Effect of Process Variables on Pulp Yield of Indigenous Raw Materials

The biggest issue before pulp and paper producers is availability of quality raw material. India is fiber deficit country, this message has become more intense after covid when price of different raw materials are increased by 20-25%, . Chemical pulp is produced by chemical delignification of wood and non-wood plants. After kraft pulping the remaining lignin is removed by oxygen delignification and bleaching to produce higher purity cellulosic pulp. The goal of delignification processes is to remove lignin from the raw material without a negative effect on the cellulose and strength of pulp. The economics of pulp and paper production is more related to the yield of unbleached and bleached pulp production. Marginal increment in pulp yield reflects into savings of crore of rupees on yearly basis. An overall vigilant concern on process conditions and variables is required during production of pulp. BAT technology for pulping along with utilization of various additives and pretreatment methods allow to fine tune these process to obtain pulp with desired yield and quality. The primary aim of this paper is to review different process variables in respect to the yield of the pulp. © 2022 Indian Pulp and Paper Technical Association. All rights reserved.

Tactile Based Intelligence Touch Technology in IoT Configured WCN in B5G/6G-A Survey

Touch-enabled sensation and actuation are expected to be the most promising, straightforward, and important uses of the B5G/6G communication networks. In light of the next generation (6G) systems' prerequisite for low latency, the infrastructure should be reconfigurable, intelligent, and interoperable in the real-time existing wireless network. It has a drastic impact on society due to its high precision, accuracy, reliability, and efficiency, combined with the ability to connect a user from remote areas. Hence, the touch-enabled interaction is primarily concerned with the real-time transmission of tactile-based haptic information over the internet, in addition to the usual audio, visual, and data traffic, thus enabling a paradigm shift towards a real-time control and steering communication system. The existing system latency and overhead often have delays and limitations on the application's usability. In light of the aforementioned concerns, the study proposes an intelligent touch-enabled system for B5G/6G and an IoT-based wireless communication network, incorporating AR/VR technologies. The tactile internet and network-slicing serve as the backbone of touch technology and incorporates intelligence from techniques such as artificial intelligence and machine/deep learning. The survey also introduces a layered and interfacing architecture with its E2E solution for the intelligent touch-based wireless communication system. It is anticipated for the upcoming 6G system to provide numerous opportunities for various sectors to utilize AR/VR technology in robotics and healthcare facilities to help in addressing several problems faced by society. Conclusively the article presents a few use cases concerning the deployment of touch infrastructure in automation, robotics, and intelligent healthcare systems, assisting in the diagnosis and treatment of the prevailing Covid-19 cases. The paper concludes with some considerable future research aspects of the proposed system with a few ongoing projects concerning the development and incorporation of the 6G wireless communication system.

Teachers' Perception about the Use of QR Code in Education

These days, QR codes are more widely used. Numerous benefits, including quick reading speed, error correction, 360-degree reading, multilingual support, robustness, and a broad range of applications, are offered by QR codes. The education industry is using QR codes on a large scale to make teaching and learning effective. In a few universities and colleges, faculties are using QR code technology in the classroom as well. The use of QR codes dramatically increased during COVID 19. It was the time when the whole world was trying to discover the potential of QR codes in different sectors. Now, QR codes have become an essential part of our lives. Almost every industry uses QR codes, including entertainment, education, sports, FMCG, textiles, restaurants, healthcare, and tourism. The objective of the study is to determine the different ways to use QR code in classroom and to find the factors that develop teachers' perception of use of QR codes in the classroom. The study also determines the role of QR code in making teaching learning effective. The data was collected from 50 faculty members of randomly selected five private universities of Haryana region through an online survey. The study found that in spite of multiple uses of QR codes in teaching, only a limited number of faculty members are using this technology in the classroom. The reason is that many teachers are not aware of different ways of using QR code in classroom. The study comes up with different factors that play an important role in developing teachers' perception of use of QR code in classroom. © 2023 IEEE.

