Street food nostalgia and COVID-19 perceptions on street food desire

With COVID-19 paralyzing street food businesses, street food vendors are trying to sustain their operations. The current study helps them by identifying the importance of five prominent stimuli viz. authenticity, quality, staff-service, ambience, and value for money in developing desire for street food in individuals in India. Furthermore, the study contributes by identifying the role of street food nostalgia (as a mediator) and perceived risk of COVID-19, age, and gender (as moderators) on the direct impact of each stimulus on the desire for street food. The study uses partial least squares path modelling to validate the hypotheses using SmartPLS. The findings are comparable to other developing Asian countries, as the proposed associations are validated with perceptual responses from three prominent cities and well-known street food destinations in India. The study showed the relative importance of the five-stimuli based on the stimulus-organism-response framework in developing a desire for street food. The findings suggest partial to complete mediation of street food nostalgia across the three samples. Lastly, the perceived risk of COVID-19 along with age and gender emerged as prominent moderators for many of the direct effects of stimuli on desires for street food. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Dynamic digital capabilities and supply chain resilience: The role of government effectiveness

Organizations in recent times are increasingly investing in building supply chain resilience following disruptions due to natural disasters, geo-political crises, and pandemics. A lack of government support has exacerbated the disruption to supply chains in some regions of the world. The positive influence of digitalization on social inclusion, government accountability, and creating a more open environment is well understood. Despite this, different countries have shown varying degrees of digital responsiveness during the pandemic as they attempted to deal with the effects of various COVID strains. The influence of government policies on the supply chain has not been examined in the literature so far and, hence, to address this research gap, we examine the interaction effect of government support effectiveness i.e., tax credits, interest deferral, digital investment, soft loans on dynamic capabilities i.e., digital adaptabilities and digital agilities and on supply chain resilience, using a multi-method approach. To understand how digital adaptability and agility improve supply chain resilience, we conducted 13 semi-structured interviews. Additionally, we pretested our measurement instrument using qualitative semi-structured interviews to validate our hypothesized relationships. We collected data at a specific point of time using a survey-based instrument (N = 203) to address our research questions. Based on data analyses of both the qualitative and survey-based data, our findings indicate that digital adaptability is an important driver of digital agility. Furthermore, the results indicate that government effectiveness is crucial to enhancing supply chain resilience by enhancing digital adaptability and agility. Our research makes some useful contributions to the dynamic capability view by enhancing theoretical understanding, of the role of government in building digital capabilities in uncertain times, to improve supply chain resilience. It also bridges the research gaps between macro and micro perspectives, as identified by management scholars. Lastly, we noted the weaknesses and limitations in the study and therefore we have offered multiple research directions forward, that could help researchers to further develop our current work. © 2023 The Authors

Role of Chest Radiography as the Imaging Tool in COVID Pandemic Era

Background: The present radiological COVID literature is mainly confined to the CT findings. Using High Resolution Computed tomography (HRCT) as a regular 1st line investigation put a large burden on radiology department and constitute a huge challenge for the infection control in CT suite. Materials and Methods: A prospective study of 700 consecutive COVID positive cases who underwent Chest Xray (CXR) and HRCT thorax were included in the study. Many of these CXR were repeated and followed up over a duration of time to see the progression of disease. Results: 392/700 (56%) were found to be negative for radiological thoracic involvement. 147/700 (21%) COVID positive patients showed lung consolidations, 115/700 (16.5%) presented with GGO, 40/700 (5.7%) with nodules and 42/700 (6%) with reticular–nodular opacities. 150/700 patients (21.4 %) had mild findings with total RALE severity score of 1-2. More extensive involvement was seen in 104/700 (14.8 %) and 43/700 (6.2%) patients, who had severity scores of 3-4 and 5-6 respectively. 11/700 patients had a severity score of >6 on their baseline CXR. Those with severity score of 5 or more than 5 (54/700, 7.7%) required aggressive treatment with mean duration of stay of 14 days, many of them died also (23/54, 42.5%). Conclusion: In cases of high clinical suspicion for COVID-19, a positive CXR may obviate the need for CT. Additionally, CXR utilization for early disease detection and followup may also play a vital role in areas around the world with limited access to CT and RT-PCR test.

