Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

Microblogging sites such as Twitter play an important role in dealing with various mass emergencies including natural disasters and pandemics. The FIRE 2022 track on Information Retrieval from Microblogs during Disasters (IRMiDis) focused on two important tasks – (i) to detect the vaccine-related stance of tweets related to COVID-19 vaccines, and (ii) to detect reporting of COVID-19 symptom in tweets. © 2022 Copyright for this paper by its authors.

Complex presentations in people with diabetes: Does HbA1c help or hinder?

HbA1c measurement is widely used for diagnosis/ management/remission of diabetes with international schemes certifying comparability. A) 75 year-old Chinese female with type 2 diabetes was admitted in April 2020 with Covid-19 and diabetic ketoacidosis. Glucose was 35mmol/l and HbA1c 150mmol/mol with previous HbA1c of 45mmol/mol on metformin and alogliptin. She was treated for ketoacidosis and once-daily Lantus introduced along with supportive management of viral illness. B) 68 year-old Afro-Caribbean with type 2 diabetes on metformin before admission, presented with new onset, jerky ballistic movements of high amplitude in right arm, 10-15 movements every 5 min. Admission glucose was >33mmol/l, ketones 1.8mmol/l and HbA1c >217mmol/ mol. Hemichorea-hemiballism, a hyperglycaemia related movement was diagnosed and insulin commenced. Glucose decreased to 8-20mmol/ l, reaching 5-15mmol/ l by time of discharge. Ballistic movements resolved when glycaemic control improved with HbA1c 169mmol/mol, 25 days after discharge. C) Several days before admission, a female with diabetes over 20 years required attention from paramedics on four occasions for hypoglycaemia. Months beforehand metformin was replaced by gliclazide due to chronic kidney disease with HbA1c 50mmol/mol, and she was transfused six weeks before admission for microcytic anaemia. Gliclazide was discontinued and her diet modified which prevented further hypoglycaemic episodes. Variant haemoglobin, beta-thalassaemia which can overestimate glycaemia;undetected by HbA1c HPLC method, invalidated HbA1c as did the blood transfusion. These cases highlight that inadequate understanding of HbA1c can lead to acute presentations of dysglycaemia. As HbA1c accuracy can be affected by multiple factors, clinical assessment and triangulation are key to the management of such patients.

Automated flood inundation mapping over Ganga basin

The worldwide appearance of COVID-19 halted all activity and caused the longest statewide lockdown. These wreaked havoc on people's livelihoods. The July 2020 floods also caused severe challenges. It adds anguish to the lives of those seeking to regulate COVID-19. It reduces catastrophe risk in other industries. Real-time information from space-based sensors is needed for a quick response. Using a cloud-based platform like Google earth engine (GEE), SAR pictures are analyzed automatically. This research shows the possibilities of automated procedures and algorithms on cloud-based systems. The findings provide flood extent maps for the lower Ganga basin, in India. Severe floods affected a large population in Bihar and West Bengal. This research provides a rapid and exact estimate of flooded regions to aid in risk assessment, notably during COVID-19. © 2023 Elsevier Inc. All rights reserved.

Interactive Communication Robot Handling Crowd Management and Content Delivery in Museums Employing Crowd Counting

Through the last decade, and particularly after the Covid period (2020 - 2022), crowd counting and localization have attracted much attention of AI researchers due to its potential applicability in crowd monitoring and control, public safety, space design, interactive content delivery etc. Once delivery objectives for a system are envisaged and the premises are fixed, we can always construct manifold technology architecture that delivers the set goals. However, in the Indian context a solution of choice needs to be optimized on frugality and ease of adaptability. In this paper we report an economic and replicable application of crowd counting and interactive content delivery in museums through unbiased knowledge systems embedded in robotic museum assistants. We intend to demonstrate a robotic system that can deliver any gallery content to groups of visitors keeping special focus on the exhibits that are popular choices. Crowd counting is used here to enable the content presentation to a group of choice in an interactive way. There are some market-ready solutions available for interactive gallery demonstration by moveable robots but they require not only huge capital investment but are also of limited use within controlled environments. Our proposed design is to multiplex an existing infrastructure of surveillance system as a smart crowd counting and gallery demonstration system along with crowd management with minimum additional hardware infusion. © 2023 IEEE.

