Seasonal Prediction of Omicron Pandemic

The ongoing coronavirus disease 2019 (COVID-19) pandemic has pushed the world in the face of another huge outbreak. In order to have a better understanding on the fast transmission of Omicron variant, we made seasonal predictions on the development of Omicron pandemic globally, as well as 11 key countries. The results demonstrated that the pandemic has an exponential-like growth rate at the initial stage of the outbreak, and will have small resurgences around April and June in north hemisphere countries and south hemisphere countries, respectively.

Kesiapan Generasi Milenial Sebagai Role Model di Era New Normal

<em>In 2020, the year the demographic bonus begins, the millennial generation is in the age range of 20 years to 40 years. This age is a productive age which is predicted to be the backbone of Indonesia's change. However, before achieving the optimization of the demographic bonus which is considered to have a positive impact, Indonesia was first hit by the Covid-19 outbreak with a very fast transmission. Not wanting to linger in uncertainty when the pandemic will end, the Government finally decided to live side by side with Cov19, one of which was by issuing a New Normal policy with the aim of keeping people productive and safe from Covid-19 during the pandemic. Therefore, the community needs a role model that can be used as an ideal role model and example. The millennial generation with all the characteristics and skills they have in this phase is seen as being able to become a role model in facing rapid changes and new orders in society. The approach used is a qualitative approach using literature study methods from books, journals and other sources relevant to the title. The result of this paper is that the productive age of the millennial generation has been in the take off phase, meaning that it can be used as a role model by the community.</em>

Oxygen Saturation (SPO2) in Covid-19 Patients

One of the emergency problems of Covid-19 patients is a decrease in oxygen saturation (SpO2). Factors that affect the decrease in oxygen saturation, age, immune system, and gender. The purpose of this study was to identify Oxygen Saturation (SpO2) in Coronavirus Disease (Covid-19) patients. The research design is descriptive. The population in this study were all Covid-19 patients at the Aminah Blitar Islamic Hospital starting last month December 2020 to March 2021 totaling 346 and the number of samples is 185. This research instrument uses data studies on the medical records of Covid-19 patients. Sampling technique using purposive sampling. Statistic testusefrequency distribution. The results of this study indicate that from 185 respondents there were 77 (41.4) respondents who did not experience hypoxemia and 108 (58.6) respondents experienced hypoxemia. Covid-19 must be watched out for because of its relatively fast transmission, has a mortality rate that cannot be ignored, and there is no definitive therapy yet. A decrease in oxygen saturation (SpO2) is an important thing that needs to be considered in Covid-19 patients.

A MAC PROTOCOL FOR ENERGY EFFICIENCY AND FAST EMERGENCY MEDICAL JUDGMENT IN WIRELESS BODY SENSOR NETWORKS

Emergency data collected from sensor nodes widely distributed in wireless body sensor networks (WBSNs) are delivered to medical staff as quickly as possible, so patients’ lives can be saved through appropriate actions and treatments. However, relevant data and vital data may be required for appropriate actions by the medical staff. Therefore, all these data must be properly delivered to the medical staff within the set time. In this paper, we propose a MAC protocol with a reservation function and an operation frame extension function to extend the overall network lifetime by reducing the energy consumption of given sensor nodes and quickly deliver information to medical systems in case of emergency. This MAC protocol makes it possible to achieve fast transmission of related data by utilizing the related-priority slots. As a result of the experiment, the transmission delay was reduced by about 12.5%, and the lifetime was increased by approximately 19% over the existing MAC protocol. It also can be seen that the proposed MAC protocol works well in an environment where emergency events often occur.

Detecting Emotional Evolution on Twitter during the COVID-19 Pandemic Using Text Analysis

Early in 2020, an unexpected and hazardous situation occurred threatening and challenging all of humankind. A new coronavirus called SARS-CoV-2 was first identified in Wuhan, China, and its related disease, called COVID-19, has induced one of the most dangerous crises at a global level since World War II. The ultra-fast transmission rate of the virus and the high mortality rate led the World Health Organization (WHO) to officially declare the situation a pandemic. Governments, for their part, were forced to implement unprecedented mobility restrictions and cease a large part of their economic activities. These facts triggered multiple reactions from people who expressed their feelings mainly through social networks (like Twitter), using them as vectors of information and opinion. In this paper, a study carried out in different Spanish speaking countries (Chile, Mexico, Peru, and Spain) is presented, which addresses the manner in which the evolution of the pandemic outbreak has affected the emotions expressed by individuals on Twitter over the last 13 months (from March 2020 to March 2021). We used a total of 3 million tweets to achieve this task. We made use of a well-known framework called EmoWeb to capture the dynamic variation in the sentimental value of pandemic-related words. The results reflect to what degree the pandemic and its derived problems have influenced and affected the population of the selected countries in different ways. The outcomes also illustrate the evolution over time of opinions published on Twitter regarding several topics related to COVID-19.

