Research on the 6XS6 of the SARS-CoV-2 Spike Protein

The 6XS6 is the structure of the SARS-CoV-2 spike protein. The physiological role of the spike protein is relative to the respiratory syndrome coronavirus and has a stronger infect on the human body than the ancestor virus. The purification of the 6XS6 is in the homo sapiens cell by the affinity chromatography, PBS supplemented and Size Exclusion chromatography. At last, using the Cryo-Electron Microscopy to see the structure. This paper is using the D614G mutation to illustrate the structure of the 6XS6. The N-terminal domain and C-terminal domain of the 6XS6 protein are ALA27 and VAL1137. Furthermore, the mutation doesn't have the hydrogen bond because the Asp614 is substituted by the Gly614, and the molecule that interacts with the Ala 647 may occur. While the 6XS6 structure has lots of non-covalent and disulfide bonds. Comparing the structure of the 6XS6 and 6VXX, both are glycoproteins, have three monomers, have two subunits, and have the same category of expression and classification. The different conformations of the two structures can affect the binding ability with the ACE2. This paper can help the researchers to further understand the structure and function of the 6XS6 which can be used in future experiments. © 2023 SPIE.

An Efficient and Intelligent Systems for Internet of Things Based Health Observance System for Covid 19 Patients

This paper describes how an IoT -based health monitoring system was conceived and built (IoT). With the proliferation of new technologies, doctors nowadays are constantly on the lookout for cutting-edge electronic tools that will make it simpler to detect abnormalities in the human body. The Internet of Things makes it possible to create cutting-edge, non-intrusive healthcare assistance systems. In this article, we introduce the Comprehensive Health Monitoring System, or CHMS. Normal people can't afford to buy separate devices or make frequent trips to hospitals. Our CHMS will monitor a patient's vitals, including temperature, heart rate, and oxygen saturation (OS), and relay that information to a portable device. To make sense of the information gathered by the physical layer's sensors, the logical layer must analyses it. The application layer then makes judgments based on the processed data from the logical layer. The primary goal is to reduce costs for average consumers. Patients will have simple access to individual healthcare, in addition to financial sustainability. This study introduces an IoT -based system that would streamline the operation of a complex medical gadget while reducing its associated cost, allowing its users to do so from the comfort of home. The public's adoption of these gadgets as aids in a given setting might have significant effects on their own lives. © 2023 IEEE.

Healthcare Diagnostics Service Using Federated Learning

Healthcare systems around the world rely on powerful computational prediction tools in order to make accurate diagnostics with regard to the human body. In order to estimate the severity of lung damage post-COVID infection, healthcare providers rely on AI prediction tools to perform diagnosis. While such tools exist at a rudimentary level, there is a growing demand for more reliable and democratised systems that train models over a diverse data-set. To that end, the focus of this research paper turns to federated learning, a distributed machine learning paradigm. The system proposed consists of a central server that pools features and weights across various nodes, thereby cutting bias in the prediction models. This also achieves data decentralisation which ensures patient privacy. An end-to-end application is realised that facilitates distributed training of batch data that is visualised in real-time with the help of sockets. The application also features an inference service, classifying chest x-rays based on whether the image displays damage in case of Pneumonia. © 2023 IEEE.

Design of Automatic Doors for Ultraviolet Sterile Box

There are several ways to sterilize them by using cleaning fluids or by using Ultra Violet (UV) light. However, the use of UV can have adverse effects if exposed to the human body. The current technology is UV sterile boxes for the benefit of UV boxes to be safe from contact with the human body. The box creates a closed space so that UV rays are not exposed to the human body. However, UV sterilizers on the market are not equipped with an automatic system that requires the user to have direct contact with the sterilizer, direct contact with the sterilizer will cause the outer side of the sterilizer to become a new medium for spreading viruses. Based on these problems, we are conducting research that aims to design an automatic door design for UV sterile boxes which are expected to minimize direct contact between the user and the sterile box so that it can stop the development of the coronavirus. Regulate the motion of the dc motor, which must rotate to move the conveyor. Arduino nano will regulate the motor driver, which functions as a switch to change the direction of motion of the dc motor. The rotating dc motor will be connected to the threaded iron to produce forward or backward movement on the conveyor. Arduino nano to it is required to be able to read the value of the proximity sensor as an indicator for stopping the conveyor. Arduino Nano is also needed to regulate the on or off of the ultraviolet lamp. At the same time, the difference in the duration of sending commands by Arduino Nano and receiving system output is 30ms. © 2022 IEEE.