Comprehending the model of omicron variant using fractional derivatives

The world is grappled with an unprecedented challenges due to Corona virus. We are all battling this epidemic together, but we have not been able to defeat this epidemic yet. A new variant of this virus, named 'Omicron' is spreading these days. The fractional differential equations are providing us with better tools to study the mathematical model with memory effects. In this paper, we will consider an extended SER mathematical model with quarantined and vaccinated compartment to speculate the Omicron variant. This extended Susceptible Exposed Infected Recovered SER model involves equations that associate with the group of individuals those are susceptible (S), exposed (E): this class includes the individuals who are infected but not yet infectious, infectious (W): this class includes the individuals who are infected but not yet Quarantined, quarantined (Q): this class includes those group of people who are infectious, confirmed and quarantined, recovered (R) this class includes the group of individuals who have recovered, and vaccinated (V): this class includes the group of individuals who have been vaccinated. The non-negativity and of the extended SER model is analysed, the equilibrium points and the basic reproduction number are also calculated. The proposed model is then extended to the mathematical model using AB derivative operator. Proof for the existence and the uniqueness for the solution of fractional mathematical model in sense of AB fractional derivative is detailed and a numerical method is detailed to obtain the numerical solutions. Further we have discussed the efficiency of the vaccine against the Omicron variant via graphical representation.

Role of Chest Radiography as the Imaging Tool in COVID Pandemic Era

Background: The present radiological COVID literature is mainly confined to the CT findings. Using High Resolution Computed tomography (HRCT) as a regular 1st line investigation put a large burden on radiology department and constitute a huge challenge for the infection control in CT suite. Material(s) and Method(s): A prospective study of 700 consecutive COVID positive cases who underwent Chest Xray (CXR) and HRCT thorax were included in the study. Many of these CXR were repeated and followed up over a duration of time to see the progression of disease. Result(s): 392/700 (56%) were found to be negative for radiological thoracic involvement. 147/700 (21%) COVID positive patients showed lung consolidations, 115/700 (16.5%) presented with GGO, 40/700 (5.7%) with nodules and 42/700 (6%) with reticular-nodular opacities. 150/700 patients (21.4 %) had mild findings with total RALE severity score of 1-2. More extensive involvement was seen in 104/700 (14.8 %) and 43/700 (6.2%) patients, who had severity scores of 3-4 and 5-6 respectively. 11/700 patients had a severity score of >6 on their baseline CXR. Those with severity score of 5 or more than 5 (54/700, 7.7%) required aggressive treatment with mean duration of stay of 14 days, many of them died also (23/54, 42.5%). Conclusion(s): In cases of high clinical suspicion for COVID-19, a positive CXR may obviate the need for CT. Additionally, CXR utilization for early disease detection and followup may also play a vital role in areas around the world with limited access to CT and RT-PCR test. Copyright © 2022, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

Deep learning-based hybrid models for prediction of COVID-19 using chest X-ray

The ongoing COVID-19 virus infection has ended up being the biggest pandemic to hit mankind in the last century. It has infected in excess of 50 Million across the globe and has taken in excess of 1.5 Million lives. It has posed problems even to the best healthcare systems across the globe. The best way to reduce the spread and damage of COVID-19 is by early detection of the infection and quarantining the infected patients with necessary medical care. COVID-19 infection can be detected by a chest X-ray. With limited rapid COVID-19 testing kits, this approach of detection with the aid of deep learning can be adopted. The only problem being, the side effects of COVID-19 infection imitate those of conventional Pneumonia, which adds some complexity in utilizing the Chest X-rays for its prediction. In this investigation, we attempt to investigate four approaches i.e., Feature Ensemble, Feature Extraction, Layer Modification and weighted Max voting utilizing State of the Art pre-trained models to accurately identify between COVID-19 Pneumonia, Non-COVID-19 Pneumonia, and Healthy Chest X-ray images. Since very few images of patients with COVID-19 are publicly available, we utilized combinations of image processing and data augmentation methods to build more samples to improve the quality of predictions. Our best model i.e., Modified VGG-16, has achieved an accuracy of 99.5216%. More importantly, this model did not predict a False Negative Normal (i.e., infected case predicted as normal), making it the most attractive feature of the study. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid in controlling it effectively. © 2022 Elsevier Inc. All rights reserved.

