Gaining Instead of Losing: The Image of Dunhuang as a Religious Heritage in a WeChat Mini-Programme

In 2020, a WeChat mini-programme called the Dunhuang E-Tour ((sic)) was launched during the COVID-19 pandemic to showcase one of China's most important religious heritage sites, the Dunhuang Mogao Grottoes (also known as the Dunhuang Caves), and it attracted a considerable number of online tourists. Unlike the colonial image of Dunhuang in Chinese public discourse, the mini-programme does not focus on Dunhuang's history;rather, it provides a dynamic and interactive representation of Dunhuang's religious murals, painted sculptures and cave architecture. To reflect the impact of the mini-programme's digital mechanisms on users' experience, this study adopts an analytical framework that combines the walkthrough method and religious tourist perspectives to explore the image of the digital Dunhuang and how it was shaped. The analysis finds that the functions of the Dunhuang E-Tour create a culturally rich image of Dunhuang, which subverts its decades-long Dunhuang image as a site of loss in Chinese public discourse. This difference in images mirrors the potential impact of China's recent cultural policy of 'cultural confidence' in relation to its cultural and creative industries.

Building a Real-Time System to Monitor Students Electronically Based on Digital Images of Face Movement

The spread of the Corona Virus pandemic on a global scale had a great impact on the trend towards e-learning. In the virtual exams the student can take his exams online without any papers, in addition to the correction and electronic monitoring of the exams. Tests are supervised and controlled by a camera and proven cheat-checking tools. This technology has opened the doors of academic institutions for distance learning to be wide spread without any problems at all. In this paper, a proposed model was built by linking a computer network using a server/client model because it is a system that distributes tasks between the two. The main computer that acts as a server (exam observer) is connected to a group of sub-computers (students) who are being tested and these devices are considered the set of clients. The proposed student face recognition system is run on each computer (client) in order to identify and verify the identity of the student. When another face is detected, the program sends a warning signal to the server. Thus, the concerned student is alerted. This mechanism helps examinees reduce cheating cases in early time. The results obtained from the face recognition showed high accuracy despite the large number of students' faces. The performance speed was in line with the test performance requirements, handling 1,081 real photos and adding 960 photos. © 2022 IEEE.

Face Mask Detection Using Multi-Task Cascaded Convolutional Neural Networks

Detecting faces is a prevalent and substantial technology in current ages. It became interesting with the use of diverse masks and facial variations. The proposed method concentrates on detecting the facial regions in the digital images from real world which contains noisy, occluded faces and finally classification of images. Multi-task cascaded convolutional neural network (MTCNN)—a hybrid model with deep learning and machine learning to facial region detection is proposed. MTCNN has been applied on face detection dataset with mask and without mask images to perform real-time face detection and to build a face mask detector with OpenCV, convolutional neural networks, TensorFlow and Keras. The proposed system can be used as an application in the recent COVID-19 pandemic situations for detecting a person wears mask or not in controlling the spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Multi-Illumination Single-Holographic-Exposure Lensless Fresnel (MISHELF) Microscopy: Principles and Biomedical Applications.

Lensless holographic microscopy (LHM) comes out as a promising label-free technique since it supplies high-quality imaging and adaptive magnification in a lens-free, compact and cost-effective way. Compact sizes and reduced prices of LHMs make them a perfect instrument for point-of-care diagnosis and increase their usability in limited-resource laboratories, remote areas, and poor countries. LHM can provide excellent intensity and phase imaging when the twin image is removed. In that sense, multi-illumination single-holographic-exposure lensless Fresnel (MISHELF) microscopy appears as a single-shot and phase-retrieved imaging technique employing multiple illumination/detection channels and a fast-iterative phase-retrieval algorithm. In this contribution, we review MISHELF microscopy through the description of the principles, the analysis of the performance, the presentation of the microscope prototypes and the inclusion of the main biomedical applications reported so far.

