Digital Fabrication Systems between Theory and Practice "Application on Metal Roofing Systems"

The tremendous development in computer technology, communication technology and information circulation is an inspiring and influential source in modern design trends, which led to the expansion of design capabilities and facilitated the preparation of designs for complex shapes quickly and accurately. The design and manufacturing process is always closely linked, starting from the stage of generating the initial ideas to the final products, especially in curved and complex shapes that are difficult to implement in traditional ways, which led to the search for new ways and methods that help in preparing and implementing their designs. The widespread spread, along with the low cost of computers, has led to its increasing use in design instead of manual means, and the computer with its superior ability to deal with many, multiple and overlapping data quickly using mathematical algorithms, which in turn facilitated the architects to use Multiple methods and programs helped to prepare and develop many different types of designs and to show problems in them before starting implementation, which helped to save time, effort and money. With the help of the computer, it was possible to design and produce virtual models from ready-made full-size molds from aluminum, iron and plastic ores. The diversity in design and manufacturing programs made possible by modern technological progress has opened the way for designers and manufacturers to prepare highly innovative and quality geometric shapes and designs, and by using these programs it has been possible to translate those shapes and designs into three-dimensional models.Hence the concept and importance of digital manufacturing systems as a link between the design process and the manufacturing process, and this is what gives the current research its importance, which crystallizes its objectives in exploring the role of digital manufacturing systems in the design, manufacture and production of metal systems with application to glass metal ceiling systems where the problem of the current research emerged from the need To keep pace with the technical development in the design and manufacturing processes of metal systems to achieve effective performance and quality, which can be achieved by taking advantage of the advantages of digital manufacturing systems.The research used the descriptive approach in theoretical studies and the experimental approach in applied studies

The benefits of parametric design methodology in developing structural solutions for metal furniture

The parametric design methodology is an updated methodology that aims to employ computer design programs (such as Maya and Rhino programs) to find a new and important design pattern that appeared after modernity. It takes care of finding a suitable design for various areas of life, starting with architecture, product design, passing through metal furniture and the smallest details of treatments. Automatically, which saves effort and time, and is unique in its smooth handling of complex blocks and highly complex structural systems in the design of metal furniture to employ these concepts in impressive designs of very complexity adapted to the era, and is characterized by the possibility of obtaining from it a dynamic design as well as sustainable design through the principle of re-employment and the use of materials, making it an almost integrated design. This research aims to clarify the role of the parametric design methodology in upgrading the design of metal furniture. The design thinking of metal furniture has evolved from the traditional approach to the parametric design methodology, and the research found that parametric furniture is now manufactured using algorithmic thinking through certain parameters and variables that are renewable and implementable, where products are made on solid supports with unconventional creative flowing shapes. The parametric design methodology gives each metal furniture design a great level of adaptability to different materials, tools and individual preferences. The parametric design methodology for manufacturing enables manufacturers anywhere to download the design file, and modify the design to suit local materials, available CNC tools, or any specific uses or need

A survey on artificial intelligence in histopathology image analysis

The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

SwinCup: Cascaded swin transformer for histopathological structures segmentation in colorectal cancer

Transformer models have recently become the dominant architecture in many computer vision tasks, including image classification, object detection, and image segmentation. The main reason behind their success is the ability to incorporate global context information into the learning process. By utilising self-attention, recent advancements in the Transformer architecture design enable models to consider long-range dependencies. In this paper, we propose a novel transformer, named Swin Transformer with Cascaded UPsampling (SwinCup) model for the segmentation of histopathology images. We use a hierarchical Swin Transformer with shifted windows as an encoder to extract global context features. The multi-scale feature extraction in a Swin transformer enables the model to attend to different areas in the image at different scales. A cascaded up-sampling decoder is used with an encoder to improve its feature aggregation. Experiments on GLAS and CRAG histopathology colorectal cancer datasets were used to validate the model, achieving an average 0.90 (F1 score) and surpassing the state-of-the-art by (23%)

Idecomp: imbalance-aware decomposition for class-decomposed classification using conditional GANs

