Effect of Surface Texturing on Friction and Film Thickness under Starved Lubrication Conditions

Tato disertační práce se zabývá vlivem mělkých mikro-textur na tření a tloušťku filmu v mazaných nekonformních kontaktech za extrémních podmínek a za podmínek hladovění kontaktu. Měření byla realizována na tribometru v konfiguraci ball-on-disk. Kontakt byl pozorován pomocí vysokorychlostní kamery. Pro stanovení součinitele tření byl využit snímač krouticího momentu. V této studii byly popsány dva typy mikrotextur – mikrovtisky a příčné mikrodrážky. Výsledky naznačují, že za podmínek hladovění vedou mikrovtisky ke snížení tření a to díky nárůstu tloušťky mazacího filmu. Mechanismus doplňování mikrovtisků čerstvým mazivem je pravděpodobně způsoben kapilárními jevy ve vstupní oblasti. Třecí plochy s příčnými mikrodrážkami, jejichž délka byla menší než průměr Hertzova kontaktu, potom obecně vykazovaly lepší tribologické parametry ve srovnání s hladkými povrchy. Příčné mikrodrážky vedly k výraznému nárůstu tloušťky mazacího filmu za podmínek hladovění i za extrémních provozních podmínek (protisměrný pohyb). Numerické simulace přechodových jevů příčných mikrodrážek ukázaly dobrou shodu s experimentálními výsledky.This PhD thesis focuses on studying the effects of shallow micro-textures on friction and film thickness of lubricated non-conformal contacts under extreme and starved conditions. Measurements were carried out using a ball-on-disc tribometer equipped with high speed camera and torque sensor. Two types of micro-textures have been assessed in this study, micro-dents and transverse micro-grooves. The results reveal that micro-dents are helpful in reducing friction under starved conditions due to the film thickness enhancement. The mechanism of filling the depleted micro-dents with fresh lubricant is probably attributed to the capillary effect in the inlet zone under starvation. On the other hand, the rubbing surfaces with transverse shallow micro-grooves with a length less than the diameter of the Hertzian contact have an improved tribological performance in comparison with smooth surfaces. Indeed, transverse shallow micro-grooves showed a significant enhancement of film thickness under starvation and under extreme operating condition (reverse motion). The numerical simulation of the transient behavior of transverse limited micro-grooves showed accepted agreement with experimental results.

Secure Cloud Storage: A Framework for Data Protection as a Service in the Multi-cloud Environment

This paper introduces Secure Cloud Storage (SCS), a framework for Data Protection as a Service (DPaaS) to cloud computing users. Compared to the existing Data Encryption as a Service (DEaaS) such as those provided by Amazon and Google, DPaaS provides more flexibility to protect data in the cloud. In addition to supporting the basic data encryption capability as DEaaS does, DPaaS allows users to define fine-grained access control policies to protect their data. Once data is put under an access control policy, it is automatically encrypted and only if the policy is satisfied, the data could be decrypted and accessed by either the data owner or anyone else specified in the policy. The key idea of the SCS framework is to separate data management from security management in addition to defining a full cycle of data security automation from encryption to decryption. As a proof-of-concept for the design, we implemented a prototype of the SCS framework that works with both BT Cloud Compute platform and Amazon EC2. Experiments on the prototype have proved the efficiency of the SCS framework

Forensic dentistry in human identification: A review of the literature

An update is provided of the literature on the role of odontology in human identification, based on a PubMed-Medline search of the last 5 years and using the terms: 'forensic dentistry' (n = 464 articles), 'forensic odontology' (n = 141 articles) and 'forensic dentistry identification' (n = 169 articles). Apart from these initial 774 articles, others considered to be important and which were generated by a manual search and cited as references in review articles were also included. Forensic dentistry requires interdisciplinary knowledge, since the data obtained from the oral cavity can contribute to identify an individual or provide information needed in a legal process. Furthermore, the data obtained from the oral cavity can narrow the search range of an individual and play a key role in the victim identification process following mass disasters or catastrophes. This literature search covering the last 5 years describes the novelties referred to buccodental studies in comparative identification, buccodental evaluation in reconstructive identification, human bites as a method for identifying the aggressor, and the role of DNA in dental identification. The oral cavity is a rich and noninvasive source of DNA, and can be used to solve problems of a social, economic or legal nature

Towards understanding phishing victims\u27 profile

Today it is known that the weakest link in the cyber security chain is the computer user. Social engineering attacks are commonly used to deceive computer users to perform actions that could leak private information. Such attacks psychologically manipulate the computer users to reveal his/her confidential information. Therefore, the computer user has been carefully studied by security researchers to understand the relationship between cyber security incidents and the victim background. In this paper, we present a breadth-first survey of recent studies that aim to understand the relationship between victims\u27 backgrounds and phishing attacks. We summarize the characteristics of the phishing victims, following a review of their demographic and personality traits. © 2012 IEEE

Political and Economic Relations between United States of America and Jordan (1990-2019)

The Kingdom of Jordan is a key U.S. partner in the Middle East. Although the United States and Jordan have never been linked by a formal treaty, the two countries have cooperated on a number of regional and international issues over the years. The research is based on a major hypothesis that, the presence of local, regional and international factors have affected the Jordanian-American relations and made them take the nature of mutual cooperation between the two countries. This research essentially contributes to deepen the understanding among those interested in politics of Jordan, in identifying the factors affecting Jordanian-American relations, and on the dimensions of the American position on all Arab issues in order to overcome negative impacts and give a strong impetus to the relations between the two parties. This research also provides an opportunity for the interested researchers and scholars of the local, regional and international affairs to familiarize themselves with the nature of Jordanian-American relations at this very important stage. The main goal is to reveal the main aspects that underpin Jordanian-American relations. The research problem revolves around a central question and which elucidates the nature of Jordanian-American relation

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses. This study reshapes the parameters of blood cell classification, harnessing the capabilities of AI and broadening the scope from 5 to 11 specific blood cell categories with the challenging 11-class PBC dataset. This transformation facilitates a more profound exploration of blood cell diversity, surpassing prior constraints in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking “Naturalize” augmentation technique produces remarkable results. The 2K-PBC dataset generated with “Naturalize” boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The “Naturalize” technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.This work is supported by grant PID2021-126701OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and by grant GIU19/027 funded by the University of the Basque Country UPV/EHU

White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope

Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigated the transfer learning process of the Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results showed that the Google ViT is an excellent DL neural solution for data scarcity. The BCCD results showed that the Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead.This work is supported by grant PID2021-126701OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and by grant GIU19/027 funded by the University of the Basque Country UPV/EHU