Mask usage recognition using vision transformer with transfer learning and data augmentation

The COVID-19 pandemic has disrupted various levels of society. The use of masks is essential in preventing the spread of COVID-19 by identifying an image of a person using a mask. Although only 23.1% of people use masks correctly, Artificial Neural Networks (ANN) can help classify the use of good masks to help slow the spread of the Covid-19 virus. However, it requires a large dataset to train an ANN that can classify the use of masks correctly. MaskedFace-Net is a suitable dataset consisting of 137016 digital images with 4 class labels, namely Mask, Mask Chin, Mask Mouth Chin, and Mask Nose Mouth. Mask classification training utilizes Vision Transformers (ViT) architecture with transfer learning method using pre-trained weights on ImageNet-21k, with random augmentation. In addition, the hyper-parameters of training of 20 epochs, an Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.03, a batch size of 64, a Gaussian Cumulative Distribution (GeLU) activation function, and a Cross-Entropy loss function are used to be applied on the training of three architectures of ViT, namely Base-16, Large-16, and Huge-14. Furthermore, comparisons of with and without augmentation and transfer learning are conducted. This study found that the best classification is transfer learning and augmentation using ViT Huge-14. Using this method on MaskedFace-Net dataset, the research reaches an accuracy of 0.9601 on training data, 0.9412 on validation data, and 0.9534 on test data. This research shows that training the ViT model with data augmentation and transfer learning improves classification of the mask usage, even better than convolutional-based Residual Network (ResNet). © 2023 The Author(s)

RSA-based Encryption Algorithm for Digital Images

With the spread of Covid-19, secure transmission of data over the Internet has drawn prior attention. Therefore, protecting these data is a very important process. This objective can be achieved through encryption. In this study, a digital image encryption algorithm is suggested depending on the RSA cryptosystem, since the RSA is a strong and well-known asymmetric encryption system. The proposed algorithm divides the image into blocks of 2×2 size rather than encrypting one pixel at a time. This improves the encryption process and then converts each block into a single vector. The vector elements are converted to binary and then into to a single binary number. After that, the binary number is converted to decimal to be compatible with using RSA algorithm. The proposed algorithm is lossless;the original image is restored without losing any information or data. Finally, the suggested algorithm is executed in MATLAB environment and tested on various images. The results of the experiment reveal the suggested encryption algorithm is both reliable, secure and applicable to image protection. © 2022 IEEE.

A Non-Instrumental Green Analytical Method Based on Surfactant-Assisted Dispersive Liquid-Liquid Microextraction-Thin-Layer Chromatography-Smartphone-Based Digital Image Colorimetry(SA-DLLME-TLC-SDIC) for Determining Favipiravir in Biological Samples.

Favipiravir (FAV) has become a promising antiviral agent for the treatment of COVID-19. Herein, a green, fast, high-sample-throughput, non-instrumental, and affordable analytical method is proposed based on surfactant-assisted dispersive liquid-liquid microextraction (SA-DLLME) combined with thin-layer chromatography-digital image colourimetry (TLC-DIC) for determining favipiravir in biological and pharmaceutical samples. Triton X-100 and dichloromethane (DCM) were used as the disperser and extraction solvents, respectively. The extract obtained after DLLME procedure was spotted on a TLC plate and allowed to develop with a mobile phase of chloroform:methanol (8:2, v/v). The developed plate was photographed using a smartphone under UV irradiation at 254 nm. The quantification of FAV was performed by analysing the digital images' spots with open-source ImageJ software. Multivariate optimisation using Plackett-Burman design (PBD) and central composite design (CCD) was performed for the screening and optimisation of significant factors. Under the optimised conditions, the method was found to be linear, ranging from 5 to 100 µg/spot, with a correlation coefficient (R2) ranging from 0.991 to 0.994. The limit of detection (LOD) and limit of quantification (LOQ) were in the ranges of 1.2-1.5 µg/spot and 3.96-4.29 µg/spot, respectively. The developed approach was successfully applied for the determination of FAV in biological (i.e., human urine and plasma) and pharmaceutical samples. The results obtained using the proposed methodology were compared to those obtained using HPLC-UV analysis and found to be in close agreement with one another. Additionally, the green character of the developed method with previously reported protocols was evaluated using the ComplexGAPI, AGREE, and Eco-Scale greenness assessment tools. The proposed method is green in nature and does not require any sophisticated high-end analytical instruments, and it can therefore be routinely applied for the analysis of FAV in various resource-limited laboratories during the COVID-19 pandemic.

