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The Personalized Job Recommender System is a subset of the custom recommendation system that provides a solution to the problem of information overload and is widely applied in numerous domains to solve a plethora of problems, such as unemployment and employment churn that we have seen emerging at higher rates in the COVID era. Furthermore, different jobs require divergent skill sets from their candidates to get hired. In this paper, we analyze the similarity techniques for Job Recommendation Systems based on the research done in the field of Job Recommendations. In our implementation, we have used three similarity measures: Tanimoto, Cosine (Orchini), and City Block similarity metrics. These techniques have been tested on a new Job Recommendation Systems Dataset taken from Kaggle. We have also analyzed the performance of similar techniques involving other distance measures, such as Euclidean distance. The performance of these similarity score-based techniques for generating the highest score-based recommendations is assessed using different evaluation metrics such as Accuracy, Precision, Recall, and F1-score respectively. © 2023 IEEE.
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Online disinformation actors are those individuals or bots who disseminate false or misleading information over social media, with the intent to sway public opinion in the information domain towards harmful social outcomes. Quantification of the degree to which users post or respond intentionally versus under social influence, remains a challenge, as individuals or organizations operating the profile are foreshadowed by their online persona. However, social influence has been shown to be measurable in the paradigm of information theory. In this paper, we introduce an information theoretic measure to quantify social media user intent, and then investigate the corroboration of intent with evolution of the social network and detection of disinformation actors related to COVID-19 discussions on Twitter. Our measurement of user intent utilizes an existing time series analysis technique for estimation of social influence using transfer entropy among the considered users. We have analyzed 4.7 million tweets originating from several countries of interest, during a 5 month period when the arrival of the first dose of COVID vaccinations were announced. Our key findings include evidence that: (i) a significant correspondence between intent and social influence;(ii) ranking over users by intent and social influence is unstable over time with evidence of shifts in the hierarchical structure;and (iii) both user intent and social influence are important when distinguishing disinformation actors from non-disinformation actors. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Introduction: Hand dermatitis causes significant physical, psychosocial, and economic burden. The internet is a major source of health education for patients. Here, we evaluate the readability, quality, and comprehensiveness of online health resources on hand dermatitis. Methods: On July 27th 2021, a Google search was conducted with terms “hand dermatitis” and “hand eczema” and the first 40 items were evaluated. Articles that were advertisements, blogs, intended for professionals, scientific papers, or irrelevant were excluded. Contents of articles were evaluated using several validated grading tools/criteria for readability and quality and Pearson’s correlation assessed the relationship between readability and quality. Results: Twenty-three articles met inclusion criteria. Average readability was at the 11th-grade level (range 7.7-15.6). University-level reading comprehension (≥13th grade) was required for 5/23 websites. The highest quality website based on the Discern instrument was Medical News Today (55.5);nearly half of the websites (48%, 11/23) rated as poor or very poor. The average JAMA benchmark score was only 1.4/4. Nineteen websites contained images (83%) and only 4 websites (21%) included images representing hand dermatitis in skin of color (SOC). Quality and readability of the articles were significantly correlated (P =.02). Conclusion: Our results demonstrate that generally, articles were too difficult to read, have low quality, and lack representation of SOC images. With increases in hand dermatitis in the setting of frequent hand-hygiene practices during the COVID-19 pandemic, it is important for online health information to improve in readability, quality, and inclusion of SOC images to optimize online patient education.
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With still ongoing COVID pandemic, there is an immediate need for a deeper understanding of how Twitter discussions (or chatters) in disinformation spreading communities get triggered. More specifically, the value is in monitoring how such trigger events in Twitter discussion do align with the timelines of relevant influencing events in the society (indicated in this work as campaign events). For campaign events in regards to COVID pandemic, we consider both NPI (Nonpharmaceutical Interventions) campaigns and disinformation spreading campaigns together. In this short paper we have presented a novel methodology to quantify, compare and relate two Twitter disinformation communities, in terms of their reaction patterns to the timelines of major campaign events. We have also analyzed these campaigns at their three geospatial granularity contexts: local county, state, and country/ federal. We have conducted a novel dataset collection on campaigns (NPI + Disinformation) at these different geospatial granularities. Then, with collected dataset on Twitter disinformation communities, we have performed a case study to validate our proposed methodology. © 2022 Public Domain.