Resetting urban human-microbial relations in pandemic times

Microbes, particularly of the viral kind, are currently preoccupying human activity and concerns due to the COVID-19 pandemic. Although for a long time there has been fear associated with ‘germs', notably viruses and bacteria and the diseases they cause, the pandemic has set these fears into overdrive. As serious as this ongoing event is, there are broader interests and important alternative narratives about the microbial world permeating current thinking, based on research that intersects with and includes biopolitical and relational research in geography. In an attempt at balancing the prevailingly negative discourses about microbes and the potential harms they can cause, and to encourage more geographers to contribute to understanding human-microbial relations, this paper draws together recent research across disciplines to discuss the prevalence and role of microbes in environments and in and on human bodies. Drawing on ideas of more-than-human care, the paper shows how geographers and other social scientists can and are already helping reset human-microbial relations, and where further work can productively be done. © 2023 The Authors. Geography Compass published by John Wiley & Sons Ltd.

Pose Estimation of Yoga Poses using ML Techniques

POSE ESTIMATION is a technique to identify joints in a human body from an image or video given as input to a computer. Pose estimation can be performed using Machine Learning (ML) techniques and Deep Learning techniques. Lately, it has been receiving lots of attention in the fields of Human Sensing and Artificial Intelligence. The main aim of pose estimation is to predict the poses of humans by locating key points like elbows, knees, wrists etc.In this work, we have proposed a model which uses Mediapipe, an ML framework, to obtain key point coordinates and ML algorithms like SVM, Gaussian Naive Bayes, Random Forest, Gradient Boost and K Neighbours classifier, which are compared and used to predict Yoga poses. Yoga is practised by people of all ages alike these days to fight issues caused both physically and mentally, thus improving the overall quality of life. Especially since the rise of the COVID-19 pandemic, the number of people practising yoga has only been increasing. In the model, human joint coordinates obtained are used as features. The model with the best accuracy and f score (MediaPipe+ SVM) is chosen for the final work.The yoga poses we used are Plank, Warrior 2, Downdog, Goddess, Tree and Cobra. On implementing the work, a real-time video feed from the webcam of the user's system is obtained, and pose estimation and classification of the yoga pose are done. Unlike in most current systems, suggestive measures to correct the yoga posture are also displayed in real-time alongside the webcam display of the person performing yoga along with some other basic pose information. © 2022 IEEE.

Cough-based COVID-19 Detection with Multi-head Deep Neural Network

Clinicians have long used audio signals created by the human body as indications to diagnose sickness or track disease progression. Preliminary research indicates promise in detecting COVID-19 from voice and coughing acoustic signals. In this paper, various popular convolutional neural networks (CNN) are employed to detect COVID-19 from cough sounds available in the Coughvid opensource dataset. The CNN models are given input in the form of hand-crafted features or raw signals represented using spectrograms. The CNN architectures for both the types of inputs has been optimized to enhance performance. COVID-19 could be detected from cough sounds with an accuracy of 77.5% using CNN on handcrafted features, and 72.5% using VGG16 on spectrograms. However, result show that the concatenation of the two in a multi-head deep neural network yield higher accuracy as compared to just using hand-extracted features or spectrograms of raw signals as input. The classification improved to 81.25% when ResNet50 was employed in the multi-head deep neural network, which was higher than that obtained with VGG16 and MobileNet. © 2022 IEEE.

Iris

As an experiment designed to question the boundary, relationship, and identity between human bodies and AI robots, Iris is endowed with independent perception and temperament by implementing facial expression recognition, bio-signal measuring, and emotion synthesis. This temperament is stimulated by PAD emotional model and expressed through algorithmic motions generated in real-Time. Emotions of the Iris are affected by the wearer's feelings through sensors that measure the biological signals of the wearer. This ability to perceive and emote reflects certain connections and differences with the wearer as if showing a split personality. Moreover, Iris noticeably affects interpersonal interactions and relationships by showing different responses to people captured by the camera. These effects were heightened during COVID-19 when our faces were covered by masks, which is, based on perception and synthesis, the Iris performs like a highly specialized organ to augment and replace humans' expressions. © 2023 Owner/Author.

