Multiple Fano Resonances in Plasmonic Waveguide Based Cavity and Detection of Pathogenic Virus

A technique is implemented for the generation of multiple Fano-resonances in a plasmonic waveguide based rectangular cavity. A rectangular cavity provides four Fano peaks which can further be increased to nine by inserting the metallic bars in it. The trapped surface plasmon polaritons by metallic bars cause the generation of multiple Fano peaks over the wavelength range of 450 nm - 1300 nm. The obtained response is validated through Fano profile and Fano shape parameter is calculated for each resonance peak. The performance of the proposed device is numerically studied as refractive index sensor and method for analyzing the detection of pathogenic virus like SARS-Cov-2 is reported. Out of nine Fano peaks, the best values of sensing performance indices are obtained with full-width, half-maxima of 1.7 nm, quality factor of 405, sensitivity of 1145.71 nm/RIU and figure of merit of 393.25 RIU-1. IEEE

Mpro Inhibition: A comparative In silico Therapeutics from Native Indian Plant Species

One of the biggest healthcare threats of this century is COVID – 19, undoubtedly. It has caused millions of deaths and raised alerts in the healthcare domain. This study focuses on the importance of 10 native Indian plant species and the phytochemicals obtained from them as a potential inhibitor to the Main protease enzyme of SARS CoV-2. About 26 phytochemicals were shortlisted for the same from the selected plants. Molecular docking was used to analyze the binding affinity of the phytochemicals in the active pocket of the Main protease enzyme to assess their effectiveness. The docking scores resulted in the selection of four compounds being more favorable than the native inhibitor N3, namely Quercetin, Withaferin A, Sominone, and Nimbin, with their binding energies being-8.42,-9.21,-9.95,-8.88 kcal/mol respectively. Furthermore, these four were further analyzed for their bioavailability scores. The studies showed that Sominone, Withaferin A are more potent inhibitors to Mpro of the SARS CoV-2 in all four. Thus further in Vitro studies can be done accordingly for the same. © 2022 by the authors.

Nanotheranostics to target antibiotic-resistant bacteria: Strategies and applications

Various health agencies, such as the European Medical Agency (EMA), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO), timely cited the upsurge of antibiotic resistance as a severe threat to the public health and global economy. Importantly, there is a rise in nosocomial infections among covid-19 patients and in-hospitalized patients with the delineating disorder. Most of nosocomial infections are related to the bacteria residing in biofilm, which are commonly formed on material surfaces. In biofilms, microcolonies of various bacteria live in syntropy;therefore, their infections require a higher antibiotic dosage or cocktail of broad-spectrum antibiotics, aggravating the severity of antibiotic resistance. Notably, the lack of intrinsic antibacterial properties in commercial-grade materials desires to develop newer functionalized materials to prevent biofilm formation on their surfaces. To devise newer strategies, materials prepared at the nanoscale demonstrated reasonable antibacterial properties or enhanced the activity of antimicrobial agents (that are encapsulated/chemically functionalized onto the material surface). In this manuscript, we compiled such nanosized materials, specifying their role in targeting specific strains of bacteria. We also enlisted the examples of nanomaterials, nanodevice, nanomachines, nano-camouflaging, and nano-antibiotics for bactericidal activity and their possible clinical implications.Copyright © 2023 The Author(s)

Total Stromal Fraction (TSF)-Fortified Adipose Tissue-Derived Stem Cells Source: An Emerging Regenerative Realm Against COVID-19 In-duced Pulmonary Compromise

The inception of the COVID-19 pandemic has jeopardized humanity with markedly dam-pening of worldwide resources. The viral infection may present with varying signs and symptoms, imitating pneumonia and seasonal flu. With a gradual course, this may progress and result in the deadliest state of acute respiratory distress syndrome (ARDS) and acute lung injury (ALI). More-over, following recovery from the severe brunt of COVID-19 infection, interstitial portions of alve-oli have been found to undergo residual scarring and further to have compromised air exchange. Such alterations in the lung microenvironment and associated systemic manifestations have been recognized to occur due to the extensive release of cytokines. The mortality rate increases with advancing age and in individuals with underlying co-morbidity. Presently, there is no availability of specific antiviral therapy or any other definitive modality to counter this progressive worsening. However, we believe principles and advancing cell-based therapy may prove fruitful in subjugating such reported worsening in these patients. This article reviews eminent knowledge and relevant ad-vancements about the amelioration of lung damage due to COVID-19 infection using adipose tis-sue-derived-total stromal fraction (TSF).Copyright © 2022 Bentham Science Publishers.

