Prediction of Unemployment using Machine Learning Approach

Unemployment is a circumstance which arises when people above a specific age are not engaged in any kind of activities which contribute to the economic welfare of the individual and country. Unemployment is becoming a rising concern which is making the daily life of people difficult. Unemployment causes poverty and depression among the citizens. Nowadays there are different opportunities in different sectors. But people are not aware of those opportunities. Different states are there where there is a lack of skilled labour whereas many states are there that have skilled labour but less opportunities. Another reason for unemployment since 2020 is the COVID-19 pandemic. We have selected this topic to spread awareness among the citizens. This work attempts to detect the states of India which are in serious need of increasing employment opportunities. We have applied the concept of Supervised Machine Learning algorithms to detect the states with the lowest employment rate. The data visualization gives a better picture of the trends in unemployment rate over years. There has been a use of different popular algorithms like Logistic Regression, Support Vector Machine, K-nearest neighbors (kNN) Algorithm and Decision Tree. In the end we have tried to find the algorithm which is going to give us more accuracy so that necessary steps can be taken for the employment of the eligible and deserving people. © 2022 IEEE.

A rare case of abdominal cocoon associated with internal hernia in an adult.

A transmesenteric internal hernia (TIH) is a protrusion of a viscus through the mesenteric defect. It is secondary to previous gastrointestinal surgery in an adult. Early diagnosis and management are warranted to prevent the strangulation of the bowel in a TIH. Here, we are reporting a case of a 24-year-old gentleman with COVID-positive status who has presented with cough, abdominal cocoon, and features of subacute intestinal obstruction (SAIO) without any previous history of abdominal surgery. A nonoperative trial is given in the management of abdominal cocoon with SAIO. In contrast, delay in surgical intervention in TIH leads to bowel gangrene. Surprisingly even on contrast-enhanced computed tomography of the abdomen, TIH was not picked up. We have diagnosed this case intraoperatively with gangrene of the bowel. In an abdominal cocoon without any history suggestive of tuberculosis or previous surgery, or any other condition that leads to an intra-abdominal reaction, an internal hernia should be kept as a differential diagnosis. The delay in diagnosis and surgical intervention is associated with potentially disastrous complications.

A rare case of concomitant inflammatory polyneuropathy and type 1 diabetes with COVID 19 in a teenager

Introduction: Reduced consciousness during diabetic ketoacidosis (DKA) is an acute medical emergency which necessitates rapid escalation of care. Rarely it can point towards unusual comorbidities too. We report a case of new- onset type 1 diabetes (T1D) presenting with severe DKA in a teenager, complicated by an evolving neuropathy. Case Report: A previously healthy 17-year-old girl presented in severe DKA following a 4-day history of vomiting, lethargy and recent history of polydipsia. She recently returned from a holiday when all family members had COVID (initial history was taken from family via telemedicine). She also intentionally lost weight by physical exercise during covid lockdown. On presentation she needed intubation and ventilation in intensive care unit. Due to profound encephalopathy she underwent CT and MRI head which were normal. Despite resolution of DKA with standard treatment protocol, she had fluctuating level of consciousness and increasing oxygen requirements. She was started on antibiotics and antiviral for chest consolidation and presumed viral meningitis respectively. She was also treated with antifungal medication for positive candida on blood culture. Due to confusion, evolving bulbar palsy with aspiration pneumoniae and seizure activity further head CT and MRIs were done which were normal. Subsequently an EEG showed features of encephalopathy but no epileptiform waves. She eventually underwent nerve conduction studies and electromyography which was suggestive of widespread demyelinating radiculoneuropathy and the cerebrospinal fluid protein was high on lumbar puncture, both consistent with the clinical diagnosis of Guillain- Barre syndrome (GBS). She was commenced on a course of plasma exchanges which improved her neurological status. Due to persistent neuropathic pain, she received extensive input from neuropsychology, speech and language, physiotherapy and occupational therapy teams. Before discharge she was started on continuous glucose monitoring in view of impaired hypoglycaemia awareness. Her GAD antibodies were positive, but her extensive autoimmune polyneuropathy screen was negative. Conclusion(s): The case shows a rare combination of GBS, T1D with DKA and COVID-19 infection in our patient. It also highlights the challenges associated with neurological manifestations during DKA. A short prodrome and severe DKA may have been precipitated by the concomitant COVID-19 infection. Persistent neurological symptoms presented a conundrum and eventually led to the diagnosis of GBS in a newly diagnosed T1D patient. It's very plausible that COVID-19 infection played part in GBS too. This underpins the importance of thinking broadly on differential diagnoses when a patient presents in severe DKA and evolving abnormal neurology.