Correlation of Radiological Findings on High-Resolution Computed Tomography Chest in Vaccinated and Unvaccinated COVID-19 Patients from Eastern India

Background: In this coronavirus disease-2019 (COVID-19) pandemic, safe and effective preventative vaccines are essential to contain the pandemic, which has had severe medical, economic, and societal consequences, despite some people still becoming infected after receiving immunisation. Methods: A total of 200 patients were examined and split into two groups: (1) 100 consecutive COVID-19-positive cases who had been vaccinated and (2) 100 consecutive COVID-19-positive patients with no vaccination. At the time of the scan, the patient's vaccination status was noted. Results: The computed tomography severity score (CTSS) of unvaccinated individuals was found to be considerably greater than that of partly or fully vaccinated patients (median 13 vs. 7, P < 0.001). Completely vaccinated individuals had a considerably lower median CTSS than partly vaccinated patients (6 vs. 9, P < 0.001). Conclusions: Individuals should be thoroughly vaccinated to avoid major lung disease. As a result, stronger dedication and motivating efforts should be made worldwide to improve the COVID-19 vaccination program.

Effective Cyber Security Using IoT to Prevent E-Threats and Hacking During Covid-19

This research work is conducted to make the analysis of digital technology is one of the most admired and effective technologies that has been applied in the global context for faster data management. Starting from business management to connectivity, everywhere the application of IoT and digital technology is undeniable. Besides the advancement of the data management, cyber security is also important to prevent the data stealing or accessing from the unauthorized data. In this context the IoT security technology focusing on the safeguarding the IoT devices connected with internet. Different technologies are taken under the consideration for developing the IoT based cyber security such as Device authentication, Secure on boarding, data encryption and creation of the bootstrap server. All of these technologies are effective to its ground for protecting the digital data. In order to prevent cyber threats and hacking activities like SQL injection, Phishing, and DoS, this research paper has proposed a newer technique of the encryption process by using the python codes and also shown the difference between typical conventional system and proposed system for understanding both the system in a better way. © 2022 by Dr. Santosh Kumar, Dr. Rajeev Yadav, Dr. Priyanka Kaushik, S B G Tilak Babu, Dr. Rajesh Kumar Dubey and Dr. Muthukumar Subramanian.

Fusion of Hand-Crafted and Automatically Generated features for improving the performance of COVID-19 X- ray image classification

In December 2019, the new disease COVID-19 was initially discovered in Wuhan, China and with a fast pace, it took over the whole world. It has impacted everyone's health, as well as the global economy and people's daily lives. It has become crucial that all the positive cases be detected quickly so it is possible to save other lives. Still, the lack of doctors and the lower availability of the test kits made it an arduous task. Recent research shows that radiological imaging techniques have played a valuable role in the detection of COVID-19. The use of artificial intelligence technology with radiological images can help identify the disease very accurately. Even in remote areas, it can be beneficial to overcome the shortage of doctors. This study proposed a method based on the aggregation of the extracted hand-crafted features with the automated ones. We used a his-togram of oriented gradients (HOG) for the hand-crafted features extraction. In addition, several techniques are investigated to get the deep learned features such as "DenseNet201","Inception ResnetV2", "VGG16","VGG19", "Inception_V3", "Resnet50", "MobileNet_V2"and "Xception"out of which "VGG19"gives optimal performance. Furthermore, for dimensionality reduction and to maintain the consistency of features, "principal component analysis (PCA)"is used. Our experiments on COVID-19 image datasets revealed that the proposed method achieves 99% classification accuracy in classifying normal and pneumonia X-ray images. © 2021 IEEE.