Medical image classification tasks frequently encounter challenges associated with class imbalance, resulting in biased model training and suboptimal classification performance. To address this issue, the combination of class decomposition and transfer learning has proven to be effective in classifying imbalanced medical imaging datasets. Nevertheless, in order to further augment the performance gains achieved through the utilisation of class decomposition within deep learning frameworks, we propose a novel model coined imbalance-Aware Decomposition for Class-Decomposed Classification (iDeComp) model. By incorporating a conditional Generative Adversarial Network (GAN) model, iDeComp is capable of generating additional samples specifically tailored to underrepresented decomposed subclasses. This paper investigates the application of iDeComp using two different medical imaging datasets. iDeComp selects underrepresented samples from the training set of the sublevel classes within each dataset, which are then employed to train separate conditional Deep Convolutional GAN (DCGAN) models and verification models. The conditional DCGAN model is responsible for generating additional samples, while the verification model critically evaluates the appropriateness of the synthesised images. Subsequently, the resulting augmented samples are utilized to train the classification model. To assess the effectiveness of iDeComp, we employ various evaluation metrics including accuracy, precision, recall, and F1 score. The results obtained from our experiments clearly indicate that iDeComp outperforms existing approaches in terms of classifying both imbalanced datasets

From Pixels to Deposits: Porphyry Mineralization With Multispectral Convolutional Neural Networks

Mineral exploration is essential to ensure a sustainable supply of raw materials for modern living and the transition to green. It implies a series of expensive operations that aim to identify areas with natural mineral concentration in the crust of the Earth. The rapid advances in artificial intelligence and remote sensing techniques can help in significantly reducing the cost of these operations. Here, we produce a robust intelligent mineral exploration model that can fingerprint potential locations of porphyry deposits, which are the world's most important source of copper and molybdenum and major source of gold, silver, and tin. We present a deep learning pipeline for assessing multispectral imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the objective of identifying hydrothermal alterations. Our approach leverages a convolutional neural network (CNN) to analyze the high-resolution images, overcoming computational challenges through a patch-based strategy that involves an overlapping window for partitioning the images into fixed-size patches. Through the utilization of manually labeled patches for image classification and identification of hydrothermal alteration areas, our results demonstrate the remarkable ability of CNN to accurately detect hydrothermal alterations. The technique is adaptable for other ore deposit models and satellite imagery types, providing a revolution in satellite image interpretation and mineral exploration

Incidence of Persistent SARS-CoV-2 Gut Infection in Patients with History of COVID-19: Insights from Endoscopic Examination

Background: Gut affection is common during acute COVID-19, and persistent SARS-CoV-2 gut infection has been reported months after the initial infection, potentially linked to long COVID syndrome. This study tested the incidence of persistent gut infection in patients with a history of COVID-19 undergoing endoscopic examination. Methods: Endoscopic biopsies were prospectively collected from patients with previous COVID-19 infection undergoing upper or lower gastrointestinal endoscopy (UGE or LGE). Immunohistochemistry was used to detect the presence of persistent SARS-CoV-2 nucleocapsid proteins. Results: A total of 166 UGEs and 83 LGE were analyzed. No significant differences were observed between patients with positive and negative immunostaining regarding the number of previous COVID-19 infections, time since the last infection, symptoms, or vaccination statuses. The incidence of positive immunostaining was significantly higher in UGE biopsies than in LGE biopsies (37.34% vs. 16.87%, p = .002). Smokers showed a significantly higher incidence of positive immunostaining in the overall cohort and UGE and LGE subgroups (p < .001). Diabetic patients exhibited a significantly higher incidence in the overall cohort (p = .002) and UGE subgroup (p = .022), with a similar trend observed in the LGE subgroup (p = .055). Conclusion: Gut mucosal tissues can act as a long-term reservoir for SARS-CoV-2, retaining viral particles for months following the primary COVID-19 infection. Smokers and individuals with diabetes may be at an increased risk of persistent viral gut infection. These findings provide insights into the dynamics of SARS-CoV-2 infection in the gut and have implications for further research