ABSTRACT

To prevent COVID-19 from resurfacing and turning into a pandemic once more, new technologies that offer quick diagnosis of infections are desperately needed. The model may fail as a result of extreme opacity from lung diseases, differences in lung structure, and trouble observing the lung region, making this approach tough. This research proposes the use of automated deep learning for the segmentation of COVID-19 infection regions in X-ray of chest images. The proposed structure is made up of multiple phases. The first stage in the procedure is to use the Gamma Correction technique to improve the contrast. This is done to increase contrast and make the infection zones more uniform. Then, by replacing the encoder with the following pre-trained networks—MobileNetv2, Efficientv1, and Densenet121—we created an enhanced version of the U-Net network. In addition to rapidly extracting features, the networks are also able to prevent gradient expansion brought on by gradient backpropagation's multiplication effect. U-Net _Densenet121 achieves exceptional segmentation performance, as shown by experimental data, with a Dice coefficient of 0.9876, loss of 0.0125, recall of 0.9866, and precision of 0.9892.

Keywords:    Coronavirus pandemic, COVID-19, Diagnosing COVID-19 infections, U-Net, X-ray of chest.

ABSTRACT

The correct prediction of the confinement effect in short reinforced concrete columns is necessary to understand the true structural behavior of this member. The traditional confinement in concrete columns is fiber reinforced polymer wraps (FRP). In this paper, a new confinement model using wire mesh installed inside the columns were used. To study the new confinement model, specimens of concrete columns type were tested. One control specimen with traditional tied reinforcement is constructed. Other five columns were confined with wire mesh installed internally with different height. The columns were tested till failure using monotonically static load using hydraulic machine. From test results, the full wire mesh confinent increase the ultimate load by 8.15% compares with column confinement with tied reinforcement. Also, the failure mode changed from brittle rapture at tied reinforcement to ductile rapture at full wire mesh confinement.

Keywords: Short Column, Wire Mesh, Confinement Effect

ABSTRACT

The project focuses on developing models and simulations using contraction flow equations to analyze movement, in passages. The research seeks to comprehend behavior in situations, such, as pipeline design, cardiovascular system studies and device development. The inquiry focus on examining the flow behaviors, pressure changes, speed variations and turbulence, within constricted passages through a combination of computer simulations and real world tests. Mathematical models derived from the Navier Stokes equations for dynamics were used to gain insights. Computational Fluid Dynamics (CFD) simulations using tools such, as ANSYS Fluent or Open FOAM is applied to visualize flow patterns pinpoint zones of shear stress and measure pressure differentials at points. To confirm the reliability of the results it is important to check if the grid is independent and if there is convergence. To validate the findings one could consider conducting experiments using Particle Image Velocimetry (PIV) to study how pressure changes, in shapes. The expected results involve examining the flow properties and providing suggestions, for engineering purposes by analyzing parameter sensitivities

Keywords: Newtonian Fluids, Contraction Flow, Fluid dynamics, Mathematical Modeling, CFD Simulations, Experimental Validation, Pressure Drop, Velocity Profiles, Turbulence

ABSTRACT

Purpose of the Study: State owned commercial banks are the market leader of banking sector of Bangladesh. Recently they are facing a severe liquidity crisis, in spite of maintaining liquidity coverage ratio (LCR) and net stable funding ratio (NSFR) above the lowest supervisory regulation. Therefore, this study aims to find out the effect of Basel III liquidity ratio LCR & NSFR on the profitability of state owned commercial banks in Bangladesh. Methodology: This study takes secondary panel data covering the period 2015-2023 and ROE, ROA as dependent variables and LCR, NSFR, NPL as independent variables for random effects regression analysis. Moreover, at first panel unit root test is done to verify the stationary property of all study variables and then conducted Hausman specification test to select random effects regression model. Results: Study results show that LCR has statistically significant negative effect, whereas NSFR has positive impact on the profitability of state owned commercial banks

in Bangladesh, i.e. if LCR increases in 1 unit, it decreases profitability by 0.123 unit, whereas if NSFR increases in 1 unit, it raises profitability by 0.04 unit. Moreover, study also shows strong negative influence of NPL on the profitability of state owned commercial banks. Implication: Management of respective banks, Regulators like central bank, ministry of finance etc., academic researchers, journalists and other stake holders may utilize the result of this research for proper liquidity management, future research regarding liquidity management. Originality: Research study regarding the impact or effect of Basel III liquidity ratio LCR and NSFR on the profitability of state owned commercial banks is hardly available in the context of Bangladesh. Hence, this is an inventive effort to examine the effect of Basel III liquidity ratio LCR & NSFR on the profitability of state owned commercial banks in Bangladesh.

