Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

Electrical characterization of carbon nanotube Y-junctions: a foundation for new nanoelectronics

A review on the syntheses and electrical characterization of Y-shaped multi-walled carbon nanotube morphologies is presented. Modified thermal CVD processes, using Ti precursors, are used to grow Y-junctions of different geometries and distribution of catalyst particles. It has been established that novel electrical switching behavior is feasible, where any one of the three branches of the Y-junction can be used for modulating the electrical current flow through the other two branches. Current blocking behavior, leading to perfect rectification, is seen which could be related to the interplay of the carrier lifetime and the transit time. The overall goal is to investigate the possibility of obtaining novel functionality at the nanoscale, which can lead to new device paradigms.     

Pressure drop for single and two‐phase flow of non‐newtonian liquids in helical coils

New experimental results on pressure loss for the single and two‐phase gas‐liquid flow with non‐Newtonian liquids in helical coils are reported. For a constant value of the curvature ratio, the value of the helix angle of the coils is varied from 2.56° to 9.37°. For single phase flow, the effect of helix angle on pressure loss is found to be negligible in laminar flow regime but pressure loss increases with the increasing value of helix angle in turbulent flow conditions. On the other hand, for the two‐phase flow, the well‐known Lockhart‐Martinelli method correlates the present results for all values of helix angle (2.56‐9.37°) satisfactorily under turbulent/laminar and turbulent/turbulent conditions over the following ranges of variables as: 0.57 ≤ n′ ≤ 1; Re′ < 4000; Re_l < 4000; Re_g < 8000; 8 ≤ x ≤ 1000 and 0.2 ≤ De′ ≤ 1000.     

Quantifying arsenic-induced morphological changes in spinach leaves: implications for remote sensing

Arsenic (As) is a common soil contaminant that can be accumulated into plant parts. The ability to detect As in contaminated plants is an important tool to minimize As-induced health risks in humans. Near-infrared (NIR) spectra are strongly affected by leaf structural characteristics. Therefore, quantitative analyses of structural changes in the arrangement of mesophyll cells caused by As will help to explain spectral responses to As. The objectives of this study were to use stereological methods to quantify internal structural changes in leaves with As treatment in spinach plants, and to relate these changes to leaf spectral properties in NIR spectra. Hydroponically grown spinach was treated with 0, 5, 10 and 20 μmol l^?1 for four weeks in a growth chamber. Spectral properties of leaves were obtained for visible and infrared frequencies. Leaf structural properties, such as mesophyll thickness and mesophyll surface area, were measured using stereological methods. Quantitative analysis of leaf structure showed that total leaf thickness and intercellular spaces in spongy mesophyll cells decreased with increasing As treatment. Changes in leaf reflectance in NIR wavelengths were strongly correlated with leaf As concentration and leaf structural changes. Multiple linear regression of leaf reflectance values at the highest correlated wavelengths (1048, 1098 and 1080 nm) generated an R ² value of 0.69. Results from this study support the hypothesis that relationships between leaf structure and reflectance may be useful in the interpretation of spectral data to detect plant leaf As concentration.     

Self-similar flows with increasing energy-3. Radiation-driven shock wave

Self-similar flows behind a radiation-driven shock wave have been investigated. Approximate analytical solutions are presented when the flow is adiabatic. These solutions are agreeing well with the exact numerical solutions. A simple method to obtain the shock propagation law has been presented. Also explicit approximate analytical solutions are obtained when the flow behind the shock is taken to be isothermal.     

Effect of fat and microflora on hepatic,small intestinal and colonic HMG CoA reductase,cytochrome P450 and cytochrome B5

High levels of dietary fat caused a significant reduction in HMG CoA reductase activity in the liver of germ-free rats whereas significantly elevated small intestinal enzyme activity was observed. Dietary fat had no significant effect on HMG CoA reductase activity in any tissue studied in the conventional rat. No significant change in colonic HMG CoA reductase activity was observed between any of the experimental groups. Rats fed a high-fat diet tended to exhibit higher cytochrome P₄₅₀ levels in all tissues studied, regardless of the presence of intestinal microflora.     

Mechanical Cues Regulating Proangiogenic Potential of Human Mesenchymal Stem Cells through YAP‐Mediated Mechanosensing

Stem cells secrete trophic factors that induce angiogenesis. These soluble factors are promising candidates for stem cell–based therapies, especially for cardiovascular diseases. Mechanical stimuli and biophysical factors presented in the stem cell microenvironment play important roles in guiding their behaviors. However, the complex interplay and precise role of these cues in directing pro‐angiogenic signaling remain unclear. Here, a platform is designed using gelatin methacryloyl hydrogels with tunable rigidity and a dynamic mechanical compression bioreactor to evaluate the influence of matrix rigidity and mechanical stimuli on the secretion of pro‐angiogenic factors from human mesenchymal stem cells (hMSCs). Cells cultured in matrices mimicking mechanical elasticity of bone tissues in vivo show elevated secretion of vascular endothelial growth factor (VEGF), one of representative signaling proteins promoting angiogenesis, as well as increased vascularization of human umbilical vein endothelial cells (HUVECs) with a supplement of conditioned media from hMSCs cultured across different conditions. When hMSCs are cultured in matrices stimulated with a range of cyclic compressions, increased VEGF secretion is observed with increasing mechanical strains, which is also in line with the enhanced tubulogenesis of HUVECs. Moreover, it is demonstrated that matrix stiffness and cyclic compression modulate secretion of pro‐angiogenic molecules from hMSCs through yes‐associated protein activity.     

A semi-empirical unified model of strain fatigue life for insulation plastics

The paper focuses on modeling the strain fatigue lives of three commonly used cable insulation polymers, namely (1) polyvinyl chloride, (2) crosslinked polyethylene, and (3) polyphenylene ether under selected strain and temperature ranges. On the basis of results obtained from their fatigue tests, Coffin–Manson model, mean/maximum strain fatigue model, and a set of new semi-empirical equations were applied to establish the relationship between fatigue lives and strains. The unified strain model, herein we name it the Wei–Wong model, is developed to predict the fatigue lives of three polymers studied and their prediction capability was examined using our experimental data. It was found that the proposed Wei–Wong model can provide a better life prediction compared to the experimental data and other methods in the literature at selected temperatures, namely −40, 25, and 65 °C.