Microstructure and Superconducting Properties of Bi-2223 Synthesized via Co-Precipitation Method: Effects of Graphene Nanoparticle Addition.

The effects of graphene addition on the phase formation and superconducting properties of (Bi1.6Pb0.4)Sr2Ca2Cu3O10 (Bi-2223) ceramics synthesized using the co-precipitation method were systematically investigated. Series samples of Bi-2223 were added with different weight percentages (x = 0.0, 0.3, 0.5 and 1.0 wt.%) of graphene nanoparticles. The samples' phase formations and crystal structures were characterized via X-ray diffraction (XRD), while the superconducting critical temperatures, Tc, were investigated using alternating current susceptibility (ACS). The XRD showed that a high-Tc phase, Bi-2223, and a small low-Tc phase, Bi-2212, dominated the samples. The volume fraction of the Bi-2223 phase increased for the sample with x = 0.3 wt.% and 0.5 wt.% of graphene and slightly reduced at x = 1.0 wt.%. The ACS showed that the onset critical temperature, Tc-onset, phase lock-in temperature, Tcj, and coupling peak temperature, TP, decreased when graphene was added to the samples. The susceptibility-temperature (χ'-T) and (χ″-T) curves of each sample, where χ' and χ″ are the real and imaginary parts of the susceptibility, respectively, were obtained. The critical temperature of the pure sample was also measured.

Phase transformation and mechanical properties of new bioactive glass-ceramics derived from CaO-P2O5-Na2O-B2O3-SiO2 glass system.

This work investigates the role of sintering temperature on bioactive glass-ceramics derived from the new composition CaO-P2O5-Na2O-B2O3-SiO2 glass system. The sintering behaviour of the samples' physical, structural, and mechanical properties is highlighted in this study. The experimental results indicated that the sintering process improved the crystallization and hardness of the final product. Results from XRD and FTIR showed the existence of carbonate apatite, pseudo-wollastonite, and wollastonite phases. From the results, the bioglass-ceramics sintered at 700 °C obtained the highest densification and optimum mechanical results. It had the value of 5.34 ± 0.21 GPa regarding microhardness and 2.99 ± 0.24 MPa m1/2 concerning fracture toughness, which falls in the range of the human enamel. Also, the sintered samples maintained their bioactivity and biodegradability after being tested in the PBS medium. The bioactivity does not affect but slows down the apatite formation rate. Overall results promoted the novel bioglass-ceramics as a candidate material for dental application.

Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI.

Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of tumour infiltration with excellent soft-tissue contrast. In this research, the robustness of semi-automatic segmentation has been evaluated using a flood-fill algorithm for quantitative feature extraction, using 30 diffusion weighted MRI images (DWI-MRI) of cervical cancer patients. The relevant features were extracted from DWI-MRI segmented images of cervical cancer. First order statistics, shape features, and textural features were extracted and analysed. The intra-class relation coefficient (ICC) was used to compare 662 radiomic features extracted from manual and semi-automatic segmentations. Notably, the features extracted from the semi-automatic segmentation and flood filling algorithm (average ICC = 0.952 0.009, p > 0.05) were significantly higher than the manual extracted features (average ICC = 0.897 0.011, p > 0.05). Henceforth, we demonstrate that the semi-automatic segmentation is slightly expanded to manual segmentation as it produces more robust and reproducible radiomic features.

Superconducting Properties of YBa2Cu3O7-δ with a Multiferroic Addition Synthesized by a Capping Agent-Aided Thermal Treatment Method.

A bulk YBa2Cu3O7-δ (Y-123) superconductor synthesized by a thermal treatment method was added with different weight percentages (x = 0.0, 0.2, 1.0, 1.5, and 2.0 wt.%) of BiFeO3 (BFO) nanoparticle. X-ray diffraction (XRD), alternating current susceptibility (ACS), and field emission scanning electron microscopy (FESEM) were used to determine the properties of the samples. From the XRD results, all samples showed an orthorhombic crystal structure with a Pmmm space group. The sample x = 1.0 wt.% gave the highest value of Y-123. The high amounts of BFO degraded the crystallite size of the sample, showing that the addition did not promote the grain growth of Y-123. From ACS results, the Tc-onset value was shown to be enhanced by the addition of the BFO nanoparticle, where x = 1.5 wt.% gave the highest Tc value (91.91 K). The sample with 1.5 wt.% showed a high value of Tp (89.15 K). The FESEM analysis showed that the average grain size of the samples decreased as BFO was introduced. However, the small grain size was expected to fill in the boundary, which would help in enhancing the grain connectivity. Overall, the addition of the BFO nanoparticles in Y-123 helped to improve the superconducting properties, mainly for x = 1.5 wt.%.

Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA).

Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features from Coronary Computed Tomography Angiography (CCTA) images and to evaluate the performance of automated machine learning (AutoML) model in classifying the atherosclerotic plaques. In total, 202 patients who underwent CCTA examination at Institut Jantung Negara (IJN) between September 2020 and May 2021 were selected as they met the inclusion criteria. Three primary coronary arteries were segmented on axial sectional images, yielding a total of 606 volume of interest (VOI). Subsequently, the first order, second order, and shape order of radiomic characteristics were extracted for each VOI. Model 1, Model 2, Model 3, and Model 4 were constructed using AutoML-based Tree-Pipeline Optimization Tools (TPOT). The heatmap confusion matrix, recall (sensitivity), precision (PPV), F1 score, accuracy, receiver operating characteristic (ROC), and area under the curve (AUC) were analysed. Notably, Model 1 with the first-order features showed superior performance in classifying the normal coronary arteries (F1 score: 0.88; Inverse F1 score: 0.94), as well as in classifying the calcified (F1 score: 0.78; Inverse F1 score: 0.91) and mixed plaques (F1 score: 0.76; Inverse F1 score: 0.86). Moreover, Model 2 consisting of second-order features was proved useful, specifically in classifying the non-calcified plaques (F1 score: 0.63; Inverse F1 score: 0.92) which are a key point for prediction of cardiac events. Nevertheless, Model 3 comprising the shape-based features did not contribute to the classification of atherosclerotic plaques. Overall, TPOT shown promising capabilities in terms of finding the best pipeline and tailoring the model using CCTA-based radiomic datasets.