Understanding the Molecular Interface of Cardiovascular Diseases and COVID-19: A Data Science Approach

Recently, mounting evidence documented an increased morbidity and mortality of COVID-19 in individuals with pre-existing cardiovascular diseases (CVDs). To better understand the pathogenesis of COVID-19 and its impact in CVDs, we designed a text-mining analysis project to evaluate the molecular interfaces between COVID-19 and several known CVDs. We assembled our data corpus from publicly accessible databases and applied text mining to COVID-19 symptoms, comorbidities, and human proteins impacted by COVID-19. Our exploration includes a statistical overview of unstructured text datasets with associated biomedical entities where the information extraction was assisted by data indexing and entity search methodologies. Using 333 human COVID-19-interacting proteins as entities and 8 CVDs classified by MeSH as categories, we examined and computed their Context Aware Semantic Analytic Processing (CaseOLAP) scores. Using this dataset, we determined associations of COVID-19 symptoms with a variety of major and minor comorbidities. Then, we further explored proteins at the interface of COVID-19 and 8 categories of CVD, evaluating relationships between the proteins and CVD categories to determine the proteins' significance in each disease. We then performed pathway analyses on those proteins of significance and their presence in each of the CVD categories. For the first time, our cluster analyses determined which COVID-19-interacting proteins are most relevant for each CVD category. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Assessment of Lifestyle Changes and Their Effect on Health among Children of <=15 Years during Covid-19 Pandemic, in North India

Objective: The objectives of the study were: (1) To assess life style changes among children of <=15 years of age during COVID-19 pandemic and (2) to find out the effect of the life style changes on health of children of <=15 years of age. Method(s): The cross-sectional comparative study conducted at department of pediatrics, Vivekananda Polyclinic and Institute of Medical Sciences, Lucknow for duration of 1 year and sample size found to be 276 on calculation by applying the formula. Result(s): Out of 278 children, about 39% (108) were female children. Most of children were studying in primary level classes (52.51%) and most of enrolled children had joint family (66.18%). Level of physical activity reduced significantly due to closure of school and restriction on outdoor activities. Weight of children increased significantly during COVID-19 pandemic seems to be due to decreased in physical activities and consumption of more fast food/fried food (high calorie intake) and sedentary life style. Conclusion(s): During COVID-19 pandemic due to closure of schools and restricted outdoor activities results in decrease level of physical activities, increased consumption of high calorie food and sedentary behavior lead to increase in weight of children and changes in sleeping pattern of children.Copyright © 2023 The Authors. Published by Innovare Academic Sciences Pvt Ltd.

A Comparative Study of Screen Time and Its Effects in Pre-Covid and Covid-Era in Children of 5 to 15 Years

Objective: The objective of this study was to compare the screen time (ST) in pre-COVID and COVID era in children aged 5-15 years and to analyse the ST effect in pre-COVID and COVID era in the children. Method(s): The study was done at Vivekananda Polyclinic and Institute of Medical Sciences, Lucknow. Two hundred and seventy-six children aged between 5 and 15 years, attending outpatient department or inpatient department were enrolled in the study. Result(s): It was observed that the ST was significantly increased in post-COVID as compared to pre-COVID time and the difference was statistically significant (p<0.0001*). It was also observed that the screening time was significantly increased in post-COVID as compared to pre-COVID time and the difference was statistically significant (p<0.0001*). Conclusion(s): The present study found that when screening duration was analysed, the screening time during COVID-19 was significantly longer than the screening time before COVID-19 which may be associated with the various health problems reported among children during COVID-19 pandemic.Copyright © 2023 The Authors. Published by Innovare Academic Sciences Pvt Ltd.

Measuring Happiness Index and Electronic Gadgets Radiations on AI IoT Systems: Return to Indian Scriptures and Science for Mental Fitness During Global Threats

The World Health Organization has unanimously accepted the four dimensions of complete human health: mental, physical, social, and spiritual. Due to the present pandemic of COVID-19, the mental and spiritual health of an individual is completely disturbed. The article is a trial enough to establish the effect of Sanskrit Vedic Mantra and Yajna, an ancient Hindu science of upliftment of consciousness and to heal self by sound science and heat and light. The article demonstrates the effect of mantra chanting on different ages and genders, rural and urban, and different age groups on human consciousness and mental peace and spiritual wellbeing. It presents the effect of sound science and mantra science over the human mind and body to relax after the stress of COVID-19, a nightmare for the world of the 21st century. Copyright © 2022, IGI Global.