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity. © 2023 CRL Publishing. All rights reserved.

Predicting the Performance of Highway Project Using Gray Numbers

Earned Value Analysis is a methodology used to monitor project performance in terms of time, scope and cost and also to deal with uncertain situations that come within. Uncertainty is a part of construction project and sometimes these situations can cause a great loss in the project's success. Recently, to deal with uncertain situations a different approach has been developed to predict the project performance in a non-deterministic way, i.e., using gray interval numbers. A framework using gray interval numbers has been developed to predict the project performance and hence this study aims at using the framework to predict the performance of a real-life highway project of total duration of approximately 2 years. The analysis involves the verbal directed data from the site by the experts which were denoted as gray interval numbers. The results indicate that the project is under budget as the CPI is 1.06 and ahead of schedule as the SPI is 1.2. The results also show the worst case scenario that the project may exceed the budget as CPI is 0.83 and may run behind the schedule as SPI is 0.69. The outcomes of the study are in the form of range which provides the overall profile of the project and also helps the project team members to not always be accurate or deterministic with the outcomes. Since the construction sector was majorly hit by an uncertain event, i.e., COVID-19, this study can be very helpful in determining the performance after facing such a huge gap. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

E-Rupi—Recent Advancement in Digital Payment System

In the era of demonetization, the banking sector has seen an exponential increase in the usage of digital payments. There has been a slew of digital payment networks proposed by both corporate and public entities. These platforms are being used by users to make payments, pay bills, and send money. The cost of Internet plans, the availability of low-cost mobile handsets, and technological savvy are just a few of the factors driving this digital revolution. Although private companies' platforms are preferred by the bulk of people using digital platforms, public players are continually bringing novel ideas to the table, such as UPI. Another new payment platform named e-Rupi has been created and released for users by the Indian government in a similar endeavor. This platform attempts to use a voucher-based system to deliver social programs, health benefits, and a variety of other services. Hence, this paper investigates the detailed functionality of the e-Rupi platform and performs an empirical evaluation and comparative analysis of e-Rupi with other digital payment platforms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Utility of 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in COVID-19 infection- a single centre retrospective observational study

Aim/Introduction: Although 18F-Fluorodeoxyglucose (FDG) Positron Emission Tomography/Computed Tomography (PET/ CT) is not routinely used for diagnosis of COVID- 19 infections, we incidentally detected lung lesions in few patients, who were referred for some other indications. The study aimed to explore a cut off value of standardized uptake values (SUV) of 18F-FDG PET/ CT in predicting active COVID-19 infection in both symptomatic and asymptomatic patients referred to our department for various oncologic indications. Material(s) and Method(s): We retrospectively analysed PET/CT studies performed from March 2020 to August 2021 done at our department, who underwent 18F-FDG PET/CT for various oncological indications. PET/CT scans were reviewed by 2 experienced nuclear medicine physicians. At first, only HRCT chest was reviewed to ascertain inclusion of the patient. CT severity score and COVID-19 Reporting and Data System (CORADS) criteria were calculated from HRCT chest. PET/CT images were reviewed and SUVmax were recorded in lung parenchyma and mediastinal blood pool and SUV ratios (SUVR) between them were calculated. Result(s): A total of 85 patients were identified and divided into 3 groups based on clinical symptoms and reverse transcription-polymerase chain reaction (RT-PCR) results * Group A- patients with symptoms of COVID- 19 and positive RT-PCR- (n=51) * Group B- patients with symptoms of COVID-19 and a negative RT-PCR- (n=13) * Group Cpatients with no symptoms of COVID-19 - (n=21). SUVR of these 3 groups (2.67+/-1.21 vs 1.86+/-0.8 vs 1.42+/-0.53 respectively) showed significant statistical difference (p<0.05). Moderate correlation was obtained between SUVR and CT severity score (r= 0.43, p<0.05), thereby correlating towards prognosis. The area under the curve (AUC) obtained for different cut-off values of SUVR was 0.74 (95% CI- 0.55-0.97, p<0.05). A SUVR cut-off value of 1.87 yielded a specificity of at least 74.3% and a sensitivity of at least 68%. Conclusion(s): An SUVR cut off value of 1.87 can yield a specificity of at least 74.3% and a sensitivity of at least 68%. HRCT chest and 18F-FDG PET/CT plays a complementary role in determining active COVID-19 infection. SUVR of pulmonary lesions can be considered as an important prognostic indicator for active COVID-19 infections.