Keywords: Basel III liquidity Ratio, LCR, NSFR, Profitability, SOCB.

ABSTRACT

The use of direct solar dryers to dry agricultural crops and other products plays a significant role in the overall drying process. However, some dryers are more efficient and advanced than others, depending on the type of solar dryer used and the efficiency of the insulating materials used to reduce energy loss. There are also natural factors related to the characteristics of the region, which in turn control the energy-efficiency of a solar-dryer. These factors include the amount of solar radiation, the average time of solar radiation, and the average air humidity. This highlights the importance of studying the region and understanding its characteristics, including humidity, the amount of solar radiation, and the average time of solar radiation, to achieve the best energy efficiency for a solar dryer.

Keywords: Direct Dryers; Solar Dryers; Solar Energy

ABSTRACT

This study presents a detailed stability analysis of Newtonian boundary layer flow over a heated flat plate, incorporating the effects of temperature-dependent viscosity. Based on the classical Blasius boundary layer framework, the research investigates how thermal expansion, buoyancy forces, and mixed convection influence flow stability. Numerical methods, including finite difference schemes and spectral collocation using MATLAB and ProSPAN, are applied to solve the Favre-averaged Navier-Stokes and energy equations, along with turbulence model equations, while accounting for rough boundary wall effects. The governing equations are non-dimensionalized and analyzed using linear and nonlinear perturbation techniques. Eigenvalue analysis identifies critical Reynolds numbers and dominant instability modes. Simulations reveal that heating and wall roughness accelerate the transition to turbulence, while thermal control strategies enhance flow stability. Comparisons with experimental data validate the numerical results and show good agreement. Sensitivity analyses demonstrate that variations in Reynolds number and viscosity are key factors influencing stability thresholds. Overall, the study extends classical theories to more realistic heated surface flows and offers insights into controlling instability in practical thermal-fluid systems. These findings provide a foundation for further investigations into transitional and turbulent boundary layer behaviors under thermal effects.

Keywords: Boundary Layer Stability, Newtonian Fluids, Heated Plate, Temperature-Dependent Viscosity, Mixed Convection, Numerical Simulation, MATLAB, Spectral Collocation, Thermal-fluid systems.

ABSTRACT

This study aims to determine the impact of modern crisis management strategies on the marketing performance of the Arab Potash Company, where the independent variable is represented by five main modern strategies (work team strategy, tactical reserve strategy, democratic participation strategy, crisis containment strategy, crisis escalation strategy), and the dependent variable, which is the marketing performance of the company, which is represented by the company's annual net sales , during the period between 2015-2024. The data obtained through the study sample, which amounted to 250 employees, out of the study population of about 1700 employees, and through hypothesis testing using the statistical program Statistical Package for the social sciences, Spss.26) at a significant level (a ≤ 0.05), it was found that there is an impact and a strong correlation between the dimensions of modern crisis management strategies under study, and the marketing performance in the Arab Potash Company. One of the most important recommendations made by the study was the need for the Arab Potash Company to have sufficient knowledge in the use of modern crisis management strategies, and work to train the company's employees to acquire the necessary skills in this field, and to form an efficient and effective team in the company, with appropriate qualifications and experience, to prepare future plans to face crises as soon as they occur, especially since the Arab Potash Company deals with foreign markets, which requires caution and efforts. Striving to maintain its market share, and even work to increase it exponentially and from year to year.

Keywords: Crisis Management, Crisis Management Strategies, Institutional Performance, Marketing Performance.

ABSTRACT

Predictive Maintenance (PdM) has emerged as a critical strategy in modern industrial operations, aimed at anticipating equipment failures and reducing unplanned downtime. Among various techniques, Acoustic Emissions (AEs) analysis has gained prominence due to its non-invasive nature and ability to detect early signs of machine degradation. The integration of machine learning, particularly Convolutional Neural Networks (CNNs), has further enhanced the accuracy and efficiency of PdM systems. This work advocates for the use of CNNs in PM using AEs in motor’s bearings to identify potential issues at an early stage. Three types of defects are considered in this work outer, roller, and inner while the normal case has been added as a fourth class. Under various operational circumstances, a dataset of features, including acoustic signatures, was gathered. The depiction of rare but important failure signatures was enhanced using data augmentation techniques. A prediction accuracy of 98.5% was attained by our model. The model also proved to be very resilient to noisy data, which makes it appropriate for real-world uses. Based on these findings, CNNs can be easily included in smart manufacturing systems and provide real-time predictions to minimize machine downtime and increase tool lifespan when customized for sensor data.

Keywords: Predictive Maintenance; Acoustic Emissions; 2D-CNN, PCA