Correlation analysis of Trial of Org 10172 in acute stroke treatment classification and National Institutes of Health Stroke Scale score in acute cerebral infarction with risk factors.

OBJECTIVE: The aim of this study was to investigate the correlation between the Trial of Org 10172 in acute stroke treatment classification and the National Institutes of Health Stroke Scale score of acute cerebral infarction as well as acute cerebral infarction's risk factors. METHODS: The clinical data of 3,996 patients with acute cerebral infarction hospitalized in Hebei Renqiu Kangjixintu Hospital from January 2014 to November 2018 were analyzed retrospectively. According to Trial of Org 10172 in acute stroke treatment, they were divided into five groups: arteriosclerosis, cardio cerebral embolism, arterial occlusion, other causes, and unknown causes. Through questionnaire design, routine physical examination, and physical and chemical analysis of fasting venous blood samples, the risk factors were evaluated, and the correlation between Trial of Org 10172 in acute stroke treatment classification and National Institutes of Health Stroke Scale classification was analyzed using multivariate logistic regression. In addition, the relationship between National Institutes of Health Stroke Scale score and risk factors in different groups was compared, and the correlation between Trial of Org 10172 in acute stroke treatment classification and National Institutes of Health Stroke Scale score was analyzed. RESULTS: Multivariate logistic regression analysis showed that diabetes, atrial fibrillation or stroke history, age, and education level were related to Trial of Org 10172 in acute stroke treatment classification. In the National Institutes of Health Stroke Scale comparison, the scores of the cardio cerebral embolism group were significantly higher than those of the other four groups, and patients with diabetes, atrial fibrillation, or stroke history had a high share, especially atrial fibrillation (33.06%). CONCLUSIONS: The nerve function defect is more serious after acute cerebral infarction with cardiogenic cerebral embolism, indicating a poor prognosis.

Correlation analysis of Trial of Org 10172 in acute stroke treatment classification and National Institutes of Health Stroke Scale score in acute cerebral infarction with risk factors

SUMMARY OBJECTIVE: The aim of this study was to investigate the correlation between the Trial of Org 10172 in acute stroke treatment classification and the National Institutes of Health Stroke Scale score of acute cerebral infarction as well as acute cerebral infarction's risk factors. METHODS: The clinical data of 3,996 patients with acute cerebral infarction hospitalized in Hebei Renqiu Kangjixintu Hospital from January 2014 to November 2018 were analyzed retrospectively. According to Trial of Org 10172 in acute stroke treatment, they were divided into five groups: arteriosclerosis, cardio cerebral embolism, arterial occlusion, other causes, and unknown causes. Through questionnaire design, routine physical examination, and physical and chemical analysis of fasting venous blood samples, the risk factors were evaluated, and the correlation between Trial of Org 10172 in acute stroke treatment classification and National Institutes of Health Stroke Scale classification was analyzed using multivariate logistic regression. In addition, the relationship between National Institutes of Health Stroke Scale score and risk factors in different groups was compared, and the correlation between Trial of Org 10172 in acute stroke treatment classification and National Institutes of Health Stroke Scale score was analyzed. RESULTS: Multivariate logistic regression analysis showed that diabetes, atrial fibrillation or stroke history, age, and education level were related to Trial of Org 10172 in acute stroke treatment classification. In the National Institutes of Health Stroke Scale comparison, the scores of the cardio cerebral embolism group were significantly higher than those of the other four groups, and patients with diabetes, atrial fibrillation, or stroke history had a high share, especially atrial fibrillation (33.06%). CONCLUSIONS: The nerve function defect is more serious after acute cerebral infarction with cardiogenic cerebral embolism, indicating a poor prognosis.

Impact fault detection of gearbox based on variational mode decomposition and coupled underdamped stochastic resonance.

In the gear fault diagnosis, the emergence of periodic impulse components in vibration signals is an important symptom of gear failure. However, heavy background noise makes it difficult to extract the weak periodic impulse features. Therefore, the paper presents an impact fault detection method of gearbox by combining variational mode decomposition (VMD) with coupled underdamped stochastic resonance (CUSR) to extract the periodic impulse features. First, the adaptive VMD is presented to decompose the vibration signal into several intrinsic mode functions (IMFs), which can automatically determine the appropriate mode number according to the correlation kurtosis (CK) of decomposition results and extract the sensitive IMF component containing the main fault information. Next, the adaptive CUSR method is developed to analyze the selected sensitive IMF component, and the optimal system parameters are obtained by the genetic algorithm using the CK index as optimization objective function. Finally, the periodic impulse features are extracted by the output signal of CUSR system accurately. Experiments and engineering application verify the effectiveness and superiority of the proposed adaptive VMD-CUSR method for extracting the periodic impulse features in gear fault diagnosis compared to other methods.

Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery.

The Kernel-RX detector (KRXD) has attracted widespread interest in hyperspectral image processing with the utilization of nonlinear information. However, the kernelization of hyperspectral data leads to poor execution efficiency in KRXD. This paper presents an approach to the progressive line processing of KRXD (PLP-KRXD) that can perform KRXD line by line (the main data acquisition pattern). Parallel causal sliding windows are defined to ensure the causality of PLP-KRXD. Then, with the employment of the Woodbury matrix identity and the matrix inversion lemma, PLP-KRXD has the capacity to recursively update the kernel matrices, thereby avoiding a great many repetitive calculations of complex matrices, and greatly reducing the algorithm's complexity. To substantiate the usefulness and effectiveness of PLP-KRXD, three groups of hyperspectral datasets are used to conduct experiments.

A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs.

The kernel RX (KRX) detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in available KRX detectors. Accordingly, this paper presents a novel weighted spatial-spectral kernel RX (WSSKRX) detector and its parallel implementation on graphics processing units (GPUs). The WSSKRX utilizes the spatial neighborhood resources to reconstruct the testing pixels by introducing a spectral factor and a spatial window, thereby effectively reducing the interference of background noise. Then, the kernel function is redesigned as a mapping trick in a KRX detector to implement the anomaly detection. In addition, a powerful architecture based on the GPU technique is designed to accelerate WSSKRX. To substantiate the performance of the proposed algorithm, both synthetic and real data are conducted for experiments.

Advanced similarity metric and its application to the band selection issue of multiple spectra identification.

A multiple spectra oriented similarity metric, referred to as n-dimensional solid spectral angle (NSSA), is addressed in this study. NSSA extends the traditional spectral angle metric (SAM)-calculating two spectra's angle-to the calculation of the high-dimensional solid angle jointly constituted by a set of spectra with any number of bands. Some significant inherent properties of NSSA are also discussed. Furthermore, as a merit of NSSA, an NSSA-based band add-on (BAO-NSSA) band selection method is derived, which displays an advantage in capturing spectra absorption features-particularly for similar classes-by operating a set of spectra instead of distinct band variables. Finally, an annular architecture in contrast with hierarchical architecture is presented by embedding the idea of BAO-NSSA into the issue of multiple class identification. Comprehensive analyses were conducted on three real hyperspectral data sets with similar and distinct classes. The proposed approach is shown to be effective for eliminating redundant bands and improving the accuracy of spectra identification.