Diagnosis of COVID-19 from the X-Ray images using BAT Algorithm with Deep Convolutional Neural Network

The COVID-19 widespread has posed a chief contest to the scientific community around the world. For patients with COVID-19 illness, the international community is working to uncover, implement, or invent new approaches for diagnosis and action. A opposite transcription-polymerase chain reaction is currently a reliable tactic for diagnosing infected people. This is a time- and money-consuming procedure. Consequently, the development of new methods is critical. Using X-ray images of the lungs, this research article developed three stages for detecting and diagnosing COVID-19 patients. The median filtering is used to remove the unwanted noised during pre-processing stage. Then, Otsu thresholding technique is used for segmenting the affected regions, where Spider Monkey Optimization (SMO) is used to select the optimal threshold. Finally, the optimized Deep Convolutional Neural Network (DCNN) is used for final classification. The benchmark COVID dataset and balanced COVIDcxr dataset are used to test projected model's performance in this study. Classification of the results shows that the optimized DCNN architecture outperforms the other pre-trained techniques with an accuracy of 95.69% and a specificity of 96.24% and sensitivity of 94.76%. To identify infected lung tissue in images, here SMO-Otsu thresholding technique is used during the segmentation stage and achieved 95.60% of sensitivity and 95.8% of specificity. © 2023 IEEE.

Optimization of sewage sampling for wastewater-based epidemiology through stochastic modeling

The proliferation of the SARS-CoV-2 global pandemic has brought to attention the need for epidemiological tools that can detect diseases in specific geographical areas through non-contact means. Such methods may protect those potentially infected by facilitating early quarantine policies to prevent the spread of the disease. Sampling of municipal wastewater has been studied as a plausible solution to detect pathogen spread, even from asymptomatic patients. However, many challenges exist in wastewater-based epidemiology such as identifying a representative sample for a population, determining the appropriate sample size, and establishing the right time and place for samples. In this work, a new approach to address these questions is assessed using stochastic modeling to represent wastewater sampling given a particular community of interest. Using estimates for various process parameters, inferences on the population infected are generated with Monte Carlo simulation output. A case study at the University of Oklahoma is examined to calibrate and evaluate the model output. Finally, extensions are provided for more efficient wastewater sampling campaigns in the future. This research provides greater insight into the effects of viral load, the percentage of the population infected, and sampling time on mean SARS-CoV-2 concentration through simulation. In doing so, an earlier warning of infection for a given population may be obtained and aid in reducing the spread of viruses. © 2023, The Author(s).

A Concept-Based Review on Generative Adversarial Network for Generating Super Resolution Medical Image Using SWOT Analysis

Several alarming health challenges are urging medical experts and practitioners to research and develop new approaches to diagnose, detect and control the early spread of deadly diseases. One of the most challenging is Coronavirus Infection (Covid-19). Models have been proposed to detect and diagnose early infection of the virus to attain proper precautions against the Covid-19 virus. However, some researchers adopt parameter optimization to attain better accuracy on the Chest X-ray images of covid-19 and other related diseases. Hence, this research work adopts a hybridized cascaded feature extraction technique (Local Binary Pattern LBP and Histogram of Oriented Gradients HOG) and Convolutional Neural Network CNN for the deep learning classification model. The merging of LBP and HOG feature extraction significantly improved the performance level of the deep-learning CNN classifier. As a result, 95% accuracy, 92% precision, and 93% recall are attained by the proposed model. © 2022 IEEE.

Automate the process of creating and conducting lessons on the Project-based method through a Web-based platform

The modern development of education is associated with its digitization and the search for our learning approaches to suit new generations and their peculiarities. During the COVID-19 crisis and the transition of learning in a remote form, the search for a new approach has become mandatory. The project-based approach has been identified as suitable for the new conditions. Learning in an electronic environment is a new way of teaching, which poses a number of challenges for teachers in creating, managing and evaluating projects. In this paper, the specific functionalities of a web-based platform for preparing and conducting projects according to the project-based method are presented. These functionalities include guided creation of projects, automated connections of project theme and educational materials and methods for the evaluation process. Detailed are the steps that the platform provides when creating a project. These steps are implemented in the form of a guide so as to facilitate as much as possible the work of teachers. Such a platform will help motivate a greater number of teachers to choose and teach by this method. © 2022 IEEE.