Prescribing safe supply: ethical considerations for clinicians.

The COVID-19 pandemic has exacerbated the drug poisoning epidemic in a number of ways: individuals use alone more often, there is decreased access to harm reduction services and there has been an increase in the toxicity of the unregulated drug supply. In response to the crisis, clinicians, policy makers and people who use drugs have been seeking ways to prevent the worst harms of unregulated opioid use. One prominent idea is safe supply. One form of safe supply enlists clinicians to prescribe opioids so that people have access to drugs of known composition and strength. In this paper, we assess the ethical case for clinicians providing this service. As we describe, there is much that is unknown about safe supply. However, given the seriousness of the overdose death epidemic and the current limited evidence for safe supply's efficacy, we argue that it is ethically permissible for clinicians to begin prescribing opioids for some select patients.

Digitalizing Folk Education: A Semiotic Modeling Approach

Cultural legacy, according to UNESCO, includes not only monuments and collections of artefacts, but also traditions and living expressions inherited from our forefathers and passed down to our successors. Folk literature, in the form of poems, lines, and melodies, is a part of cultural legacy, and its preservation for future generations appears to be crucial. The advent of technology in the post-covid era gave rise to a new perspective on the educational system. Folk education, which has never been regarded as a separate discipline in the sphere of primary education, is now in jeopardy as it is losing its cultural heritage in this digital age. It can only be preserved if it can be linked to newly developed technologies. The current research proposes the Socio-Digital Knowledge System, or SDKS, in light of this. This model deals with the detour of transition from the 'as is' to the 'as ought to be' reality, which can be considered the main grounds of folk education. The status 'as it is' of folk society represents the remaining varieties, not yet to be accounted for by any other educational developmental agencies. There may not be such instances of 'remaining varieties' in real. On the other hand, the 'as ought to be' educational system remains under development determined by surrounding political and socio-cultural parameters. This effort focuses on the nature of the psychology of the monolingual speakers' collective self when they are obliged to maintain a tumultuous social environment with two poles, 'as it is' and 'as it ought to be.' In Indian communities, our model focuses on the ambiguous interpretation of social mobilization and hierarchy principles, as well as the imprecise term 'class.' To comprehend the mentioned bipolar possibility in terms of educational growth, we employ semiotic devices as a methodology.

COVID 19 Prediction Using Chest X-Ray And CNN Hyperparameter Optimizer

According to mid-June 2020, the abrupt escalation of coronavirus reported widespread fear and crossed 16 million confirmed cases. To fight against this growth, clinical imaging is recommended, and for illustration, X-Ray images can be applied for opinion. This paper categorizes chest X-ray images into three classes- COVID-19 positive, normal, and pneumonia affected. We have used a CNN model for analysis, and hyperparameters are used to train and optimize the CNN layers. Swarm-based artificial intelligent algorithm - Grey Wolf Optimizer algorithm has been used for further analysis. We have tested our proposed methodology, and comparative analysis has been done with two openly accessible dataset containing COVID- 19 affected, pneumonia affected, and normal images. The optimized CNN model features delicacy, insight, values of F1 scores of 97.77, 97.74, 96.24 to 92.86, uniqueness, and perfection, which are better than models at the leading edge of technology. © 2022 IEEE.

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.

Delayed IBD diagnosis leads to worse outcomes over years, with multiple implications on service delivery

