In the present work experiments were conducted to study  the effect of solid loading (1,5 and 9 vol.%) on the enhancement of carbon dioxide absorption in bubble column at various volumetric gas flow rate (0.75, 1 and 1.5 m³/h) and absorbent concentration (caustic soda)( 0.1,0.5 and 1 M  ). Activated carbon and alumina oxide (Al₂O₃) are used as solid particles. The Danckwerts method was used to calculate interfacial area and individual mass transfer coefficients during absorption of carbon dioxide in a bubble column. The results show that the absorption rate was increased with increasing volumetric gas flow rate, caustic soda concentration and solid loading. Mass transfer coefficient and interfac

This study presents a rapid, sensitive, and straightforward approach to measure chlorpheniramine maleate (CPM) by using turbidity CFIA. The method involves CPM reacting with sodium nitroprusside (Nitropress) to produce a pale white precipitate. The NAG-SSP-5S1D analyzer was used to measure turbidity at 0°–180° angle to detect the attenuation of incident light as a result of collision on the surfaces of the precipitate particles. The linear range of CPM measurements was between 0.008 and 11 m.mol/L, with correlation coefficient of 0.9983 and R2% = 99.65. The limit of detection was determined to be 0.0328 µg/sample from the lowest concentration in the calibration curve, and the repeatability of the method (RSD%) was less than 0.4% (n = 6

The research dealt with a comparative study between some semi-parametric estimation methods to the Partial linear Single Index Model using simulation. There are two approaches to model estimation two-stage procedure and MADE to estimate this model. Simulations were used to study the finite sample performance of estimating methods based on different Single Index models, error variances, and different sample sizes , and the mean average squared errors were used as a comparison criterion between the methods were used. The results showed a preference for the two-stage procedure depending on all the cases that were used

    The Atoms in Molecules (AIM) analysis for triosmium cluster, which contains trihydridede, carbon, carbonyl and 2-methylbenzothiazolide ligands, [Os₃(µ-H)₃(µ³-ɳ²-CC₇H₃(2-CH₃)NS)(CO)₈] is reported. Bonding features in this cluster has been analyzed based on QTAIM ("Quantum Theory of Atoms in Molecules") in this work. The topological indices derived from electron density of relevant interactions in triosmium compound have been studied. The major interesting point of the AIM analyses is that the core of part (Os₃H₃) reveals the absence of any critical points and bond paths connecting any pairs of O

Crop diseases are usually caused by inoculum of pathogens which might exist on alternate hosts or weeds as endophytes. These endophytes, cum pathogens, usually confer some beneficial attributes to these weeds or alternate hosts from protection against herbivores, disease resistance, stress tolerance to secondary metabolites production. This study was therefore carried out to isolate potential crop pathogens which exist as endophytes on weed species in the University of Ilorin plantations. Green asymptomatic leaves were collected from 10 weed species across the plantations, and processed for their endophytic fungi isolation. Isolates were purified into pure cultures and used for molecular identification using the internal transcribed spac

This study employs a critical discourse analysis approach to investigate the linguistic and discursive mechanisms employed by the prominent Russian online news platform Gazeta.ru in its coverage of social news. Drawing on an interdisciplinary framework integrating critical discourse analysis (CDA), media discourse analysis, and sociolinguistic perspectives, the research examines how language is used to construct and disseminate societal narratives. The analysis focuses on a dataset of Gazeta.ru articles published in March 2024, encompassing topics such as health, travel, and consumer affairs. Through a multi-level analytical approach, the study explores macro-level discursive strategies and microlevel linguistic choices, unveiling the intri

Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM

Spatial Autoregressive Model (SAR) is one of the modeling frameworks that indicates a spatial dependence in the response variable. SAR model has a weakness, which is represented by the unknown variance of the residuals. Therefore, an alternative model has used titled Spatial Autoregressive Quantile Regression (SARQR) model That which is obtained by combining SAR and Quantile Regression (QR) models, is a regression method with the approach of dividing the data into particular quantiles that are likely to have different estimate values. This alternative model addresses the variance issues in SAR models. Additionally, the SARQR model not only resolves the issue of spatial variance but also serves as a solution for dealing with non-normal data

In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

In order to improve the effectiveness, increase the life cycle, and avoid the blade structural failure of wind turbines, the blades need to be perfectly designed. Knowing the flow angle and the geometric characteristics of the blade is necessary to calculate the values of the induction factors (axial and tangential), which are the basis of the Blade Element Momentum theory (BEM). The aforementioned equations form an implicit and nonlinear system. Consequently, a straightforward iterative solution process can be used to solve this problem. A theoretical study of the aerodynamic performance of a horizontal-axis wind turbine blade was introduced using the BEM. The main objective of the current work is to examine the wind turbine blade’s perf