Comparison of Strain and Conventional Echocardiography in Predicting Icu Needs in Mis-C

INTRODUCTION: Multisystem Inflammatory Syndrome in Children (MIS-C) after SARS-CoV-2 infection is often complicated by ventricular dysfunction. Our prior work suggests that strain echocardiography (SE) detects abnormalities in cardiac function in ICU patients unrecognized by conventional echocardiography (CE). This study compares the relative strengths of CE and SE in predicting needs for ICU level therapies in MIS-C patients. METHOD(S): A retrospective, single center analysis was performed on patients admitted to a quaternary-care children's hospital with the diagnosis of MIS-C from March 2020 to January 2022. Only patients with CE and SE measured prior to initiation of vasoactives were included. Abnormal LVEF was defined as < 55% and abnormal strain as <= -17.2%. Clinical metrics included use of positive pressure ventilation (PPV), vasoactives, or any ICU level care. RESULT(S): Of 90 patients diagnosed with MIS-C, median LVEF was normal in both normal (64.0%) and abnormal strain (57.2%) groups. We found 97.6% of patients with normal strain had normal LVEF but only 57.1% of those with normal LVEF had normal strain. Median LVEF was normal in both those who did and did not require ICU level care (63% vs 57.1%), and those who did and did not require vasoactives (62.4% vs 56.5%). Strain was abnormal in patients who required ICU level care, PPV, and vasoactives (-13.8%, -12.9%, -13.8% respectively), compared to those who did not (all -19.3%, p all < 0.001). Sensitivity analysis showed strain was superior to LVEF in identifying need for ICU level care (75.8% vs 39%), PPV (88% vs 48%), and vasoactives (90% vs 55%). Negative predictive value of strain was superior to LVEF for similar outcomes (ICU level care (80.9% vs 72%), PPV (92.7% vs 81.9%) and vasoactives (95.1% vs 87.2%)). CONCLUSION(S): We demonstrate that SE detects abnormalities in cardiac function in MIS-C patients unrecognized by CE. In both ICU and non-ICU patients, we found compared to LVEF, detection of abnormal strain better identifies patients who will need ICU level therapies. Similarly, a measurement of normal strain is superior to a normal LVEF in predicting those who will not need these interventions.

Brain phaeohyphomycosis secondary to immunosuppressant therapy due to Rhinocladiella mackenziei

Two cases of Rhinocladiella mackenziei have been noted in our institute, the first case in 2015 (post-renal transplant) and the second case in 2021 (post-COVID infection). Both the patients had received immunosuppressants for varying dura-tion. Both the cases presented to the hospital with neurological deficit secondary to brain abscess. On initial assessment, the melanized fungus was noted which was later identified as Rhinocladiella on culture and further confirmed with molecular meth-ods. Both the cases were treated with injection of L AmB, voriconazole and 5FC for a prolonged duration and later discharged when the condition improved. The renal transplant patient was advised lifelong voriconazole since he would continue to be on immunosuppressants. To our knowledge, the second patient diagnosed post-COVID could be the first case report of invasive dematiaceous fungal infection in an apparently immunocompetent individual. Both cases also highlight the challenges in man-agement such as designing an appropriate regimen, deciding the optimum duration of antifungal therapy, and managing the toxicities associated with long-term antifungal use. R. mackenziei is a frequently fatal melanized neurotropic fungus known to carry almost 100% mortality despite the combination of antifungal agents and surgery. Central nervous system infections due to R. mackenziei have been exclusively reported from the Middle East, except for cases recently reported from India.

