Name Entity Recognition on Covid-19 Dataset using Machine Learning algorithms

The COVID-19 pandemic has been accompanied by such an explosive increase in media coverage and scientific publications that researchers find it difficult to keep up. So we are working on COVID-19 dataset on Omicron variant to recognise the name entity from a given text. We collect the COVID related data from newspaper or from tweets. This article covered the name entity like COVID variant name, organization name and location name, vaccine name. It include tokenisation, POS tagging, Chunking, levelling, editing and for run the program. It will help us to recognise the name entity like where the COVID spread (location) most, which variant spread most (variant name), which vaccine has been given (vaccine name) from huge dataset. In this work, we have identified the names. If we assume unemployment, economic downfall, death, recovery, depression, as a topic we can identify the topic names also, and in which phase it occurred. © 2022 IEEE.

STOPPAGE: Spatio-temporal data driven cloud-fog-edge computing framework for pandemic monitoring and management

Several global health incidents and evidences show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data-sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio-temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio-temporal data and computing infrastructure using fog/edge based architecture;and (ii) spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by (Formula presented.) (18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio-temporal datasets and reduces the delay in predicting health status compared to the existing studies. © 2022 John Wiley & Sons Ltd.

Sustainable green production model considering variable demand, partial outsourcing, and rework

Social activities, economic benefits, and environmental friendly approach are very much essential for a sustainable production system. This is widely observed during the Covid-19 pandemic situation. The demand for essential goods in the business sector is always changing due to different unavoidable situations. The proposed study introduces a variable demand for controlling the fluctuating demand. However, a reworking of produced imperfect products makes the production model more profitable. Partial outsourcing of the good quality products has made the production system more popular and profitable. Separate holding cost for the reworked and produced products are very helpful idea for the proposed model. Moreover, consumption of energy during various purpose are considered. Separate green investment make the model more sustainable and eco-friendly. The main focus of the model is to find the maximum profit through considering optimum value of lot size quantity, average selling price, and green investment. The classical optimization technique is utilized here for optimizing the solution theoretically. The use of concave 3D graphs, different examples, and sensitivity analyses are considered here. Furthermore, managerial insights from this study can be used for industry improvement. © 2022 the Author(s), licensee AIMS Press.

Graphene-metasurface-based biosensor for SARS-CoV-2 detection

The COVID-19 pandemic attributed to the SARs-Cov-2 virus has disrupted the lives of individuals in every corner of the world, causing millions of infections and numerous deaths worldwide. Identifying and isolating infected people is very crucial to slow down the spread of the disease. In this paper, we report a design of highly sensitive, graphene-metasurface based biosensor for detecting the S1 spike protein expressed on the surface of the SARSCoV-2 virus in the terahertz band. Our structure consists of a silicon dioxide substrate sandwiched between a complete gold layer at the bottom, and a graphene monolayer on top etched with a phi-shaped slot tilted at 45 degree, which performs a wideband reflective-type cross-polarization conversion of the incident electromagnetic (EM) wave. The optimized polarization conversion ratio (PCR) has been achieved at 0.75eV chemical potential value of the graphene layer. When samples of Sars-CoV-2 virus contained in a phosphate buffer saline (PBS) solvent is put on top of proposed design of the sensing surface, the spike proteins of the virus interact with the spike antibody grown on the sensing surface;and it changes the refractive index of the overall system (Biosensor + Analyte), which in turn changes the PCR and the corresponding frequency of the reflected wave. The biosensor response has been computed using the Finite Integration Technique (FIT) in the terahertz region. The sensitivity of the biosensor is found to be 354 GHz/RIU at the PCR of 0.9. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

A production-inventory model for perishable items with demand dependent production rate, shortages and variable holding cost

This model investigates the optimal time at which a production cycle should be stopped and then resumed in a production-inventory system, in order to keep shortages under control. This study considers a perishable item that decays at a constant rate. Year 2020 has seen a sudden surge in the demand of masks, PPEs, etc. to combat COVID-19. Such demand, which follows an exponential distribution, has been considered. The production rate is taken to be a linear function of demand, so as to cope up with an exponential market demand. The holding cost is taken to be a linear function of time. Shortages are allowed to occur and are completely backlogged. This model handles variable production, variable demand and variable holding cost simultaneously. The model is illustrated by a numerical example. Sensitivity analysis is carried out and has been detailed with the help of graphs. A case study has also been done. Copyright © 2022 Inderscience Enterprises Ltd.

