Bill Gates and the 'new normal' COVID-19 conspiracy theories: 'it's a new thing' or nothing new under the sun?

The outbreak of SARS-CoV-2 in late 2019 and the subsequent lockdowns led to widespread conspiracy theories often involving one particular actor: Bill Gates. Adherents of these conspiracy theories believed Gates was behind the pandemic for some nefarious purpose, including chipping and/or eugenics. This was, however, no fringe sentiment: celebrities and other prominent voices articulated some iteration of the Gates-COVID-19 conspiracy theory beginning in 2020. Though the conspiracy theory appears to have come out of nowhere, it does have a (pre-)history. Some have tried to point to a single or recent origin, but it is in fact much older, more complex, and informed by real developments over the previous two decades. This article traces the origins of the conspiracy theory going back to its prehistory in the 1990s, describes the narrative in its various iterations and (per)mutations - along with Gates's shifting role in them - and charts the dissemination of this dynamic conspiracy theory while examining some of its notable tropes.

Dancing Plague: Archives of Celebration and Care in Bill T. Jones and Arnie Zane's Secret Pastures

Following the cancellation of influential contemporary choreographer Bill T. Jones's highly anticipated return to the stage in spring 2020, Jones reflected that COVID-19 was his "second plague." In referencing the AIDS epidemic that upended his career and personal life, Jones located methods of enduring not in the "unprecedented" present, but in the past. This essay considers the irreverent and buoyant Secret Pastures (1984), a work that reemerged as streaming media during the pandemic, as part of Jones's AIDS repertory. I describe how Secret Pastures' artistic and social archives, and the collaboration and friendship among Jones, Arnie Zane, Keith Haring, Peter Gordon, and Willi Smith documented therein, contain crucial practices of queer survival, particularly that of "alongsidedness." The essay argues that contexts for endurance can be found in the allegedly frivolous, glamorous, playful, humorous, and excessive aesthetics of Secret Pastures as much as they can be identified in, and more typically are ascribed to, more formalist, austere, and tonally serious works like D-Man in the Waters (1989) and Still/Here (1994). Modeling a toolkit of perseverance and flourishing, Secret Pastures reorients popular and academic views of minoritarian, particularly Black, and queer art and life as structured through trauma and scarcity. Secret Pastures shows how the onstage performance serves as a context for offstage friendships. Amid the ongoing hostilities of government abandonment, homophobia, white supremacy, and viral attack, art-making functions as a laboratory for modes of relationality that can endure.

The right to housing and shelter in South Africa: A COVID-19 informed perspective

This chapter critically evaluates the right to housing and shelter in South Africa within the COVID-19 framework. Such a context includes pertinent policy interventions such as the prohibition of evictions, protection provided to those who reside in informal settlements, protection extended to the homeless, and protection provided to renters and mortgage payers. This analysis is done to identify some legislative and policy gaps and propose solutions for addressing the challenges identified. While the contribution is about the right to housing, it does adopt a broader approach whereby it regards the full realisation of the right as interwoven with other rights in the Bill of Rights of the Constitution of the Republic of South Africa, 1996, particularly the socio-economic rights. This approach, as the study argues, is essential for protecting and providing the right to housing, especially during a pandemic, in a meaningful manner. © 2023 Nova Science Publishers, Inc.

Technological intelligence for circular supply chain: a co-citation analysis approach

PurposeThe COVID-19 pandemic has disrupted the supply chains, and this brought researchers and practitioners to think about more on circular supply chains (CSC). The CSC concept has been discussed in the literature more than a decade. This study aims to find out the theoretical roots of CSC by analyzing scholarly articles in literature.Design/methodology/approachThis paper uses document cocitation analysis for reaching this aim. After retrieving data from Web of Science database, this paper explores data set by considering used references of these publications and clusters them based on their title terms and visualizes them by using cosine similarity index.FindingsThis paper concludes that CSC is mainly discussed along with sustainability, sustainable supply chains and green supply chains, where the focus is integrating circular economy principles to supply chain management (SCM). On the other hand, although resiliency is an essential characteristic of SCM, contribution of CSC to supply chain resilience is neglected in the literature.Research limitations/implicationsCSC will gain importance in near future to increase national/firm productivity, and findings of this paper will give researchers insights for furthering their studies.Social implicationsTo avoid supply chain and market collapse caused by natural disruptions, supply chain resilience is of paramount importance. According to the findings of this paper, the authors believe that business resilience in COVID-19-restricted environment may have exit point by using circular perspective.Originality/valueThe importance of CSC has increased due to the disruptions caused by the COVID-19 pandemic. Therefore, this unique study was undertaken to investigate the evolution of this crucial topic with evidence from the literature through a cocitation analysis.

Energy Assessment of Academic Campus: IIT Gandhinagar as a Case Study

This case study examines the Indian Institute of Technology Gandhinagar (IITGN) campus's monthly energy consumption profile in detail to understand how it varies according to academic calendar, seasonal variability, and the recent COVID 19 pandemic. In addition, a detailed assessment of the electricity bill and its sub-component calculations are intended to understand how the energy consumption pattern affects the overall monthly electricity bill. From this study, it is observed that the energy consumption of academic areas, hostel areas, and chiller plants account for 80-90% of total energy consumption. The on-site solar PV energy generation at IITGN campus accounts for 1014% of total monthly energy consumption, which varies greatly by season. The analyses performed in this paper were inferred by three years of historical data of actual energy consumption and monthly electricity bills. Based on the analysis presented in this paper some recommendations towards the energy conservation measures are also given. © 2022 IEEE.

