Vol. 6 No. 06 (2024)

In the process of separating cotton fiber from seed in a sawed fiber separator, the formation of raw material depends on several factors. The most important of them are the speed of rotation of the raw material, fiber, density, the amount of seeds separated from the fiber, etc. In addition, it is necessary to take into account the force of friction created by the walls of the working chamber due to the pressure created in the raw material. A high coefficient of friction of the colossal working surface has a negative effect on work efficiency, in particular, frictional forces cause the seed separated from the fiber to remain on the colossal working surface. These factors have an effect on the performance of the fiber separator and the quality of the extracted fiber. It is known that in the process of ginning, 25% of the fiber and seeds separated from the fiber remain in the raw material pile, causing mechanical damage.

The main goal of the research is to prevent fiber and seed damage in the process of separating cotton fiber from the seed by installing a colosnik structure with the new recommended composition, to increase the efficiency of the seed exit from the working chamber, and at the same time to study ways to reduce energy consumption during the ginning process.

This research paper presents an analysis of the results obtained after installing one of the working parts of the gin machine on the DL-10 gin machine of the existing and recommended composition of colosniks. The main purpose of introducing the new construction is to increase the efficiency of the DL-10 gin machine of the society, to develop it by filling it with new technologies, and to obtain quality fiber and seed.

This article describes in detail the procedure and sequence of survey the technical condition of frame buildings made of multi-storey reinforced concrete structures.

Recent advancements in data science, coupled with the revolution in digital and satellite technology, have catalyzed the potential for artificial intelligence (AI) applications in forestry and wildlife sectors. Recognizing the critical importance of addressing land degradation and promoting regeneration for climate regulation, ecosystem services, and population well-being, there is a pressing need for effective land use planning and interventions. Traditional regression approaches often fail to capture underlying drivers' complexity and nonlinearity. In response, this research investigates the efficacy of AI in monitoring, predicting, and managing deforestation and forest degradation compared to conventional methods, with a goal to bolster global forest conservation endeavors. Employing a fusion of satellite imagery analysis and machine learning algorithms, such as convolutional neural networks and predictive modelling, the study focuses on key forest regions, including the Amazon Basin, Central Africa, and Southeast Asia. Through the utilization of these AI-driven strategies, critical deforestation hotspots have been successfully identified with an accuracy surpassing 85%, markedly higher than traditional methods. This breakthrough underscores the transformative potential of AI in enhancing the precision and efficiency of forest conservation measures, offering a formidable tool for combating deforestation and degradation on a global scale.

The production process for medical devices must precisely follow quality assurance (QA) procedures to comply with the sector's stringent regulatory requirements. Although conventional QA procedures are generally effective, they can be time-consuming and resource-intensive, which can lead to problems and increased costs. With its unprecedented potential for increased productivity, accuracy, and scalability, Artificial Intelligence (AI) has revolutionized quality assurance (QA) approaches across industries since its inception. In this study, we look at how artificial intelligence (AI) could improve medical device quality assurance procedures. Artificial intelligence (AI) methods such as computer vision, machine learning, and natural language processing can automate and optimize critical QA operations, allowing manufacturers to expedite production workflows, while improving product quality. Systems powered by artificial intelligence can sift through mountains of data in search of irregularities, defects, and faults, and they can do it in real-time. This lessens the likelihood of non-compliance problems and enables proactive response. Furthermore, QA systems driven by AI offer the ability to learn and adapt, which allows them to continuously improve performance by analyzing input and meeting evolving regulatory requirements.