The Impact of Bahrain's Adaptive Sports on Quality of Life

The importance of research and review of the advantages of physical exercise for humans and the importance of sports and the extent of its impact on the lives of individuals in various aspects. Bahrain Economic Vision 2030 consider sport is one of the most important factors to increase immunity in the human body, and that sport is important to confront COVID-19 and all diseases, so there are positives to the need to adhere to sports practices. Literature Review shows Public sector support for sport and physical activity unleash promote wellbeing and health, pursue positive social goals and promoting genders equality (Giulianotti et al. 2019) and how Physical activities enhance quality of life as exercise are very great for the general health and physical and psychological health of those who exercise regularly. (Shen et al. 2020).The method of the research is systematically literature review the articles related how sport enhancing quality of life, global initiatives regarding sport as social innovation and Bahrain overview about physical activity. In the Conclusion, Sport is one of the main things in a healthy person's life, which maintains public health and prevents diseases. Also, Bahrain 2030 vision improve health system by promoting and encouraging a healthy lifestyle by offering more attractive public spaces and modern facilities to boost sports activities. Finally, For future research academic institutions need to conduct researches about how sport can result outcomes beyond the playing field (Sanders et al. 2017;Camp 2020), what is the role of innovation in sport for development and peace to develop solutions for social challenges (Svensson and Cohen 2020). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Arduino Based Automatic Sanitizer Dispenser Controlled Door

The newly discovered infectious disease COVID-19 infected many people. The coronavirus causes respiratory problems and also gets affected in many parts of the human body. This virus is transmitted from one body to another body through the air or by touch. The only way to stop the transmission is to use a mask on the face and washing or sanitizing hands frequently. Sanitizers are liquid, gel, or foam which is designed to eliminate germs on skin or any other object. In daily lives also sanitization is necessary to prevent various germs which can make you ill. One of the main modes of contact with germs is our hands because in our daily lives we touch many infected surfaces and objects unknowingly hence to prevent any type of illness due to those germs, hand sanitization is necessary, especially for a person working in a closed and congested environment like offices, canteens, restaurants, schools, etc. This paper aims to design a low-cost Arduino-based automatic sanitizer dispenser-controlled door and it will be of great help in public places where people don't follow hand hygiene properly. This automatic sanitizer system will be placed at the entry point of the main gate and it will only allow entry from that gate if and only if the person goes through the sanitization process first. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comparison of adaptive thermal comfort with face masks in library building in Guangzhou, China

During the COVID-19 pandemic, wearing masks in public spaces has become a protective strategy. Field tests and questionnaire surveys were carried out at a university library in Guangzhou, China, during June 2021 and January 2022. The indoor environmental parameters were observed, thermal sensation votes of students on various environmental parameters were collected, symptoms of students wearing masks were quantified, and the appropriate amount of time to wear masks was established. To identify acceptable and comfortable temperature ranges, the relationship between thermal sensation and thermal index was investigated. During summer and winter, people wearing masks are symptomatic for a certain duration. The most frequently voted symptom was facial heat (62.7 % and 54.6 % during summer and winter, respectively), followed by dyspnea. During summer, more than 80 % of the participants subjects were uncomfortable and showed some symptoms after wearing masks for more than 2 h (3 h during winter). In the summer air conditioning environment in Guangzhou, the neutral Top was 26.4 °C, and the comfortable Top range was 25.1–27.7 °C. Under the natural ventilation environment in winter, the neutral Top was 20.5 °C, and the comfortable Top range was 18.5–22.5 °C. This study may provide guidance for indoor office work and learning to wear masks in Guangzhou. © 2022 Elsevier Ltd

Study on the Simultaneous Measurement of Multiple Positions of the Human Body Using Two-Dimensional Millimeter-Wave Array Radar

Radar-based non contact measurement of physiological signals and vital signs has been of great interest, partly because of the COVID-19 pandemic. Existing studies reported that different physiological signals can be extracted from different positions of the human body. In this study, we demonstrate the measurement of multiple positions of the human body using a radar system with a two-dimensional antenna array. Using a 79-GHz 48-channel multiple-input multiple-output antenna array, we image multiple body parts of participants and separate the echoes using array signal processing. We present experimental results to show the feasibility of the proposed approach. © 2022 The Institute of Electronics Information and Communication Engineers (IEICE) of Japan.

