Nuclei detection and segmentation in histopathological images is a prerequisite step for quantitative analysis including morphological shape and size to help in identifying cancer prognosis. Digital pathology field aims to improve the quality of cancer diagnosis and has helped pathologists to reduce their efforts and time. Different deep learning architectures are widely used recently in Digital pathology field, yielding promising results in different problems. However, Deep convolutional neural networks (CNNs) need a large subset of labelled data that are not easily available all the time in

In this work, gamma irradiation from a cobalt 60Co source was used to graft Copolymerize α-methyl styrene (AMS) onto Polyethersulfone (PES) films. Grafting reaction was performed at ambient temperature by simultaneous method applying different dose rates for a total absorbed dose of 30 kGy. The effects of reaction conditions including, dose rate, monomer concentration and absorbed dose on the grafting yield (DOG) were studied. Results showed that the grafting conditions influence considerably DOG. In addition, the depth understanding of the graft copolymerization reaction kinetics under

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer. Chronic hepatitis and liver cirrhosis lead to accumulation of genetic alterations driving HCC pathogenesis. This study is designed to explore genomic landscape of HCC in Egyptian patients by whole exome sequencing. Methods: Whole exome sequencing using Ion Torrent was done on 13 HCC patients, who underwent surgical intervention (7 patients underwent living donor liver transplantation (LDLT) and 6 patients had surgical resection}. Results: Mutational signature was mostly S1, S5, S6, and S12 in HCC. Analysis of

The increase in population, food demand, and the pollution levels of the environment are considered major problems of this era. For these reasons, the traditional ways of farming are no longer suitable for early and accurate detection of biotic stress. Recently, precision agriculture has been extensively used as a potential solution for the aforementioned problems using high resolution optical sensors and data analysis methods that are able to cope with the resolution, size and complexity of the signals from these sensors. In this paper, several methods of machine learning have been utilized

Dissolved oxygen (DO) is an important water quality parameter. It is considered the most important parameter. DO concentration in water is affected by different parameters such as volume flow rate, water velocity, and re-aeration rate. Those parameters are directly affected by the geometry of the waterway. Thus, studying the impact of changing channel geometry on DO is very important. Many researchers studied the effect of influential parameters on water quality variables but the influence of channel geometric parameters on DO was not studied thoroughly before. This research aims to study the

Corrosion occurs in all sectors including oil pipelines, drinking water and sewerage in the majority of cases linked to corrosion of steel. Good corrosion management includes optimising corrosion control actions and minimising lifecycle corrosion costs whilst meeting environmental goals. The toxicity of commonly used synthetic inhibitors are the subject of recent legislations (REACH and PARCOM) have led to search on more eco-friendly corrosion inhibitors. Extensive research is conducted to assess the corrosion inhibition rate of diverse green inhibitors. However, it was not adequately

This paper describes a field study conducted over a period of 11-months on labour productivity observed during the construction of a new university campus in Cairo, Egypt. The campus is being built on 127 acres and the field study was conducted during the construction of two main buildings; each of 20,000 m 2 built up area. The study utilized work sampling (WS), craftsman questionnaire (CQ), and foreman delay survey (FDS) methods to analyze labour productivity of three indicative and labour-intensive trades, namely formwork, masonry work, and HVAC duct installation. The results were also

In the era of digital transformation, a mass disruption in the global industries have been detected. Big data, the Internet of Things (IoT) and Artificial Intelligence (AI) are just examples of technologies that are holding such digital disruptive power. On the other hand, retailing is a high-intensity competition and disruptive industry driving the global economy and the second largest globally in employment after the agriculture. AI has large potential to contribute to global economic activity and the biggest sector gains would be in retail. AI is the engine that is poised to drive the

Multi-slice short-axis acquisitions of the left ventricle are fundamental for estimating the volume and mass of the left ventricle in cardiac MRI scans. Manual segmentation of the myocardium in all time frames per each cross-section is a cumbersome task. Therefore, automatic myocardium segmentation methods are essential for cardiac functional analysis. Region growing has been proposed to segment the myocardium. Although the technique is simple and fast, non uniform intensity and low-contrast interfaces of the myocardium are major challenges of the technique that limit its use in myocardial

One of the key challenges in applying deep learning to solve real-life problems is the lack of large annotated datasets. Furthermore, for a deep learning model to perform well on the test set, all samples in the training and test sets should be independent and identically distributed (i.i.d.), which means that test samples should be similar to the samples that were used to train the model. In many cases, however, the underlying training and test set distributions are different. In such cases, it is common to adapt the test samples by transforming them to their equivalent counterparts in the