Direct Measurement of Transformation Zone Strains in Toughened Zirconia

Residual strains responsible for crack tip shielding have been measured within transformation zones surrounding cracks in Mg-PSZ. Two techniques were used for strain measurement: moiré interferometry and high-resolution image matching. Both methods provide maps of differential in-plane displacements within the specimen surface intersected by the crack, the latter method with the higher resolution. The results are compared with finite-element analysis to assess surface relaxation effects, and the measured strains are used to evaluate the crack tip shielding stress intensity factor. Calculations based on the assumption that the unconstrained transformation strain is hydrostatic dilatation yielded results that were significantly smaller than the measured toughness increases.     

An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions

The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

     

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.     

Designing customer-oriented catalogs in e-CRM using an effective self-adaptive genetic algorithm

Analysis of customer interactions for electronic customer relationship management (e-CRM) can be performed by way of using data mining (DM), optimization methods, or combined approaches. The microeconomic framework for data mining addresses maximizing the overall utility of an enterprise where transaction of a customer is a function of the data available on that customer. In this paper, we investigate an alternative problem formulation for the catalog segmentation problem. Moreover, a self-adaptive genetic algorithm has been developed to solve the problem. It includes clever features to avoid getting trapped in a local optimum. The results of an extensive computational study using real and synthetic data sets show the performance of the algorithm. In comparison with classical catalog segmentation algorithms, the proposed approach achieves better performance in Fitness and CPU-time.     

Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro‐fuzzy inference system and K‐means clustering

In this paper, a fuzzy expert system based on adaptive neuro‐fuzzy inference system (ANFIS) is introduced to assess the mortality after coronary bypass surgery. In preprocessing phase, the attributes were reduced using a univariant analysis in order to make the classifier system more effective. Prognostic factors with a p‐value of less than 0.05 in chi‐square or t‐student analysis were given to inputs ANFIS classifier. The correct diagnosis performance of the proposed fuzzy system was calculated in 824 samples. To demonstrate the usefulness of the proposed system, the study compared the performance of fuzzy system based on ANFIS method through the binary logistic regression with the same attributes. The experimental results showed that the fuzzy model (accuracy: 96.4%; sensitivity: 66.6%; specificity: 97.2%; and area under receiver operating characteristic curve: 0.82) consistently outperformed the logistic regression (accuracy: 89.4%; sensitivity: 47.6%; specificity: 89.4%; and area under receiver operating characteristic curve: 0.62). The obtained classification accuracy of fuzzy expert system was very promising with regard to the traditional statistical methods to predict mortality after coronary bypass surgery such as binary logistic regression model.     

A Novel Approach for Bolted T-Stub Connections

This paper reports an investigation into new connection types and their behaviors determined using full-scale experiments. T-shaped connections were created using the IPE standard profile. The aim of this study was to analyze the influence of T connections based on the IPE standard profile, height of beam to height of T-stub joint (H) of T-stub joints, and lengths (X) of T-stub joints on the behavior of steel connections, in order to provide the necessary data for improving Eurocode 3 and enable efficient use of residue IPE standard profiles and back to the consumption cycle. While the moment resistance values increased with an increase in H from H_min to H_max in model groups with X of 126 mm, and the energy dissipation increased with an increase in H from H_min to H_max and also with an increase in the lengths (X) of T-stub joints from 54 to 126 mm.     

Nanoelectromechanical Chip (NELMEC) Combination of Nanoelectronics and Microfluidics to Diagnose Epithelial and Mesenchymal Circulating Tumor Cells from Leukocytes

An integrated nano‐electromechanical chip (NELMEC) has been developed for the label‐free distinguishing of both epithelial and mesenchymal circulating tumor cells (ECTCs and MCTCs, respectively) from white blood cells (WBCs). This nanoelectronic microfluidic chip fabricated by silicon micromachining can trap large single cells (>12 µm) at the opening of the analysis microchannel arrays. The nature of the captured cells is detected using silicon nanograss (SiNG) electrodes patterned at the entrance of the channels. There is an observable difference between the membrane capacitance of the ECTCs and MCTCs and that of WBCs (measured using SiNG electrodes), which is the key indication for our diagnosis. The NELMEC chip not only solves the problem of the size overlap between CTCs and WBCs but also detects MCTCs without the need for any markers or tagging processes, which has been an important problem in previously reported CTC detection systems. The great conductivity of the gold‐coated SiNG nanocontacts as well as their safe penetration into the membrane of captured cells, facilitate a precise and direct signal extraction to distinguish the type of captured cell. The results achieved from epithelial (MCF‐7) and mesenchymal (MDA‐MB231) breast cancer cells circulated in unprocessed blood suggest the significant applications for these diagnostic abilities of NELMEC.     

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research.