Analysis of high-resolution computed tomography chest in interferon gamma release assay negative COVID-19 patients: From a COVID hospital of Odisha, India

Background: Atypical category of COVID-19 could not be differentiated from tuberculosis (TB) in high-resolution computed tomography (HRCT) of the chest because of similar imaging features. This study aims to distinguish between the HRCT features of TB and atypical COVID-19. Methodology: Interferon-gamma release assay (IGRA) was performed in all the 54 COVID-positive patients, showing atypical COVID features that are suspicious of TB on the HRCT chest. Atypical imaging features such as a tree in bud nodules, patchy consolidations, cavitation with surrounding consolidation, discrete nodules, mediastinal lymphadenopathy, and pleural effusion were analyzed in 50 IGRA-negative patients. Results: We found trees in bud nodules (93%) and consolidations (56%) involving predominantly lower lobes, i.e., superior and posterobasal segments. Discrete nodules and cavitation with surrounding consolidation were seen involving predominantly upper lobes (78 and 57% cases, respectively), i.e., apicoposterior and lingular segments of the left upper lobe. The maximum number (67%) of right paratracheal enlarged nodes and bilateral pleural effusions (71%) were found in IGRA-negative COVID-19 patients. Conclusions: It is not always possible to differentiate features of atypical COVID-19 from TB based on HRCT chest alone because of similar appearances and distribution of tree in bud nodules, consolidation, cavitation, and lymphadenopathy in HRCT chest. Since both bilateral and unilateral pleural effusions may be seen in TB, it is impossible to differentiate COVID-19 from TB based on pleural effusion. Therefore, exclusion of TB will need supportive, relevant laboratory investigations (Sputum acid fast bacilli, cartridge-based nucleic acid amplification test, and IGRA) for appropriate diagnosis and management.

Spectrum of barotraumatic events in COVID-19 patients on high-resolution computed tomography

Barotrauma has many different presentations, including pneumothorax, subcutaneous emphysema, pneumoperitoneum, and pneumomediastinum. We have presented and analyzed some interesting cases of barotrauma in this case series. Case 1 in our series developed a thin-walled new cavity due to barotrauma, mimicking pneumatocele and fungal cavity. Case 2 presented with coexistence of pneumothorax and cavity with fungal infestation. Severity of barotrauma due to positive pressure ventilation has been shown in case 3. An interesting case of barotrauma in a 36-week primigravida, post cesarean section, causing dehiscence of scar, presented as case 4 in our series. Early and rapid imaging diagnosis of barotrauma should be pursued. In patients with mechanical ventilation, identifying small changes in imaging characteristics of cavitary lesions, such as fungal, bacterial, or transient cavities, would aid physicians in offering a correct treatment plan.

The rising burden of invasive fungal infections in COVID-19, can structured CT thorax change the game

Background: The occurrence of invasive fungal infections in COVID-19 patients is on surge in countries like India. Several reports related to rhino-nasal-sinus mucormycosis in COVID patients have been published in recent times;however, very less has been reported about invasive pulmonary fungal infections caused mainly by mucor, aspergillus or invasive candida species. We aimed to present 6 sputum culture proved cases of invasive pulmonary fungal infection (four mucormycosis and two invasive candidiasis) in COVID patients, the clues for the diagnosis of fungal invasion as well as difficulties in diagnosing it due to superimposed COVID imaging features. Case presentation: The HRCT imaging features of the all 6 patients showed signs of fungal invasion in the form of cavities formation in the pre-existing reverse halo lesions or development of new irregular margined soft tissue attenuating growth within the pre-existing or in newly formed cavities. Five out of six patients were diabetics. Cavities in cases 1, 2, 3 and 4 of mucormycosis were aggressive and relatively larger and showed relatively faster progression into cavities in comparison with cases 5 and 6 of invasive candidiasis. Conclusion: In poorly managed diabetics or with other immunosuppressed conditions, invasive fungal infection (mucormycosis, invasive aspergillosis and invasive candidiasis) should be considered in the differential diagnosis of cavitary lung lesions.

