Near-field exposure of pathogen-laden respiratory particles based on statistical evaluation of one emitting person indoors

Risk assessment models typically assume ideal mixing, in which the pathogen-laden aerosol particles emitted by a person are evenly distributed in the room. This study points out the local deviation from this idealized assumption and a correlation between the level of pathogen concentration and the distance from the emitter. For this purpose, several numerical studies (CFD) were analyzed, and a validation experiment was performed. Statistical evaluation of the spatial pathogen distribution was used to determine the potential exposure to elevated pathogen concentrations. Compared to an ideally mixed room, at a distance of 1.5 m, the mixing ventilation cases show a 25% risk of being exposed to twice the amount of pathogens and a 5% risk to more than 5 times the assumed value. For displacement ventilation there is a 75% chance of being exposed to less pathogens than in complete mixing at a distance of 1 m. The measurement values agree with the simulation results. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Early Prediction of COVID-19 Infection with IoT and Machine Learning

The deadly virus COVID-19 has heavily impacted all countries and brought a dramatic loss of human life. It is an unprecedented scenario and poses an extreme challenge to the healthcare sector. The disruption to society and the economy is devastating, causing millions of people to live in poverty. Most citizens live in exceptional hardship and are exposed to the contagious virus while being vulnerable due to the inaccessibility of quality healthcare services. This study introduces ubiquitous computing as a state-of-The-Art method to mitigate the spread of COVID-19 and spare more ICU beds for those truly needed. Ubiquitous computing offers a great solution with the concept of being accessible anywhere and anytime. As COVID-19 is highly complicated and unpredictable, people infected with COVID-19 may be unaware and still live on with their life. This resulted in the spread of COVID-19 being uncontrollable. Therefore, it is essential to identify the COVID-19 infection early, not only because of the mitigation of spread but also for optimal treatment. This way, the concept of wearable sensors to collect health information and use it as an input to feed into machine learning to determine COVID-19 infection or COVID-19 status monitoring is introduced in this study. © 2023 IEEE.

Tolerance Increase in Escherichia coli O157:H7 and Methicillin-Resistant Staphylococcus Aureus USA300 Exposed to Low-Power Continuous Ultraviolet Radiation from Narrow-Wavelength Sources

Escherichia coli O157:H7 is a major cause of foodborne disease outbreaks throughout the world, while methicillin-resistant Staphylococcus aureus (MRSA) is responsible for many difficult-to-treat infections in humans. Ultraviolet (UV) irradiation is commonly used for disinfection in food processing, medical facilities, and water treatment to prevent the transmission of these pathogen. With increased use of UV disinfection technologies over the last few years because of COVID-19 and concerns about other communicable disease, it has become a concern that microbial species could develop tolerance to UV irradiation, especially when it is applied continuously. To elucidate the effect of continuous UV exposure at different wavelengths and power levels on the tolerance development of bacteria, Escherichia coli O157:H7 and MRSA)USA300 growths were investigated by continuously exposing inoculated agar plates to six different commercially available UV sources at wavelengths of 222 nm, 254 nm, 275 nm, and 405 nm. The agar plates in these experiments were partially covered by a thin acrylic sheet, which provided either complete protection from the UV to the cells directly under the sheet, no protection if significantly away from the sheet, or partial protection near the edges of the sheet due to shading or small amounts of UV reflection under the sheet at the edges. In these experiments, tolerant cells of E. coli and S. aureus were found from the 222 nm, the 405 nm, and one of the 254 nm sources. Upon examination of the power of each UV source, it was shown that the 275 nm and 254 nm sources that resulted in no tolerant cells had surface power densities [at 25 cm (10 in.)] that were more than 10-200 times greater than those that had tolerant cells. These results suggests that bacterial cells have a higher chance to develop UV tolerance under lower power UV sources (under the experimental conditions in our laboratory). Genome investigation of the tolerant colonies revealed that there are no significant differences between the cells that developed tolerance and the original organism, hinting at the need to explore the role of epigenetics mechanisms in the development of UV tolerance in these bacteria. © 2023 American Society of Civil Engineers.