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India lacks a formal social security system. However, there is scope to develop a few systems, empowering some desired sections financially. In the recent past, post the onset of COVID19, a number of Indians have chosen to adopt a ‘Start Up’ mechanism using proprietary trade secrets. These mechanisms are not new, have existed informally and have been carried forward through generations. This paper is an attempt to lay the blueprint of a social security system which can be adopted by the elderly urban middle class of India, giving them a more profitable, safe and lucrative money generation means, through their real estate assets. The rental laws of India are fluid, allowing the middle-class real estate owners to fall prey to unknown and unexpected complications, creating legal turmoil. The call of the hour is to restructure rental arrangements, keeping the safety of the owner as priority without neglecting the preferred requirements of the tenants. A probable symbiotic arrangement, which by all means can provide professionals, a stay and vacation option. This dwelling space would allow them to work and holiday from anywhere, away from their designated office space. These stay and vacation options can also create the scope of living in larger spaces for professionals with pocket suiting investment. The profiteering objective of Rental Management of Real Estate renting process can be redefined easily by the creation of a ‘Renting Appraisal App’, making it convenient and transparent for the property owner and the rent seeker. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Power systems are designed to be operated under expected weather conditions. Unexpected weather conditions sometimes create widespread damage to the power system. The failure rate of equipment in power system along with redundancy and accuracy of forecasting decide the scope of damage. Eastern coast of Indian sub-continent experiences cyclones of varying intensity every year. These cyclones have severe impact on the infrastructure including the power infrastructure. The proactive operation strategy to counter each stage of uncertainty helps not only in managing the power system but also in early restoration. Amid the NCOVID-19 pandemic, Indian power system witnessed super cyclone named 'AMPHAN' which originated in the Bay of Bengal. The cyclone, after landfall passed through densely populated regions affecting the load centers. Cyclone of matching severity was last witnessed by India in the year 1999. The landfall of cyclone started during afternoon hours of 20th May 2020. The proactive action strategy based on past experience resulted in minimization of loss to electrical system and power supply outage. This paper presents the proactive measures taken by POSOCO and power utilities across all key sectors, viz., generation, transmission, distribution, during different phases of cyclone trajectory and the impact of cyclone on Indian power system. © 2021 IEEE.
Subject(s)
Angiomatosis , Exanthema , Skin Diseases, Vascular , Vaccines , Adult , ChAdOx1 nCoV-19 , Humans , ImmunizationABSTRACT
BACKGROUND: The first months of the SARS-CoV-2 epidemic in Spain resulted in high incidence and mortality. A national sero-epidemiological survey suggests higher cumulative incidence of infection in older individuals than in younger individuals. However, little is known about the epidemic dynamics in different age groups, including the relative effect of the lockdown measures introduced on March 15, and strengthened on March 30 to April 14, 2020 when only essential workers continued to work. METHODS: We used data from the National Epidemiological Surveillance Network (RENAVE in Spanish) on the daily number of reported COVID-19 cases (by date of symptom onset) in eleven 5-year age groups: 15-19y through 65-69y. For each age group g, we computed the proportion E(g) of individuals in age group g among all reported cases aged 15-69y during the pre-lockdown period (March 1-10, 2020) and the corresponding proportion L(g) during two lockdown periods (March 25-April 3 and April 8-17, 2020). For each lockdown period, we computed the proportion ratios PR(g)= L(g)/E(g). For each pair of age groups g1,g2, PR(g1)>PR(g2) implies a relative increase in the incidence of detected SARS-CoV-2 infection in the age group g1 compared with g2 for the later vs. early period. RESULTS: For the first lockdown period, the highest PR values were in age groups 50-54y (PR=1.21;95% CI: 1.12,1.30) and 55-59y (PR=1.19;1.11,1.27). For the second lockdown period, the highest PR values were in age groups 15-19y (PR=1.26;0.95,1.68) and 50-54y (PR=1.20;1.09,1.31). CONCLUSIONS: Our results suggest that different outbreak control measures led to different changes in the relative incidence by age group. During the first lockdown period, when non-essential work was allowed, individuals aged 40-64y, particularly those aged 50-59y presented with higher COVID-19 relative incidence compared to pre-lockdown period, while younger adults/older adolescents (together with persons aged 50-59y) had increased relative incidence during the later, strengthened lockdown. The role of different age groups during the epidemic should be considered when implementing future mitigation efforts.
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The NCOVID-19 pandemic has been an unforeseen calamity which affected almost all the countries in the world. On March 11, 2020, World Health Organization (WHO) declared NCOVID-19 as a pandemic. The Government of India (GOI) took several actions to control the spread of the pandemic in the country and issued several guidelines for public and organizations. The adversity caused by pandemic required continuity of electric supply to consumers. The latter was achieved through proper planning and execution. The Indian Power System Operator, POSOCO through its National and Regional Load Dispatch Centers (NLDC and RLDCs) assessed the situation early and took all planned efforts for the Indian power system operation which is essential for keeping 'lights ON' during these hours of crisis. Strategic team at the top management level and tactical teams at the individual control center level were formed to handle any unforeseen circumstances. This paper has discussed the various actions taken by POSOCO during NCOVID-19 scenario and impact of it on Indian power system. Impact of exceptional events as faced during pandemic period, like, Janata Curfew, Lights-Off event, Super Cyclonic Storms 'Amphan', 'Nisarga', and Solar eclipse is also discussed in the paper. The insights gained during these events may enhance the capability to envisage and handle such multiple high impact low probability events in the future. © 2021 IEEE.