SARS-CoV-2: Potential Drug Targets and Its Virtual Screening

The well-established structure of SARS-CoV-2 proteins has opened the door for the drug development of the potent inhibitors. The interaction of spike proteins of the virus with human angiotensin converting enzyme (ACE-2) via receptor binding domain start the journey of the virus in the host cell. The entry of corona virus by endocytosis is followed by genomic replication, transcription and formation of the positive sense RNA. The assembling of genomic material with viral structure proteins, endoplasmic reticulum & golgi intermediate forms mature viron, which on exocytosis completes the SARS-CoV-2 life cycle. The entire cycle highlights the importance of different proteins involved in functioning and virulence of the virus. Targeting some of these proteins such as spike proteins, 3C like protease (3CL pro), papaine like protease (PL pro), helicase, RNA dependent RNA polymerase (RdRp) and N protein plays important role in the development of the antiviral drugs. Using computational approach the researchers are investigating important targets interaction with ligands (novel or existing) for the development of potent antiviral drugs. Number of existing FDA approved drug molecules are repurposed against 3CL pro, PL pro, RdRp and few of them, e.g. remdisivir, favipiravir and lvermectin, are currently used in clinical application against SARS-CoV-2. Numbers of small molecules libraries are also high through output screened for the identification of novel ligand molecule for new drug development. In the present chapter various approaches and strategies for the development of antiviral drugs using the computation tools has been highlighted for the main targets of SARS-CoV-2. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Molecular docking of a bioactive compound of C. sinensis n-heptadecanol-1 with opportunistic fungi

With the expansion of immune-impaired people during second wave of Covid-19 the case of mucormycosis and aspergillosis caused by different fungi such as Rhizopus oryzae, Candida albicans and Aspergillus niger arose in different states. The current study reveals the molecular interaction of ligand and protein to find the novel and natural drug. The molecular docking was carried out by CB-dock tool and the ligand compound n-heptadecanol-1 were docked with different protein of opportunistic fungi. The most significant outcomes were depicted against C. albicans (−4.6 kcal/mol) followed by R. oryzae (−3.9 kcal/mol) and A. niger (−3.0 kcal/mol). n-heptadecanol-1 showed therapeutic potential and eliminates the issue of drug inadequacy that could act as potential anti-fungal agent.

COVITALE 2020 from eastern Indian population: imageologists perspective, a learning curve

Background: High-resolution computed tomography (HRCT) chest becomes a valuable diagnostic tool for identifying patients infected with Coronavirus Disease 2019 (COVID-19) in the early stage, where patients may be asymptomatic or with non-specific pulmonary symptoms. An early diagnosis of COVID-19 is of utmost importance, so that patients can be isolated and treated in time, eventually preventing spread of the disease, improving the prognosis and reducing the mortality. In this paper, we have highlighted our radiological experience of dealing with the pandemic crisis of 2020 through the study of HRCT thorax, lung ultrasonography, chest X-rays and artificial intelligence (AI). Results: Results of CT thorax analysis have been given in detail. We had also compared CT severity score (CTSS) with clinical and laboratory parameters. Correlation of CTSS with SpO2 values and comorbidities was also studied. We also analysed manual CTSS with the CTSS scored calculated by the AI software. Conclusions: CTSS and use of COVID-19 Reporting and Data System (CORADS) result in accuracy and uniform percolation of information among the clinicians. Bed-side X-rays and ultrasonography have played a role where the patients could not be shifted for CT scan. The possibility of predicting impending or progression of hypoxia was not possible when SpO2 mapping was correlated with the CTSS. AI was alternatively tried with available software (CT pneumonia analysis) which was not so appropriate considering the imaging patterns in the bulk of atypical category.

Multi-objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection Using Medical Image Data.