Influence of the Covid-19 Pandemic on Migrant Workers' Quality of Life in Trichirappalli, Tamilnadu

A pandemic is an epidemic that spreads widely beyond national lines and has an impact on the entire planet. It produces a lot of illnesses that can be fatal. For instance, although though cancer claims many lives, the disease is not considered a pandemic because it cannot be spread readily or even be infectious. Pandemics, which meaning 'everyone' in Greek, are where the word 'pandemic' originates. The term 'demo' refers to the populace. All people are Pan. Therefore, the idea of a 'pan demo' assumes that the entire world's population will be exposed to this illness and that some of them will get sick. Sadly, an unforeseen cause has struck the world with the corona virus's growth in India in 2020 and other nations throughout the time of 2019. A research study on migrant workers is required because they have significantly impacted the socioeconomic sector. The goal of this study is to ascertain how the Covid-19epidemic has affected migrant workers' quality of life in Tiruchirappalli, Tamil Nadu. Due to the fact that this is an empirical study, the field survey method and personal interview techniques were used to collect the necessary data from the respondents. The researcher interacted with each visitor at the job site and collected the necessary data through interviews and scheduling. A total of 487 persons were included in the sample size for the investigation. Statistics were employed for this inquiry, including frequency analysis, regression, and correlation analysis. The interpretation's output is the findings and observations of the analytical study. © 2022 IEEE.

Measuring COVID-19 Opinion in the Online Debate using an Unsupervised Model

A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE

Hand-breathe: Non-Contact Monitoring of Breathing Abnormalities from Hand Palm

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method. IEEE

Impacts of Information Source and Perceived Health on Information Behavior

This preliminary study revisits a fundamental information problem of information behavior, focusing on needs, overload, and information source use, in the context of the COVID-19 pandemic. The associations between the impact of information source use on the extent of information needs, being exposed to information, and feeling of overload was examined. Furthermore, to understand the impact of context on information behavior, the differences in the degree of information resource use, needs, exposure, and overload between the two groups with different levels of health were investigated. 85th Annual Meeting of the Association for Information Science & Technology ;Oct. 29 – Nov. 1, 2022 ;Pittsburgh, PA. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

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.

System for Prioritizing Covid-19 Patients Based on Certain Parameters

Over numerous nations on earth, a brand-new virus known as the corona virus has been spreading like wildfire. Hospitals are exposed to many people, and it might be difficult to follow COVID-19 and take into consideration all patients. We have created a decision-making method based on a few parameters and X-rays to identify the priority of patients. The Mask R- CNN technique is used to train and test on the dataset to classify patients as having COVID infection or not. On chest X-ray pictures, the mask R CNN technique enhances COVID - 19 detection performance. © 2022 IEEE.

Intelligent Ventilation System for COVID-19 Prevention using the IoT and Decision Tree

COVID-19 can be spread through the air, caused by inhaling smaller droplets1 containing SARS-CoV-2 in an indoor environment. In particular, both people with symptoms and people without symptoms make many small droplets and respiratory droplets when they breathe, sneeze, cough, or speak. When these tiny droplets are exposed to the air around them, they can react with the particles (PM) and stay in the air for a long time. The survival period depends on various conditions, including the type of surface, temperature, and relative humidity. The ventilation system is one way to solve this problem. This research makes a significant contribution to the development of intelligent ventilation systems utilizing the Internet of Things (IoT) and decision tree algorithms. The function of this system is to get a clean room environment from viruses by spraying disinfectant automatically and controlling the temperature and humidity of the air. Based on the results of this study, the system is able to control and categorize room conditions based on temperature and humidity factors. The classification value using the C4.5 decision tree algorithm is 92.33% with an average temperature and humidity value of 25oC and 49%. © 2022 IEEE.

