Search Results (3)

Rationale: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure the delivery of effective care. Objective: Our objective was to develop predictive measures of tumor response to NAC prior to starting for LABC using machine learning and textural computed tomography (CT) features in different level of frequencies. Materials and Methods: A total of 851 textural biomarkers were determined from CT images and their wavelet coefficients for 117 patients with LABC to evaluate the response to NAC. A machine learning pipeline was designed to classify response to NAC treatment for patients with LABC. For training predictive models, three models including all features (wavelet and original image features), only wavelet and only original-image features were considered. We determined features from CT images in different level of frequencies using wavelet transform. Additionally, we conducted a comparison of feature selection methods including mRMR, Relief, Rref QR decomposition, nonnegative matrix factorization and perturbation theory feature selection techniques. Results: Of the 117 patients with LABC evaluated, 82 (70%) had clinical–pathological response to chemotherapy and 35 (30%) had no response to chemotherapy. The best performance for hold-out data splitting was obtained using the KNN classifier using the Top-5 features, which were obtained by mRMR, for all features (accuracy = 77%, specificity = 80%, sensitivity = 56%, and balanced-accuracy = 68%). Likewise, the best performance for leave-one-out data splitting could be obtained by the KNN classifier using the Top-5 features, which was obtained by mRMR, for all features (accuracy = 75%, specificity = 76%, sensitivity = 62%, and balanced-accuracy = 72%). Conclusions: The combination of original textural features and wavelet features results in a greater predictive accuracy of NAC response for LABC patients. This predictive model can be utilized to predict treatment outcomes prior to starting, and clinicians can use it as a recommender system to modify treatment. Full article

This paper proposed a modified Rock Mass Rating (RMR) system, the RMR_HLW system, for evaluating the rock quality of High-level Radioactive Waste (HLW) geological disposal engineering. Some salient factors, including the weakening of groundwater and temperature on the uniaxial compressive strength, the continuity of index values, the geostress, the rock permeability, and the groundwater chemical properties, were further incorporated based on the widely used RMR system. The proposed RMR_HLW system was then verified by the case study of selection of nine candidate sites for HLW disposal engineering in China. The results indicated that the rock quality of the Xinchang site was the best and ranked as the most appropriate site, while the Jiujing site ranked the worst. Compared with the traditional RMR system, the proposed RMR_HLW system can further consider crucial factors related to the long-term safety of HLW disposal and better reflect the differences between the potential sites. It can facilitate engineers to preliminarily evaluate the rock quality of the potential sites for High-level Radioactive Waste geological disposal engineering. Full article

Low frequency oscillations (LFOs) in power systems usually fall into two types, i.e., forced oscillations and natural oscillations. Waveforms of the two are similar, but the suppression methods are different. Therefore, it is important to accurately identify LFO type. In this paper, a method for discriminating LFO type based on multi-dimensional features and a feature selection algorithm combining ReliefF and minimum redundancy maximum relevance algorithm (mRMR) is proposed. Firstly, 53 features are constructed from six aspects—time domain, frequency domain, energy, correlation, complexity, and modal analysis—which comprehensively characterize the multidimensional features of LFO. Then, the optimal feature subset with greater relevance and less redundancy is extracted by ReliefF-mRMR. In order to improve the classification performance, a modified Support Vector Machine (SVM) with Genetic Algorithm (GA) optimizing the key parameters is adopted, which is conducted in MATLAB. Finally, in 179-bus system, the samples of LFOs are generated by the Power System Analysis Toolbox (PSAT) and the accuracy of the LFO type identification model is verified. In ISO New England and East China power grid, it is proven that the proposed method can accurately identify LFO type considering the influences of noise, oscillation mode, and data incompletion. Hence, it has good robustness, noise immunity, and practicability. Full article