Metal Dusting of Heat-Resistant Alloys

Metal dusting leads to disintegration of such alloys as iron and nickel-based into a “dust” of particulate metal, metal carbide, carbon, and/or oxide. It occurs in strongly carburising environments at 400-900°C. Literature survey has shown that alloys behave differently in metal dusting conditions based on their composition and the environment. Metal dusting mechanisms for iron and nickel-based alloys have been proposed but, nevertheless, have not been agreed upon and numerous modifications to them have been suggested. Further adding to the complexity, the mechanisms were found to have differed due to operating condition alterations. In view of that, this research was carried out to gain a better understanding of metal dusting process(s) by evaluating the performance of heat-resistant alloys, namely KHR35C HiSi© (HP), KHR45A LC© (35Cr-45Ni), and UCX©, in metal dusting conditions. HP, which is an iron-based alloy, was modified by adding more silicon in order to improve its resistance through the development of SiO2 at the surface. The carbon content in the nickel-based alloy, 35Cr-45Ni, was lowered to delay the attack onset by accommodating more diffused carbon. UCX©, however, has the highest nickel and chromium levels. The alloys were exposed to a gas containing 80 vol% CO+20 vol% H2 at 650, 750, and 850ºC for 100, 500, and 1000h. Analyses including visual inspection, XRD, and SEM/EDX revealed that the alloys suffered localised attacks at the three temperatures but to varying degrees and in different shapes. In general, the attack initiated at the matrix rather than the primary carbides and also progressed through the matrix. Increasing the exposure temperature caused less carbon deposition and more oxides formation on the alloy surfaces leading to a reduction in the attack aggressiveness. UCX© exhibited the highest resistance to metal dusting whilst HP suffered the severest attack. The presence of high concentrations of chromium at the surface catalysed a quick formation of Cr2O3 scale that reduced the extent of metal dusting. Also, the increase in nickel content might have slowed down the carbon diffusion into the alloy. In addition, the presence of other oxide and carbide-forming elements such as silicon and tungsten might well have enhanced the alloy performance. Diffused carbon binds with free tungsten, niobium, and chromium to form carbides prolonging the incubation period prior to the attack initiation

Medical Student’s Attitudes and Perceptions Toward Artificial Intelligence Applications

To evaluate medical students' perceptions in radiology and medical applications of artificial intelligence (AI). Students at 10 prestigious medical schools were issued an online survey that was created using Survey Monkey. It was divided into many parts with the goal of assessing the students' past understanding of AI in radiology and beyond as well as their attitudes about AI in medicine more generally. Anonymity of the respondents was protected. A total of 263 students—166 female and 94 male—with a median age of 23—replied to the survey. Concerning 52 percent of respondents were aware of the current debate about AI in radiology, while 68 percent said they were ignorant of the underlying technology. abnormalities in radiological scans, but they believed that AI would not be able to provide a definitive diagnosis (56 percent). In contrast to claims that human radiologists would be displaced, the majority (77 percent and 86 percent) believed that AI would revolutionize and enhance radiology (83 percent). Over two-thirds of respondents felt that medical education must include AI (71 percent). Male and tech-savvy respondents had higher levels of confidence in the advantages of AI and lower levels of fear of these technologies in sub-group analyses. In conclusion, Contrary to what has been mentioned in the media, medical students are aware of the possible applications and effects of AI on radiology and medicine and do not worry that it will replace human radiologists. The situations in which artificial intelligence has reportedly substituted human radiologists are known to medical students. Since it is their duty, the field of radiology must take the initiative in teaching students about these freshly developed tools

Medical student’s attitudes and perceptions toward artificial intelligence applications

To evaluate medical students' perceptions in radiology and medical applications of artificial intelligence (AI). Students at 10 prestigious medical schools were issued an online survey that was created using Survey Monkey. It was divided into many parts with the goal of assessing the students' past understanding of AI in radiology and beyond as well as their attitudes about AI in medicine more generally. Anonymity of the respondents was protected. A total of 263 students—166 female and 94 male—with a median age of 23—replied to the survey. Concerning 52 percent of respondents were aware of the current debate about AI in radiology, while 68 percent said they were ignorant of the underlying technology. abnormalities in radiological scans, but they believed that AI would not be able to provide a definitive diagnosis (56 percent). In contrast to claims that human radiologists would be displaced, the majority (77 percent and 86 percent) believed that AI would revolutionize and enhance radiology (83 percent). Over two-thirds of respondents felt that medical education must include AI (71 percent). Male and tech-savvy respondents had higher levels of confidence in the advantages of AI and lower levels of fear of these technologies in sub-group analyses. In conclusion, Contrary to what has been mentioned in the media, medical students are aware of the possible applications and effects of AI on radiology and medicine and do not worry that it will replace human radiologists. The situations in which artificial intelligence has reportedly substituted human radiologists are known to medical students. Since it is their duty, the field of radiology must take the initiative in teaching students about these freshly developed tools

Dermatological Emergencies in Family Medicine: Recognition, Management, and Referral Considerations