Design and Implementation of IoT-Based System for Tracking and Monitoring of Suspected COVID-19 Patient

Coronavirus is the most challenging threat today in our society. This virus has caused a huge number of deaths worldwide. Experts are still searching for an effective and long-term vaccine. In the present scenario, people are relying on wearing mask, self-quarantine, social-distancing and sanitizing hands for defending this infectious coronavirus. The health-care units, academicians, researchers and governments as well as business houses are trying their level best to prevent the spread of this disease. In this paper, design and implementation of an Internet of Things (IoT)-based smart and cost-effective system for tracking and monitoring of COVID-19 suspected patients have been introduced. Proposed system has significant social impact as it can be used to reduce the chances of community transmission of this deadly virus. Proposed reusable cost-effective system can help the hospital and local authority to monitor and track the movement of quarantined people. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Recent Developments Focusing Disinfectant Systems for Effective Inactivation of Coronaviruses

The enduring Covid-19 pandemic has turned out to be extremely damaging, creating a severe health emergency around the globe. The exponential rise in confirmed cases raises alarm over the rapid nature of the spread of the virus, and is indicative of the need for mandatory safety measures to be implemented in order to keep rising infections at bay. The situation necessitates following all possible recommended measures such as social distancing, wearing face masks, regular hand hygiene, and respiratory practices. In addition to these measures the use of disinfectants for the inactivation of public, exposed, and high-risk areas and surfaces, along with day-to-day sanitizing practices, can go a long way to repel the surge of the virus. Though there are a variety of disinfectants available for inanimate and animate surfaces, their appropriate use and dosage is crucial for maximum efficacy and for avoiding any related human health hazards. This chapter focusses on the various physical and chemical disinfectants available that are now being employed for inactivation of coronaviruses. The optimum dosages and modes of operation, along with their efficacies, have been elaborated. This chapter is expected to provide a better understanding of the disinfectant technologies available, with suitable dosages for their appropriate use, in the employment of the long fight against coronaviruses for the foreseeable future. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Perspective of Organic-Based Antimicrobial Coating Materials: Implication Toward COVID-19

In the current scenario emerging COVID-19 pandemic has caused severe global impact over the health of the human. It was started from China at the end of November or beginning of December 2019. It is noted that the existence/survival of novel coronavirus in harsh environmental condition for 72 h virus which cause rapid spread from one person to another. To stop the communal spread of this novel coronavirus various safety measures have been taken globally including hand sanitizers, social distancing, quarantine, and the most important thing is mask. Among the commendable safety measures mask is a key technology is moderately mature, while most of them cannot fight against the effect of viral infection. The non-woven material plays an important role for the manufacture of mask which offer barrier in flow of virus infiltration. If the causes filtration and the antiviral effects can be instantaneously unified into the mask, then it will be more efficient. In this chapter, we discussed about the advanced organic material used for antiviral coating over the safety measures like mask gloves and personnel protective equipment (PPE) kit including the causes, spread, and challenges to develop materials in cost-effective manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Crisis

Currently, the whole world is under a serious threat to public health by the novel coronavirus (COVID-19), which is believed to begin in Wuhan, China, in December 2019. As of 11 May 2020 more than 4 million cases of COVID‐19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), have been confirmed. SARS‐CoV‐2 is a highly pathogenic and transmissible coronavirus that primarily spreads through respiratory droplets and close contact. A growing body of clinical data suggests that a cytokine storm is associated with COVID‐19 severity and is also a crucial cause of death from COVID‐19. It becomes more disastrous when the viral epidemiology has been varied making the virulence trajectories of nascent coronaviruses difficult to envisage. More uncertainties of the disease will be revealed, and more response actions will be implemented. It is currently paid increasing attention to this crisis over the study period, and substantial efforts are being made to identify insufficiency, incongruency and supply chain issues all across the globe. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining

Rich temporal information and variations in viewpoints make video data an attractive choice for learning image representations using unsupervised contrastive learning (UCL) techniques. State-of-the-art (SOTA) contrastive learning techniques consider frames within a video as positives in the embedding space, whereas the frames from other videos are considered negatives. We observe that unlike multiple views of an object in natural scene videos, an Ultrasound (US) video captures different 2D slices of an organ. Hence, there is almost no similarity between the temporally distant frames of even the same US video. In this paper we propose to instead utilize such frames as hard negatives. We advocate mining both intra-video and cross-video negatives in a hardness-sensitive negative mining curriculum in a UCL framework to learn rich image representations. We deploy our framework to learn the representations of Gallbladder (GB) malignancy from US videos. We also construct the first large-scale US video dataset containing 64 videos and 15,800 frames for learning GB representations. We show that the standard ResNet50 backbone trained with our framework improves the accuracy of models pretrained with SOTA UCL techniques as well as supervised pretrained models on ImageNet for the GB malignancy detection task by 2–6%. We further validate the generalizability of our method on a publicly available lung US image dataset of COVID-19 pathologies and show an improvement of 1.5% compared to SOTA. Source code, dataset, and models are available at https://gbc-iitd.github.io/usucl. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Epidemic and Infectious Disease Surveillance Rise of the Security-Military Framework

