In this study, we employed first-principles Density Functional Theory (DFT) calculations using the Perdew–Burke–Ernzerhof (PBE) pseudopotential in Quantum ESPRESSO code to investigate the structural, electronic, mechanical, vibrational, and thermodynamic properties of MgAg and MgAu intermetallic compounds. Structural optimizations confirmed that both compounds crystallize in the cubic Tetraauricupride structure with excellent agreement between the calculated lattice constants and available literature. The electronic band structures and projected density of states (PDOS) revealed the metallic nature of both compounds, with distinct orbital contributions near the Fermi level. Mechanical stability was confirmed through elastic constants satisfying the Born-Huang criteria, with MgAu demonstrating higher stiffness, lower compressibility, and slightly greater ductility compared to MgAg. Phonon dispersion relations and density of states confirmed the dynamical stability of both compounds, showing positive phonon frequencies across the Brillouin zone. Thermodynamic analyses, including vibrational free energy, entropy, heat capacities, and the Grüneisen parameter, indicated that MgAu exhibits enhanced thermal stability and stronger thermal expansion effects. The consistency between results obtained from phonon density of states and Brillouin zone integration further validates the accuracy of the predictions. These findings provide valuable insights into the potential applications of MgAg and MgAu alloys in areas such as materials design, catalysis, and electronic devices.

The extraordinary compressive strength of concrete makes it one of the most popular building materials, second only to water. However, early reinforcement corrosion is a common problem with concrete structures reinforced with carbon steel (CS). This issue underscores the significance of understanding corrosion-retarding systems that incorporate inhibitors. This research evaluates the effectiveness of leaf extract from Elaeocarpus angustifolius Blume (LEEA) as an environmentally friendly corrosion inhibitor in a saturated calcium hydroxide solution that simulates a concrete pore solution (SCPS) with a pH level exceeding 11.5. The objective was to evaluate the corrosion-inhibiting capacity of LEEA at concentrations of 500, 1000, 2000, and 4000 ppm in SCPS to reduce reinforcing carbon steel (RCS) corrosion at a laboratory temperature of 298K over four months or more using gravimetric weight loss (GrWL) and electrochemical polarization (ECP) techniques. GrWL and ECP methods yielded the maximal inhibition efficiencies of 93.9% and 81.7%, respectively, at 4000 ppm LEEA in SCPS. The results of ECP experiments demonstrated that the corrosion current density (CCD) decreased with increasing LEEA concentrations. This observation indicates that LEEA exhibits a significant inhibitory effect in the SCPS environments. Phyto-compounds (PCs), including polyphenols, alkaloids, and flavonoids found in LEEA, can inhibit both cathodic and anodic (i.e., mixed) processes by promoting the best-fitting Langmuir adsorption isotherm model on the RCS surface. Surface analyses, such as white light interferometry (WLI), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX), confirmed the corrosion-inhibiting effectiveness of LEEA in enhancing concrete's anti-corrosion properties through a passive layer on RCS.

Human intervention cannot halt natural disasters like earthquakes, but machine learning application expertise can be utilized to detect patterns in data and increase understanding and predictive power. Recent development of machine learning models has increasingly developed interest in forecasting and predicting the magnitude of earthquakes. In this work, Random Forest Regressor (RFR), Multi-Layer Perceptron Regressor(MLPR), and Support Vector Regression (SVR) models were employed to predict the magnitude of greater than 6 mb earthquakes that occurred in the year 2015 in the central Himalaya. We noticed RFR method had been able to predict the magnitude of the Gorkha earthquake (6.9mb), and the Kodariearthquake (6.7 mb) in comparison with the other two models. We also checked the performance of these models by three parameters Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) and noticed the better performance of RFR model. The findings illustrate that RFR is achieving better performance than the other two algorithms, as the predicted magnitudes are close to the actual magnitudes.