Portable Technology of Low-Cost Image Digitizing for the Screening of Uterine Cancer in Latin America

Cervical cancer death rates have been increasing during the last five years in the world, especially in Brazil and Latin America. One of the reasons for this phenomenon is the stalling of preventive cervical cancer screening tests, due to the lack of specialists with proper technologies for massive screening. The COVID-19 pandemic has worsened the situation as several Pap smearing tests have been postponed. The goal of this work is to present the design of a portable, low-cost device for digital screen test analysis to support cervical cancer detection in low-income and remote areas. We were able to design and test a low-cost, easy to implement device for conventional Pap smearing with high accuracy to detect anomalies in cervical cells. The total cost of the device is lower than 400 dollars. © 2022 IEEE.

Ingenious Methodology to Detect Disguised Human Faces with Masks using Deep Neural Network

Face recognition is now ubiquitous as an efficient and non-invasive method to verify identity. A facial recognition system works by comparison of a digital image or video frame showing a person's face with a database storing face images. Face masks are considered a required biosafety measure during this COVID-19 pandemic. Use of masks led to various issues to emerge and impact the functioning of earlier facial recognition algorithms and that has motivated our research. The construction of a real-time face recognition system that recognizes faces with and without masks is described in this paper. ResNet10 is used to perform the feature extraction. Then, to perform face detection and recognition, it is paired with a machine learning algorithm such as SVM. Without a mask, the maximum recognition accuracy is 99.40%, while with a mask, it is 98.30%. © 2022 IEEE.

Investigation of the Properties of Digital Chromosome Microscopy Images for the Telemedicine Applications

Deep cytogenetic examination of chromosomes properties is very specialized test that can be performed only in some scientific laboratories. Many problems occurred with sending the cytohistological micro preparations to laboratories of other countries because of COVID-19 pandemic. The only one way was available-to use telecommunication systems and send digital images of micro preparations to laboratories for their analysis in the limited time. Due to technical features of digital images of microslides, their informative value can vary significantly. Subjective and qualitative estimations of the parameters of microobjects are also a significant factor, which leads to decreasing the accuracy of laboratory diagnostics and complicating the repeatability of the results in scientific research. The aim of this article is to investigate the chromosome image parameters that carry the fullest informative value out of the images. © 2021 IEEE.

A System for Conversion of Hand-drawn Electrical circuit to Digital circuit: A Deep learning approach

The conversion of hand-drawn circuit to digital image is very essential for designers to save their rough sketch. Due to COVID-19, the teaching-learning process has become completely virtual. In this scenario, the present work on conversion of hand-drawn circuit to digital circuit is expected to help the faculty and students of engineering circuit branches. The MobileNet SSD architecture is used for detection of resistors, capacitors, inductors and battery in the hand-drawn circuit. Image overlay and skeletonization technique are used to obtain the digital image. In the present work, very few electrical components are considered for modeling;however, the proposed system can be extended (trained) for more number of electrical components and can be deployed in mobile applications with ease. © 2021 IEEE.

The Rise Efficiency of Coronavirus Disease Classification Employing Feature Extraction

COVID-19 has been an epidemic since the end of 2019. The number of patients with COVID-19 continues to escalate until new variants emerge. The COVID-19 detection procedure begins with detecting early symptoms, furthermore, confirmed by the swab and Chest X-Ray methods. The process of swab and Chest X-Ray takes a relatively long time since in Chest X-Ray some patients have the same symptoms as pneumonia. This study carried out the classification of COVID-19 and not COVID-19 with Discrete Wavelet Transform as feature extraction techniques and deep learning as the classification method. The result of this study capable to identify Chest X-Ray with COVID-19 and the accuracy increased of more than 10% on Support Vector Machine, Decision Tree and Deep Learning. So that, the comparison result showed that feature extraction was able to significantly improve accuracy. © 2021 IEEE.

