The influence of risk management on the project portfolio success – proposal of a risk intensity matrix

This article aims to understand how the risk management influences the project portfolio success. Two methodological approaches were selected in this research: a bibliometric survey followed by a case study. The object of study was the new products project portfolio of an organization from the industrial sector, manufacturer of durable goods. The findings revealed a low intensity of project portfolio risk management. This is aligned with the bibliometric survey results and with the evidence from the investigation performed on the case study unit. In order to evaluate the risk management influence over the portfolio success, this article proposes a matrix which suggests the risk management intensity associated with the project portfolio management processes. The proposed matrix application can be considered a contribution element to deepen the knowledge of the risk management influence on the project portfolio success.

The regional economic spatial pattern of Northeast China and its economic integration trend based on network perspective

Based on the modified gravity model and social network analysis method, the paper constructed the economic linkage intensity matrix of 34 prefecture-level cities of Northeast China and analyzed the density and structure of urban linkage networks. The results show that at present, the urban linkage network in the Northeast region is getting closer, and the radiation role of core cities is gradually increasing. However, the degree of regional integration in Northeast China is not high, and some remote cities are still semi-isolated from the network.

Ultrasonic Flaw Recognition by Multi-Angle Phased Array Data Integration

This study presents a method of ultrasonic flaw identification using phased array ultrasonic inspection data. Raw data from each individual channel of the phased array ultrasonic inspection are obtained. The data trimming and de-noising are employed to retain the data within the boundary of the inspected object and remove the speckle noise components from the raw data, respectively. The resulting data are passed into a sequence of signal processing operations to identify embedded flaws. A shape-based filtering method is proposed to reduce the intensity of geometric noise components due to the non-uniform microstructures introduced in the manufacturing process. The resulting data matrices are integrated to obtain the intensity matrix of the possible flaw regions. Thresholding is applied to the intensity matrix to obtain the potential flaw regions, followed by a connected component analysis to identify the flaws. The overall method is demonstrated and validated using realistic phased array experimental data.

Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer

Background: Novel radiomic features are enabling the extraction of biological data from routine sequences of MRI images. This study’s purpose was to establish a new model, based on the joint intensity matrix (JIM), to predict the Gleason score (GS) of prostate cancer (PCa) patients. Methods: A retrospective dataset comprised of the diagnostic imaging data of 99 PCa patients was used, extracted from The Cancer Imaging Archive’s (TCIA) T2-Weighted (T2-WI) and apparent diffusion coefficient (ADC) images. Radiomic features derived from JIM and the grey level co-occurrence matrix (GLCM) were extracted from the reported tumor locations. The Kruskal-Wallis test and Spearman’s rank correlation identified features related to the GS. The Random Forest classifier model was implemented to identify the best performing signature of JIM and GLCM radiomic features to predict for GS. Results: Five JIM-derived features: contrast, homogeneity, difference variance, dissimilarity, and inverse difference were independent predictors of GS (p < 0.05). Combined JIM and GLCM analysis provided the best performing area-under-the-curve, with values of 78.40% for GS ≤ 6, 82.35% for GS = 3 + 4, and 64.76% for GS ≥ 4 + 3. Conclusion: This retrospective study produced a novel predictive model for GS by the incorporation of JIM data from standard diagnostic MRI images.

A Parallel Algorithm for Wavelet Transform-Based Color Image Compression

Wavelet transforms emerge as one of the popular techniques in image compression. This technique is accepted by the JPEG Committee for the next-generation image compression standard JPEG-2000. Convolution-based strategy is widely used in calculating the wavelet transform of the image. A convolution-based wavelet transform consists of a large number of multiplications and additions. A color image consists of a two-dimensional matrix each for red, green, and blue colors. An ordinary way to calculate the wavelet transform of a color image includes calculating the transform of the intensity matrix of the red, green, and blue components. In this article, we present a parallel algorithm for calculating the convolution-based wavelet transform of the red, green, and blue intensity components simultaneously in color images, which can run on commonly used processors. This means that it needs no extra hardware. The results are also compared to the nonparallel algorithm based on compression time, mean square error, compression ratio, and peak signal-to-noise ratio. Complexity analysis and comparative complexity analysis with some other papers are also shown here.