Retrofiting Existing Optical Fiber Infrastructure to Mitigate Geohazard Risk: The TGP Case

Optical fiber cables (OFC) are well known for their use in communications. They offer long distance and fast transmission rate capabilities. OFC are the perfect companion of hydrocarbon and water transport system as part of the physical layer of the communication services and SCADA of the operating companies. As an example, the TGP system has more than 1400 km of cables laid in its Right-of-Way (ROW) which are in use since the beginning of its operation in 2004. More recently OFC started to be used as sensors. In such applications, a communication cable (CC) can be turned into a continuous temperature sensor allowing for leak and erosion detection. A strain monitoring cable (SMC) can also be spliced to the CC for landslide and subsidence detection in selected areas. In the case of very large soil displacement, it is common to observe the strain induced on the CC. From what precedes, existing OFC infrastructures can be taken advantage of to retrofit pipelines with monitoring instrumentation. The current work describes how an existing CC is retrofitted to provide information about the TGP transport system’s integrity. When accurate monitoring of a landslide is required, a dedicated sensing cable is installed locally and connected to the CC. Elsewhere the CC is being measured to detect and locate events as erosion or landslide in position where the geohazard risk present lower probability. Such approach not only improves geohazard risk management, but it also indicates early sign of stress on the cable that can lead to its rupture, mitigating service interruption probability.

Electrochemical Biosensors for COVID-19

Today COVID-19 pandemic caused by the SARS-CoV-2 virus is the most challenging health issue due to the fast transmission rate and its impact on different aspects of human life. Although RT-PCR is the primary method to detect SARS-CoV-2, other reliable methods are being developed to detect this pathogen. Biosensors can provide fast, reliable, and point-of-care diagnostic. Among them, electrochemical biosensors attract considerable interest. In this mini-review, I will summarize few electrochemical biosensors which have been developed to detect the SARS-CoV-2 virus.

Fog computing scheduling algorithm for smart city

With the advent of the number of smart devices across the globe, increasing the number of users using the Internet. The main aim of the fog computing (FC) paradigm is to connect huge number of smart objects (billions of object) that can make a bright future for smart cities. Due to the large deployments of smart devices, devices are expected to generate huge amounts of data and forward the data through the Internet. FC also refers to an edge computing framework that mitigates the issue by applying the process of knowledge discovery using a data analysis approach to the edges. Thus, the FC approaches can work together with the internet of things (IoT) world, which can build a sustainable infrastructure for smart cities. In this paper, we propose a scheduling algorithm namely the weighted round-robin (WRR) scheduling algorithm to execute the task from one fog node (FN) to another fog node to the cloud. Firstly, a fog simulator is used with the emergent concept of FC to design IoT infrastructure for smart cities. Then, spanning-tree routing (STP) protocol is used for data collection and routing. Further, 5G networks are proposed to establish fast transmission and communication between users. Finally, the performance of our proposed system is evaluated in terms of response time, latency, and amount of data used.

Design of a Convolution Neural Network Model to Predict COVID-19

Coronavirus is a type of viral infection. There are many different kinds, and some cause disease. A newly identified coronavirus, has caused a worldwide pandemic of respiratory illness, called COVID-19. When the virus reaches the lungs, and it causes inflammation, resulting in fluid accumulation and difficulty of breathing. When fluid enters the air in the lungs where gas exchange occurs, it leads to low blood oxygen levels. This condition is termed pneumonia. There are about four to five tests which are used to identify the presence of coronavirus in humans but among them, there are only two tests which are recommended by WHO as they take less time and a low risk to identify the virus During recent times there is a fast transmission of Covid-19, And in some countries that are unable to purchase laboratory kits for testing. We aimed to present the use of Machine learning for the high-accuracy detection of Covid-19 using chest X-ray and these images are publicly available. A convolutional neural network with minimized layers is capable of detecting Covid-19 in a limited number of chest X-ray images. This model can also detect SARS, MERS and severe pneumonia using chest X-ray images has life-saving importance for both patients and doctors.