Background: Delays in diagnosis can be patient and health-system related. Such delays have been reported to increase overall complications in Inflammatory Bowel Diseases (IBD). The aim of our study was to report on the impact of delays on IBD-related adverse outcomes (AOs), as hospitals currently face challenges with long waiting lists in the post-COVID-19 era. Method(s): New patients referred for suspected IBD to a single tertiary care centre between Jan 2013 to Dec 2017 were identified using EMR. A cut-off time was set for each delay-type based on best average hospital waiting times. Reasons for delays until start of treatment and data on pre-defined AOs (steroid & other rescue therapies, hospitalisations, surgery) were recorded for each patient until end of June 2021. Data was analysed using multiple Pearson correlations and Cox proportional Hazard model to determine if there was a difference in survival without AOs between patients with and without delay. Result(s): 105 patients were identified using strict criteria (M=58;median age=32y) with a median follow-up of 55 months. The most frequent presenting complaints were abdominal pain (44, 41.9%), loose stools (40, 38.1%), bloody diarrhoea (37, 35.2%) and bleeding perrectum (33, 31.4%). 65, 27 and 13 patients had a final diagnosis of Ulcerative colitis, Crohn's disease and Unclassified colitis respectively, and were analysed jointly. The longest delay-types noted: Patients seeking medical attention (median= 4 months;range 1 to 84 months);arranging gastroenterology clinic review after GP referral (median=5 weeks;1 to 30 weeks);and waiting for index endoscopy (median=3 weeks;1 to 36 weeks). Patient stratification based on delay-type, using specific cut-off times for each showed a statistically significant difference in survival without AOs for all (when comparing delay vs no delay). - delay in seeking medical attention (cut-off=1m;p=0.004) (Fig 1A) - delay in GP referral to specialty review (cut-off=1w;p=0.048) - delay in index endoscopy (cut-off=4w;p=0.01) (Fig 1B) - delay in starting treatment (cut-off=4w;p=0.03) Conclusion(s): Several bottlenecks of delays increase AOs in IBD over the follow-up period. A delay as short as a week, between GP referral to specialty review, is significant in determining AOs, relevant for specialist IBD centres particularly in the post-Covid period. Endoscopy units should prioritise suspected IBD patients to reduce AOs, which is likely to have implications on service delivery and planning. Long delays observed in patients seeking medical attention highlights the need for better patient education in the community.

MODELING AND SIMULATION FOR THE SPREAD OF COVID-19 IN AN INDIAN CITY: A CASE STUDY

We present a case study on modeling and predicting the course of Covid-19 in the Indian city of Pune. The results presented in this paper are concerned primarily with the wave of infections triggered by the Delta variant during the period between February and June 2021. Our work demonstrates the necessity for bringing together compartmental stock-and-flow and agent-based models and the limitations of each approach when used individually. Some of the work presented here was carried out in the process of advising the local city administration and reflects the challenges associated with employing these models in a real-world environment with its uncertainties and time pressures. Our experience, described in the paper, also highlights the risks associated with forecasting the course of an epidemic with evolving variants. © 2022 IEEE.

A Methodology for Energy Usage Prediction in Long-Lasting Abnormal Events

Accurate energy consumption prediction is critical for proper resource allocation, meeting energy demand, and energy supply security. This work aims at developing a methodology for accurately modeling and predicting electricity consumption during abnormal long-lasting events, such as COVID-19 pandemic, which considerably affect consumption patterns in different types of premises. The proposed methodology involves three steps: (A) selects among multiple models the most accurate one in energy consumption prediction under normal conditions, (B) uses the selected model to analyze the impact of a specific abnormal event on energy consumption for various classes of premises, and (C) investigates which features contribute most to energy consumption prediction for abnormal conditions and which features can be added to improve such predictions.We use COVID-19 as a case study with datasets obtained from Fort Collins Utilities, which contain energy consumption data for residential and different sizes of commercial and industrial premises in the city of Fort Collins, Colorado, USA. We also use temperature records from NOAA and COVID-19 public orders from Larimer County.We validate the methodology by demonstrating that the methodology can help design a model suited for the pandemic situation using representative features, and as a result, accurately predict the energy consumption. Our results show that the MLP model selected by our methodology performs better than the other models even when they all use the COVID-related features. We also demonstrate that the methodology can help measure the impacts of the pandemic on the energy consumption. © 2022 IEEE.

Analyzing the nexus between pandemic, policy uncertainty, and international tourists' behavior in Taiwan

The tourism and hospitality industry is the hardest-hit industry, owing to the disruptions from COVID-19. The tourism sector witnessed a mounting loss of about 2.86 trillion dollars during the pandemic period. Exploring how inbound international tourists' perception gets affected by uncertainty originating from the pandemics can have important insights to revive tourism during the new normality. Against this backdrop, this paper explores the impact of the pandemics and global economic uncertainty on international inbound tourist arrivals to Taiwan, a major travel destination of the East during the period 1997 to 2020. In particular, we augment the traditional demand model of tourism with economic uncertainty indicators and disaster and pandemic dummies, to explore the impact on visitor arrivals in Taiwan from major countries around Asia, Africa, Oceania, Europe, and America. To this end, the Autoregressive Distributed Lag (ARDL) along with the modified Wald test of Toda Yamamoto (T-Y) was applied. The empirical results depict that apart from the pandemics, the global economic policy uncertainty has adverse implications on international tourism demand. The findings have important policy implications. Recovery of tourism demand should move along: i) new concepts on products;ii) new destination imagery and iii) marketing strategies through collaboration from the state. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Business Responses to COVID-19 Through CSR: A Study of Selected Companies in India