Purpose: In early 2020, the world is amid a significant pandemic due to the novel coronavirus disease outbreak, commonly called the COVID-19. Coronavirus is a lung infection disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2). Because of its high transmission rate, it is crucial to detect cases as soon as possible to effectively control the spread of this pandemic and treat patients in the early stages. RT-PCR-based kits are the current standard kits used for COVID-19 diagnosis, but these tests take much time despite their high precision. A faster automated diagnostic tool is required for the effective screening of COVID-19. Methods: In this study, a new semi-supervised feature learning technique is proposed to screen COVID-19 patients using chest CT scans. The model proposed in this study uses a three-step architecture, consisting of a convolutional autoencoder based unsupervised feature extractor, a multi-objective genetic algorithm (MOGA) based feature selector, and a Bagging Ensemble of support vector machines based binary classifier. The proposed architecture has been designed to provide precise and robust diagnostics for binary classification (COVID vs.nonCOVID). A dataset of 1252 COVID-19 CT scan images, collected from 60 patients, has been used to train and evaluate the model. Results: The best performing classifier within 127 ms per image achieved an accuracy of 98.79%, the precision of 98.47%, area under curve of 0.998, and an F1 score of 98.85% on 497 test images. The proposed model outperforms the current state of the art COVID-19 diagnostic techniques in terms of speed and accuracy. Conclusion: The experimental results prove the superiority of the proposed methodology in comparison to existing methods.The study also comprehensively compares various feature selection techniques and highlights the importance of feature selection in medical image data problems.

How frugal innovation shape global sustainable supply chains during the pandemic crisis: lessons from the COVID-19

Purpose: The COVID-19 crisis has created enormous strain in global supply chains. The disruption has caused severe shortages of critical items, including personal protective equipment (e.g. face masks), ventilators and diagnostics. The failure of the industry to meet the sudden demand for these necessary items has caused a severe humanitarian crisis. These situations, resulting from the COVID-19, crisis have led to the informal growth of frugal innovation in sustainable global supply chains. This paper aims to provide a detailed overview of drivers of frugal-oriented sustainable global supply chains, following lessons acquired from emerging countries’ attempts to deal with the COVID-19 pandemic. Design/methodology/approach: The authors used a focused group approach to identify the drivers and this paper further validated them using existing literature published in international peer-reviewed journals and reports. The authors adopted total interpretive structural modeling (TISM) to analyze the complex relationships among identified drivers. Findings: The authors present a theoretical framework to explain how the drivers are interlinked. This paper has developed the framework through a synthesis of the TISM modeling and Matrice d’impacts croisés multiplication appliquée á un classment analysis. This paper observed that government financial support, policies and regulations, under the mediating effect of leadership and the moderating effect of national culture and international rules and regulations, has a significant effect on the adoption of emerging technology, volunteering initiatives and values and ethics. Further, emerging technology, volunteering initiative and values and ethics have a significant effect on supply chain talent and frugal engineering. These results provide some useful theoretical insights that may help in further investigating the role of frugal innovations in other contexts. Originality/value: The authors find that outcomes of the methodical contributions and the resulting managerial insights can be categorized into four levels. Industry and researchers alike can use the study to develop the decision-support systems guiding frugal-oriented sustainable global supply chains amid the COVID-19 pandemic and in recovering them thereafter. Suggestions for future research directions are offered and discussed. © 2021, Emerald Publishing Limited.

Increased tracheostomy rates in head and neck cancer surgery during the COVID-19 pandemic.

Surgical practice during the coronavirus disease 2019 (COVID-19) pandemic has changed significantly, without supporting data. With increasing experience, a dichotomy of practice is emerging, challenging existing consensus guidelines. One such practice is elective tracheostomy. Here, we share our initial experience of head and neck cancer surgery in a COVID-19 tertiary care centre, emphasizing the evolved protocol of perioperative care when compared to pre-COVID-19 times. This was a prospective study of 21 patients with head and neck cancers undergoing surgery during the COVID-19 pandemic, compared to 193 historical controls. Changes in anaesthesia, surgery, and operating room practices were evaluated. A strict protocol was followed. One patient tested positive for COVID-19 preoperatively. There was a significant increase in pre-induction tracheostomies (28.6% vs 6.7%, P=0.005), median hospital stay (10 vs 7 days, P=0.001), and postponements of surgery (57.1% vs 27.5%, P=0.01), along with a significant decrease in flap reconstructions (33.3% vs 59.6%, P=0.03). There was no mortality and no difference in postoperative morbidity. No healthcare personnel became symptomatic for COVID-19 during this period. Tracheostomy is safe during the COVID-19 pandemic and rates have increased. Despite increased rescheduling of surgeries and longer hospital stays, definitive cancer care surgery has not been deferred and maximum patient and healthcare worker safety has been ensured.