination of features that better determines the priority of a bug.To evaluate the bug priority recommender,we use precision and recall measures and also propose two new measures,Nearest False Negatives （NFN） and Nearest False Positives （NFP）,which provide insight into the results produced by precision and recall.Our findings are that the results of SVM are better than the Na¨ ve Bayes algorithm for text features,whereas for categorical features,Na¨ ve Bayes performance is better than SVM.The highest accuracy is achieved with SVM when categorical and text features are combined for training.