On the 30 January 2020, the World Health Organization (WHO) declared COVID-19 a public health emergency of international concern. Such a global health crisis has resulted in restructuring of resources in terms of both speed and scale of mobilisation. Corporate Social Responsibility (CSR) is playing a crucial role in the age of this pandemic COVID-19, where business is trying their best to cope with this tremendous challenging time. On 23 March 2020, the Indian government declared that all expenditures incurred on activities related to COVID-19 would be regarded to be CSR expenditure. Since the announcement of the PM CARES Fund and its inclusion in Schedule VII of the Companies Act, 2013, through a subsequent amendment, a huge amount of funding has also been directed from corporates to the PM CARES Fund. In this chapter we have studied the business responses to COVID-19 through the lens of CSR of the top 50 companies ranked on the basis of market capitalization for the years 2019–2020 to 2020–2021 by constructing a Corporate Health Disclosure Index (CHDI). Our study showed that business response towards health during COVID-19 was average. Businesses have mostly concentrated on short-term plans, primarily supporting healthcare infrastructure, assisting in vaccination programmes and contributing to the PM CARES Fund. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Efficacy Evaluation of Silty-Sandy Soil and Chrysopogon zizanioides to Attenuate Doxycycline from Wastewater in a Constructed Wetland System

In recent years, the emergence of COVID-19 has created disastrous health effects worldwide. Doxycycline, a member of the tetracycline group, has been prescribed as a treatment companion for attending this catastrophe. Due to extensive use and high solubility, a significant amount of un-metabolized doxycycline has been found to reach water bodies within a short time, and consumption of this water may lead to the development of fatal resistance in organisms and create health problems. Therefore, it has become necessary to develop suitable technologies from a geoenvironmental point of view to remove these unwanted antibiotics from wastewater. In this context, locally obtainable silty-sandy soil was explored as a low-cost material in a constructed wetland with Chrysopogon zizanioides (vetiver sp.) for phytoremediation to mitigate doxycycline spiked wastewater. The obtained soil hydraulic conductivity was 1.63×10-7 m/s. Batch adsorption tests conducted on silty-sandy soil, vetiver leaf, and vetiver root provided maximum removal efficiencies of 90%, 72%, and 80% percent, respectively, at optimal sorbent doses of 10 g/L, 17 g/L, and 16 g/L, and contaminant concentrations of 25 mg/L, 20 mg/L, and 23 mg/L, with a 30-min time of contact. The Freundlich isotherm was the best fit, indicative of sufficient sorption capacity of all the adsorbents for doxycycline. The best match in the kinetic research was pseudo-second-order kinetics. A one dimensional vertical column test with the used soil on doxycycline revealed a 90% breakthrough in 24 h for a soil depth of 30 mm. Studies on a laboratory-scale wetland and numerically modeled yielded removal of around 92% by the selected soil and about 98% combined with Chrysopogon zizanioides for 25 mg/L of initial doxycycline concentration, which is considered quite satisfactory. Simulated results matched the laboratory tests very well. The study is expected to provide insight into remedies for similar practical problems. © 2023 American Society of Civil Engineers.

Application of Deep Learning in Healthcare

Automated Patient monitoring is rising to importance in the mobile healthcare services as it makes day-to-day activities risk-free, by continuously monitoring their vital signs. Clinical solutions are being provided to patients in no time, which is made possible due to the latest improvements in the "Internet of Things (IoT), cloud computing, and fog computing”. "Machine learning and Deep learning” are now being extensively used for various applications in healthcare such as extracting relations from vast amounts of patient data, analyzing patterns to predict the propagation of diseases, classify reports and X-rays to detect diseases, to name a few. In this paper, a deep learning-based model is proposed to monitor Covid-affected patients within hospitals. Our model can provide an online link between a patient and medical facility while also collecting patient data. This will enhance the care taken for patients. At the hospital end, we present a deep learning model using ResNet-50 that could classify chest X-rays as Covid positive or No Covid. Through this model we expect to quicken the process of COVID-19 detection while lowering the healthcare expenses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Face Mask Detector Using Convolutional Neural Networks