RESOURCE PERCEPTIONS AND EMPLOYEE WELL-BEING DURING THE COVID-19 PANDEMIC: THE ROLE OF PERSONAL CONTROL AS A MODERATOR

This paper used Hobfoll's conservation of resources (COR) theory to investigate the extent to which interpersonal and financial resources predict the well-being of salaried employees in the United States. Data were collected from a nationally representative survey of adults in the United States conducted by the RAND Corporation1. Two measures of well-being (depressive symptoms and life satisfaction), along with an actual loss of financial resources and a perceived lack of interpersonal and financial resources, were examined. The role of perceived control as a moderator in the relationship between resource deficit perceptions and well-being was also examined. The results of the regression analysis indicate that the perceived lack of resources was associated with a decline in well-being. Perceived control was found to buffer the negative effects of resource deficit perceptions. Employees with high levels of perceived control showed less of a reduction in well-being than those with low levels of perceived control as the perceived deficit of resources increased. The study also revealed that actual loss of resources, measured as a decrease in wages, is associated with a decrease in life satisfaction but is not associated with depressive symptoms. We conclude by discussing the theoretical and practical implications of our research on the relationship between resource deficits and well-being during a public health crisis. © 2022, Universiti Malaysia Sarawak. All rights reserved.

Multi-Level Image Thresolding Entropy Based Methods for Covid X-Ray Image Segmentation Using FS-AOSMA

The Technology of Image Processing has been incredibly used in many era of application like Medical Diagnosis using Image Segmentation, Face Recognition, HandWriting Analysis using Pattern Recognition. It has created its' own identity and has been fascinating all over the Research studies. Our paper is based on Image Processing called as "MULTI-LEVEL IMAGE THRESOLDING METHODS FOR COVID X-RAY IMAGE SEGMENTATION ". The mirror of the whole paper is summarized in this part. The people life has been affected due to the ongoing commotion due to COVID-19.The Researcher's left no stone unturned to deal out with Corona virus. Many methods had been applied like RT-PCR, CT Scan, Image Segmentation, uses of Meta-heuristic Algorithm: PSO, Cuckoo search, MRFO, MRFO Algorithm, MRFO-OBL, etc. © 2022 IEEE.

Development of bioreactors: current scenario and future challenges

Bioreactors since their invention have eased the feasibility of lab-scale processes to the industrial-scale level. They have been an integral part of the downstream processes. Apart from the product development, they have also been part of sustainable environmental practices such as reduction of wastage, treatment of waste products, wastewater, detoxification. The chapter focuses on the recent scenario of reactor development in terms of waste management. As the onset of the SARS-CoV2 pandemic has changed the course of action the bioreactors do have a major role to play in the handling of COVID waste as well. © 2022 Elsevier Inc. All rights reserved.

E-Rupi—Recent Advancement in Digital Payment System

In the era of demonetization, the banking sector has seen an exponential increase in the usage of digital payments. There has been a slew of digital payment networks proposed by both corporate and public entities. These platforms are being used by users to make payments, pay bills, and send money. The cost of Internet plans, the availability of low-cost mobile handsets, and technological savvy are just a few of the factors driving this digital revolution. Although private companies’ platforms are preferred by the bulk of people using digital platforms, public players are continually bringing novel ideas to the table, such as UPI. Another new payment platform named e-Rupi has been created and released for users by the Indian government in a similar endeavor. This platform attempts to use a voucher-based system to deliver social programs, health benefits, and a variety of other services. Hence, this paper investigates the detailed functionality of the e-Rupi platform and performs an empirical evaluation and comparative analysis of e-Rupi with other digital payment platforms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Predicting the Performance of Highway Project Using Gray Numbers

Earned Value Analysis is a methodology used to monitor project performance in terms of time, scope and cost and also to deal with uncertain situations that come within. Uncertainty is a part of construction project and sometimes these situations can cause a great loss in the project’s success. Recently, to deal with uncertain situations a different approach has been developed to predict the project performance in a non-deterministic way, i.e., using gray interval numbers. A framework using gray interval numbers has been developed to predict the project performance and hence this study aims at using the framework to predict the performance of a real-life highway project of total duration of approximately 2 years. The analysis involves the verbal directed data from the site by the experts which were denoted as gray interval numbers. The results indicate that the project is under budget as the CPI is 1.06 and ahead of schedule as the SPI is 1.2. The results also show the worst case scenario that the project may exceed the budget as CPI is 0.83 and may run behind the schedule as SPI is 0.69. The outcomes of the study are in the form of range which provides the overall profile of the project and also helps the project team members to not always be accurate or deterministic with the outcomes. Since the construction sector was majorly hit by an uncertain event, i.e., COVID-19, this study can be very helpful in determining the performance after facing such a huge gap. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