A Broadband Biosensor using Graphene-Metasurface Based Cross-Polarization Converter

We present the design and analysis of a graphene metasurface-based cross polarization converter operating within the terahertz gap for detecting biomolecules over a broad spectral range, taking the SARS-CoV-2 virus as a specific example. To the best of our knowledge, our design reports the widest band of operation in the THz region of a graphene-based metasensor. Each meta-atom comprises a graphene pattern on silicon dioxide atop a continuous gold layer and exhibits near-unity cross polarization conversion ratio (PCR) and a 90% PCR bandwidth of 0.926 THz within the desired band (1.88 THz-2.81 THz). The proposed device demonstrates additional benefits which include a thin configuration (λ/7.84) and compact lattice size (λ/10.66), which are significantly better than other recently reported graphene metasurface biosensors. The device provides a sensitivity up to 490 GHz/RIU and a figure of merit (FoM) of 0.377 over a wide span of 0.926 THz within the terahertz gap. The electromagnetic response of this device has been validated via rigorous numerical analyses of simulated outputs as well as by developing a detailed circuit model representation of the same. The device demonstrates angular stability of nearly 40°under oblique incidence of the incident wave. IEEE

Climate Change Impact and Comprehensive Disaster Management Approach in Bangladesh: A Review

Due to its geographical condition and geophysical location Bangladesh is one of the world’s most vulnerable country, which will become more vulnerable to the impact of climate change. According to the Global Climate Risk Index 2020 and Intergovernmental Panel on Climate Change (IPCC) 2011, Bangladesh is the seventh most climate change-affected nation in the world. This chapter elaborates on the possible impacts of climate change in Bangladesh through various natural disasters, i.e., increasing temperature, sea level rise, salinity intrusion, cyclone, storm surges, drought, etc. and also discusses the comprehensive disaster management approach in Bangladesh. It is now a worry in the scientific community that climate change could dramatically change weather patterns like the disease spread of epidemics (such as COVID-19) from vulnerable regions to invulnerable regions. All sectors will be affected by the impact of climate change, not only Bangladesh but also other South Asian countries. In Bangladesh, both the government and nongovernmental organizations (NGOs) are trying to prevent and alter the impacts of climate change by enhancing several adaptation and mitigation approaches. But still, coastal districts and northern areas in Bangladesh are facing many climatic issues, such as flash floods, super cyclones, salinity intrusion, storm surges, drought and riverbank erosion etc. Moreover, the government is taking the immediate response of shifting people in a cyclone center at the moment of extreme natural events but most of the peoples of the coastal districts in Bangladesh are illiterate so that they very careless about the awareness. On the basis of current information, it is suggested that the government should make some policy in disaster management for a sustainable solution for coastal areas in Bangladesh. © 2021, SpringerNature Switzerland AG.

Machine Learning Based Anxiety Prediction of General Public from Tweets During COVID-19

In this crisis of COVID19, everyone is staying in touch with the world through social media. This has led to social media becoming a significant source of new information for many people and unfortunately this phenomenon has given birth to a lot of misinformation, chaos and fear in people’s minds. This fear is often due to the inadequate and wrong information. Therefore, there is a important need to understand this crisis. Patterns need to be established between popular tweets and its effect on the public’s sentiments, especially their fear. So, tweets of three different countries namely United States of America, Federative Republic of Brazil and Republic of India. Sentiment analysis reveals that fear of this unknown and mysterious nature of the coronavirus is dominant among the public. Predominant analysis of tweets within past two months will be done and then a model will be built to predict future reaction of the general public based on the crisis level in the country. Machine Learning algorithms such as ‘Logistic Regression (LR)’, ‘Multinomial Naïve Bayes’ and ‘Support Vector Machine (SVM)’are used for classification purpose preceded by the pre-processing steps of raw data from each country. 90% of accuracy has been achieved from sentiment classification result. Insights to the fear, sentiments have also been provided. Tweets with negative sentiment and emotion indicates the cases for the pandemic outbreak. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Fully Homomorphic Encryption based Privacy-Preserving Data Acquisition and Computation for Contact Tracing

For public health surveillance systems, privacy is a major issue in storing and sharing of personal medical data. Often, patients and organizations are unwilling to divulge personal medical data for fear of compromising their privacy because although the data may be encrypted, the encrypted values typically need to be first decrypted to perform any computation on the data. Unfortunately, such a barrier in easy sharing of data can severely hamper the ability to respond in a timely and effective manner to a crisis scenario, as evident in the case of the ongoing COVID-19 pandemic. To overcome this critical obstacle, we propose in this paper a novel privacy-preserving encryption mechanism for storage and computation of sensitive healthcare data. Our scheme is based on the use of a secure fully homomorphic encryption scheme, so that the required computations can be performed directly on the encrypted data values without the need for any decryption. The ability to execute queries or computation directly on encrypted data, without the need for decryption, is not present in any existing public-health surveillance system. We propose a novel computational model and also develop an algorithm for contact tracing with COVID-19 pandemic as a case study. We have simulated our proposed approach using the ElGamal encryption algorithm to check the correctness and effectiveness of our proposed approach. The results show that our proposed solution is effective in providing adequate security while supporting the computational needs for contact-tracing. Besides contact-tracing, our new data-encryption technique can have a much broader impact in the field of healthcare. By executing queries or computations directly on encrypted data, our innovative solution would make the sharing of data in healthcare-related research and industry significantly simpler and faster. The use of such a data encryption scheme to store and transmit sensitive healthcare data over a network can not only allay the fear of compromising sensitive information but also ensure HIPAA-compliance.