SIMULATING AN ENGINEERING WORKPLACE: A NEW APPROACH TO PROTOTYPE-BASED TEAM PROJECT

This paper documents the remote management of a first-year foundations of engineering course with special focus on students' learning by completing a prototype-based project in an online course. The COVID-19 pandemic brought on unprecedented challenges to the teaching and learning communities around the world. Educators made purposeful changes in their teaching approaches, shifting rapidly from in-person to online mode of instruction. This study documents a project-based course that adopted an asynchronous mode of instruction as a part of the general engineering curriculum at a large Southeast university in the United States during the pandemic. This asynchronous course - through implementing necessary changes and adaptations - simulated the experience of a cross-border engineering workplace. The course content focuses on engineering design and problem-solving, physical prototyping, simulated data collection and analysis, contemporary software tools, and professional practices and expectations (e.g., communication, teamwork, and ethics). Learning activities are designed to introduce students to the types of work that engineers do daily and to challenge students' knowledge and abilities as they explore the different elements of engineering by completing an aesthetic wind turbine project. Our paper reports on the development of the course site as informed by recent national developments in scholarship and practice for online teaching and learning. The principles of course design alignment as well as instructor presence and learner interaction as suggested by these national standards are discussed. Further, the study records strategies adapted to enable students to complete a successful prototype-based project working in geographically distributed and virtual, international teams. © 2022 SEFI 2022 - 50th Annual Conference of the European Society for Engineering Education, Proceedings. All rights reserved.

VIRTUAL REALITY TRAINING OF EMPLOYEES IN FOREST-BASED SMES AS A PART OF A CRISIS MANAGEMENT

Science and manufacturing have always been a generator and conduit of innovations in every field of human life. The innovations are of both fundamental and purely applied nature. The first environment for testing these innovations is the internal firm's educational system. In this regard, the last two years circumstances around the pandemic of COVID-19 served as a catalyst for the training in companies to adopt contemporary, interactive and attractive methods of training processes. Of course, some of these methods have been used in the pre-pandemic environment, but they have not been widespread. This confirms the rule related to a crisis management, namely that any crisis must be seen not only as a threat, but also as an opportunity to master new approaches and to show their effectiveness in practice. The aim of this paper is to focus on the possibilities of using virtual reality in training employees in forest-based SMEs such as specific manufacturing procedures, healthy work condition, organization of manufacturing etc. A number of research methods will be used. These will include: literature research, retrospective analysis, method of comparison etc. © 2022 15th International Scientific Conference WoodEMA 2022 - Crisis Management and Safety Foresight in Forest-Based Sector and SMES Operating in the Global Environment. All rights reserved.

Nanozyme: a New Approach for Anti-microbial Infections

Bacteria and viruses always posed a threat to human health. Most impressively, SARS-CoV-2 has raged around the world for almost three years, causing huge loss to human health. Facing increasing challenges of drug-resistance and poor treatment efficacy, new solutions are urgently needed to combat pathogenic microorganisms. Recently, nanozymes with intrinsic enzyme-like activities emerged as a promising new type of "antibiotics”. Nanozymes exhibit superior antibacterial and antiviral activities under physiological conditions by efficiently catalyzing generation of a large number of reactive oxygen species. Moreover, enhanced therapeutic effects are achieved in nanozyme-based therapy aided by the unique physicochemical properties of nanomaterials such as photothermal and photodynamic effects. This paper reviews the latest research progress in the field of anti-microbial nanozymes, systematically summarizes and analyzes the principles of nanozymes in the treatment of bacteria and viruses from a mechanistic point of view. An outlook on the future direction and the challenges of new anti-microbial infection nanomaterials are proposed to provide inspiration for developing next generation anti-microbial nanozymes. © 2023 Science Press. All rights reserved.

Enhanced Chest X-Ray Classification Model for Covid-19 Patients Using HOG and LBP

Several alarming health challenges are urging medical experts and practitioners to research and develop new approaches to diagnose, detect and control the early spread of deadly diseases. One of the most challenging is Coronavirus Infection (Covid-19). Models have been proposed to detect and diagnose early infection of the virus to attain proper precautions against the Covid-19 virus. However, some researchers adopt parameter optimization to attain better accuracy on the Chest X-ray images of covid-19 and other related diseases. Hence, this research work adopts a hybridized cascaded feature extraction technique (Local Binary Pattern LBP and Histogram of Oriented Gradients HOG) and Convolutional Neural Network (CNN) for the deep learning classification model. The merging of LBP and HOG feature extraction significantly improved the performance level of the deep-learning CNN classifier. As a result, 95% accuracy, 92% precision, and 93% recall are attained by the proposed model. © 2022 IEEE.