Quarantined-subject monitoring and indoor positioning with anti-Absconding artificial intelligence (Project BANTAY)

Quarantine is the process of restricting and separating the movement of people who have been exposed to a contagious disease to avoid proliferation. Quarantined subjects were monitored manually, and patients tended to abscond or 'run away.' In the Philippines, there was a lack of research and the absence of similar technology commercially available related to this matter. Project Bantay integrated Artificial Intelligence of Things (AIoT), indoor positioning systems, and wearable technology to alleviate the shortage of personnel, long-Term savings on workforce utilization in the government, and predict absconding or 'run-Away' potentially infectious individuals in our current and future quarantine facilities. Also, it included information system development for monitoring quarantined subjects' heart rates and temperatures that would eventually help the government combat and prepare for a similar unexpected pandemic. The prototype would start by turning on the device after being placed on the quarantined subject. The device must be linked to the router, and the sensors must then be calibrated. The web interface should receive and be able to see data readings, including temperature, heart rate, the location of the subject being quarantined, and the removal of the device via an IR reading. The temperature, heart rate sensor, and indoor positioning all measured above a p-value of 0.05, which accepted the null hypothesis, confirming that the actual and commercial product versus Project Bantay's temperature, heart rate, and indoor positioning accuracy was statistically the same. The anti-Absconding tendencies of the hypothetical dataset using machine learning data analytics showed that among four inherently multioutput machine learning regression algorithms, the Decision Tree Regression Algorithm could output a much better result in determining the tendencies of a subject to abscond from the quarantine facility. © 2022 IEEE.

Contact Tracing Platform in OSN for Prevention of Infectious Disease Outbreaks

To limit the spread of COVID-19, social distancing measurements and contact tracing have become popular strategies implemented worldwide. In addition to manual contact tracing, smartphone-based applications based on proximity detection have emerged to speed up the discovery of potential infectious individuals. However, so far, their effectiveness has been limited, mainly due to privacy issues. A new tracing mechanism is represented by Online Social Networks (OSNs), which provide a successful way to track, share and exchange information in real-time. Being extremely popular and largely used by citizens, OSNs are less exposed to privacy concerns. In this paper, we present an OSN-based contact tracing platform called TraceMe to reduce the spread of the epidemic. The proposal currently targets COVID-19, but it can be used in presence of other infectious diseases, like Ebola, swine flue, etc. TraceMe implements conventional contact tracing based on physical proximity and, in addition, it leverages OSNs to identify other contacts potentially exposed to the virus. To efficiently find the targeted social community, while saving the time complexity, a clique-based method is applied. Performance evaluation based on a realistic dataset shows that TraceMe is able to analyse large-scale social networks in order to find, and then alert, the tight communities of contacts that are at high risk of infection. © 2023 IEEE.

A Staging Prediction Model for COVID-19 Pandemic under Strong Public Health Interventions

With the extensive implementation of the strong public health interventions in China, many models proposed to predict COVID-19 epidemic are no longer applicable to the current epidemic development. In this paper, a COVID-19 prediction method is proposed based on a staging SEITR model with consideration of strong public health interventions in China. The method simulates preventive and control measures such as mass nucleic acid testing and quarantine of close contacts by introducing the role of Isolates and the transformation of Exposed to Isolated. The experimental evaluation uses real epidemic data from six cities including Nanjing, Yangzhou, and etc. The accuracy of prediction for total number of infections reaches 95.8% with the data of the first 15 days of the outbreak. In addition, the prediction accuracy of the end of the pandemic is 95.07%. These show that the proposed method can effectively predict the course of the epidemic and it is practical for relevant departments to formulate reasonable prevention and control measures. © 2022 IEEE.

COVID-19 and Caregiver Risk Factors for Child Maltreatment: The Pandemic in Review.

The COVID-19 pandemic has fostered an environment for increased risk of child maltreatment (CM) as families experience increased psychosocial and financial burdens and spend unprecedented amounts of time together in the home. This narrative review aimed to summarize empirical findings on existing or new pandemic-related risk factors among caregivers. A combination of search terms related to CM and COVID-19 were used to identify articles published within five databases between February 2020 and July 2022. Literature searches produced 113 articles, of which 26 published across 12 countries met inclusion criteria. Four previously well-established risk factors for CM perpetration continued to persist during the pandemic, including stress, parental mental health, financial concerns, and parental substance use. Of note, inconsistent definitions and measures were used to capture these risk factors. Several additional emerging and understudied risk factors were also identified among limited articles, such as food insecurity and parental education. Findings emphasize the ongoing need for evidence-based interventions to address CM risk during the pandemic, including parent training programs. However, consolidated measures and consistent conceptualization of risk factors are needed to advance the study of CM. Going forward, practitioners and researchers should (a) strengthen the identification process for families at greatest risk for CM, and particularly those vulnerable to pandemic-related stressors; and (b) augment delivery of CM prevention strategies and evidence-based programs to fit the pandemic context.