A convolutional neural network (CNN) has one or more layers and is mainly used for image processing, classification, segmentation. CNN is commonly used for satellite image capturing or classifying hand written letters and digits. In this particular project, a convolutional neural network is trained to predict whether a person is wearing a mask or not. The training is done by using a set of masked and unmasked images which constitutes the training data. The performance of the trained model is evaluated on the test dataset, and the accuracy of the prediction is observed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

India and the pandemic: Democratic governance at crossroads

Covid-19 seems to have unlocked the reality of democracy's ongoing tension in many parts of the world, including India. The present government, led by Hindu nationalist Bharatiya Janata Party, enjoys absolute majority in the lower House of Samshad (Indian Parliament);thus satisfies WHO requirement of strong political leadership for meeting the challenge of Covid-19 pandemic. Through analysis of various acts, rules, notifications, social media behaviour, media-representations and reports, two aspects of governance become relevant: The process of policy-communication on the pandemic, particularly while declaring and extending lockdowns, through widely publicised speeches of the Prime Minister, packed with emotive appeals and policy-propaganda. However, government's several omissions and commissions have defied the norms of democratic accountability. In response, opposition political parties and civil society activism have continuously contested these trends, for stretching the democratic space wider and achieving better governance outcomes. © The Author(s), 2021. Published by Cambridge University Press.

Surgical staples: Current state-of-the-art and future prospective

Wound closing is one of the widely performed and prominent clinical practices in the surgical intervention process. A physician or surgeon has several options ranging from surgical sutures and adhesive strips to fibrin glue for effective wound closure to close the commonly occurring surgical cuts and deep skin tissue injuries. However, all the commercially available wound closure devices have some limitations in each and another perspective. From the beginning of the late 90s, surgical staples got tremendous attention as efficient wound closure devices for their time-effective and sufficient mechanical strength, performance feasibility, fewer chances of surgical site infection and require minimal expertise characteristics in consideration of remote location. Even in the context of the recent COVID19 pandemic, the clinical acceptance and patient compliance for the staples have increased due to minimizing the chances of prolonged interaction between the patient and physicians. The surgical staples application is extensive and diversified, ranging from common external cuts to highly complex surgery procedures like laparoscopic appendectomy, intestinal anastomosis, etc. Thus, in this literature review, we try to give a comprehensive glimpse of the development and current state-of-the-art surgical staples in consideration with research from a commercial point of view. On a special note, this review also describes a very brief outline of the regulatory aspects and some common internationally acceptable 'de jure standards for the development of commercially viable surgical staples. Copyright © 2022 The Author(s)

Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

Microblogging sites such as Twitter play an important role in dealing with various mass emergencies including natural disasters and pandemics. Over the last several years, the track on Information Retrieval from Microblogs during Disasters (IRMiDis), organized as part of the FIRE conference series, has provided annotated datasets for developing ML/NLP techniques for utilizing microblogs for various practical tasks that would help authorities better deal with disaster situations. In particular, the FIRE 2022 IRMiDis track focused on two important tasks-(i) to detect the vaccine-related stance of tweets related to COVID-19 vaccines, and (ii) to detect reporting of COVID-19 symptom in tweets. © 2022 Owner/Author.

A Comparative Overview of Various Fungal Menaces Amidst Corona Virus Disease

The disastrous Coronavirus Disease outbreak declared as a global pandemic, has become a potential hazard to public health. The second wave of COVID-19 in India has been strongly linked to the rising cases of various fungal infections including mucormycosis, aspergillosis, candidiasis, and mucor septicus. These fungal infections have been a cause for alarm for the general public. The color-coding of the fungal infections is primarily based on the symptoms observed in the infected patients and not based on the color of the fungi itself. For busting the myths behind fungal infections, a comprehensive and deeper understanding of the facts is needed to overcome this challenge. Rapid diagnosis, reversal of underlying predispositions, surgical excision or debridement, and optimal antifungal therapy are some of the crucial factors in combating these fungal infections. This article provides a comparative review of literature on various fungal infections during COVID-19, that have been threatening worldwide, predominantly in India. A well-established databases literature search was conducted. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.