Feature-Modified SEIR Model for Pandemic Simulation and Evaluation of Intervention Approaches

SEIR (susceptible-exposed-infected-recovered) model has been widely used to study infectious disease dynamics. For instance, there have been many applications of SEIR analyzing the spread of COVID to provide suggestions on pandemic/epidemic interventions. Nonetheless, existing models simplify the population, regardless of different demographic features and activities related to the spread of the disease. This paper provides a comprehensive SEIR model to enhance the prediction quality and effectiveness of intervention strategies. The new SEIR model estimates the exposed population via a new approach involving health conditions (sensitivity to disease) and social activity level (contact rate). To validate our model, we compare the estimated infection cases via our model with actual confirmed cases from CDC and the classic SEIR model. We also consider various protocols and strategies to utilize our modified SEIR model on many simulations and evaluate their effectiveness. © 2022 IEEE.

Fifth international conference on materials and environmental science: Role of new materials in sustainable development

The Fifth International Conference on Materials and Environmental Science (ICMES20221), is an interdisciplinary platform to promote a multi-sectoral and collaborative approach in the field of development of new and innovative approaches in materials, their applications in energy and renewable energy, environmental science, sustainable development, health, biotechnology and electrical engineering. The scientific committee of ICMES2022 agreed that the health session was the priority since the Covid19 pandemic still constitutes a Public Health Emergency of International Concern. There are many multifunctional materials available by the advent of nanotechnology, ranging from carbon nanotubes, graphene, inorganic nanoparticles, conducting polymers, 2D materials, CO2 material capture, etc… Materials science Conference is an event that brings together leading researchers spanning the field of materials science and engineering to present and discuss cutting edge research with other experts in the field: exchanging ideas to advance current understanding towards the future of materials science. © 2022

New Approach of Covid-19 Prevention by Implemented Combination of Decision Support System Algorithm

Indonesia and Malaysia from 2020 to 2021 were exposed to COVID-19 pandemic. Both countries implemented a policy of restricting entry areas based on almost the same criteria, In Indonesia namely as PPKM which applying some level of exposure to those infected with covid-19. The determination of this level was all based on the growth in numbers exposed to covid-19, but on pandemic cases, the number of people who do not suffer from COVID-19 disease but have the same symptoms as the symptoms of COVID-19 also need to be considered as the pandemic agent to their environment. We named it as Precaution Covid-19 Pandemic (PCP) Level. The current level of the COVID-19 pandemic has not been fully determined by this idea. So, the idea of this research is to determine the pre-pandemic or precaution level of covid-19 in an area interfere by surrounding area. PCP level was not based on the growth of those infected with the covid-19 disease, but influenced by the number of patients whose have the symptoms similar to the dominant symptoms of the covid-19. The PCP Level determination can be used for precaution policy and support the previous Level Pandemic Methods. To accomplish this idea, three algorithms are used, they are K-Mean algorithm as a pattern clustering and the AHP algorithm as a level determination of the Covid-19 pandemic, While the relationship of candidate symptom pairs to Covid-19 transmission is carried out using the Naïve Bayes algorithm. The results of this study show that the combination of the three proposed algorithms provides and using data symptoms closely to dominant covid-19 symptoms can give an alternative for precaution level of covid-19 pandemic. The model for determining Covid-19 transmission based on four candidate symptoms has 89% precision and 85% accuracy. © 2022 IEEE.

COVID-Net Network and Application on Support Diagnosis COVID-19 over X-ray Images

Early diagnosis through X-ray images is the diagnosis with low cost, often used in hospitals to assist doctors in making health treatment plans. This paper presents a new approach for supporting the diagnosis of Covid-19 based on chest X-ray images. Specifically, this paper proposes using the Covid-net model for classifying the damage as Covid-19 or other causes. Data augmentation using seam carving was also researched and evaluated with different energy functions. The experimented results done on different databases are promising. © 2022 IEEE.

Lessons Learned from COVID That Have Been Transferred to Post-COVID Teaching and Learning

The COVID-19 pandemic required a rapid shift in course content delivery. Educators were faced with the challenge of providing some sort of continuity to student learning. Several content delivery modalities were used, including asynchronous, synchronous, and hybrid. The term HyFlex gained popularity, representing simultaneous offering of courses in-person, asynchronously online, and synchronously online, with students given the flexibility to engage through any of the modalities. New and innovative approaches to interactive learning were developed and implemented. Additionally, a transition to the online performance of laboratory experiments was required. Some of these new methods have carried over as we have moved back into more traditional education operations. In this paper, faculty from multiple institutions share success stories from techniques developed during the transition to online learning that have been transferred to or refined for the post-COVID in-person learning environment. © 2022 First-Year Engineering Experience, FYEE 2022. All rights reserved.