Assimilation of aircraft observations over the Indian monsoon region: Investigation of the effects of COVID-19 on a reanalysis

Since March 2020, the COVID-19 pandemic has significantly reduced the availability of global aircraft-based observations (ABOs), which has been restored later in 2021. This study focuses on the impact of ABOs on a regional reanalysis. Indian Monsoon Data Assimilation and Analysis (IMDAA) is a regional reanalysis for a period from 1979 to 2020 (originally up to 2018) over India and surrounding regions produced at the National Centre for Medium Range Weather Forecasting (NCMRWF), India, in collaboration with the UK Met Office. A comparison of the impact of ABOs on other conventional and satellite observations assimilated in the NCMRWF global model and IMDAA during 2019 and 2020 revealed the importance of ABOs, particularly in IMDAA, since it did not assimilate the latest satellite data as the IMDAA system was frozen in October 2016. A data denial experiment that removes all the ABOs from the IMDAA assimilation system for a period from March to November 2019 is designed. The results from the IMDAA reanalysis run, which assimilates ABOs during the same period, are compared with the data denial experiment. Assimilation of ABOs strengthened the upper tropospheric circulation, the Tropical Easterly Jet (TEJ), during the Indian summer monsoon compared to the data denial experiment. Analysis of the features of two cyclones that developed over the North Indian Ocean during the study period revealed that ABO assimilation played a key role in simulating the track and intensity of these cyclones when they were in the ‘severe' category. Since the sample is small, more cyclone cases need to be analysed to consolidate the result. © 2023 Royal Meteorological Society.

The trend of COVID-19 pandemic in India: a methodical comparison with Italy, UK, and the US

Introduction: To statistically compare the trends of epidemiological indicators of COVID-19 in India with Italy, the UK, and the US. Methodology: In this descriptive analysis, epidemiological indicators were calculated and their trends were plotted and compared statistically. Regression analysis was done to predict the fatalities. Results: The trends of total and active cases per million populations are rising in India and US, while Italy has achieved the plateau in the total cases per million populations, and active cases have been sharply declining with time. The UK is about to achieve the same. India has remained far behind the other three countries in the number of tests per million populations (p < 0.05). In the initial phase, the test positivity rate of India was quite lower but has overtaken Italy and UK. India has always reported a higher recovery rate than US and lower than Italy. CFRs have achieved a plateau in Italy and UK, in US it is declining, while it remained almost constant in India throughout the pandemic. Testing was a significant covariate in predicting the fatalities. Conclusions: India was able to manage the initial phase of this pandemic due to early and strict government interventions and strong public health responses.

Low dose (ICMR) hydroxychloroquine prophylaxis for COVID-19 fails - a prospective study

Since the COVID-19 pandemic, the world began a frantic search for possible prophylactic options. We conducted a study to assess the role of hydroxychloroquine for COVID-19 prophylaxis in health-care workers. The study was a prospective cohort with four arms (high, medium, low dose, and control) of HCQ prophylaxis. Participants were grouped as per their opting for any one arm on a voluntary basis as per institute policy. The outcomes studied were COVID-19 positivity by RT-PCR and its severity assessed by WHO COVID-19 severity scale. Total 486 participants were enrolled, of which 29 (6%) opted for low dose, 2 (<1%) medium dose, and none for high dose HCQ while 455 (93.6%) were in the control arm. Of the 164 participants who underwent RT-PCR, 96 (58.2%) tested positive. Out of these 96 positive cases, 79 [82.3%] were ambulatory and were managed conservatively at home. Only 17.7% participants, all from the control group, required hospitalization with the mild-moderate disease. None of the participants had severe disease, COVID-related complications, ICU stay, or death. The difference in the outcome was statistically insignificant (p value >0.05). This single-centre study demonstrated that HCQ prophylaxis in healthcare workers does not cause a significant reduction in COVID-19 as well as mitigating its severity in those infected. At present, most of the trials have not shown any benefit. Though COVID-19 vaccines have reduced the need for prophylaxis, the search for a safe and reasonable chemoprophylaxis should continue until a large population of individuals gets vaccinated, especially in underdeveloped countries.

