Current Advances in Nanomaterial-associated Micro and Nano-devices for SARS-CoV-2 Detection

COVID-19 spread rapidly around the world in 18 months, with various forms of variants caused by severe acute respiratory syndrome (SARS-CoV). This has put pressure on the world community and created an urgent need for understanding its early occurrence through rapid, simple, cheap, and yet highly accurate diagnosis. The most widely adopted method as of today is the real-time reverse-transcriptase polymerase chain reaction. This test has shown the potential for rapid testing, but unfortunately, the test is not rapid and, in some cases, displays false negatives or false positives. The nanomaterials play an important role in creating highly sensitive systems, and have been thought to significantly improve the performance of the SARSCoV- 2 protocols. Several biosensors based on micro-and nano-sensors for SARS-CoV-2 detection have been reported, and they employ multi-dimensional hybrids on sensing surfaces with devices having different sizes and geometries. Zero-to-three-dimension nanomaterial hybrids on sensing surfaces, including nanofilm hybrids for SARS-CoV-2 detection, were employed with unprecedented sensitivity and accuracy. Furthermore, the sensors were nanofluidic and mediated high-performance SARS-CoV-2 detection. This breakthrough has brought the possibility of making a biosystem on a chip (Bio-SoC) for rapid, cheap, and point-of-care detection. This review summarises various advancements in nanomaterial-associated nanodevices and metasurface devices for detecting SARS-CoV-2.

Fault detection model for a variable speed heat pump

The impact of the COVID pandemic has resulted in many people cultivating a remote working culture and increasing building energy use. A reduction in the energy use of heating, ventilation, and air-conditioning (HVAC) systems is necessary for decreasing the energy use in buildings. The refrigerant charge of a heat pump greatly affects its energy use. However, refrigerant leakage causes a significant increase in the energy use of HVAC systems. The development of refrigerant charge fault detection models is, therefore, important to prevent unwarranted energy consumption and CO2 emissions in heat pumps. This paper examines refrigerant charge faults and their effect on a variable speed heat pump and the most accurate method between a multiple linear regression and multilayer perceptron model to use in detecting the refrigerant charge fault using the discharge temperature of the compressor, outdoor entering water temperature and compressor speed as inputs, and refrigerant charge as the output. The COP of the heat pump decreased when it was not operating at the optimum refrigerant charge, while an increase in compressor speed compensated for the degradation in the capacity during refrigerant leakage. Furthermore, the multilayer perception was found to have a higher prediction accuracy of the refrigerant charge fault with a mean square error of ± 3.7%, while the multiple linear regression model had a mean square error of ± 4.5%. The study also found that the multilayer perception model requires 7 neurons in the hidden layer to make viable predictions on any subsequent test sets fed into it under similar experimental conditions and parameters of the heat pump used in this study.

Smart Home Application for Challenged Along with Human Temperature Detector

A smart home is a component of the Internet of Things (IoT) technology implementations that help people with their daily activities. To link devices to the Internet of Things, a variety of communication methods can be used. Impairments restrict the activities that disabled people can participate in. This paper proposes an automation system that enables disabled people to control televisions (TVs), lights, and fans, any other electrical devices at home, using just voice commands without moving. The Google Assistant feature for mobile phones is used to achieve voice recognition on electronic components. This system also contains the concept of human temperature measurement where the temperature sensor, fixed to the door, checks the temperature of the person and opens when it is normal. This prevents the user from getting infected by the illness, keeping in mind the present situation of covid19. © 2023 IEEE.

The Pi-CON Methodology Applied: Operator Errors and Preference Tracking of a Novel Ubiquitous Vital Signs Sensor and Its User Interface

Remote Patient Monitoring has enjoyed strong growth to new heights driven by several factors, such as the COVID-19 pandemic or advances in technology, allowing consumers and patients to continuously record health data by themselves. This does not come without its challenges, however. A literature review was completed and highlights usability gaps when using wearables or home use medical devices in a virtual environment. Based on these findings, the Pi-CON methodology was applied to close these gaps by utilizing a novel sensor that allows the acquisition of vital signs at a distance, without any sensors touching the patient. Pi-CON stands for passive, continuous and non-contact, and describes the ability to acquire vital signs continuously and passively, with limited user interaction. The preference of vital sign acquisition with a newly developed sensor was tested and compared to vital sign tests taken with patient generated health-data devices (ear thermometer, pulse oximeter) measuring heart rate, respiratory rate and body temperature. In addition, the amount of operator errors and the user interfaces were tested and compared. Results show that participants preferred vital signs acquisition with the novel sensor and the developed user interface of the sensor. Results also revealed that participants had a mean error of .85 per vital sign measurement with the patient-generated health data devices and .33 with the developed sensor, confirming the beneficial impact available when using the developed sensor based on the Pi-CON methodology.

