An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

IIR system identification using cat swarm optimization

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.     

An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases

Intelligent computing system (ICS) and knowledge-based system (KBS) have been widely used in the detection and interpretation of EMG (electromyography) based diseases. Heuristic-based detection methods of EMG parameters for a particular disease have also been reported in the literature but little effort has been made by researchers to combine rule-based reasoning (RBR) and case-based reasoning of KBS, and ANN (artificial neural nets) of ICS. Integrating the methods in KBS and ICS improves the computational and reasoning efficiency of the problem-solving strategy. We have developed an integrated model of CBR and RBR for generating cases, and ANN for matching cases for the interpretation and diagnosis of neuromuscular diseases. We have hierarchically structured the neuromuscular diseases in terms of their physio-pyscho (muscular, cognitive and psychological) parameters and EMG based parameters (amplitude, duration, phase etc.). Cumulative confidence factor is computed at different node from lowest to highest level of hierarchal structure in the process of diagnosis of the neuromuscular diseases. The diseases considered are Duchenne muscular dystrophy, Polymyostits, Endocrine myopathy, Metabolic myopathy, Neuropathy, Poliomyletis and Myasthenia gravis. The basic objective of this work is to develop an integrated model of RBR, CBR and ANN in which RBR is used to hierarchically correlate the sign and symptom of the disease and also to compute cumulative confidence factor (CCF) of the diseases. CBR is used for diagnosing the neuromuscular diseases and to find the relative importance of sign and symptoms of a diseases to other diseases and ANN is used for matching process in CBR.     

Differential Physio-Biochemical and Metabolic Responses of Peanut (Arachis hypogaea L.) under Multiple Abiotic Stress Conditions

The frequency and severity of extreme climatic conditions such as drought, salinity, cold, and heat are increasing due to climate change. Moreover, in the field, plants are affected by multiple abiotic stresses simultaneously or sequentially. Thus, it is imperative to compare the effects of stress combinations on crop plants relative to individual stresses. This study investigated the differential regulation of physio-biochemical and metabolomics parameters in peanut (Arachis hypogaea L.) under individual (salt, drought, cold, and heat) and combined stress treatments using multivariate correlation analysis. The results showed that combined heat, salt, and drought stress compounds the stress effect of individual stresses. Combined stresses that included heat had the highest electrolyte leakage and lowest relative water content. Lipid peroxidation and chlorophyll contents did not significantly change under combined stresses. Biochemical parameters, such as free amino acids, polyphenol, starch, and sugars, significantly changed under combined stresses compared to individual stresses. Free amino acids increased under combined stresses that included heat; starch, sugars, and polyphenols increased under combined stresses that included drought; proline concentration increased under combined stresses that included salt. Metabolomics data that were obtained under different individual and combined stresses can be used to identify molecular phenotypes that are involved in the acclimation response of plants under changing abiotic stress conditions. Peanut metabolomics identified 160 metabolites, including amino acids, sugars, sugar alcohols, organic acids, fatty acids, sugar acids, and other organic compounds. Pathway enrichment analysis revealed that abiotic stresses significantly affected amino acid, amino sugar, and sugar metabolism. The stress treatments affected the metabolites that were associated with the tricarboxylic acid (TCA) and urea cycles and associated amino acid biosynthesis pathway intermediates. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and heatmap analysis identified potential marker metabolites (pinitol, malic acid, and xylopyranose) that were associated with abiotic stress combinations, which could be used in breeding efforts to develop peanut cultivars that are resilient to climate change. The study will also facilitate researchers to explore different stress indicators to identify resistant cultivars for future crop improvement programs.     

Light-induced changes in the solar cell parameters and temperature coefficient of n-C/p-Si heterojunction solar cell

Amorphous carbon (a-C) is a potential material for the development of low-cost and high-efficiency solar cell. We report the study of the influence of light soaking up to 100 h on n-C/p-Si heterojunction solar cell. It is observed that the deterioration in the fill factor and the efficiency are significantly smaller as compared to that observed in a-Si:H solar cell. Variations in the temperature coefficients of the I–V characteristics subjected to light degradation and recovery has also been investigated. A good correlation between change in the temperature coefficient and the degradation/recovery state of cell's conversion efficiency has been observed.     

Hydrogeochemistry,Elemental Flux,and Quality Assessment of Mine Water in the Pootkee-Balihari Mining Area,Jharia Coalfield,India

Ninety nine mine water discharge samples were collected and analyzed for pH, electrical conductivity (EC), major cations, anions, and trace metals in the Pootkee-Balihari coal mining area of the Jharia coalfield. The mines of the area annually discharge 34.80 × 10⁶ m³ of mine water and 39,099 t of solute loads. The pH of the analyzed mine waters ranged from 6.97 to 8.62. EC values ranged from 711 μS cm⁻¹ to 1862 μS cm⁻¹, and reflect variations in lithology, geochemical processes, and hydrological regimes in the mines. The cation and anion chemistry indicate the general ionic abundance as: Mg²⁺ > Ca²⁺ > Na⁺ > K⁺ and HCO₃ ⁻ > SO₄ ²⁻ > Cl⁻ > NO₃ ⁻ > F⁻, respectively. Elevated SO₄ ²⁻ concentrations in the Gopalichuck, Kendwadih, and Kachhi-Balihari mine waters are attributed to pyrite weathering. The water quality assessment indicated that TDS, hardness, Mg²⁺, and SO₄ ²⁻ are the major parameters of concern in the study area. Except for Fe, all of the measured metals in the mine water were well within the levels recommended for drinking water. With only a few exceptions, the mine water is of good to permissible quality and suitable for irrigation.     

