Vol. 4 No. 09 (2024): Volume 04 Issue 09

The work studied the antibacterial activity of a flavonoid extract isolated from the medicinal plant Physalis alkekengi against conditionally pathogenic and pathogenic bacteria, in particular, Proteus mirabilis 9, Escherichia coli NC 101, Listeria monocytogenes and Bacillus subtilis. The immunostimulating effect of the plant flavonoid extract in the lymphoid organs of immunized animals was also studied in vivo.

The article is devoted to the experience of Uzbekistan in organizing the activities of regional cotton-textile clusters. It analyzes the capabilities of regional clusters in the economic development of industries and regions. The goals of clustering the cotton-textile industry in Uzbekistan and the measures being implemented to achieve these goals are described.

A process for obtaining naphthalene, indene and phthalic anhydride based on distillation of liquid and solid fractions of heavy pyrolysis products has been implemented. The quantitative and qualitative composition of resin samples and the products of processing their fractions have been determined. Superplasticizers-additives for cement have also been created, their strength, average density of cement particles, the effect of the amount of superplasticizers and the duration of their action on the properties of the material have been studied.

This article provides detailed information on aircraft maintenance and repair using information technology. In addition, special attention is paid to the development of models and algorithms for solving these problems.

Automation in manufacturing refers to using technology and machines to perform specific tasks without the need for humans to intervene. The goal of automation is to increase efficiency, productivity, and accuracy in the production process, reducing manual labour and minimizing the risk of human error.Recent supply chain disruptions and a squeeze on labour markets means automation has now become more important than ever.According to the International Federation of Robotics, there are around 3.5 million industrial robots in operation around the globe. The Federation noted this represents a growth rate of more than 30% year-on-year, with a massive surge in demand since the pandemic.

The removal of nitrogen compounds in wastewater treatment is a critical process in maintaining environmental standards and preventing eutrophication. This study investigates the impact of thermal conditions on the efficiency of nitrogen compound removal in activated sludge systems. Using a series of controlled laboratory experiments, the study explores how variations in temperature influence key processes such as nitrification and denitrification. The findings indicate that temperature plays a significant role in the activity and growth rates of nitrifying and denitrifying bacteria, with optimal performance observed within a specific temperature range. Higher temperatures accelerated the removal of ammonium, while lower temperatures hindered the denitrification process, leading to incomplete nitrogen removal. The study also discusses the implications of these results for the design and operation of wastewater treatment plants, particularly in regions subject to temperature fluctuations. Understanding the relationship between temperature and nitrogen removal efficiency provides valuable insights for optimizing activated sludge systems and achieving more sustainable wastewater management practices.

Department of MCA, Siddaganga Institute of Technology, Tumkur, Karnataka, IndiaEar biometrics have emerged as a reliable and non-invasive method for personal identification, offering distinct advantages in terms of stability and ease of acquisition. This study explores the application of Support Vector Machines (SVM) for the classification of ear biometric data, aiming to enhance the accuracy and efficiency of identification systems. By leveraging a comprehensive dataset of ear images, we employed SVM techniques to classify individual ear patterns, addressing challenges such as variability in image quality and alignment.

The research focuses on optimizing SVM parameters to improve classification performance, comparing linear and nonlinear kernels to determine the most effective approach. Our results demonstrate a significant improvement in classification accuracy, with SVM proving to be a robust method for ear biometric identification. This study highlights the potential of advanced SVM techniques in developing more reliable and efficient biometric systems, contributing to the broader field of biometric security.