An IoT based Smart Robot that Aids in the Prevention of COVID19 Spread

The Internet of Things revolution is transforming current healthcare practices by combining technological, economic, and social aspects. Since December 2019, the global spread of COVID19 has influenced the global economy. The COVID19 epidemic has forced governments all around the world to implement lockdowns to prevent viral infections. Wearing a face mask in a public location, according to survey results, greatly minimizes the risk of infection. The suggested robotics design includes an IoT solution for facemask detection, body temperature detection, an automatic dispenser for hand sanitizing, and a social distance monitoring system that can be used in any public space as a single IoT solution. Our goal was to use IoT-enabled technology to help prevent the spread of COVID19, with encouraging results and a future Smart Robot that Aids in COVID19 Prevention. Arduino NANO, MCU unit, ultrasonic sensor, IR sensor, temperature sensor, and buzzer are all part of our suggested implementation system. Our system's processing components, the Arduino UNO and MCU modules are all employed to process and output data. Countries with large populations, such as India and Bangladesh, as well as any other developing country, will benefit from using our cost-effective, trustworthy, and portable smart robots to effectively reduce COVID-19 viral transmission. © 2022 IEEE.

Smart and functional textiles

Smart and Functional Textiles is an application-oriented book covering a wide range of areas from multifunctional nanofinished textiles, coated and laminated textiles, wearable e-textiles, textile-based sensors and actuators, thermoregulating textiles, to smart medical textiles and stimuli-responsive textiles. It also includes chapters on 3D printed smart textiles, automotive smart textiles, smart textiles in military and defense, as well as functional textiles used in care and diagnosis of Covid-19. • Overview of smart textiles and their multidirectional applications • Materials, processes, advanced techniques, design and performance of smart fabrics • Fundamentals, advancements, current challenges and future perspectives of smart textiles. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

IOT based Temperature and Oxygen level monitoring and Data visualization

The Covid-19 pandemic that hit us in 2020 changed our lifestyle in every way. There was tremendous damage to people's lives. It is now predicted that other variants of Coronavirus are affecting people's health throughout the world. We must remain vigilant against upcoming dangers. The Indian health ministry has also advised people to take the necessary precautions. In this paper, we will focus on automating temperature and oxygen monitoring using the Internet of Things. According to our proposed model, data generated by the temperature sensor (MLX90614) and oxygen saturation sensor (MAX30102) will be stored in a relational database. Using this data, future data analyses can be conducted. We are also going to visualize the data by building an interactive dashboard using Power BI. Overall, health monitoring will become much more convenient and speedier. © 2023 IEEE.

Recent advances on CRISPR/Cas system-enabled portable detection devices for on-site agri-food safety assay

Agri-food safety has been considered as one of the most important public concerns worldwide. From farm to table, food crops and foods are extremely vulnerable to the contamination by a variety of pollutants from their growth and processing. Moreover, the SARS-CoV-2 detected in the food supply chain during COVID-19 pandemic has posed a greater challenge for rapid and on-site detection of agri-food contaminants in complex and volatile environments. Therefore, the development of rapid, accurate, and on-site detection technologies and portable detection devices is of great importance to ensure the agri-food security. This review comprehensively summarized the recent advances on the construction of CRISPR/Cas systems-based biosensing technologies and their portable detection devices, as well as their promising applications in the field of agri-food safety. First of all, the classification and working principles of CRISPR/Cas systems were introduced. Then, the latest advances on the CRISPR/Cas system-based on-site detection technologies and portable detection devices were also systematically summarized. Most importantly, the state-of-the-art applications of CRISPR/Cas systems-based on-site detection technologies and portable detection devices in the fields of agri-food safety were comprehensively summarized. Impressively, the future opportunities and challenges in this emerging and promising field were proposed. Emerging CRISPR/Cas system-based on-site detection technologies have showed a great potential in the detection of agri-food safety. Impressively, the integration of CRISPR/Cas systems-based biosensing technologies with portable detection devices (e.g., nanopore-based detection devices, lateral flow assay, smartphone-based detection devices, and microfluidic devices) is very promising for the on-site detection of agri-food contaminants. Additionally, CRISPR/Cas system-based biosensing technologies can be further integrated with much more innovative technologies for the development of novel detection platforms to realize the more reliable on-site detection of agri-food safety.