Physiochemical characterizations of hydroxyapatite extracted from bovine bones by three different methods: Extraction of biologically desirable HAp

In the present study, subcritical water and alkaline hydrolysis methods are proposed methodologies for extraction of natural hydroxyapatite bioceramic from bovine bone. In these processes, the bovine bones powder were treated by high pressure water at 250 °C for 1 h and 25% (wt) sodium hydroxide at 250 °C for 5 h, respectively. Also the conventional calcination methodology has been utilized as well (T = 850 °C for 1 h). The obtained apatites from the three treatment processes have been characterized by powder X-ray diffraction analysis (XRD), Fourier transform infrared spectroscopy (FT IR), transmission electron microscopy (TEM), thermal gravimetric analysis (TGA), electron scanning microscopy (SEM), energy dispersive X-ray analysis (EDX) and field emission scanning electron microscopy (FE SEM). FT IR and XRD results affirmed that both the proposed methods and the traditional one can eliminate the collagen and other organic materials present in the bovine bones. The physiochemical characterizations for the obtained apatites have proved that the subcritical water and the alkaline hydrolysis relatively preserve the carbonate content present in the biological apatite, so they yield carbonated hydroxyapatite which is medically preferable. While, the thermal process produces almost hydroxyapatite carbonate-free.     

Study of glasses/glass-ceramics in the SrO-ZnO-SiO2 system as high temperature sealant for SOFC applications

Glasses having composition (in wt.%) 51SrO-9ZnO-(40−x)SiO₂ (SZS), where x represents the additives like B₂O₃, Al₂O₃, V₂O₅, and Cr₂O₃, were prepared by melt-quench method and transformed into glass-ceramics by controlled crystallization based on differential thermal analysis (DTA) data. Glasses and glass-ceramics were characterized using dilatometry, X-ray diffraction (XRD), microhardness, and Raman spectroscopy. XRD revealed that glass-ceramics are composed of mainly Sr₂ZnSi₂O₇ and SrSiO₃ crystalline phases along with residual glassy phase. Raman spectroscopy showed that main constitutes of the glass network are the Q¹ and Q² silicate structural units. With the addition of B₂O₃ and other additives silicate glass network depolymerizes and concentration of Q¹ structural units increases at the expense of Q² units. Formation of phases during crystallization depends on the presence of different silicate structural units in the glass matrix. B₂O₃ goes into the glass network as triangular (BO₃) borate structural units and at higher concentration of B₂O₃, only a part of the B₂O₃ forms tetragonal (BO₄) structural units. Investigated glasses and glass-ceramics have thermal expansion coefficient (TEC) in the range of 105-120 × 10⁻⁷/°C which matches with TEC of other cell components. B₂O₃ containing SZS glasses show good adhesion/bonding with YSZ and Crofer 22 APU. Elemental line scans indicate that interdiffusion of Fe, Cr and Si across interface is responsible for good bonding with Crofer 22 APU and interdiffusion of Sr, Si, Y and Zr is responsible for good bonding with YSZ.     

Quality Assessment of Mine Water in the Raniganj Coalfield Area,India

In a qualitative assessment of mine water from the Raniganj coalfield, 77 mine water samples were analyzed to assess water quality and suitability for domestic, industrial, and irrigation uses. The pH of the mine water ranged from 6.5 to 8.8. Total dissolved solids (TDS) ranged from 171 to 1,626 mg L⁻¹; spatial differences between the TDS values reflect variations in lithology, activities, and prevailing hydrological regime. The anion chemistry was dominated by HCO₃ ⁻ and SO₄ ²⁻. On average, Cl⁻ contributes 10 and 19% of the total anionic balance, respectively, in the Barakar and Raniganj Formation mine water. F⁻ and NO₃ ⁻ contribute <2% to the total anions. The cation chemistry is dominated by Mg²⁺ and Ca²⁺ in the mine water of the Barakar Formation and Na⁺ in the Raniganj Formation mines. Much of the mine water, especially of the Barakar Formation area, has high TDS, total hardness, and SO₄ concentrations. Concentrations of some trace metals (i.e. Fe, Cr, Ni) were found to be above the levels recommended for drinking water. However, the mine water can be used for irrigation, except at some sites, especially in the Raniganj Formation area, where high salinity, sodium adsorption ratio, %Na, residual sodium carbonate, and excess Mg restrict its suitability for agricultural uses.