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.     

Hydrogen production from thermal decomposition of ammonia-contaminated acid gas using a detailed reaction mechanism

The increase in the production of acid gas consisting of H₂S, CO₂, and associated impurities such as ammonia and hydrocarbons from oil and gas plants and gasification facilities has stimulated the interest in the development of alternative means of acid gas utilization to produce hydrogen and sulfur, simultaneously. The present literature lacks a detailed reaction mechanism that can reliably predict the thermal destruction of NH₃ and its blend with H₂S and CO₂ to facilitate process optimization and commercialization. In this paper, a detailed mechanism of NH₃ pyrolysis is developed and is merged with the reactions of NH₃ oxidation and H₂S/CO₂ thermal decomposition from our previous works. The mechanism is validated successfully using different sets of experimental data on the pyrolysis and oxidation of NH₃, H₂S, and CO₂. The proposed mechanism predicts the experimental data on NH₃ pyrolysis remarkably better than the existing mechanisms in the literature. The mechanism is used to investigate the effects of NH₃ concentration (0–20%) and reactor temperature (1000–1800 K) on the thermal decomposition of H₂S and CO₂. A synergistic effect is observed in the simultaneous decomposition of NH₃ and CO₂, i.e., NH₃ conversion is improved in the presence of CO₂ and the decomposition CO₂ to CO is enhanced in the presence of NH₃. The presence of H₂S suppressed NH₃ conversion, while the conversion of H₂S remained unchanged with increasing NH₃ concentration at temperature below 1400 K due to the low conversion of NH₃ (up to 18%). At temperature above 1400 K, NH₃ conversion increased rapidly and it triggered a decrease in H₂S conversion as well as the yields of H₂ and S₂. The major reactions involved in the decomposition of H₂S, CO₂, and NH₃ and the production of major products such as H₂, S₂, and CO are identified. The detailed reaction mechanism can facilitate the design and optimization of acid gas thermal decomposition to produce hydrogen and sulfur, simultaneously.     

Fabrication of core–shell nanocomposites with enhanced photocatalytic efficacy

The current study establishes the unprecedented involvement in the evolution and production of novel core–shell nanocomposites composed of nanosized titanium dioxide and aniline‐o‐phenylenediamine copolymer. TiO₂@copoly(aniline and o‐phenylenediamine) (TiO₂@PANI‐o‐PDA) core–shell nanocomposites were chemically synthesized in a molar ratio of 5:1 of the particular monomers and several weights of nano‐TiO₂ via oxidative copolymerization. The construction of the TiO₂@PANI‐o‐PDA core–shell nanocomposites was ascertained from Fourier transform IR spectroscopy, UV–visible spectroscopy and XRD. A reasonable thermal behavior for the original copolymer and the TiO₂@PANI‐o‐PDA core–shell nanocomposites was investigated. The bare PANI‐o‐PDA copolymer was thermally less stable than the TiO₂@PANI‐o‐PDA nanocomposites. The core–shell feature of the nanocomposites was found to have core and shell sizes of 17 nm and 19–26 nm, respectively. In addition, it was found that the addition of a high ratio of TiO₂ nanoparticles increases the electrical conductivity and consequently lowers the electrical resistivity of the TiO₂@PANI‐o‐PDA core–shell nanocomposites. The hybrid photocatalysts exhibit a dramatic photocatalytic efficacy of methylene blue degradation under solar light irradiation. A plausible interpretation of the photocatalytic degradation results of methylene blue is also demonstrated. Our setup introduces a facile, inexpensive, unique and efficient technique for developing new core–shell nanomaterials with various required functionalities and colloidal stabilities. © 2018 Society of Chemical Industry     

A novel binary gaining–sharing knowledge-based optimization algorithm for feature selection

Neural Computing and Applications - To obtain the optimal set of features in feature selection problems is the most challenging and prominent problem in machine learning. Very few human-related...     

Purification,structural data and biological properties of polysaccharide from Prunus amygdalus gum

This work demonstrates the efficiency of almond gum polysaccharides (AGPs) as bioactive compounds. AGPs were first extracted using H₂O₂, in the presence of NaOH, at different times and temperatures. The optimal extraction conditions were 4% H₂O₂ and 2 N NaOH, for 7 h at 50 °C, leading to an extraction yield of 58.2% (w/w). After a purification step, the retained AGPs were characterised using high‐performance liquid chromatography showing a molecular weight of 99.3 kDa. The monosaccharide composition of AGPs were assessed using gas chromatography–mass spectrometry. AGPs were found to be a complex heteropolysaccharide with a repeating unit mainly composed of galactose, arabinose, xylose, mannose, rhamnose, and glucuronic acid with the respective ratios: 45:26:7:10:1:11. The acidic nature of the polysaccharide is due to the presence of glucuronic acid. Total antioxidant activity, free radical‐scavenging activity and reducing power assay of AGPs were investigated. The obtained results showed high antioxidant activities of AGPs. Furthermore, beyond 60 mg mL^?1, AGPs exhibited bacterial growth inhibition for five pathogenic strains: Escherichia coli, Staphylococcus aureus, Enterococcus feacalis, Pseudomonas aeruginosa and Salmonella typhimurium.     

Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.     

Harmful effects of pyrethroid ester insecticide on the male reproductive system mainly through affecting testicular function and inflammatory markers

Pyrethroid esters are widely used as insecticides worldwide. In this study, we aimed to evaluate the harmful effect of deltamethrin on the male reproductive system through the assessment of reproductive hormones, inflammatory markers, and testicular function. To achieve our aim, eighty male 7-9-week-old, Wistar rats were taken, weighed, and divided into four experimental groups. The first group was kept as a control group, and the other three groups were given deltamethrin orally at different concentrations (0.87, 8.7, and 17.4 mg/kg body weight) for nine weeks. The results indicated that deltamethrin administration associated with a significant decrease in reproductive hormones, especially FSH, LH, and significant elevation in the interleukin 2 (IL2), interleukin 6 (IL6), histamine, and cortisol levels. Also, the significance of inhibition of sperm motility and viability, decreased testis weights, sperm count, and fructose in semen were noted. These findings clarify the harmful effect of deltamethrin on the male reproductive system by producing a significant alteration in reproductive hormones, inflammatory markers as well as testicular function.