Multi‐bit Boolean model for chemotactic drift of Escherichia coli

Dynamic biological systems can be modelled to an equivalent modular structure using Boolean networks (BNs) due to their simple construction and relative ease of integration. The chemotaxis network of the bacterium Escherichia coli (E. coli ) is one of the most investigated biological systems. In this study, the authors developed a multi‐bit Boolean approach to model the drifting behaviour of the E. coli chemotaxis system. Their approach, which is slightly different than the conventional BNs, is designed to provide finer resolution to mimic high‐level functional behaviour. Using this approach, they simulated the transient and steady‐state responses of the chemoreceptor sensory module. Furthermore, they estimated the drift velocity under conditions of the exponential nutrient gradient. Their predictions on chemotactic drifting are in good agreement with the experimental measurements under similar input conditions. Taken together, by simulating chemotactic drifting, they propose that multi‐bit Boolean methodology can be used for modelling complex biological networks. Application of the method towards designing bio‐inspired systems such as nano‐bots is discussed.Inspec keywords: cell motility, microorganisms, Boolean functionsOther keywords: multibit Boolean approach, conventional BNs, high‐level functional behaviour, steady‐state responses, chemoreceptor sensory module, drift velocity, chemotactic drifting, multibit Boolean methodology, complex biological networks, bio‐inspired systems, multibit Boolean model, chemotactic drift, dynamic biological systems, equivalent modular structure, Boolean networks, simple construction, chemotaxis network, bacterium Escherichia coli, biological systems     

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.     

Machine Learning-Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high-quality datasets at appropriate volumes. This article extends the suitability of such ML-enabled approaches to a new technological domain of EHD spraying and drop-on-demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree-based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.     

A stabilized volume‐averaging finite element method for flow in porous media and binary alloy solidification processes

A stabilized equal‐order velocity–pressure finite element algorithm is presented for the analysis of flow in porous media and in the solidification of binary alloys. The adopted governing macroscopic conservation equations of momentum, energy and species transport are derived from their microscopic counterparts using the volume‐averaging method. The analysis is performed in a single domain with a fixed numerical grid. The fluid flow scheme developed includes SUPG (streamline‐upwind/Petrov–Galerkin), PSPG (pressure stabilizing/Petrov–Galerkin) and DSPG (Darcy stabilizing/Petrov–Galerkin) stabilization terms in a variable porosity medium. For the energy and species equations a classical SUPG‐based finite element method is employed. The developed algorithms were tested extensively with bilinear elements and were shown to perform stably and with nearly quadratic convergence in high Rayleigh number flows in varying porosity media. Examples are shown in natural and double diffusive convection in porous media and in the directional solidification of a binary‐alloy. Copyright © 2004 John Wiley & Sons, Ltd.     

Synthesis of mesoporous geopolymeric powder from LD slag as superior adsorbent for Zinc (II) removal

Linz-Donawitz(LD) slag Geopolymer(LDSGP), a porous aluminosillicate geopolymeric adsorbent, has been synthesized from steel plant LD slag for efficient removal of Zinc(II) ions from wastewater, thus presenting a solution for converting industrial waste to adsorbent for wastewater treatment. The colloid paste of raw LD slag and the alkaline activator (10?M NaOH?+?sodium silicate (1:1 w/w)) has been cured for 3?days at low temperature to geopolymerize the calcium oxide rich LD slag. The BET surface area of LDSGP adsorbent (30.84?m²/g) has improved considerably compared to raw LDS (4.85?m²/g) and the FESEM and HRTEM images reveal the presence of micropetal and cauliflower like structures at outer surface of the adsorbent particles. The mesoporous nature of LDSGP adsorbent can be understood by analyzing N₂ adsorption-desorption and pore size distribution plot. The PXRD pattern of LDSGP adsorbent powder confirms the presence of Ca₂SiO₄ and Ca₃SiO₅ in the geopolymeric matrix. Langmuir isotherm model correlates the batch adsorption data of Zn²⁺ ions onto LDSGP particles at 298?K, 308?K and 318?K. The maximum Zn²⁺ ions adsorption capacity of LDSGP is 86?mg/g at 318?K. The adsorption kinetic data is correlated with pseudo-second-order model indicating chemisorption of Zn²⁺ ions onto LD slag geopolymeric powder adsorbent.     

Microwave-assisted route for synthesis of nanosized metal oxides

Microwave-assisted route for the synthesis of nanomaterials has gained importance in the field of synthetic technology because of its faster, cleaner and cost effectiveness than the other conventional and wet chemical methods for the preparation of metal oxide nanoparticles. In the present work, synthesis of metal oxide nanoparticles viz., γ-Fe₂O₃, NiO, ZnO, CuO and Co-γ-Fe₂O₃ were carried out by microwave-assisted route through the thermal decomposition of their respective metal oxalate precursors employing polyvinyl alcohol as a fuel. The metal oxide nanoparticles are then characterized for their size and γ to α (in γ-Fe₂O₃) transition and structure by employing powder X-ray diffraction (XRD) pattern, high-temperature X-ray diffraction (HTXRD) pattern and Fourier transform infrared (FT-IR) spectral studies. The morphology of the samples ranged from nanorods to irregular-shaped particles for different metal oxide samples on the basis of scanning electron microscopy and transmission electron microscopy images. Frequency-dependent dielectric study of the ferrite samples (γ-Fe₂O₃ and Co-Fe₂O₃) showed a similar behaviour, where the dielectric constant decreased rapidly with increase in frequency. Possible explanation for this behaviour is given.     

Electrocoagulation of methylene blue and eosin yellowish using mild steel electrodes

The paper presents the study of electrocoagulation (EC) of aqueous dye solutions of two different industrial dyes in a batch stirred cell. Experiments were carried out with 200 mg/l individual concentration of methylene blue (MB) and eosin yellowish (EY) in presence of NaCl as electrolyte. Effect of operating time and current density on the decolorization of dye solutions, reduction of chemical oxygen demand (COD) and variation in conductivity, pH during treatment has been studied. Small difference between color diminution and COD reduction has been found with the progress of treatment. First-order rate equation for dye removal has been developed from the experimental results. Sludge formation during EC and problems associated with this solid waste generation and disposal has been assessed. Energy consumption in KWh/m(3) with reduction of COD (kg) during treatment has been reported. Electric power consumption of 1.5 KWh reduces 0.21 and 0.11 kg COD from 0.24 and 0.14 kg of initial COD for MB and EY, respectively, starting from 200 mg/l dye concentration.