It is very important to know the degree of disintegration beforehand in open quarry bench blasting in terms of blasting efficiency. Kuznetsov (1973), Cunningham (1987), andOuchterlony (2005) have developed the Rosin-Rammler distribution function for estimating the degrees of fragmentation (Rosin & Rammler, 1933). However, models they developed do not give realistic results for blast surfaces where there are a lot of discontinuity characteristics with broken and fissured areas due to the difference in structural characteristics of the rocks. In this study, average fragmentative distribution of the heap formed as a result of several blasting tests in a quarry belonging to BATIÇİM have been separately determined by using a dimensional analysis program with Wipfrag image processing technique and Kuz-Ram estimation model. Average granular size correction has been carried out with the approach of accepting as fine-grain the areas that couldn't be determined by means of image analysis programs and that were neglected in the analysis. Subsequently, the land coefficient in Kuz-Ram model was determined to be 0.0383 rather than 0.06 for the said quarry by taking the average dimension values determined by Wipfrag method as reference point.Keywords: average muck pile fragmentation, estimation muck pile fragmentation, blasting efficiency, Image processing technique Kluczowym zagadnieniem jest znajomość stopnia rozdrobnienia materiału przed przystąpieniem do prac strzałowych w kamieniołomach, dla określenia skuteczności strzelania. Kuznetsov (1973), Cunninghma (1987 i Ouchterlony (2005) wyprowadzili dystrybuantę bazującą na równaniu Rosina--Rammlera (1933) w celu estymacji stopnia rozdrobnienia materiału. Jednakże opracowane modele nie oddają rzeczywistych wyników gdy prace strzałowe prowadzone są na powierzchniach w których znajdują się liczne strefy nieciągłości, załamań oraz spękań wskutek różnic we właściwościach struktur skalnych. W pracy tej obliczono średnią wielkość fragmentów materiałów rozdrabnianych w trakcie trwania prac strzałowych w kamieniołomie należącym do przedsiębiorstwa BATICIM. Wielkości te zostały określone oddzielnie przy użyciu programu do analiz wymiarowych wykorzystującego techniki przetwarzania
Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia-burden of COVID-19 disease. However, the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous AI software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes.
Material and Methods: Fifty COVID-19 and 50 age-matched negative control patients were analyzed using the proposed technique and commercially available artificial intelligence (AI) software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts.
Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between ₋642.4 HU and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1 ± 4.4%, 6.1 ± 4.5, and 11.3 ± 7.3% for the COVID-19 cohort, respectively, and statistically different (P < 0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P < 0.01).
Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.
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