Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework’s development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.
Many researchers are interested in biofuels because it isenvironmentally friendly and potentially reduce global warming. Incorporating nanoparticles into biodiesel has increased its performance and emission characteristics. The current study examines the influence of magnesium oxide nanoadditions on the performance and emissions of a diesel engine that runs on C. vulgaris algae biodiesel. The transesterification process produced methyl ester from C. vulgaris algae biodiesel.The morphology of nanoadditives was studied using scanning electron microscopy, transmission electron microscopy, and energy-dispersive X-ray spectroscopy. The fuel sample consisted of biodiesel blends with and without magnesium oxide nanoadditives. The fuel properties of the prepared C. vulgaris methyl ester were found to conform with the ASTM standards. The experimental results were determined by running a single-cylinder four-stroke diesel engine at different load conditions. When compared to B20, a B20 blend containing 100 ppm magnesium oxide nanoparticles enhanced brake thermal efficiency while reducing specific fuel consumption, according to the research. When MgO nanoparticles were introduced to B20, engine emissions of HC, CO, and smoke were decreased.
This study evaluates improvements made to a biodiesel production process from Chlorella sp. micro algae in a locomotive pilot plant using simulation. Energy and the main variables of the operation such as temperature, reaction time, alcohol molar concentration, vegetable oil, and use of homogeneous and heterogeneous catalysts and their concentration, mixing intensity, and moisture control were collected from operational data, and mass balances were tested in the SuperPro Designer retail package v.9.5. The result was an increase in the efficiency of the process of obtaining company biodiesel from 86% to 92% by volume, the same that were scaled taking into account the species’ production locality, and the results obtained showed that 26% was met by obtaining 10 MM (millions) of liters of biodiesel from the scaled plant.
Over the past few decades, manufacturing and production have undergone rapid development, particularly through the combination of additive manufacturing (AM) and other digitally driven manufacturing machines, creating hybrid additive manufacturing (hybrid-AM). However, despite significant growth, hybrid-AM has not yet gained acceptance at an industrial level due to certain limitations. This article aims to provide the latest information and discuss recent research trends, opportunities, challenges, and indicators in the field of hybrid-AM. Specifically, it will review and analyze literature related to the development of hybrid additives and subtractive processes known as HASPs, and identify future research avenues. Additionally, the article will identify key traits and research work in HASP systems, as well as present the future of HASPs and other types of hybrid machine tools based on recent trends.
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