Membranes for CO2 /CH4 and CO2/N2 Gas Separation

Membrane technology has emerged as a leading tool worldwide for effective CO₂ separation because of its well-known advantages, including high surface area, compact design, ease of maintenance, environmentally friendly nature, and cost-effectiveness. Polymeric and inorganic membranes are generally utilized for the separation of gas mixtures. The mixed-matrix membrane (MMM) utilizes the advantages of both polymeric and inorganic membranes to surpass the trade-off limits. The high permeability and selectivity of MMMs by incorporating different types of fillers exhibit the best performance for CO₂ separation from natural gas and other flue gases. The recent progress made in the field of MMMs having different types of fillers is emphasized. Specifically, CO₂/CH₄ and CO₂/N₂ separation from various types of MMMs are comprehensively reviewed that are closely relevant to natural gas purification and compositional flue gas treatment     

Electrochemical performance of NiFe2O4 nanostructures incorporating activated carbon as an efficient electrode material

The quest for cost-efficient electro-active materials exhibiting high specific capacitance is currently a key focus in energy-related research. Owing to their high capacitance values, metal oxides (MOs) are preferably being utilized for energy storage applications as electrode materials in supercapacitors. However, the electrochemical performance of MOs is hindered due to less specific surface area and high tendency towards aggregation. Therefore, tuning in electrochemical activity of MOs is essential. In this framework, NiFe₂O₄ was prepared using a facile and cost-effective citrate-gel followed by auto-ignition method, and was incorporated with activated carbon contents to tune the electrochemical performance. Formation of inverse spinel structure of NFO and its stability throughout the compositions was examined using X-ray diffraction analysis. Well-dispersed, spherical and porous morphological features were visualized using a field emission scanning electron microscope. The electrochemical analysis was conducted using CH instruments 660 E via freshly prepared 4 M KOH solution. Cyclic voltammetry was carried out at constant potential window of 0.25–0.65 V and different scan rates (0.009–0.08 Vs^-1). The pseudo-capacitive behavior was perceived from occurrences of oxidation/reduction peaks. In addition, charge/discharge curves revealed cyclic stability over long range cycles. Specific capacitance, discharge time, energy and power density values were also measured for all the compositions and NFO with 1% activated carbon was found to be the most suitable candidate for use as electrode materials in the present work.     

Adomian decomposition solution for propulsion of dissipative magnetic Jeffrey biofluid in a ciliated channel containing a porous medium with forced convection heat transfer

Physiological transport phenomena often feature ciliated internal walls. Heat, momentum, and multispecies mass transfer may arise and additionally non‐Newtonian biofluid characteristics are common in smaller vessels. Blood (containing hemoglobin) or other physiological fluids containing ionic constituents in the human body respond to magnetic body forces when subjected to external (extracorporeal) magnetic fields. Inspired by such applications, in the present work we have considered the forced convective flow of an electrically conducting viscoelastic physiological fluid through a ciliated channel under the action of a transverse magnetic field. The presence of deposits (fats, cholesterol, etc.) in the channel is mimicked with a Darcy porous medium drag force model. The effect of energy loss is simulated via the inclusion of viscous dissipation in the energy conservation (heat) equation. The velocity, temperature, and pressure distribution are computed in the form of infinite series constructed by Adomian decomposition method and numerically evaluated in a symbolic software (Mathematica). The influence of Hartmann number (magnetic parameter), Jeffrey first and second viscoelastic parameters, permeability parameter (modified Darcy number), and Brinkman number (viscous heating parameter) on velocity, temperature, pressure gradient, and bolus dynamics is visualized graphically.     

Synthesis and characterizations of a fluoride-releasing dental restorative material

The aim was to develop an obturating material which has the tendency to release fluoride and minimize interfaces with tooth. Nano-fluorapatite (nFA) powder was synthesized by sol–gel. The composite based on polyurethane (PU) was obtained by chemically binding the nFA (10, 15, 20% wt/wt) to the diisocyanate component by utilizing in-situ polymerization. The procedure involved stepwise addition of monomeric units of PU, and optimizing the reagent concentrations to synthesize composite. The structural, phase and morphological analysis of nFA was evaluated. The structural, fluoride release and in-vitro adhesion analysis with tooth structure of PU/nFA was conducted. For fluoride release analysis the samples were stored in artificial saliva and deionized water for periodical time intervals. Bond strength of composites was analyzed by push-out test. Chemical linkage was achieved between PU and nFA without intermediate coupling agent. The insignificant difference of fluoride release pattern was observed in artificial saliva and (p ≥ 0.05) deionized water. The PU/nFA composite provided sustained release of fluoride over a long period of time. The composite showed more adhesion toward tooth structure with the increase in concentration of nFA. Bond strength of composite was in accordance with root canal filling material, hence, the material with anti-cariogenic properties can be used as an obturating material.     