RAPTOR: Ravenous Throughput Computing

We describe the design, implementation and performance of the RADICAL-Pilot task overlay (RAPTOR). RAPTOR enables the execution of heterogeneous tasks-i.e., functions and executables with arbitrary duration-on HPC platforms, pro-viding high throughput and high resource utilization. RAPTOR supports the high throughput virtual screening requirements of DOE's National Virtual Biotechnology Laboratory effort to find therapeutic solutions for COVID-19. RAPTOR has been used on 8300 compute nodes to sustain 144M/hour docking hits, and to screen 1011 ligands. To the best of our knowledge, both the throughput rate and aggregated number of executed tasks are a factor of two greater than previously reported in literature. RAPTOR represents important progress towards improvement of computational drug discovery, in terms of size of libraries screened, and for the possibility of generating training data fast enough to serve the last generation of docking surrogate models. © 2022 IEEE.

Cerebellar ataxia: a rare toxicity of commonly used drug

Background and Aim: Metronidazole is commonly prescribed drug for amoebiasis and is usually well tolerated, and safe but can cause serious neurological adverse events including peripheral neuropathy which is relatively common but CNS toxicity is rare. We report a case of cerebellar ataxia who had taken metronidazole inadvertently for amoebic liver abscess. Case summary: Young male with history of toddy inking admitted for management of amoebic liver abscess. He was managed with percutaneous ain and intravenous metronidazole. He was discharged on oral metronidazole tablet for a total duration of 10 days. Due to COVID-19 pandemic, he did not turn up and continued taking metronidazole. Two months’ later patient presented with progressive slurring of speech and unsteady gait. On examination, cerebellar sign was present with normal motor and sensory system. Blood investigations including complete blood count, liver function test, kidney function test and thyroid profile were normal. Vitamin B12 and fasting blood sugar levels were normal. Non-contrast computed tomographic (NCCT) scan of brain was normal. Magnetic resonance imaging (MRI) scan of the brain showed areas of hyperintense signal change in dentate nucleus of cerebellum, two small foci in dorsal pons and splenium of corpus callosum with no restriction in T2 FLAIR, DWI and ADC sequences suggestive of interstitial edema. On stopping metronidazole, his sign and symptoms started waning and was symptom free after 10 days. Conclusions: Neurological toxicity may be related to prolonged administration, high doses, or high cumulative doses of metronidazole and prompt identification of neuropathy and cerebellar ataxia is essential to avoid permanent damage. Clinicians should avoid the use of metronidazole for more than 2 weeks in case of amoebic liver abscess.

A Risk Management Tool for Construction Sector India During Covid-19 Crisis

In construction industry, with the management of time and cost of the project, risk management is also especially important. Recently, COVID-19 pandemic has brought a huge crisis on construction sector. During this crisis, risk management becomes even more crucial to avoid further losses in the project. This study aims at identifying the risks involved in construction project during COVID-19 crisis, analyse them and develop a plan to bring the project back on schedule. Possible risks involved in construction sector due to COVID-19 are identified and defined. The risks are classified based on the categories like commercial risk, health and safety risk, completion risk etc. The project was analysed for all categories of risks using Expected Value Method (EVM) for statistical analysis. EVM evaluates the average outcome when the future events may or may not happen. Based on the analysis, Composite Likelihood factor, Composite Impact factor and risk severity has been computed. The EVM results shown that the commercial risk would be at a high level with a risk severity equals to 0.034 and completion risk would be at a low level with a risk severity equals to 0.003. Using this approach, the occurrence of risks at various stages of the project can also be predicted. EVM is found to be a convenient and accurate method to identify risks that might occur and prepare a contingency plan to avoid further losses. © 2023, Canadian Society for Civil Engineering.

