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.     

Impact of adhesive ingredients on adhesion between rubber and brass-plated steel wire in tire

Proficiency on underlying mechanism of rubber-metal adhesion has been increased significantly in the last few decades. Researchers have investigated the effect of various ingredients, such as hexamethoxymethyl melamine, resorcinol, cobalt stearate, and silica, on rubber-metal interface. The role of each ingredient on rubber-metal interfacial adhesion is still a subject of scrutiny. In this article, a typical belt skim compound of truck radial tire is selected and the effect of each adhesive ingredient on adhesion strength is explored. Out of these ingredients, the effect of cobalt stearate is found noteworthy. It has improved adhesion strength by 12% (without aging) and by 11% (humid-aged), respectively, over control compound. For detailed understanding of the effect of cobalt stearate on adhesion, scanning electron microscopy and energy dispersive spectroscopy are utilized to ascertain the rubber coverage and distribution of elements. X-ray photoelectron spectroscopy results helped us to understand the impact of Cu_XS layer depth on rubber-metal adhesion. The depth profile of the Cu_XS layer was found to be one of the dominant factors of rubber-metal adhesion retention. Thus, this study has made an attempt to find the impact of different adhesive ingredients on the formation of Cu_XS layer depth at rubber-metal interface and establish a correlation with adhesion strength simultaneously.     

Detection and analysis of transition from annular to intermittent flow in vertical tubes

In vertical co-current gas-liquid flow, the transition from annular to intermittent flow occurs when gas core becomes interrupted by liquid bridges due to the instability of the interfacial capillary waves. An analytical model is formulated to explain the liquid bridging in terms of the growth of finite amplitude interfacial capillary waves. Experimental results show that the longest wave length, which is associated with the transition, is about eight times the wave length of waves moving with the velocity of the liquid film.     

Plasma immersion ion implantation for SOI synthesis: SIMOX and ion-cut

We have demonstrated feasibility to form silicon-on-insulator (SOI) substrates using plasma immersion ion implantation (PIII) for both separation by implantation of oxygen and ion-cut. This high throughput technique can substantially lower the high cost of SOI substrates due to the simpler implanter design as well as ease of maintenance. For separation by plasma implantation of oxygen wafers, secondary ion mass spectrometry analysis and cross-sectional transmission electron micrographs show continuous buried oxide formation under a single-crystal silicon overlayer with sharp Si/SiO₂ interfaces after oxygen plasma implantation and high-temperature (1300°C) annealing. Ion-cut SOI wafer fabrication technique is implemented for the first time using PIII. The hydrogen plasma can be optimized so that only one ion species is dominant in concentration and there are minimal effects by other residual ions on the ion-cut process. The physical mechanism of hydrogen induced silicon surface layer cleavage has been investigated. An ideal gas law model of the microcavity internal pressure combined with a two-dimensional finite element fracture mechanics model is used to approximate the fracture driving force which is sufficient to overcome the silicon fracture resistance.     

Microwave-Assisted Synthesis of Fine Particle Oxides Employing Wet Redox Mixtures

Interaction of electromagnetic radiation with a physical mixture of metal nitrates and amides/hydrazides is observed to initiate high-temperature reactions, useful for realizing several high-temperature ceramic materials. A judicious choice of such redox mixtures undergoes exothermic reactions when they couple with microwave radiation. The coupling of electromagnetic radiation with metal salts and amides/hydrazides depends on the dielectric properties of the individual components in the reaction mixture. The approach has been used to prepare γ-Fe₂O₃, Fe₃O₄, MgCr₂O₄, α-CaCr₂O₄, and La_0.7Ba_0.3MnO₃.     

Isothermal oxidation behavior of FeCo–2V intermetallic alloy

Isothermal oxidation behavior and the nature of oxide layer formed during oxidation of FeCo–2V alloy were characterized in the temperature range of 500–600 °C. Oxidation kinetics of the alloy follows a parabolic rate law. SEM and XRD studies indicate the formation of an iron rich outer oxide layer and an inner solute rich layer containing cobalt and vanadium rich oxides. The oxidation mechanism of the FeCo–2V alloy is similar to that of low alloy steels. During the initial stages, preferential oxidation of iron and cobalt occurs at the alloy surface and leads to the formation of a solute rich inner layer. Continued oxidation occurs through oxidation of iron and cobalt at the outer layer and internal oxidation of inner layer. The iron rich oxide layer formed at the surface on oxidation of FeCo alloy is semi-conducting in nature and may not provide the necessary insulating barrier required at the surface to minimize eddy current losses during A.C. applications.     

An effective deep learning model for grading abnormalities in retinal fundus images using variational auto-encoders

Diabetic retinopathy (DR) and Diabetic Macular Edema (DME) are severe diseases that affect the eyes due to damage in blood vessels. Computer-aided automated grading will help clinicians conduct disease diagnoses at ease. Experiments of automated image processing with deep learning techniques using CNN produce promising results, especially in the medical imaging domain. However, the disease grading tasks in retinal images using CNN struggle to retain high-quality information at the output. A novel deep learning model based on variational auto-encoder to grade DR and DME abnormalities in retinal images is proposed. The objective of the proposed model is to extract the most relevant retinal image features efficiently. It focuses on addressing less relevant candidate region generation and translational invariance present in images. The experiments are conducted in IDRID dataset and evaluated using accuracy, U-kappa, sensitivity, specificity and precision metrics. The results outperform compared with other state-of-art techniques.     

Architectural design and FPGA implementation of radix-4 CORDIC processor

A new scaled radix-4 CORDIC architecture that incorporates pipelining and parallelism is presented. The latency of the architecture is n/2 clock cycles and throughput rate is one valid result per n/2 clocks for n bit precision. A 16 bit radix-4 CORDIC architecture is implemented on the available FPGA platform. The corresponding latency of the architecture is eight clock cycles and throughput rate is one valid result per eight clock cycles. The entire scaled architecture operates at 56.96 MHz of clock rate with a power consumption of 380 mW. The speed can be enhanced with the upgraded version of FPGA device. A speed-area optimized processor is obtained through this architecture and is suitable for real time applications.