Numerous people with skin disorders who have real dermatologic crises show up at the emergency room. Family doctors need to be able to identify potentially fatal dermatological disorders quickly since they could be the first to encounter patients with these illnesses. The purpose of this review is to provide guidance for early recognition, help identify distinct symptoms, and enable early diagnosis of emerging dermatological conditions. Necrotizing fasciitis, Stevens-Johnson syndrome, toxic epidermal necrolysis, Rocky Mountain spotted fever, and other possible emergencies that might manifest as dermatological symptoms are examples of these conditions. In this article we will be discussing the dermatological emergencies present at primary care settings and encountered by family physician, recognition and management of those emergencies, referral considerations, role of family medicine in dermatological emergencies and other topics

Diagnosis of Histopathological Images to Distinguish Types of Malignant Lymphomas Using Hybrid Techniques Based on Fusion Features

Malignant lymphoma is one of the types of malignant tumors that can lead to death. The diagnostic method for identifying malignant lymphoma is a histopathological analysis of lymphoma tissue images. Because of the similar morphological characteristics of the lymphoma types, it is difficult for doctors and specialists to manually distinguish the types of lymphomas. Therefore, deep and automated learning techniques aim to solve this problem and help clinicians reconsider their diagnostic decisions. Because of the similarity of the morphological characteristics between lymphoma types, this study aimed to extract features using various algorithms and deep learning models and combine them together into feature vectors. Two datasets have been applied, each with two different systems for the reliable diagnosis of malignant lymphoma. The first system was a hybrid system between DenseNet-121 and ResNet-50 to extract deep features and reduce their dimensions by the principal component analysis (PCA) method, using the support vector machine (SVM) algorithm for classifying low-dimensional deep features. The second system was based on extracting the features using DenseNet-121 and ResNet-50 and combining them with the hand-crafted features extracted by gray level co-occurrence matrix (GLCM), fuzzy color histogram (FCH), discrete wavelet transform (DWT), and local binary pattern (LBP) algorithms and classifying them using a feed-forward neural network (FFNN) classifier. All systems achieved superior results in diagnosing the two datasets of malignant lymphomas. An FFNN classifier with features of ResNet-50 and hand-crafted features reached an accuracy of 99.5%, specificity of 100%, sensitivity of 99.33%, and AUC of 99.86% for the first dataset. In contrast, the same technique reached 100% for all measures to diagnose the second dataset

Hybrid Techniques for Diagnosis with WSIs for Early Detection of Cervical Cancer Based on Fusion Features

Cervical cancer is a global health problem that threatens the lives of women. Liquid-based cytology (LBC) is one of the most used techniques for diagnosing cervical cancer; converting from vitreous slides to whole-slide images (WSIs) allows images to be evaluated by artificial intelligence techniques. Because of the lack of cytologists and cytology devices, it is major to promote automated systems that receive and diagnose huge amounts of images quickly and accurately, which are useful in hospitals and clinical laboratories. This study aims to extract features in a hybrid method to obtain representative features to achieve promising results. Three proposed approaches have been applied with different methods and materials as follows: The first approach is a hybrid method called VGG-16 with SVM and GoogLeNet with SVM. The second approach is to classify the cervical abnormal cell images by ANN classifier with hybrid features extracted by the VGG-16 and GoogLeNet. A third approach is to classify the images of abnormal cervical cells by an ANN classifier with features extracted by the VGG-16 and GoogLeNet and combine them with hand-crafted features, which are extracted using Fuzzy Color Histogram (FCH), Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms. Based on the mixed features of CNN with features of FCH, GLCM, and LBP (hand-crafted), the ANN classifier reached the best results for diagnosing abnormal cells of the cervix. The ANN network achieved with the hybrid features of VGG-16 and hand-crafted an accuracy of 99.4%, specificity of 100%, sensitivity of 99.35%, AUC of 99.89% and precision of 99.42%

Fresh Produce as a Potential Vector and Reservoir for Human Bacterial Pathogens: Revealing the Ambiguity of Interaction and Transmission

The consumer demand for fresh produce (vegetables and fruits) has considerably increased since the 1980s for more nutritious foods and healthier life practices, particularly in developed countries. Currently, several foodborne outbreaks have been linked to fresh produce. The global rise in fresh produce associated with human infections may be due to the use of wastewater or any contaminated water for the cultivation of fruits and vegetables, the firm attachment of the foodborne pathogens on the plant surface, and the internalization of these agents deep inside the tissue of the plant, poor disinfection practices and human consumption of raw fresh produce. Several investigations have been established related to the human microbial pathogens (HMPs) interaction, their internalization, and survival on/within plant tissue. Previous studies have displayed that HMPs are comprised of several cellular constituents to attach and adapt to the plant’s intracellular niches. In addition, there are several plant-associated factors, such as surface morphology, nutrient content, and plant–HMP interactions, that determine the internalization and subsequent transmission to humans. Based on documented findings, the internalized HMPs are not susceptible to sanitation or decontaminants applied on the surface of the fresh produce. Therefore, the contamination of fresh produce by HMPs could pose significant food safety hazards. This review provides a comprehensive overview of the interaction between fresh produce and HMPs and reveals the ambiguity of interaction and transmission of the agents to humans