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The rapid spread of the coronavirus disease 2019 (COVID-19) epidemic poses a threat to human civilization. This infectious outbreak induced a global menace, resulting in day-to-day community and social services standstill. Countries like China and Italy are positioned at an alarming stage of this pandemic, and India is also testifying a rapid outbreak of the COVID-19.This unprecedented scenario warrants the formulation of a robust mechanism to estimate the misfortunes of this pandemic in these three countries to assist governments in countermeasuring the COVID-19 catastrophe. In the light of fast varying fatality data rendered by the World Health Organization (WHO), a spectrum of case-based fatality assessments for the COVID-19 is presented that differs considerably in measurements. This publication elucidates the scope of the curve-fitting methods in terms of the goodness-of-fit statistics and support vector machine-based regression (SVR) in estimating the misfortunes of COVID-19 in China, Italy, and India in a given time frame. Consequently, we achieved a reasonably small root mean squared error (RMSE) for the SVR method in predicting the adversities induced by this global pandemic in China and India. In contrast, conventional regression offers a better estimate to sketch the outbreak pattern in Italy. © 2022, Springer Nature Switzerland AG.
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Recently the COVID-19 pandemic outbreak is inflicting devastation on human civilization. This infectious virus spreads like wildfire, already affected millions worldwide, and the numbers are still increasing. This situation warrants a comprehensive strategy backed by futuristic estimations to counter COVID-19 adversities. Like any other country globally, India is also encountering an uphill task to fight against this unfortunate pandemic, with six million-plus COVID-19 cumulative infected cases by the first week of October 2020. This publication elucidates the use of four state-of-art models, namely the Abbasov - Mamedova (AM) Fuzzy, proposed Multilayer Perceptron (MLP), Auto-ARIMA, and Auto-MLP, to forecast the number of cumulative infected COVID-19 cases in India. These models exhibited high forecast accuracy for 30 days ahead scenario with MAPE ranges from 0.44 to 1.83% in the test condition, whereas a MAPE range of 1.09 to 2.39% in real-time. We estimated the COVID-19 cases fortnightly and observed that the proposed MLP exhibited the flattening of the COVID-19 curve, whereas other models exhibited a rising trend. Though our proposed MLP outperformed other models, we employed all four methods and estimated a range between 8.53 to 13.77 million COVID-19 positives by 4th January 2021 in India. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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INTRODUCTION: Social vulnerability is a known determinant of health in respiratory diseases. Our aim was to identify whether there are socio-demographic factors among COVID-19 patients hospitalized in Spain and their potential impact on health outcomes during the hospitalization. METHODS: A multicentric retrospective case series study based on administrative databases that included all COVID-19 cases admitted in 19 Spanish hospitals from 1 March to 15 April 2020. Socio-demographic data were collected. Outcomes were critical care admission and in-hospital mortality. RESULTS: We included 10,110 COVID-19 patients admitted to 18 Spanish hospitals (median age 68 (IQR 54-80) years old; 44.5% female; 14.8% were not born in Spain). Among these, 779 (7.7%) cases were admitted to critical care units and 1678 (16.6%) patients died during the hospitalization. Age, male gender, being immigrant, and low hospital saturation were independently associated with being admitted to an intensive care unit. Age, male gender, being immigrant, percentile of average per capita income, and hospital experience were independently associated with in-hospital mortality. CONCLUSIONS: Social determinants such as residence in low-income areas and being born in Latin American countries were associated with increased odds of being admitted to an intensive care unit and of in-hospital mortality. There was considerable variation in outcomes between different Spanish centers.
Subject(s)
COVID-19 , Aged , Aged, 80 and over , Female , Hospital Mortality , Hospitalization , Humans , Intensive Care Units , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2 , Social VulnerabilityABSTRACT
The novel coronavirus (nCoV-2019) was first apparent in Wuhan city in China, which impacted the world and its peoples. This epidemic severely influenced the global equilibrium of humankind, including the USA, where the number of affected cases reached more than 4,323,160 by the end of July 2020. Therefore, the COVID-2019 outbreak scenario warrants a sound forecasting model to accurately predict the catastrophe in human lives that resulted from this pandemic. In this study, the Fuzzy Time Series (FTS) forecasting model for COVID-19 employed to analyze and predict the number of cumulative infected cases of the USA by employing the Abbasov and Mamedova model. Our experiment used 145 days of infected cases of the USA rendered from the World Health Organization (WHO). The optimized model achieved through tuning three hyper parameters of the Abbasov and Mamedova model. To estimate the model performance, we evaluated the forecast accuracy through the lenses of Mean Absolute Percentage Error (MAPE) and Theil U statistics, followed by a comparison between the forecasted with actual observations. We observed that the recommended FTS model’s forecasting is reliable and acceptable up to 35 days ahead of forecasting. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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World over, COVID-19 pandemic has affected the behavior and livelihoods of the people. The impact is reflected in the power system operations and electricity markets. This paper examines the impact of COVID-19 on different Indian electricity market segments. The utilities on both demand side and supply side responded to situation with depressed volumes and prices. The paper also describes the measures taken by the system operator to cope up with the situation. The paper traces the resilience exhibited by Indian power system stakeholders in handling exceptional events during lockdown such as cyclones, eclipse and lights switch-off event. The implementation of initiatives such as Real Time Market and expansion of Security Constrained Economic Despatch pilot in this challenging environment is discussed briefly. The paper concludes with possible course of action contemplated for the future of Indian electricity market post COVID-19. © 2020 IEEE