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.

The Indoor Air Quality Trilemma: Improving Air Quality, Using Less Energy, and Meeting Stakeholder Requirements

Good indoor air quality (IAQ) is critically important for many aspects of our lives, including as we've found recently in reducing the transmission of airborne diseases such as COVID-19. Delivering good IAQ poses several challenges to organisations: it can require changes in working practices, be bounded by infrastructure capabilities such as buildings and their heating and ventilation systems, and result in substantial energy usage. In this study we have conducted a preliminary investigation measuring IAQ in a typical 'science lab' classroom, and engaging with stakeholders to jointly explore these data. Our mixed methods approach uncovers an indoor air quality 'trilemma', which relates air quality, energy usage, and stakeholder practices that can be mediated by, and understood as, a site for potentially impactful future HCI designs. © 2023 Owner/Author.

Probing environmental and tectonic changes underneath Mexico City with the urban seismic field

The sediments underneath Mexico City have unique mechanical properties that give rise to strong site effects. We investigated temporal changes in the seismic velocity at strong-motion and broadband seismic stations throughout Mexico City, including sites with different geologic characteristics ranging from city center locations situated on lacustrine clay to hillside locations on volcanic bedrock. We used autocorrelations of urban seismic noise, enhanced by waveform clustering, to extract subtle seismic velocity changes by coda wave interferometry. We observed and modeled seasonal, co- and post-seismic changes, as well as a long-term linear trend in seismic velocity. Seasonal variations can be explained by self-consistent models of thermoelastic and poroelastic changes in the subsurface shear wave velocity. Overall, sites on lacustrine clay-rich sediments appear to be more sensitive to seasonal surface temperature changes, whereas sites on alluvial and volcaniclastic sediments and on bedrock are sensitive to precipitation. The 2017 Mw 7.1 Puebla and 2020 Mw 7.4 Oaxaca earthquakes both caused a clear drop in seismic velocity, followed by a time-logarithmic recovery that may still be ongoing for the 2017 event at several sites or that may remain incomplete. The slope of the linear trend in seismic velocity is correlated with the downward vertical displacement of the ground measured by interferometric synthetic aperture radar, suggesting a causative relationship and supporting earlier studies on changes in the resonance frequency of sites in the Mexico City basin due to groundwater extraction. Our findings show how sensitively shallow seismic velocity and, in consequence, site effects react to environmental, tectonic and anthropogenic processes. They also demonstrate that urban strong-motion stations provide useful data for coda wave monitoring given sufficiently high-amplitude urban seismic noise.

mCrutch: A Novel m-Health Approach Supporting Continuity of Care.

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype's architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation.

Internet of Things Based Intelligent Transportation of Food Products During COVID.

Transportation management plays a vital role in the development of the country, with the help of IoT smart transportation has become a reality. Developing a smart and secured transportation system of food products to various shops during this pandemic period is an important task. The vehicle tracking system is the technology that is used by many companies and individuals to track a vehicle by using many ways like GPS that operates using satellites and ground-based stations. In this paper an Internet of Things based application is developed to monitor the moving vehicle, this proposed model provides a monitoring solution for a moving vehicle with the help of sensors Blind Spot Assist sensor, Collision Prevention sensor, Fuel Monitoring sensor, Door Sensor, and GPS/GPRS tracking module are integrated to make a smart vehicle prototype using raspberry pi. In this model, a Blind spot sensor is used to monitor the nearby vehicles, a Collision Prevision sensor is used to avoid the collision between the vehicles, a Fuel monitoring sensor is used to monitor the fuel level in the vehicle, the Door sensor is used to check the status of the door and GPS/GPRS tracking module is used to track the current location of the moving vehicle during the COVID-19 Pandemic period.