Impact of Covid 19 on Work Integrated Learning for Computing Education

Work Integrated Learning (WIL) for university graduates allows students to gain employability skills through relevant employment experience and make them work-ready. However, the disruptions caused by COVID-19 pandemic has changed how universities deliver WIL programs for students in higher education. Students faced multiple challenges to be workplace ready during COVID-19. Enforced social distancing has impacted the delivery of WIL education in many ways, which requires the attention of all the stakeholders so that students are not missing out on the opportunities provided by WIL education and hence their career in the future. To deliver WIL in computing is different to delivering WIL in other disciplines. Consequently, there is a need to summarise the current practices adopted by higher education in delivering WIL education for computing students and how COVID19 has impacted it. This paper aims to briefly review WIL practices in computing education due to COVID-19, its impact on supervisors/academics, students, and student-industry relationships, and the challenges for developing student's professional capabilities to make them employable in computing field.

Long Term Health Consequences of COVID-19 in Hospitalized Patients from North India: A follow up study of upto 12 months

Background: Long-COVID syndrome is now a real and pressing public health concern. We cannot reliably predict who will recover quickly or suffer with mild debilitating long COVID-19 symptoms or battle life-threatening complications. In order to address some of these questions, we studied the presence of (post covid) symptoms and various correlates in COVID-19 patients who were discharged from hospital, 3 months and up to 12 months after acute COVID-19 illness. Methods: This is an observational follow-up study of RT-PCR confirmed COVID-19 patients admitted at 3 hospitals in north India between April – August 2020. Patients were interviewed telephonically using a questionnaire regarding the post-COVID symptoms. The first tele-calling was done in the month of September 2020, which corresponded to 4- 16 weeks after disease onset. All those who reported presence of long COVID symptoms, were followed-up with a second call, in the month of March 2021, corresponding to around 9-12 months after the onset of disease. Results: Of 990 patients who responded to the first call, 615 (62.2%) had mild illness, 227 (22.9%) had moderate and 148 (15.0%) had severe COVID-19 illness at the time of admission. Nearly 40% (399) of these 990 patients reported at least one symptom at that time. Of these 399 long-COVID patients, 311 (almost 78%) responded to the second follow-up. Nearly 8% reported ongoing symptomatic COVID, lasting 1-3 months and 32% patients having post-COVID phase with symptoms lasting 3-12 months. Nearly 11% patients continued to have at least one symptom even at the time of the second interview (9-12 months after the disease onset). Overall, we observed Long-COVID in almost 40% of our study group. Incidence of the symptoms in both the follow-ups remained almost same across age-groups, gender, severity of illness at admission and presence of comorbidity, with no significant association with any of them. Most common symptoms experienced in long COVID phase in our cohort were fatigue, myalgia, neuro-psychiatric symptoms like depression, anxiety, “brain fog” and sleep disorder, and breathlessness. Fatigue was found to be significantly more often reported in the elderly population and in those patients who had a severe COVID-19 illness at the time of admission. Persistence of breathlessness was also reported significantly more often in those who had severe disease at the onset. The overall median duration of long COVID symptoms was 16.9 weeks with inter-quartile range of 12.4 to 35.6 weeks. The duration of symptom resolution was not associated with age, gender or comorbidity but was significantly associated with severity of illness at the time of admission (P=0.006). Conclusions: Long-COVID was seen in almost 40% of our study group with no correlation to age, gender, comorbidities or to the disease severity. The duration of symptom resolution was significantly associated with severity of illness at the time of admission (P = 0.006). In our study, all patients reported minor symptoms such as fatigue, myalgia, neuro-psychiatric symptoms like depression, anxiety, “brain fog” and sleep disorder and persistence of breathlessness.

An Improved Procedure for Face Mask Detection using Convolution Neural Network

The present COVID-19 pandemic scenario where entire world is facing a lot of unforeseen medical challenges, can be somehow controlled by following some medical guidelines and procedures when monitored strictly. Wearing a specifically recommended face mask is one of the easiest ways to prevent it. However, due to negligence by the common people for avoiding face masks, need a meticulous system to find such people and appropriate action may be taken against them. Medical guidelines state that primarily COVID-19 and its variants outspread typically through nose discharge or droplets of saliva by the infected person's coughs or sneezes. Droplets of saliva all around us could infect us and others. So, People need to wear masks as it's one of the methods which can effectively depress the growth of its spreading. Identifying masks over the faces could be done using many methodologies in Computer Science. The primary concern is to work upon identifying masks on people's faces and increasing their recognition accuracy with simple methodology and network. Keep the fact in mind, this paper presents a methodology for the identification of masks on people's faces using Convolution Neural Networks (CNN), for this, a training dataset has been used to generate more augmented images. Afterward, it has been pre-processed by applying CNN. The method has been evaluated using a CNN-based algorithm on the test dataset. The simulation result shows satisfactory performance and accuracy through different curves. © 2022 IEEE.