Diabetes mellitus poses a significant risk for cardiovascular complications, including the formation of blood clots, which can lead to severe consequences such as heart attacks and strokes. In this study, we employ mathematical modeling and computational analysis to investigate the mechanisms underlying the increased risk of clot formation in diabetic patients. Our approach integrates physiological data, hemodynamic principles, and mathematical equations to simulate blood flow dynamics and clot formation processes within the vasculature of diabetic individuals. By incorporating key factors such as altered blood viscosity, resistance to flow, endothelial dysfunction, and platelet aggregation, we obtained insights into the complex interplay between diabetes related factors and clotting propensity. Changes in blood composition, such as increased levels of fibrinogen and other clotting factors, can make blood thicker and more prone to clotting and the reason for increased resistance to flow and viscosity. As blood clots enlarge in blood vessels, they obstruct blood flow, increasing resistance. This makes blood movement harder. Clot size also affects nearby blood viscosity. Accumulating cells and clotting factors thicken blood, worsening circulation. Larger clots heighten flow resistance and viscosity, potentially causing issues like tissue damage. Thus, larger clots worsen blood flow and cardiovascular health. Through computational simulations, we explored various scenarios to assess the impact of different parameters on clot formation risk, thereby offering valuable insights for the development of preventive strategies and targeted interventions for diabetic patients.

Kidney stone is an alarming global disease due to its rising incidence and prevalence. FTIR spectroscopic analysis reveals that calcium oxalate is one of the most frequent chemical constituents in kidney stones. DFT calculations indicate that the calcium oxalate can interact through charge transfer process in biological activities. Among various proteins, lysozyme is one of the promoter proteins in nephrolithiasis of calcium oxalate type kidney stone. The location, conformation and interactions of calcium oxalate with the active residues of lysozyme contribute the binding energy of -4.18 kcal/mol. The characterization of kidney stones, DFT calculations of calcium oxalate, and binding interactions of calcium oxalate-lysozyme complex contribute to the understanding of nephrolithiasis.

This research work is concerned with the comprehensive study of the electrical and optical characterization of an atmospheric pressure circular dielectric barrier discharge (APCDBD) in natural air. The effect of airflow and input voltage on the power supply’s behavior has been investigated to keep the electrode gap constant. The energy and power dissipated during the discharge cycle are calculated by the Lissajous plot and the time average of the voltage and current curve. For the optical characterization of APCDBD, light emission during the plasma discharge is examined electron excitation temperature, rotational temperature, vibrational temperature, and plasma density have been computed. The Boltzmann plot method is used to estimate the electron excitation temperature and vibrational temperature. The rotational and also vibrational temperatures are calculated from online MassiveOES software. It is found that multiple filamentry micro discharge is produced increasing the airflow and input voltage of the power supply. Energy per cycle of the discharge is increased for increasing input voltage and vice-versa in airflow. The discrepancy in the calculation of energy dissipation from discharge between time-averaged and Lissajous’s plot method is estimated. However, the fluctuation is less in the Lissajous plot method and hence, the calculation of energy from the Lissajous plot method is more suitable for plasma discharge. The power consumption of the plasma reactor is found to be dependent on the input of the power supply. Electron excitation temperature and the rotational temperature of the discharge decrease with increasing the airflow in the discharge and increase with increasing input voltage. Therefore, by using the airflow in dielectric barrier discharge, we can move closer to the room temperature of atmospheric pressure plasma.

This work presents the comparative study of the properties of dust within the W51 Giant Molecular Cloud (GMC) located at (RA, DEC) (J2000): 290.91°, +14.51°. In the infrared data of Improved Reprocessing of the IRAS (IRIS) the dust structure seems single and isolated having size 0.45°×0.45° but in AKARI infrared survey it breaks into two isolated regions having size 0.15°×0.15° and 0.10°×0.10°, represented by AKARI-I and AKARI-II respectively. In IRIS map, 60 and 100 mm and in AKARI map, 90 and 140 mm image are used for extraction of the infrared flux. The dust color temperature (Td) and dust mass (Md) are calculated from the infrared flux and found to 30.34 K in IRIS and 27.40 K and 23.55 K in AKARI-I and AKARI-II. A linear relationship between the infrared flux at two wavelength is found except AKADI-I. The dust mass per pixels in the isolated (core) region is found to be increase compare to total study region. To quantify the relation between flux, dust color temperature and dust mass the regression analysis is used and observed spectrum of relation. A huge SIMBAD background objected is found embedded in the dust cloud which might have been shaping the spatial variation of temperature and mass within dust cloud.