Distance Learning in an International Context: A Case of Success

From 2020, due to the COVID-19 pandemic and its aggravation, the whole world turned to the transfer of information, that is, from the onsite to the distance learning modality, which involved a series of technological adjustments and mechanisms. However, such mechanisms of assisted technology are not recent, and it is possible to get a detailed observation over more than two decades of development of the most different possibilities and formats. Currently, adhesion is massive in everything that involves the virtual learning environment, and much is discussed about all stages of the processes. Thus, this article aims to demonstrate the importance of internationalization in the context of distance learning, through the analysis of a case study that consists of a set of partnerships involving the Training Institute of Portuguese Speaking Countries (Portugal), the Federal University of Rio de Janeiro (Brazil), through its School of Music and School of Communication, and the Arts National Foundation (Brazil), when conducting a course in Photography and Digital Image. This was the first product of the partnership between the institutions in this context, and because it was a success, the decision was made to report, through this article, its development, and the respective results obtained. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework.

Pulmonary diseases, driven by pollution, industrial farming, vaping, and the infamous COVID-19 pandemic, lead morbidity and mortality rates worldwide. Computational biomechanical models can enhance predictive capabilities to understand fundamental lung physiology; however, such investigations are hindered by the lung's complex and hierarchical structure, and the lack of mechanical experiments linking the load-bearing organ-level response to local behaviors. In this study we address these impedances by introducing a novel reduced-order surface model of the lung, combining the response of the intricate bronchial network, parenchymal tissue, and visceral pleura. The inverse finite element analysis (IFEA) framework is developed using 3-D digital image correlation (DIC) from experimentally measured non-contact strains and displacements from an ex-vivo porcine lung specimen for the first time. A custom-designed inflation device is employed to uniquely correlate the multiscale classical pressure-volume bulk breathing measures to local-level deformation topologies and principal expansion directions. Optimal material parameters are found by minimizing the error between experimental and simulation-based lung surface displacement values, using both classes of gradient-based and gradient-free optimization algorithms and by developing an adjoint formulation for efficiency. The heterogeneous and anisotropic characteristics of pulmonary breathing are represented using various hyperelastic continuum formulations to divulge compound material parameters and evaluate the best performing model. While accounting for tissue anisotropy with fibers assumed along medial-lateral direction did not benefit model calibration, allowing for regional material heterogeneity enabled accurate reconstruction of lung deformations when compared to the homogeneous model. The proof-of-concept framework established here can be readily applied to investigate the impact of assorted organ-level ventilation strategies on local pulmonary force and strain distributions, and to further explore how diseased states may alter the load-bearing material behavior of the lung. In the age of a respiratory pandemic, advancing our understanding of lung biomechanics is more pressing than ever before.

An innovative user-friendly platform for Covid-19 pandemic databases and resources.

BACKGROUND: The current coronavirus disease-19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global outbreak of a disease from a new coronavirus. Several databases have been published on this pandemic, but the research community still needs an easy way to get comprehensive information on COVID-19. OBJECTIVES: COVID-19 pandemic database (CO-19 PDB) aims to provide wonderful insights for COVID-19 researchers with the well-gathered of all the COVID-19 data to one platform, which is a global challenge for the research community these days. METHODS: We gathered 59 updated databases since December-2019 until May 2021 and divided them into six categories: digital image database, genomic database, literature database, visualization tools database, chemical structure database, and social science database. These categories focus on taking number of functions from the images, information from gene sequences, updates from relevant papers, essays, reports, articles, and books, the data or information in the form of maps, graphs, and charts, information of bonds between atoms, and updates about events of the physical and social environment, respectively. RESULTS: Users can search the information of interest in two ways including typing the name of the database in the search bar or by clicking the right category directly. Computer languages such as CSS, PHP, HTML, Java, etc. are utilized to construct CO-19 PDB. CONCLUSION: This article attempts to compile up-to-date appropriate COVID-19 datasets and resources that have not been compiled and given in such an accessible and user-friendly manner. As a result, the CO-19 PDB offers extensive open data sharing for both worldwide research communities and local people. Further, we have planned future development of new features, that will be awesome for future study.