Metabolic Changes of VOCs in Human Caused by Wearing Masks: A Preliminary Study Based on Electronic Nose

Wearing masks has been generally recommended to reduce the spreading of COVID-19. However, little is known about its effects on metabolic VOC changes in human body. To explore how the duration of wearing masks influences VOC metabolism in the human body, the essay used a self-developed electronic nose to analyse exhaled breath samples from 10 healthy individuals in this study. Firstly, polytetrafluoroethylene sampling bags are used to collect breath samples after volunteers wearing masks for 1h, 2h, 3h, 4h, and 5h. Secondly, data pre-processing, including baseline calibration and normalization are carried out. Thirdly, the study used LDA for dimensionality reduction on the original data to extract 4 features. Fourthly, differences in the length of time of wearing masks are analysed. Then, 4 algorithms were applied for cluster analysis based on extracted features. Moreover, 3 supervised classification algorithms were used to recognize the duration of wearing masks. Finally, multi-dimensional linear regression is used to study the possibility of predicting the duration of wearing masks based on breath signals acquired through electronic noses. As a result, the first feature extracted by LDA significantly differs from each other in the duration of wearing masks (p<0.05). Cluster analysis results show that the optimal internal parameters Adjusted Rand Index, Adjusted Mutual Information, Homogeneity and V-measure reach 80.2%, 81.5%, 83.5% and 83.7% respectively. Using 5-fold cross-validation on the K nearest neighbour classification model, the best accuracy of recognizing durations of wearing a mask reaches 88%. R-square of multi-dimensional linear regression reaches 92.5%, which shows excellent fitting performance. It can be concluded that the VOC metabolism of the human may change with the duration of wearing masks. Further, "breath prints” obtained by electronic nose may have the potential to predict the effective time and even the quality of masks. © 2022 SPIE.

COVID-19 Data Analysis and Appropriate Vaccine Prediction using Machine Learning

COVID-19 highly contagious virus, it has wreaked devastation on the earth. To help the world overcome this challenging situation, scientists and professionals across many disciplines are working relentlessly to develop vaccines and prevention measures. Many people are getting sick because they don't know which of the discovered coronavirus vaccines are beneficial for the human body. The appropriate vaccine has been predicted by analyzing the types of diseases that people have in our data set and the types of diseases that people get after giving the first dose and second dose. From these data, we can predict what kind of vaccine will be appropriate for any disease and there will be no side effects in the first doses and no side effects in the second doses. Here three algorithms such as SVM, Random Forest, and Bagging Classifier of machine learning are used to get the appropriate vaccine. Finally, we can say that the vaccine made by machine learning will help reduce the death rate of the coronavirus and increase the immunity of our body. © 2022 IEEE.

Arduino Based Automatic Sanitizer Dispenser Controlled Door

The newly discovered infectious disease COVID-19 infected many people. The coronavirus causes respiratory problems and also gets affected in many parts of the human body. This virus is transmitted from one body to another body through the air or by touch. The only way to stop the transmission is to use a mask on the face and washing or sanitizing hands frequently. Sanitizers are liquid, gel, or foam which is designed to eliminate germs on skin or any other object. In daily lives also sanitization is necessary to prevent various germs which can make you ill. One of the main modes of contact with germs is our hands because in our daily lives we touch many infected surfaces and objects unknowingly hence to prevent any type of illness due to those germs, hand sanitization is necessary, especially for a person working in a closed and congested environment like offices, canteens, restaurants, schools, etc. This paper aims to design a low-cost Arduino-based automatic sanitizer dispenser-controlled door and it will be of great help in public places where people don't follow hand hygiene properly. This automatic sanitizer system will be placed at the entry point of the main gate and it will only allow entry from that gate if and only if the person goes through the sanitization process first. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Small Scale COVID-19 Diagnosis Program: A Philippine Perspective