Reducing lattice thermal conductivity (κl) that reflects the material’s heat-carrying capacity through lattice phonon vibrations is crucial for optimizing the figure of merit (zT). Using density functional theory (DFT) and density functional perturbation theory (DFPT), present work considers the structural, electronic, magnetic, and phonon properties of the XNiSn (X=Ti, Zr, Hf ) half Heusler (hH) compounds. TiNiSn, ZrNiSn, and HfNiSn are identified as semiconductors with indirect band gaps of 0.43 eV, 0.47 eV and 0.39 eV, respectively, exhibiting dynamical stability. The temperature- dependent κl of hH XNiSn are compared using Slack’s model with two approaches for analyzing phonon velocity: elastic constants from quasi- harmonic approximation (QHA) as implemented in the Thermo_pw code and slope of phonon bands based on DFPT. At room temperature, TiNiSn, ZrNiSn and HfNiSn have κl values of 28.61 Wm−1K−1, 34.61 Wm−1K−1 and 29.85 Wm−1K−1 respectively using phonon velocity from slopes of phonon bands based on DFPT. These values show deviation of 1.48%, 6.29%, and 5.82% to those κl values 29.01 Wm−1K−1’, 36.79 Wm−1K−1 and 31.59 Wm−1K−1 for TiNiSn, ZrNiSn and HfNiSn respectively using QHA.

In this work, we have studied the dust properties of the North-East part of Perseus cloud having a size of 0.5o × 0.5o located at RA (ICRS): 56.14o DEC (ICRS): +32.15o, within the Open Cluster IC 348, using IRIS and AKARI data. An isolated region of size 0.39o×0.23o in IRIS and 0.34o×0.16o in AKARI data is detected within it. The infrared fluxes extracted using Aladin v11.0 are used to study the dust temperature and dust mass. The distance of the selected dust structure is calculated using Gaia EDR3, which is 309.98 pc. The infrared flux density is found to be increased for long-wavelength data. The average dust color temperature calculated from short-wavelength IRIS data is more than that calculated from long-wavelength AKARI data, which are 26.34 K±0.11 K for IRIS and 17.63 K±0.02 K for AKARI data. The mass of dust within the entire dust structure is 0.06 Mʘ for IRIS data and 37.44 Mʘ for AKARI data. Jeans mass for isolated region gives contradictory results in two surveys, for IRIS survey total mass is smaller than Jeans mass but for AKARI survey the total mass is larger than the Jeans mass. A good correlation between infrared fluxes is noticed for linear regression. The study background sources observed from the SIMBAD database explore the large number of stars, X-ray sources, YSOs, etc., embedded within the dust structure; some of them are responsible for dust heating and some for the contribution of dust mass. The contour map shows the identical distribution between infrared fluxes, dust color temperature and Planck’s function and dissimilar distribution between dust mass and visual extinction in IRIS and AKARI data. The high temperature at the central region suggests that the core of dust structure is thermally active, radiating the large thermal radiation in comparison to the outer region.

Postharvest loss of vegetables and fruits is higher in developing countries due to the lack of post-processing equipment. Although the production of ginger and turmeric is high in Nepal, farmers practices open sun drying, which leads to poor quality, and the products do not get to the market at reasonable prices. In this study, a simple, easy to assemble and disassemble mixed-mode solar dryer has been designed, fabricated, and biochemical and performance analyses were done. The dryer was designed considering the solar irradiation, sunshine hours, latitude of the location, locally available materials, moisture content (MC) of the product, and drying capacity. The Computational Fluid Dynamics (CFD) analysis was performed using ANSYS Fluent simulation software to predict the dryer’s temperature and air flow behavior. The experiment was carried out in November in the harvesting season of ginger. The dryer performance is compared with the open sun drying using ginger and turmeric. The biochemical properties such as Oleoresin and Essential oil were determined using Soxhlet and Steam distillation methods and compared with the product of open sun drying. The experiment results showed that ginger and turmeric took only about 14 sunshine hours to reach from 88% to 10-12% moisture content. The maximum collector and drying efficiency were recorded at about 45.32% and 31.364%, respectively. The temperature distribution inside the dryer obtained from the experiment correlated with the results obtained from the CFD analysis. The retention of essential oil was found more in product drying with dryer than open sun drying. Among the various drying models, Wang and Singh model was found suitable for describing the drying model. It is concluded that the designed dryer is suitable for ginger and turmeric drying into a safe moisture level, ensuring product quality. BIBECHANA 19 (2021) 40-60