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This paper aims to study the resulting Brag peak and secondary particles (neutrons, photons, deuterons, alpha, helium_3, and tritons) along protons’ path in tissue.

MATLAB program and MCNP code were used to read abdomen Digital Imaging and Communications in Medicine (DICOM) images and build a 3D phantom to liver in purpose to study resulting Bragg peak and secondary particles (neutrons, photons, deuterons, alpha, helium_3 and tritons) along protons’ path.

From the study, it was found that Bragg peak varies from a 2 cm depth within the tissue for 50 MeV protons to a 14.2 cm depth for 150 MeV protons; in the other hand, the total deposited energy decreases from 0.656 [MeV/g]/proton, at the depth 2 cm and 50 MeV protons, to the value 0.220 [MeV/g]/proton, at the depth 14 cm and 150 MeV protons.

As for the flow rate of secondary neutrons and photons, the flow rate of secondary neutrons takes a maximum value (peak) in the middle of the proton path, i.e. when the energy of the protons drops to the value of 30 MeV, and this maximum value of the neutrons flow rate is accompanied by a maximum value of the photon flow rate, as for the rest of the secondary particles produced (alpha particles, deuterons, electrons, tritons and triple helium), they deposit most of their energy locally.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

To decrease wear and friction, zinc dialkyldithiophosphate (ZDDP) has been used in engine oil for several decades, but the mechanism of the tribofilm formation is still unclear. The purpose of this study is to characterize the chemical details of the tribofilm by using high-resolution approaching.

An ISO VG 100 mineral oil mixed with ZDDP was used in sliding tests on cylindrical roller bearings. Tribofilm formation was observed after 2 h of the sliding test. X-ray photoelectron spectroscopy (XPS) and atom probe tomography (APT) were used for chemical analysis of the tribofilm.

The results show that the ZDDP tribofilm consists of the common ZDDP elements along with iron oxides. A considerable amount of zinc and a small amount of sulfur were observed. In particular, an oxide interlayer with sulfur enrichment was revealed by APT between the tribofilm and the steel substrate. The depth profile of the chemical composition was obtained, and a tribofilm of approximately 40 nm thickness was identified by XPS.

A sulfur enrichment at the interface is observed by APT, which is beneath an oxygen enrichment. The clear evidence of the S interlayer confirms the hard and soft acids and bases principle.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2020-0035/

In this article, the ideas and methods behind the “patent-paper citation” are scrutinised by following the intellectual and technical development of approaches and ideas in early work on patentometrics. The aim is to study how references from patents to papers came to play a crucial role in establishing a link between science and technology.

The study comprises a conceptual history of the “patent paper citation” and its emergence as an important indicator of science and technology interaction. By tracing key references in the field, it analyses the overarching frameworks and ideas, the conceptual “hinterland”, in which the approach of studying patent references emerged.

The analysis explains how interest in patents – not only as legal and economic artefacts but also as scientific documents – became evident in the 1980s. The focus on patent citations was sparked by a need for relevant and objective indicators and by the greater availability of databases and methods. Yet, the development of patentometrics also relied on earlier research, and established theories, on the relation between science and technology.

This is the first attempt at situating patentometrics in a larger societal and scientific context. The paper offers a reflexive and nuanced analysis of the “patent-paper citation” as a theoretical and historical construct, and it calls for a broader and contextualised understanding of patent references, including their social, legal and rhetorical function.