Advancing aircraft engine RUL predictions: an interpretable integrated approach of feature engineering and aggregated feature importance.

In this study, we present a comprehensive approach for predicting the remaining useful life (RUL) of aircraft engines, incorporating advanced feature engineering, dimensionality reduction, feature selection techniques, and machine learning models. The process begins with a rolling time series window, followed by the extraction of a multitude of statistical features, and the application of principal component analysis for dimensionality reduction. We utilize a variety of feature selection methods, such as Genetic Algorithm, Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator Regression, and Feature Importances from a Random Forest model. As a significant contribution, we introduce the novel aggregated feature importances with cross-validation (AFICv) technique, which ranks features based on their mean importance. We establish a selection criterion that retains features with a cumulative mean sum equal to 70%, thereby reducing the complexity of machine learning models and enhancing their generalizability. Four machine learning regression models-Natural and Extreme Gradient Boosting, Random Forest, and Multi-Layer Perceptron-were employed to evaluate the effectiveness of the selected features. The performance of our proposed method is assessed by the evaluation metrics Root Mean Square Error (RMSE) and R2 Score, and also considered within-interval percentages and relative accuracy metrics. Importantly, a novel PCA interpretability was introduced to provide real-world context and enhance the utility of our findings for domain experts. Our results indicate that the proposed AFICv technique efficiently achieves competitive performance across the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) sub-datasets using a significantly smaller subset of features, thus contributing to a more effective and interpretable RUL prediction methodology for aircraft engines.

Part orientation optimization for Wire and Arc Additive Manufacturing process for convex and non-convex shapes.

Building orientation optimization for Additive Manufacturing (AM) process is a crucial step because it has a vital effect on the accuracy and performance of the created part. Wire and Arc Additive Manufacturing's (WAAM) working space is less limited, and the production time is significantly shorter than the other metal 3D printers. However, one of the adverse effects of WAAM is the defect at the start and endpoints of the welding beads. In this paper, an algorithm has been invented to define the optimal printing position, reducing the number of these defects by rotating the 3D object in a loop around the X and Y axes by a small constant degree and then selecting the degree of rotation that has the fewest uninterrupted surfaces and the largest area of the first layer. The welding process will be interrupted as little as possible by the torch if there are the fewest possible uninterrupted surfaces. As a result, there will be fewer defects in the production and finishing of the welding beads. In order to have a sufficient connection surface with the build tray, which will aid in holding the workpiece in place, the largest first layer should also be sought. Therefore, it has been found that a properly defined orientation relative to the build tray can reduce the number of uninterrupted surfaces within the layers, which will improve the expected dimensional accuracy of the parts. The efficiency of the process is highly affected by the shape of the part, but in most cases, the print errors can be drastically minimized.

Oral contraceptives and colorectal cancer risk - A meta-analysis and systematic review.

There is limited understanding of the potential relationship between the risk of colorectal cancer and oral contraceptive use among women of different ages. Further investigation on the issue helps develop an informed choice of contraception. Data for this meta-analysis were derived from case-control and cohort studies of colorectal cancer and oral contraceptive use conducted between June 2000 and May 2022. The studies had a very high heterogeneity, as shown by an I2 of 99%, and a confidence interval of 95% was considered significant. Other results from the meta-analysis were as follows; Heterogeneity: Chi2 = 585.13, df = 6 (P < 0.00001). A test of the overall effect of ever use versus never use of oral contraceptives was Z = 21.85 (P < 0.00001). All the studies had a pooled risk ratio (RR) of 0.53. The use of oral contraceptives is associated with reduced risk of developing colorectal cancer. There is a need for further research into the biological mechanisms underlying these relationships, which may lead to insights into potential preventive interventions for colorectal carcinogenesis in women. The keywords used to locate studies included in this meta-analysis include Keywords targeting oral contraceptives included oral contraceptive pills, and birth control pills. Search keywords targeting colorectal carcinogenesis included neoplasms, tumors, or colon and rectal cancer.

Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue.

Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

Automatic detection and quantification of WBCs and RBCs using iterative structured circle detection algorithm.

Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.