The COVID-19 pandemic has seen some Asian countries employ sophisticated mass-surveillance technologies- normally employed to gather intelligence for domestic security purposes-to contain the spread of infection in their populations. There has also been an intrusion of military and allied national security actors into the traditionally civilian domain of public health, in the form of disease surveillance. These emerging developments in the pandemic response provide a pretext for a limited historical review, beginning from World War II to the present, centred on the intersection between infectious disease surveillance and control, national security, and military in the Western world. © 2022 Economic and Political Weekly. All rights reserved.

Classifying COVID-19 Variants Based on Genetic Sequences Using Deep Learning Models

The COrona VIrus Disease (COVID-19) pandemic led to the occurrence of several variants with time. This has led to an increased importance of understanding sequence data related to COVID-19. In this chapter, we propose an alignment-free k-mer based LSTM (Long Short-Term Memory) deep learning model that can classify 20 different variants of COVID-19. We handle the class imbalance problem by sampling a fixed number of sequences for each class label. We handle the vanishing gradient problem in LSTMs arising from long sequences by dividing the sequence into fixed lengths and obtaining results on individual runs. Our results show that one-vs-all classifiers have test accuracies as high as 92.5% with tuned hyperparameters compared to the multi-class classifier model. Our experiments show higher overall accuracies for B.1.1.214, B.1.177.21, B.1.1.7, B.1.526, and P.1 on the one-vs-all classifiers, suggesting the presence of distinct mutations in these variants. Our results show that embedding vector size and batch sizes have insignificant improvement in accuracies, but changing from 2-mers to 3-mers mostly improves accuracies. We also studied individual runs which show that most accuracies improved after the 20th run, indicating that these sequence positions may have more contributions to distinguishing among different COVID-19 variants. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Penetration and aerosolization of cough droplet spray through face masks: A unique pathway of transmission of infection

The advent of the COVID-19 pandemic has necessitated the use of face masks, making them an integral part of the daily routine. Face masks occlude the infectious droplets during any respiratory event contributing to source control. In the current study, spray impingement experiments were conducted on porous surfaces like masks having a different porosity, pore size, and thickness. The spray mimics actual cough or a mild sneeze with respect to the droplet size distribution (20-500 mu m) and velocity scale (0-14 m / s), which makes the experimental findings physiologically realistic. The penetration dynamics through the mask showed that droplets of all sizes beyond a critical velocity penetrate through the mask fabric and atomize into daughter droplets in the aerosolization range, leading to harmful effects due to the extended airborne lifetime of aerosols. By incorporating spray characteristics along with surface tension and viscous dissipation of the fluid passing through the mask, multi-step penetration criteria have been formulated. The daughter droplet size and velocity distribution after atomizing through multi-layered masks and its effects have been discussed. Moreover, the virus-emulating particle-laden surrogate respiratory droplets are used in impingement experiments to study the filtration and entrapment of virus-like nanoparticles in the mask. Furthermore, the efficacy of the mask from the perspective of a susceptible person has been investigated.& nbsp;Published under an exclusive license by AIP Publishing

Inferring COVID-19 Biological Pathways from Clinical Phenotypes Via Topological Analysis

COVID-19 has caused thousands of deaths around the world and also resulted in a large international economic disruption. Identifying the pathways associated with this illness can help medical researchers to better understand the properties of the condition. This process can be carried out by analyzing the medical records. It is crucial to develop tools and models that can aid researchers with this process in a timely manner. However, medical records are often unstructured clinical notes, and this poses significant challenges to developing the automated systems. In this article, we propose a pipeline to aid practitioners in analyzing clinical notes and revealing the pathways associated with this disease. Our pipeline relies on topological properties and consists of three phases: (1) pre-processing the clinical notes to extract the salient concepts, (2) constructing a feature space of the patients to characterize the extracted concepts, and finally, (3) leveraging the topological properties to distill the available knowledge and visualize the result. Our experiments on a publicly available dataset of COVID-19 clinical notes testify that our pipeline can indeed extract meaningful pathways. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Tracking 2020 Decreases in Carbon Dioxide Due to the COVID19 Pandemic in NASA’s GEOS Modeling system: Implications for Space-Based Carbon Monitoring