The world is still currently facing a pandemic. In the Philippines, the number of cases is rapidly rising. Since there is yet a cure to be found, the best cure to such is prevention such as being aware of the adverse effects that it has on people along with the symptoms commonly felt by those who have the disease. Constant sanitation is also necessary to kill the bacteria causing the disease before it even has the chance to spread throughout the human body. In this research, a small scale AI program that could diagnose a person with the probability of having the disease was developed. Theprogram used patients' symptoms who have the disease, along with the corresponding severities of such, as input. Fuzzy logic was used in developing the program through the development and integration of a fuzzy inference system (FIS). Moreover, the testing accuracy of the proposed system was 70.83% which was based on the number of diagnoses that produced a medium or high verdict of a patient contracting the virus. The inputs for such diagnoses were the symptoms felt by confirmed COVID-19 patients along with their corresponding severities which were obtained from the data set acquired containing information regarding COVID-19 patients in the Philippines. Additionally, MATLAB was the software used to develop both the program and the FIS. © 2022 IEEE.

Academy Camp VR: Serious Game Constructions as Active Learning Experiences for Children

We have been running children’s play and learning camps since 2011, but we are withholding physical gathering after 2020 during the COVID-19 pandemic. The problem with online videoconference camps is that it is difficult to design activities that encourage children to move spontaneously and engage with the world around them. Starting in 2021, we have launched a series of VR (virtual reality) camps with each camp aiming for the construction of a VR world. We believe that by having the children actively involved in the creation of the 3D objects that make up the worlds and in game design, we are fostering a sense of efficacy that allows them to actively work on the world surrounding them and change it. In this paper, we summarize our attempts, especially our experience of constructing a VR world with children in which the immune system of the human body is turned into a serious game. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Radiant soma

The human body has historically been a constant source of fascination in the arts and sciences. With the impact of the COVID-19 pandemic, the debate over the virtues and disadvantages of physical versus virtual bodies has increased dramatically. One of the most difficult attributes of the human body to translate into other forms is the essence of human movement and, by extension, energy. Radiant Soma emphasizes the ephemerality of human movement by visualizing motion capture data with light. The installation of lasers and phosphorescent objects transforms choreography that the original performer can no longer perform into a constant stream of lively spirits. © 2022 Owner/Author.

Contrast Enhancement and Noise Reduction of chest X-ray images

X-ray radiography is used to get medical images of body parts such as chest, bones etc. These images help in detection of anomaly in inspected body part, for eg- Chest X-ray are used for detection of many diseases such as Covid-19, Pneumonia and Cancer. However, images obtained from radiography are low in contrast and with high noise level. Enhancement of an image is very crucial for the diagnostic purpose, as currently medical images are very helpful in identifying various disease and problem in human body. With the technical support, the enhancement is considered one of the first-rate methods for the betterment of visualization and raising the standard for understanding and clearing the image details. In our work, we have focused on the contrast enhancement and noise reduction, using Histogram equalization, CLAHE (Contrast Limited Adaptive Histogram Equalization), median filter and DCT filter for chest X-ray images of COVID-19 positive patients. The dataset of 6,334 images are collected from the Kaggle repository. All these methods are combined and as a result, has provided the best output by giving a colored enhanced image, highlighting the major details. This work will be helpful in the diagnosis of various kind of the diseases from radiographic approach. In the future, we will extend the process for the diagnostic part of COVID-19 from the enhanced images dataset, which will help in easy detection and work as a technological support to healthcare system. © 2022 IEEE.

Comparative Analysis of Image Enhancement Techniques for Chest X-ray Images

X-ray radiography plays a crucial part in diagnosis of various diseases in human body like Covid-19, Cancer and Pneumonia. The images obtained through X-ray radiography is interpreted by Surgeons, Pathologists and Radiologists for detecting anomaly in scanned body part. Chest X-ray is one of the cheapest and easily accessible tests of functioning of chest and lungs. However, images obtained through X-ray are not very clear, low in contrast and with lesser variation in gray level. Image enhancement is done for better visualization of images and bringing forward the underlying details of image. The Kaggle repository of total 6334 chest X-ray images were used for experimentation and calculation works. In this paper, we have compared various combinations of contrast enhancement techniques such as CLAHE, Morphological operations (black and white hat transforms) and noise reduction techniques like Median filter, DCT and DWT. The Comparison was done on the basis of image quality assessment parameters such as MSE, PSNR, and AMBE. The results showed that fusion of CLAHE and DWT techniques gave best results with highest PSNR value and lowest AMBE among the various models discussed. The proposed methodology shall be very helpful in diagnosis of diseases from chest X-ray images. © 2022 IEEE.