The dynamics of three-mode coupling in a trapped Bose gas

ABSTRACT

Multiple mode couplings in topological coherent modes of Bose–Einstein condensate are considered, by introducing an external alternating (resonating) field in the system. This analysis is based on the analytical solutions of nonlinear Gross–Pitaevskii equation for a trapped Bose gas at nearly absolute zero temperature. The dynamics of fractional populations of the generated coherent modes are analysed, particularly for a three-level system in the limit of small to large detuning of the intermediate state. These coupled topological modes, though nonlinear, are analogous to a resonant atom and exhibit a variety of significant non-trivial phenomena (effects), like: dynamic phase transitions, interference patterns, critical phenomena, mode-locking and chaotic motion.     

Wavelets-based facial expression recognition using a bank of support vector machines

A human face does not play its role in the identification of an individual but also communicates useful information about a person’s emotional state at a particular time. No wonder automatic face expression recognition has become an area of great interest within the computer science, psychology, medicine, and human–computer interaction research communities. Various feature extraction techniques based on statistical to geometrical data have been used for recognition of expressions from static images as well as real-time videos. In this paper, we present a method for automatic recognition of facial expressions from face images by providing discrete wavelet transform features to a bank of seven parallel support vector machines (SVMs). Each SVM is trained to recognize a particular facial expression, so that it is most sensitive to that expression. Multi-classification is achieved by combining multiple SVMs performing binary classification using one-against-all approach. The outputs of all SVMs are combined using a maximum function. The classification efficiency is tested on static images from the publicly available Japanese Female Facial Expression database. The experiments using the proposed method demonstrate promising results.     

An Ensemble Machine Learning Technique for Stroke Prognosis

Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those in Machine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this research proposed an ensemble voting model based on three Machine Learning (ML) algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). We apply data preprocessing to manage the outliers and useless instances in the dataset. Furthermore, to address the problem of imbalanced data, we enhance the minority class’s representation using the Synthetic Minority Over-Sampling Technique (SMOTE), allowing it to engage in the learning process actively. Results reveal that the suggested model outperforms existing studies and other classifiers with 0.96% accuracy, 0.97% precision, 0.97% recall, and 0.96% F1-score. The experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches.     

Ensemble Learning for Fetal Health Classification

: Cardiotocography (CTG) represents the fetus’s health inside the womb during labor. However, assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician. Digital signals from fetal monitors acquire parameters (i.e., fetal heart rate, contractions, acceleration). Objective:: This paper aims to classify the CTG readings containing imbalanced healthy, suspected, and pathological fetus readings. Method:: We perform two sets of experiments. Firstly, we employ five classifiers: Random Forest (RF), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) without over-sampling to classify CTG readings into three categories: healthy, suspected, and pathological. Secondly, we employ an ensemble of the above-described classifiers with the over-sampling method. We use a random over-sampling technique to balance CTG records to train the ensemble models. We use 3602 CTG readings to train the ensemble classifiers and 1201 records to evaluate them. The outcomes of these classifiers are then fed into the soft voting classifier to obtain the most accurate results. Results:: Each classifier evaluates accuracy, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values. Results reveal that the XGBoost, LGBM, and CatBoost classifiers yielded 99% accuracy. Conclusion:: Using ensemble classifiers over a balanced CTG dataset improves the detection accuracy compared to the previous studies and our first experiment. A soft voting classifier then eliminates the weakness of one individual classifier to yield superior performance of the overall model.     

Elemental composition of rice using calibration free laser induced breakdown spectroscopy

In this paper, we apply laser induce breakdown spectroscopy (LIBS) to determine the elemental composition of different parts (root, stem and seed) of the rice plant and determine their weighted concentration using calibration free laser induced breakdown spectroscopy (CF-LIBS) technique. Ca, Fe and K are identified as major elements, while C, Ti, Mg, Si, Li, Ba, Sr, Cr, Na and Al as minor elements. We also detect the H-alpha line of hydrogen in the spectrum and determine the electron number density. The electron number density and its behavior as a function of laser energy, laser wavelength and the detector position are investigated. The plasma temperatures of samples are determined, and the validity of the assumption of the local thermodynamic equilibrium (LTE) is discussed.