Role of Digital Healthcare in Rehabilitation During a Pandemic

The pandemic turned life upside down, including causing unavailability and an inability to access rehabilitation in the hospital. However, the need to be fit and healed does not stop, so rehabilitation innovation from the digital sectors plays a role in approaching the patient, as the patient requires a medical professional to be healed. Rehabilitation via a digital pathway is fraught with difficulties, but advances in technology and research have enabled it to be used to the greatest extent possible in this disaster. Digital health has increased its efficacy in response to the pandemic, as it is now available in developing countries where there is an inability to visit a clinic for rehabilitation, and now the rehabilitation tool is accessible to the patients in their hands and they can connect to their therapist at any time. The rehabilitation is designed based on the patient's illness, feedback, and health data stored on the application devices, which regulate and provide feedback from both sides, from the patient and other improvement changes gathered with the help of digital applications. Digital health allows for online consultation, assessment, and 24-h monitoring, all of which are directly shared with the rehabilitation team. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Clinico-epidemiological Trends of Leprosy in 21st Century and During COVID-19 Pandemic

Leprosy is the oldest disease affecting humankind since ancient times. Despite MDT's availability for disease curability, vast pockets of multi-bacillary (MB) cases persist in the community. We conducted this study to know the clinico-epidemiological trends of leprosy over four years and five months in this era of the COVID-19 pandemic (C19P). A total of 90 cases were registered;59 (65.5%) were males, and 31 (34.5%) were females. The majority (69%) of cases were in the 15-45 age groups. Childhood leprosy was detected in 3(3.3%) cases. A history of contact with leprosy patients could be established in 16 (17.8%) cases. The cases comprised 54.5% local inhabitants and 45.5% were migrants. The MB cases 77 out of 90 (85.6%) were in higher proportion than pauci-bacillary (PB) cases. In the clinical spectrum, BL leprosy was most common in 39% of cases, followed by LL and BT leprosy. Thirty-seven (41%) patients were suffering from lepra reactions (LR), and out of them, 59.4% had type 2 reactions (T2R), and the rest had type 1 reactions (T1R). Disabilities were found in a total of 56 (62.2%) cases, and grade 2 disabilities (G2D) were recorded in 25 (44.6%) patients. Ulnar nerve (UN) was most commonly affected nerve in 64.4% of cases, followed by lateral peroneal (LPN) and posterior tibial nerve (PTN). We observed the impact of Covid 19 infection peak C19P in two ways;firstly, during the C19P peak in 2020, there was a drastic fall in total registered cases (TRC) to 4/year against 22/year in pre-C19P with a relative increase in LRs and disabilities. In post-C19P peak periods, not only was there a marked rise in TRC (20/5 months), but LR (50%) and disabilities (75%) also showed a significant rise. A high proportion of MB cases, LRs and disability rates indicate the need for population-based studies and subsequent public health measures for early diagnosis and treatment. Further large sample-sized, in-depth studies can tell the exact impact of C19P on leprosy.Copyright © Hind Kusht Nivaran Sangh, New Delhi.

Immunosuppressant Drugs and Covid-19: Associated Risks, Drug-Drug Interactions and Contraindications

Immunosuppressant drugs like Etanercept, Mycophenolate mofetil, Sirolimus, Cyclos-porine, and Rituximab can weaken the immune system and make patients susceptible to SARS nCoV-2 virus. These drugs make immunocompromised persons more vulnerable to complications associated with COVID-19. Moreover, it can also increase mortality and morbidity, as a weakened immune system can lead to a longer duration of infection. This study discusses the guidelines on immunosuppressant drugs and their associated risk factors with COVID-19, issued by the U.S CDC (Centers for Disease Control and Prevention), WHO (World Health Organization), U.S FDA (Food and Drug Administration), and other accredited global health organizations. Moreover, it also includes information about pharmaceutical properties, mechanism of action, COVID-19 associated risk factors, adverse drug reactions, contraindications, and drug-drug interactions. Our study will help government partners and international health organizations to understand COVID-19 health risks associated with immunosuppressants. Increased public awareness about effective drug therapy for autoimmune diseases, cancer treatment, immunocompromised, and organ transplant patients will help lower the mortality and morbidity associated with the disease amid the COVID-19 pandemic.Copyright © 2021 Bentham Science Publishers.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

