Evaluation of Moisture Diffusion by IR Thermography

It is well known that IRT is among the preferred instruments in the qualitative monitoring of humidity in buildings. The evaporation of water leads to a sink of thermal energy that eventually manifests as a decreasing of the temperature. The imaging and non-contact characteristics of IRT make the monitoring of this temperature decrease particularly easy and effective. Nonetheless, the quantitative extraction of some figures that make the qualitative observation more reliable is still an open problem.

Development of MATLAB GUI Based on Blob Analysis for Automatic Extraction of Defects

This study introduces a graphical user interface (GUI) based on MATLAB to realize the automatic ex-traction of sizes of defects from the infrared sequence. To obtain the edge of the defect at deeper layer, a fusion stratagem of the maximum of gray values is adopted for an image subset in the sequence. Blob analysis to the fusion image is used to obtain the general information of defects of a specimen including the distributions and numbers of defects. The frame image is determined for a certain defect according to the peak of the time history curve of sensitive region variance. It can yield a region of interest (ROI) to expand the blob in the selected frame and the defect can be acquired by image segmentation. The results show that through this GUI, a better thermal image can be selected from a set of infrared sequence diagrams for quantitative extraction of different buried depth defect areas, which realizes automatic defect extraction, and its relative error is within 5%. The research on infrared automatic detection technology has certain significance.

Recovery of Palladium and Gold from PGM Ore and Concentrate Leachates Using Fe3O4@SiO2@Mg-Al-LDH Nanocomposite

In this work, we developed a core–shell nanostructured magnetic composite by functionalizing layered double hydroxide (Mg-Al-LDH) microspheres with Fe3O4@SiO2, for the recovery of Au(III) and Pd(II). The magnetic Fe3O4 nanoparticles provided effective magnetic separation of the adsorbent from aqueous solutions. While silica protected the Fe3O4 nanoparticles, increased the adsorption sites and the stability of the material. Finally, Mg-Al-LDH was chosen because of its large anion sorption capacities which lead to the improved adsorption capacity of Fe3O4@SiO2@ Mg-Al-LDH nanocomposite. The morphology and structural composition of the nanocomposite were characterized using various analytical techniques. It was satisfactorily established that silica was coated on iron oxide and layered double hydroxide was immobilized on Fe3O4@SiO2. Parameters affecting adsorption of the composite towards Au(III) and Pd(II), such as effects of sample pH, mass of adsorbent, extraction time, eluent type and concentration were investigated using response methodology based on central composite design. Maximum adsorption capacities of Fe3O4@SiO2@ Mg-Al-LDH for Au(III) and Pd(II) were 289 mg g−1 and 313 mg g−1, respectively. Under optimum conditions, the proposed method displayed good analytical performance suggesting that the adsorbent is a good candidate for quantitative extraction of Au(III) and Pd(II) from secondary sources. Additionally, %recoveries ranging from 85%–99.6% were obtained revealing that Fe3O4@SiO2@ Mg-Al-LDH could selectively extract Au(III) and Pd(II) from leaching solutions of SARM 107 PGM ore and SARM 186 PGM concentrate.

Extraction of medicinal compounds of the benzodiazepines class from aqueous solutions and their following gas chromatographic determination

In extraction systems hexane–water, hexane–aqueous solutions of inorganic salts (sodium chloride, dipotassium phosphate and potassium carbonate), chloroform–water using the method of gas chromatography–mass spectrometry, the distribution constants of psychotropic substances, limited to movement through the customs border of the Republic of Belarus – alprazolam, lorazepam, temazepam, and as well as clozapine, a medicinal product permitted for movement, were determined. The greatest dynamics of the growth of the distribution constant versus salt concentration was observed in alprazolam. It was found that replacing hexane with chloroform leads to increasing the distribution constants and the quantitative extraction of all substances. However, for the selective extraction of substances from various objects and the simultaneous removal of matrix components, it is necessary to use the systems as hexane–aqueous solutions of salts or hexane–water. Based on the obtained values of the distribution constants of substances, extraction-chromatographic technique for the determination of organic non­electrolytes (benzodiazepines) in various objects is proposed. Metrological characteristics of the techniques are: relative standard deviation does not exceed 2 %, the range of determined concentrations in solutions is 0,05– 1,00 g/dm3.

Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations

Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.

Extraction behavior of Pu(IV), Th(IV), and fission product elements with hexapropyl and hexabutyl phosphoramides

Hexapropyl phosphoramide (HPPA) and hexabutyl phosphoramide (HBPA) were examined for the macrolevel extraction and stripping of tetravalent metal ions such as Pu(IV) and Th(IV). The solvation number of Pu(IV) with both the phosphoramides were measured. The number of stages was evaluated for the quantitative extraction and stripping of Pu(IV) and Th(IV) in cross-current mode with 1.1 M phosphoramides in xylene from the nitric acid medium. The distribution ratios for the extraction of Th(IV) were measured with 1.1 M phosphoramides in xylene as a function of equilibrium aqueous phase Th(IV) and nitric acid concentration. In addition, distribution ratios for the extraction of fission product elements such as Zr, Tc, Ru, lanthanides (La, Pr, Nd and Gd) and activation product Am were also measured with 1.1 M HPPA and HBPA in n-Dodecane (n-DD) system as a function of aqueous phase nitric acid concentration. The extraction data generated with phosphoramides were compared with a 1.1 M TBP/n-DD system.

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

Hepatocellular carcinoma (HCC) is a common cancer worldwide. Recent international guidelines request an identification of the stage and patient background/condition for an appropriate decision for the management direction. Radiomics is a technology based on the quantitative extraction of image characteristics from radiological imaging modalities. Artificial intelligence (AI) algorithms are the principal axis of the radiomics procedure and may provide various results from large data sets beyond conventional techniques. This review article focused on the application of the radiomics-related diagnosis of HCC using radiological imaging (computed tomography, magnetic resonance imaging, and ultrasound (B-mode, contrast-enhanced ultrasound, and elastography)), and discussed the current role, limitation and future of ultrasound. Although the evidence has shown the positive effect of AI-based ultrasound in the prediction of tumor characteristics and malignant potential, posttreatment response and prognosis, there are still a number of issues in the practical management of patients with HCC. It is highly expected that the wide range of applications of AI for ultrasound will support the further improvement of the diagnostic ability of HCC and provide a great benefit to the patients.

Radiomics and Prostate MRI: Current Role and Future Applications

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.