Analysis of the Homozygosity of Microsatellite Markers by Using Fuzzy Sets

Genetics is a highly relevant field of science. During the time of COVID-19 pandemic, it has gained additional importance. In this paper, a novel approach to genetic research using fuzzy sets is presented. Such a synergy of two so far rarely interacting scientific disciplines opens new avenues of research. The proposed approach shows only a sample of the possibilities offered by interdisciplinary research. In this study, a new approach using fuzzy set-based techniques to analyze the phenomena of homozygosity of microsatellite markers is presented. The analyses carried out using one of the most intuitive types of membership functions allowed us to achieve results that shed new light on the examined data. Moreover, the analysis of the distributions of individual markers using fuzzy sets allowed for a more in-depth study of the problem under consideration. © 2022 IEEE.

Comparative Analysis of Face Mask Detection Models

With an ongoing episode of Covid, the world health security and precaution need reformation and a new approach to be dealt with. The health concerns of the individual is a topic of utmost importance for every nation fighting the pandemic. With limited healthcare staff and the large public to look after, the assistance of Computer vision and AI is needed. Social distancing is a very effective way of containing the spread of a pandemic. Social distancing becomes difficult when dealing with a number of subjects like at gateways of offices, Airports, and many other sectors that have significant footfall in a day. In this paper we have tried to compare the different models for the recognition of mask on the face, for doing so we have used Real world masked face dataset (RMFD) (Iqbal et al, Renewable power for sustainable growth, Springer Nature, Berlin, LNEE, 2020) and Kaggle (Tomar et al, Machine learning, advances in computing, renewable energy and communication, vol 768. Springer Nature, Berlin, LNEE, 2020) dataset. At first we gather the images where face have actual mask on it and also augmented the image with editing the image of unmasked face with mask so that model can learn very details of the image and result will come more accurate and clean. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Third Path: A New Approach to Industry-based Undergraduate Engineering and Technical Education in the United States

The driving forces changing how we work and the jobs that we do are impacting organizations of all sizes across all sectors. The global pandemic has accelerated the pace of change and disruption to a level not experienced before. The combination of Industry 4.0, the Fourth Industrial Revolution and COVID-19 are creating a new sense of urgency to drive collaboration between industry and education. In 2022, academic institutions offer three paths to prospective engineering students, which students qualify for via standardized testing;Path 1) 4-year bachelor degrees with “R1” research focus: typically following on to postgraduate degrees and careers in research or academia. Path 2) 2-year associate degree (community college): typically leading to a career based on a technical skill or trade. Path 3) 4-year bachelor degree with industry focus: typically leading to careers in technical-based industries This paper presents a new approach to the “third path,” the industry-based bachelor degrees. The new approach is an alternative to the traditional programs currently offered by the majority of engineering schools in the United States. The traditional academic approach is failing to fill the talent pipeline. Academic policies and practices are unable to keep pace with the exponential growth of technology, the evolving motivations of a four-generation workforce (soon to be 5 generation) and the unpredictable development of new engineering business models [1-4]. The global competitiveness of the United States is at risk, the stakes are too high to stay on the traditional course. The authors contend that paths 1 and 2, despite shortcomings of their own, are in far better shape than the third path, so they are not addressed in this paper. This paper, written more like a position paper, proposes a new model for the third path;it is based on extensive research that was discussed in prior publications by the same authors [10,11,24-26]. The Third Path model proposes revised roles for the four key stakeholders involved in undergraduate engineering and technical education. The stakeholders are: 1) Industry (United States), 2) Academic institutions, 3) Federal and State Governments, and most importantly 4) next-generation student-engineers and technicians. © American Society for Engineering Education, 2022.

Increasing Safety and Privacy Against COVID-19 using IoTs and Enhanced Positioning Methods

This paper introduces and demonstrates a new approach to enhance safety against COVID-19, or other dangerous and contagious diseases, on mainly indoor public spaces, using enhanced privacy protection and an enhanced localization techniques. In most of the existing COVID-19 tracing systems and mobile apps, the focus is on identifying possible infected individuals, that were closed to a human source of transmission. This work includes primarily results and demonstrates a mobile app prototype and a corresponding support system to identify unsafe 'COVID-19 areas', from where infected individuals have recently crossed, so these spots to be avoided by individuals, until they will be characterized again as open to be used. © 2022 IEEE.

