Wine component tracing method based on near infrared spectrum fusion machine learning

An intelligent wine detection and traceability method based on infrared spec-troscopy and machine learning is proposed, in order to meet the needs of online rapid nondestructive testing of wine. On the basis of extracting infrared spectrum of wine, the principal component analysis (PCA) – support vector machine (SVM) model was modified by chemometrics. A total of 300 grape wine samples were collected from six production areas. The composition of the samples was analyzed by ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). According to the experimental results, indole, sulfacetamide and caffeine were selected as characteristics of different origins. Near infrared spectral wavelengths of wine samples were compressed between 900 and 2,500 nm. The ranges of 1,000 nm ~ 1,400 nm and 1,500 nm ~ 1800 nm were selected for PCA principal component analysis and key spectral wavelengths were extracted. The unsupervised learning model of SVM is used to classify and identify key spectral wavelengths. The experimental results show that the algorithm has higher classification accuracy than traditional PCA-LDA, PCA and other algorithms. The classification accuracy of the algorithm is improved from 98.3 to 99.75%. The improved PCA-SVM algorithm can achieve fast and loss-less source tracing of wine

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (S ( I ), THI, and SHI, where S ( I ) is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (S ( I ),THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services

Disorder induced multifractal superconductivity in monolayer niobium dichalcogenides

The interplay between disorder and superconductivity is a subtle and fascinating phenomenon in quantum many body physics. The conventional superconductors are insensitive to dilute nonmagnetic impurities, known as the Anderson's theorem. Destruction of superconductivity and even superconductor-insulator transitions occur in the regime of strong disorder. Hence disorder-enhanced superconductivity is rare and has only been observed in some alloys or granular states. Because of the entanglement of various effects, the mechanism of enhancement is still under debate. Here we report well-controlled disorder effect in the recently discovered monolayer NbSe$_2$ superconductor. The superconducting transition temperatures of NbSe$_2$ monolayers are substantially increased by disorder. Realistic theoretical modeling shows that the unusual enhancement possibly arises from the multifractality of electron wave functions. This work provides the first experimental evidence of the multifractal superconducting state

Femoral–tibial contact stresses on fixed rotational femur models

ObjectivesThis study aims to quantitatively evaluate the femoral–tibial contact pressure on the knee under certain malrotaional degrees.MethodsFemoral–tibial contact pressure was carried out on 14 fixed rotational knee models under 200/400/600 N vertical load using ultra-low-pressure sensitive film technology, rotation angles including neutral position (0°, anatomically reduced), 5°, 10°, and 15° internally and externally. Data were collected and analyzed with SPSS software.ResultsThere are significant statistical differences between the medial contact pressure among rotational deformities (including neutral position) (P < 0.01), the increase in the degree of fixed internal malrotation of the femur resulted in a linear increase in the medial femoral–tibial contact pressures (P < 0.05) under 200/400/600 N vertical load, while increase in the degree of fixed external malrotation resulted in a linear decrease (P < 0.05). Except the 200 N compression, we can't find significant differences in lateral contact pressures (P > 0.05). In the comparison of medial to lateral contact pressures, no statistically significant differences were found in neutral and 5° internal rotation under 200/400 N, neutral, 5° internal rotation, and 15° external rotation under 600 N. In contrast, medial contact pressures were higher than lateral at other angles (P < 0.05).ConclusionObvious contact pressure changes were observed in rotatory femur. Doctors should detect rotational deformity as much as possible during operation and perform anatomical reduction. For patients with residual rotational deformities, indication of osteotomy should not be too broad