A novel design proposal for low-cost vein-viewer for medical and non-contact biometric applications using NIR imaging.

The vein-viewer is a new revolution in the health industry. In fact, it is one of the must-have gadgets for any medical professional. The vein-viewer is device that helps to access easily veins when trying to collect a blood sample or for administering Intravenous (IV) cannulation. It is also an aid for dermatologist/aesthetic physician to access client's veins for sclerotherapy procedures or avoiding veins in cosmetic procedures. The vein-viewer is highly applicable where vascular positioning is really difficult; examples while canulating infants, obese, hairy/dark skins, dialysis/cancer patients etc. In addition, frequent attempts affect patients, causing trauma and subcutaneous haemorrhage. As palm/finger vein patterns are unique and complex, difficult to duplicate or steal as it is beneath the skin. So, in this Covid19 pandemic time, the vein-viewer finds applications in the secure non-contact bio-metric authentications for secure banking and attendance registering system to identify an individual. In this article I am trying to explain the design overview of vein-viewer system, its design challenges, cost aspects, its availability and also sharing a few inputs for the new compact, low-cost design and implementation.

Novel Mechanical Strain Characterization of Ventilated ex vivo Porcine and Murine Lung using Digital Image Correlation.

Respiratory illnesses, such as bronchitis, emphysema, asthma, and COVID-19, substantially remodel lung tissue, deteriorate function, and culminate in a compromised breathing ability. Yet, the structural mechanics of the lung is significantly understudied. Classical pressure-volume air or saline inflation studies of the lung have attempted to characterize the organ's elasticity and compliance, measuring deviatory responses in diseased states; however, these investigations are exclusively limited to the bulk composite or global response of the entire lung and disregard local expansion and stretch phenomena within the lung lobes, overlooking potentially valuable physiological insights, as particularly related to mechanical ventilation. Here, we present a method to collect the first non-contact, full-field deformation measures of ex vivo porcine and murine lungs and interface with a pressure-volume ventilation system to investigate lung behavior in real time. We share preliminary observations of heterogeneous and anisotropic strain distributions of the parenchymal surface, associative pressure-volume-strain loading dependencies during continuous loading, and consider the influence of inflation rate and maximum volume. This study serves as a crucial basis for future works to comprehensively characterize the regional response of the lung across various species, link local strains to global lung mechanics, examine the effect of breathing frequencies and volumes, investigate deformation gradients and evolutionary behaviors during breathing, and contrast healthy and pathological states. Measurements collected in this framework ultimately aim to inform predictive computational models and enable the effective development of ventilators and early diagnostic strategies.

Novel expandable architected breathing tube for improving airway securement in emergency care.

Life-saving interventions utilize endotracheal intubation to secure a patient's airway, but performance of the clinical standard of care endotracheal tube (ETT) is inadequate. For instance, in the current COVID-19 crisis, patients can expect prolonged intubation. This protracted intubation may produce health complications such as tracheal stenosis, pneumonia, and necrosis of tracheal tissue, as current ETTs are not designed for extended use. In this work, we propose an improved ETT design that seeks to overcome these limitations by utilizing unique geometries which enable a novel expanding cylinder. The mechanism provides a better distribution of the contact forces between the ETT and the trachea, which should enhance patient tolerability. Results show that at full expansion, our new ETT exerts pressures in a silicone tracheal phantom well within the recommended standard of care. Also, preliminary manikin tests demonstrated that the new ETT can deliver similar performance in terms of air pressure and air volume when compared with the current gold standard ETT. The potential benefits of this new architected ETT are threefold, by limiting exposure of healthcare providers to patient pathogens through streamlining the intubation process, reducing downstream complications, and eliminating the need of multiple size ETT as one architected ETT fits all.