All-fiber pyro- and piezo-electric nanogenerator for IoT based self-powered health-care monitoring

In this work, an all-fiber pyro- and piezo-electric nanogenerator (PPNG) is designed using multiwall carbon nanotube (MWCNT) doped poly(vinylidene fluoride) (PVDF) electrospun nanofibers as the active layer and an interlocked conducting micro-fiber based electrode for converting both thermal and mechanical energies into useful electrical power. The PPNG generates high electrical throughput (output voltage similar to 35 V, maximum power density similar to 34 mu W cm(-2) and power conversion efficiency (eta(piezo)) similar to 19.3%) with an ultra-fast response time of similar to 10 ms. Owing to the higher piezoelectric charge co-efficient (;d(33);similar to 51.3 pC N-1) and figure of merit (FoM approximate to 5.95 x 10(-11) Pa-1) of PVDF-MWCNT nanofibers in comparison to the neat PVDF nanofibers (;d(33);similar to 22 pC N-1 and FoM approximate to 9.7 x 10(-12) Pa-1) the PPNG operates a range of consumer electronic components such as capacitors and light emitting diodes. Furthermore, the electroactive phase content (similar to 87%) is improved in the active layer due to the interfacial interaction between the surface charges at from the pi-electron cloud of the MWCNT and -CH2- dipoles of the PVDF chain. Additionally, the PVDF-MWCNT nanofibers possess fifteen times higher pyroelectric coefficient (similar to 60 nC m(-2) K-1) compared to that of neat PVDF nanofibers (4 nC m(-2) K-1). As a result, the PPNG is capable of converting very large temperature fluctuations (Delta T similar to 14.30 K) to electrical energy (such as the open-circuit voltage of 250 mV and a short-circuit current of 83 pA). Besides this, it is capable of detecting very low-level thermal fluctuations (as low as Delta T similar to 5.4 K) with responsivity of similar to 1.48 s and possesses very high mechano-sensitivity (similar to 7.5 V kPa(-1)) which makes it feasible for use as a biomedical sensor since the body temperature and bio-mechanical signals (such as breathing temperature, pulse rate, vocal cord vibrations, coughing sound, and so on) have an immense signature of health conditions. As a proof-of-concept, the all-fiber PPNG is employed as a biomedical sensor by integrating with the Internet of Things (IoT) based human health care monitoring system as well as for remote care of infectious diseases (e.g., applicable for pneumonia, COVID-19) by transferring the pulse response, body temperature, coughing and laughing response wirelessly to a smartphone.

Cosmic ray flux and lockdown due to COVID-19 in Kolkata - Any correlation?

Cosmic ray muon flux is measured by the coincidence technique using plastic scintillation detectors in the High Energy Physics Detector Laboratory at Bose Institute, Kolkata. Due to the COVID-19 outbreak and nationwide complete lockdown, the laboratory was closed from the end of March 2020 till the end of May 2020. After lockdown, although the city is not in its normal state, we still were able to take data on some days. The lockdown imposed a strict restriction on the transport service other than the emergency ones and also most of the industries were shut down in and around the city. This lockdown has significant effect on the atmospheric conditions in terms of change in the concentration of air pollutants. We have measured the cosmic ray flux before and after the lockdown to observe the apparent change if any, due to change in the atmospheric conditions. In this article, we report the measured cosmic ray flux at Kolkata (22.58 ∘ N 88.42 ∘ E and 11 m above the Sea Level) along with the major air pollutants present in the atmosphere before and after the lockdown.

Analyzing Impact of Climate Variability on COVID-19 Outbreak: A Semantically-enhanced Theory-guided Data-driven Approach

With the intention of complementing the current worldwide actions to fight against novel coronavirus disease (COVID-19), substantial number of research works have been put forth during past few months so as to explore whether or how the various climatic factors influence the spread of this potentially fatal disease. However, because of uneven distribution as well as inadequate number of COVID tests, and also, due to lack of data transparency, these research findings are often found to be contradictory. In order to tackle such data inadequacy and uncertainty issues, in this work, we propose a theory-guided data-driven probabilistic framework with embedded technology of upgrading the impact analysis through incorporated climate domain semantics. Infusion of both the theoretical knowledge from epidemiology and the semantic knowledge from climatological domain helps the framework in better dealing with the uncertainty while appropriately capturing the pandemic characteristics of the disease. The effectiveness of our semantically-enhanced theory-guided data-driven approach is validated in terms of analyzing the causal influence as well as impact of climate variability on COVID-19 outbreak in several states of India. © 2021 ACM.