New monitoring interface for the AMS experiment

The Alpha Magnetic Spectrometer (AMS) is constantly exposed to harsh condition on the ISS. As such, there is a need to constantly monitor and perform adjustments to ensure the AMS operates safely and efficiently. With the addition of the Upgraded Tracker Thermal Pump System, the legacy monitoring interface was no longer suitable for use. This paper describes the new AMS Monitoring Interface (AMI). The AMI is built with state-of-the-art time series database and analytics software. It uses a custom feeder program to process AMS Raw Data as time series data points, feeds them into InfluxDB databases, and uses Grafana as a visualization tool. It follows modern design principles, allowing client CPUs to handle the processing work, distributed creation of AMI dashboards, and up-to-date security protocols. In addition, it offers a more simple way of modifying the AMI and allows the use of APIs to automate backup and synchronization. The new AMI has been in use since January 2020 and was a crucial component in remote shift taking during the COVID-19 pandemic. © 2022 Elsevier B.V.

High efficacy of activated carbon fabric filters and masks developed to prevent the inhalation of microorganisms and particles associated with respiratory tract infections

As a major international public health emergency, COVID-19 has posed many challenges for healthcare professionals who have been heavily exposed to contamination. This article describes the development of a high-filtration capacity mask consisting of filter-element layers interspersed with super-activated carbon fiber fabric, non-woven polypropylene for dental–medical–hospital use and antiviral polyamide with nanostructured SiO2 thin film coating. The study found 98.18% particle filtration efficiency and determined 2.11 mmH2O/cm2 differential pressure, while fluid repellency complied with Brazilian standard NBR ABNT 15052:2004. © The Author(s) 2022.

Cyberattacks and Solutions for Future Factories

This paper discusses the major technologies used in modern factories and highlights some important types of cyberattacks. Industrial espionage and information theft are the most common cyberattacks because of the digitalization of industrial processes. Some of the solutions used are also discussed for the security issues in future factories. The paper mentioned the importance of data security and the steps to achieve it in modern factories. It is worth noting the Corona crisis (Covid-19) and its impact on life in all respects, and certainly had an impact on modern factories, and during this period modern factories were exposed to many cyber-attacks. The last thing focused on in this paper is the cybersecurity challenges of large industrial IoT systems. © 2022 IEEE.

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.

IoT Based Cytokine Storm Symptoms Early Warning System for Covid-19 Self-Isolating Patient

Covid-19, which has spread throughout the world, has reportedly caused millions of deaths. Among the causes of the patient's death is the phase after the patient is declared negative for COVID, but there is a cytokine storm. In this study, an IoT-based technology was proposed to be able to detect abnormalities in COVID-19 patients, even though they already had a negative Covid status based on the PCR test. The implementation of this technology allows former Covid patients to be monitored from anywhere as long as they are connected to the internet, using designed wearable devices and dedicated mobile apps for them. Based on experiment result, all the sensors have the ability to work and sense patient body indicators with error below 5%. This study demonstrated the flawless use of a mobile app dedicated to monitor patients' health during the pandemic. When patient health condition indicating exposed to cytokine storm, a warning notification is appear at the mobile app. © 2022 IEEE.

Blockchain based access control for home hospitalization during covid-19

With the emergence of the Covid-19 disease, hospitals and health care professionals (HP) had to deal with a huge number of patients presenting with the virus that kept increasing every day, resulting in the increase of the pandemic transmission. To deal with this issue, minimize patients' and healthcare professionals' exposure, and be able to treat all patients, HP turned to home hospitalization. In-home hospitalization, doctors need to monitor their patient's health status remotely, and patients need to share this data with them. But in this scenario, patient privacy is exposed to several external threats and intrusions, sometimes resulting in the loss of patient life. To deal with the above issues, our focus in this article is on access control (AC). Thus, we propose a Blockchain (BC) smart contract-based model, where each object owner creates one ACC (Access control contract) for each subject in the system and defines his access policies in it. © 2022 IEEE.