Fake news believability: The effects of political beliefs and espoused cultural values

Fake news has led to a polarized society as evidenced by diametrically opposed perceptions of and reactions to global events such as the Coronavirus Disease 2019 (COVID-19) pandemic and presidential campaigns. Popular press has linked individuals' political beliefs and cultural values to the extent to which they believe in false content shared on social networking sites (SNS). However, sweeping generalizations run the risk of helping exacerbate divisiveness in already polarized societies. This study examines the effects of individuals' political beliefs and espoused cultural values on fake news believability using a repeated-measures design (that exposes individuals to a variety of fake news scenarios). Results from online questionnaire-based survey data collected from participants in the US and India help confirm that conservative individuals tend to exhibit increasing fake news believability and show that collectivists tend to do the same. This study advances knowledge on characteristics that make individuals more susceptible to lending credence to fake news. In addition, this study explores the influence exerted by control variables (i.e., age, sex, and Internet usage). Findings are used to provide implications for theory as well as actionable insights. © 2022 The Author(s)

Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images.

Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier's generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics.

Designerly Tele-Experiences: A New Approach to Remote Yet Still Situated Co-Design

The COVID-19 pandemic disrupted processes interaction designers took for granted, challenging some of our most commonplace design practices. Participatory and situated approaches have been impacted the most: where we engaged stakeholders in-person and in-context, during this time we must co-design remotely and in virtual environments. Such a dramatic change calls for new co-design methods. In this article, we present a novel remote strategy for involving stakeholders to co-design interactive technology: Designerly Tele-Experiences (DTE). Our methodological proposal enables participants to experience early design concepts in-the-wild as a provocation to contribute new ideas that resonate with their experiential preferences. Here we describe the rationale for DTE, unpack how it builds on and extends existing methods, and provide actionable guidelines from our experience of using it in our work. Our contribution will empower interaction designers to embrace participatory and situated approaches even when engaging stakeholders in person is not possible or desirable.

Imagining Caring Futures for Frontline Health Work

Care workers are increasingly using digital technology in their daily lives, for monitoring, financial compensation, training, coordination, and more. State and corporate actors have invested significant resources to enable this digital shift, particularly during the COVID-19 pandemic. However, care work has remained chronically underpaid, and continues to rely on women from minoritized and marginalized backgrounds. Our paper examines how care workers carefully navigate digitization, precarity, and complex social relationships, in an attempt to care for their communities and each other. We analyze the emerging digital ecosystem for frontline health workers in India during the COVID-19 pandemic where these dynamics have been highly visible. Our research draws attention to four interconnected ways in which workers practiced care, by directing their efforts towards survival, resilience, advocacy, and/or resistance. We suggest these also as care orientations that can be adopted by researchers and practitioners, to critically reflect on and direct technology design towards enabling more caring futures, for (and with) workers and communities. © 2022 Owner/Author.

Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images

Covid-19 is a highly infectious disease that spreads extremely fast and is transmitted through indirect or direct contact. The scientists have categorized the Covid-19 cases into five different types: severe, critical, asymptomatic, moderate, and mild. Up to May 2021 more than 133.2 million peoples have been infected and almost 2.9 million people have lost their lives from Covid-19. To diagnose Covid-19, practitioners use RT-PCR tests that suffer from many False Positive (FP) and False Negative (FN) results while they take a long time. One solution to this is the conduction of a greater number of tests simultaneously to improve the True Positive (TP) ratio. However, CT-scan and X-ray images can also be used for early detection of Covid-19 related pneumonia. By the use of modern deep learning techniques, accuracy of more than 95% can be achieved. We used eight CNN (CovNet)-based deep learning models, namely ResNet 152 v2, InceptionResNet v2, Xception, Inception v3, ResNet 50, NASNetLarge, DenseNet 201, and VGG 16 for both X-rays and CT-scans to diagnose pneumonia. The achieved comparative results show that the proposed models are able to differentiate the Covid-19 positive cases.