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Objective To investigate the changes of apolipoprotein A1 （ApoA1） and alkaline phosphatase in hepatitis B virus （HBV）-related liver cancer， and evaluate the diagnostic performance of alpha-fetoprotein （AFP） combined with other indexes for HBV-related liver cancer. Methods 1 089 patients with chronic hepatitis B， compensated cirrhosis， non-compensated cirrhosis post-hepatitis B and chronic B-related liver cancer were enrolled. Among them， 745 cases were included in non-liver cancer group， and 344 cases in the HBV-related liver cancer group （liver cancer group）. The influencing factors of HBV-related liver cancer were identified by univariate and multivariate Logistic regression analyses. The area under the receiver operating characteristic （ROC） curve （AUC） was adopted to evaluate the diagnostic efficacy of AFP combined with other serological indexes in HBV-related liver cancer. Results Significant differences were observed in serum levels of ApoA1， alkaline phosphatase and AFP between the non-liver cancer and liver cancer groups （all P

In order to address the energy imbalance issue of a series-connected lithium-iron battery pack, this paper proposes an active equalization method based on a reduced-order solving strategy for the Hanoi Tower problem. The proposed scheme utilizes a combined structure of a switching-network circuit and a bidirectional Cuk converter and leverages an ultracapacitor cell as the energy-transfer carrier. Simulation and comparison demonstrate that the exchange of unbalanced energy within the battery pack can be achieved. The proposed approach can effectively achieve various balancing modes such as cell-to-cell, cell-to-string, string-to-cell, and string-to-string with a relatively fast balancing speed. [ABSTRACT FROM AUTHOR]

A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc. Existing outlier detection algorithms, which can be divided into supervised methods, semi-supervised methods, and unsupervised methods, suffer from missing labeled data, the curse of dimensionality, low interpretability, etc. To address these issues, in this paper, we present an unsupervised outlier detection method based on quantiles and skewness coefficients called ISOD (Interpretable Single dimension Outlier Detection). ISOD first fulfils the empirical cumulative distribution function before computing the quantile and skewness coefficients of each dimension. Finally, it outputs the outlier score. This paper's contributions are as follows: (1) we propose an unsupervised outlier detection algorithm called ISOD, which has high interpretability and scalability; (2) massive experiments on benchmark datasets demonstrated the superior performance of the ISOD algorithm compared with state-of-the-art baselines in terms of ROC and AP. [ABSTRACT FROM AUTHOR]

Outlier detection is an essential research field in data mining, especially in the areas of network security, credit card fraud detection, industrial flaw detection, etc. The existing outlier detection algorithms, which can be divided into supervised methods and unsupervised methods, suffer from the following problems: curse of dimensionality, lack of labeled data, and hyperparameter tuning. To address these issues, we present a novel unsupervised outlier detection algorithm based on mutual information and reduced spectral clustering, called MISC-OD (Mutual Information and reduced Spectral Clustering—Outlier Detection). MISC-OD first constructs a mutual information matrix between features, then, by applying reduced spectral clustering, divides the feature set into subsets, utilizing the LOF (Local Outlier Factor) for outlier detection within each subset and combining the outlier scores found within each subset. Finally, it outputs the outlier score. Our contributions are as follows: (1) we propose a novel outlier detection method called MISC-OD with high interpretability and scalability; (2) numerous experiments on 18 benchmark datasets demonstrate the superior performance of the MISC-OD algorithm compared with eight state-of-the-art baselines in terms of ROC (receiver operating characteristic) and AP (average precision). [ABSTRACT FROM AUTHOR]