The COVID19 pandemic led to abrupt, worldwide changes in human activity and related emissions of air pollutants and greenhouse gases that are unprecedented in modern times. NASA satellites have demonstrated their ability to observe some of these impacts, particularly in relation to short-lived gases like nitrogen dioxide (NO2), which is emitted primarily from transportation. Bottom-up analyses of carbon dioxide (CO2) emissions suggest that the growth of atmospheric CO2 has also slowed, but differences are much more subtle than for NO2 because of the lifetime of atmospheric CO2 and sectoral differences in emission reductions. Estimates of CO2 from NASA’s Orbiting Carbon Observatory 2 (OCO-2) provide a unique view of COVID19 impacts, but observed changes in the column-average CO2 can be difficult to interpret because of gaps in spatial coverage. Assimilating these data into the Goddard Earth Observing System (GEOS), an integrated Earth system model with an advanced Constituent Data Assimilation System (CoDAS), helped to reveal changes in CO2 that are consistent with separate, bottom-up analyses of emissions reductions. Both indicate that from February-April of 2020, the growth in CO2 over Europe, North America, and Asia was roughly 0.3 ppm less than during the previous four years. Anomalies derived from gap-filled GEOS OCO-2 CoDAS products contribute to a joint effort by the world’s space agencies to track COVID19 impacts on the Earth. However, attribution of these changes is complicated by interannual variability in atmospheric circulation and the influence of climate on ocean and land carbon sinks. We discuss these results from the perspective of space-based carbon monitoring, which has received considerable support over the past decade from NASA. Our results demonstrate the accomplishments of current sensors and data assimilation systems, but also highlight challenges in providing high quality, low latency information to the public. In particular, understanding and attributing CO2 changes during 2020 requires year-specific information about land and ocean fluxes, which is often delayed for months or even years. We discuss current limitations and potential solutions to address these lags, which would support more reliable and timely space-based carbon monitoring.

COVID19 Anomalies Using the GEOS-OCO L3 Dataset

No abstract available.

Extension of Search Facilities Provided by ‘CoWIN’ Using Google’s Geocoding API and APIs of ‘CoWIN’ and ‘openweathermap.org’

Pandemic COVID-19 creates devastating impacts on healthcare, education, economy, and society. Herd immunity against COVID-19 would permit society to return to normal. To achieve herd immunity against COVID-19 harmlessly, a significant portion of a population would need to be vaccinated. India is the second most populated country globally, already administered the second-highest number of doses to its citizen successfully. The Government of India’s popular web portal/app ‘CoWIN’ is used to register and monitor vaccinations. It helps Indians to find the nearest available vaccination slots. In the present work, an Android application is developed to enhance the search facility provided by ‘CoWIN’ to make it more convenient. It helps the users to find all available vaccination slots within a 15 km radius in one search. This application also provides a weather forecast for the searched location and search date. Users are also able to know how much fees are charged by a center during search time. This application has tried to make the ‘CoWIN’s search by map facility more informative. It is expected that this application will improve the user’s vaccination slots search experiences and make the searchings more convenient. © 2022, Springer Nature Switzerland AG.

A Review on Effect of Social Marketing during COVID-19 in India

In the view of Philip Kotler, Nancy Lee, and Michael Rothschild (2006) "Social Marketing is a process that applies marketing principles and techniques to create, communicate, and deliver value in order to influence target audience behaviours that benefit society as well as the intended audience. Social Marketing does not work to exploit turnover or sales;rather the aim of Social Marketing campaigns is to change the activities in the society that will support the overall public of a country to use only competent lighting to preserve the energy or persuading more individuals to use seat belts. Our study is based on only India and this chapter explored how Social Marketing can bear a significant impact during COVID -19 pandemic and with the help of various reviews we will catch out the positive and negative effects of Social Marketing in COVID - 19 in India. Methods: A systematic review conducted on the recent COVID situation for the last one-year (i.e. published papers along the last one year) in Scopus, Web of Science, Science Direct and Sage. For this, we have searched digital marketing, restricted to the title, "Interest" and "Market", COVID lockdown. Results: Since the COVID has broken down for the past one year only and there are very few papers are published based on the very contemporary title considered for the article hence this study identified45 articles in the scientific literature, but only 17 articles were classified as eligible according to the previously established criteria. Discussion: This methodical review highlight the positively association between COVID on Social Marketing in digital era.