Risk Area Alert Through Heterogeneous Mobile Networks: A New Approach to Fight COVID-19 and Beyond

COVID-19 has now been sweeping the whole world, and fundamentally affecting our daily life. An effective mechanism to further fight against COVID-19 and prevent the spread of this pandemic is to alert people when they are in the vicinity of areas with a high infection risk, yielding them to adjust their routes and consequently, leave these areas. Inspired by the fact that mobile communication networks are capable of precise positioning, data processing and information broadcasting, as well as are available for almost every person, in this paper, we propose a mobile network assisted Risk arEa ALerting scheme, named REAL, which exploits heterogeneous mobile networks to alert users who are in/near to the areas with high risks of COVID- 19 infection. Specifically, in REAL scheme, all base stations (BSs) periodically estimate their serving users' locations, which are then analyzed by macro BSs (MBSs) to identify risk areas. Next, each MBS transmits the information about risk areas to small BSs (SBSs), which in their turn adjust the beamforming direction to cover these areas and send alerts to users located therein. Simulation results validate the effectiveness of the proposed REAL scheme. In addition, some key challenges associated with implementing REAL are discussed at the end. IEEE

Remote Reach: Innovating K-12 Education in a Time of Social Distancing and Beyond

In the face of the COVID-19 crisis, parents and educators were tasked with the enormous responsibility of creating a dedicated learning environment for their students at home. While shifting to an online medium was challenging, it provided a unique opportunity for AIChE at UCLA to support K-12 students through its new Remote Reach project. In Remote Reach, designing and delivering personalized interactive lessons to students has proved to be a rewarding and enjoyable experience for members involved. The prospect of online education has enabled our chapter to establish several connections with schools and community groups and develop a new approach to outreach. This year, our chapter's Remote Reach, was a year-long project that was dedicated to helping elementary and high school students learn and get excited about STEM topics while learning from home. We partnered with 3 main schools, 2 elementary schools and 1 high school. For each elementary school, we had a team of 5-10 AIChE members make and present a unique STEM module biweekly to 100+ students. Topics were chosen based on teacher, school, and member input and were designed to include demos, videos, and interactive content. The goal was to facilitate learning and discussion amongst the students using different mediums, rather than just our members lecturing them. These biweekly modules gave the elementary students the opportunity to learn new topics on a regular basis and become comfortable with our members. For the high school, we designed modules that would teach them about what ChemE's do and how wide-ranging our careers can be with videos and an interactive Process Flow Diagram activity. At this workshop, we will introduce innovative ways our chapter has served hundreds of students in the Greater Los Angeles community online, and how all student chapters can grow their own outreach programs in this ever-changing climate. Additionally, we will detail how Remote Reach's techniques can be utilized in both an in-person and online format to best serve your community as guidelines change. © 2021 American Institute of Chemical Engineers. All rights reserved.

Passive Method for Wax Casting in the Production of the Fuel Grain for in-space Hybrid Rocket

The Space Enabled Research Group at MIT is conducting a multiyear research effort to better understand the technical and logistical challenges posed by the implementation of a wax-based hybrid chemical in-space propulsion system. Paraffin and beeswax are being considered as candidate fuels. The overarching effort includes imagery analysis conducted on paraffin and beeswax centrifugal casting tests conducted onboard progressively higher-fidelity experimental platforms within transparent hardware which aids in optical investigations. Such platforms include a laboratory optical table experiment, as well as a vacuum chamber test, a parabolic trajectory microgravity aircraft (three flights to date), the Blue Origin New Shepard suborbital launch vehicle (three experiments onboard and scheduled for mid to late 2022), and potentially the Destiny laboratory module of the International Space Station. Each of these platforms allows for testing in a new environment or increasingly longer-duration microgravity. The main focus of this paper is in regards to a Suborbital flight experiment. This experimental setup had multiple limiting factors such a size, 10 cm x 10 cm x 20 cm and power of approximately 5 W. This lead to trying a new approach to the spin casting approach used previously by the team, as the method of forming the fuel grain annulus. This new approach was passive, meaning it did not require any additional power other than to melt the wax, and relied heavily on the surface tension properties of the containment chamber. The surface tension of the end caps was modified by using an oleophobic substance to repel the wax. Unfortunately, due to the Covid-19 pandemic, delays on flight caused results to not be ready before the date of publication of this paper. © 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.