Presidential Policies' Effects on Oil and Gas Price and Productions Within the United States from 1977-2021

Amidst the ongoing COVID-19 pandemic, the contentious U.S. 2020 presidential election featured candidates with quite different stances on regulating the oil and gas industry, leaving many to question the longevity of fossil fuel use. However, little research explores the relationship between presidential policies and the oil market. In this paper, extensive research into presidential energy policies and their effects on domestic oil prices and production dating back to 1977 helps us identify whether we can predict the industry's future under Joe Biden's administration. The paper's results suggest the domestic oil industry is more dependent on external foreign events - with presidential policies offering almost negligible effects on prices and production.

Smart Energy Meter with Cloud Connectivity

The main tools for allowing customers to communicate openly and transparently with the company and other stakeholders are their electricity bills. However, last year due to pandemics, some residents petitioned the Madras high court claiming TANGEDCO's technique to measure power was arbitrary and unreasonable during the lockdown that COVID provoked. This was done amid complaints about excessive electricity bills. When compared to bills from other states, TANGEDCO's white meter card for energy bills is found to provide insufficient information on use and rates, according to a survey by the certain associations. Power bills urgently need to be redesigned to include comprehensive billing details and accurate assessments of electricity consumption from closed homes or homes in restricted zones. [4] We proposed and designed a Smart Home Energy Meter Monitoring System to solve this crisis. It consists of three systems. First system: Customized built energy meter with LCD. [6] Second system: Wi-Fi module with the Microcontroller (ATMega328P) and an alert system. Third system: Database (MySQL database). [12] The quantity of power used by the device is measured by an energy meter user and every two months, the final reading of the power consumption is taken by the micro controller where the electricity bill calculation program has been pre-programmed to give the value of power consumed during the two months and amount to be paid by the user and [4] It will be shown on the Energy Meter's LCD. The micro controller with a built-in Wi-Fi module (ESP8266) will send these displayed data to the service provider's database. [2] An alert system has been added to counteract the hefty usage and electricity bills to create awareness to the consumer about the slab-wise tariffs increase in the per-unit cost data that has been set by TANGEDCO. [10] The alert has been set in a way that the consumer receives a message for every 200 unit usage of power. The third system is a database created using MySQL database to transport the data to the service provider. © 2022 IEEE.

A Machine Learning Approach to Predict the Personalized Next Payment Date of An Online Payment Platform

Use of digital payments has risen exponentially in the recent past especially due to the COVID-19 pandemic. This is because online payment methods offer many benefits in performing their day-to-day transactions and paying utility bills such as electricity bills, water bills, telephone bills and etc. Knowing when a consumer will perform a specific online transaction, or bill payment is beneficial t o a n o nline payment platform to plan marketing campaigns since targeted marketing has become very prevalent nowadays. However, predicting this is not an easy task since thousands of transactions are happening in each and every minute of an online payment platform. This paper presents the results of a study that investigated predicting the customer personalized, utility bill payment type wise next payment date of a financial c ompany i n S ri Lanka by using machine learning techniques. This is accomplished by analyzing not only online transaction history but also customer characteristics and a holiday calendar which is specific t o Sri Lanka. At the end of the study, it was identified t hat XGBoost Regressor is the most suitable machine learning algorithm, etc deal with this scenario which provided 91.02% accuracy. These predictions will be used for sending personalized reminders and discount offers to customers without sending general common notifications w hen t hey a re p lanning t o d o a n o nline payment. Such reminders and offers will be notified o n t he m obile devices of the customers and, ultimately both customers and the business owners will be benefited by this. © 2022 IEEE.

A Dynamic Analysis of Conspiratorial Narratives on Twitter During the Pandemic.

Since the breakout of COVID-19 in late 2019, various conspiracy theories have spread widely on social media and other channels, fueling misinformation about the origins of COVID-19 and the motives of those working to combat it. This study analyzes tweets (N = 313,088) collected over a 9-month period in 2020, which mention a set of well-known conspiracy theories about the role of Bill Gates during the pandemic. Using a topic modeling technique (i.e., Biterm Topic Model), this study identified ten salient topics surrounding Bill Gates on Twitter, and we further investigated the interactions between different topics using Granger causality tests. The results demonstrate that emotionally charged conspiratorial narratives are more likely to breed other conspiratorial narratives in the following days. The findings show that each conspiracy theory is not isolated by itself. Instead, they are highly dynamic and interwoven. This study presents new empirical insights into how conspiracy theories spread and interact during crises. Practical and theoretical implications are also discussed.

Bill of lading data in international trade research with an application to the COVID-19 pandemic

We evaluate high-frequency bill of lading data for international trade research. These data offer some advantages over both other publicly accessible trade data and confidential datasets, but they also have drawbacks. We analyze three aspects of trade during the COVID-19 pandemic. First, we show how the high-frequency data capture the within-month collapse of trade between the United States and India that are not observable in official monthly data. Second, we demonstrate how U.S. buyers shifted their purchases across suppliers over time during the recovery. And third, we show how the data can measure vessel delivery bottlenecks in near real time.

Deep state phobia: Narrative convergence in coronavirus conspiracism on Instagram

Recent scholarship has established that conspiracist narratives proliferated in mainstream online discourse during the coronavirus pandemic. This proliferation has been provocatively characterized as a 'conspiracy singularity' in which previously divergent conspiracy narratives converged into a single, overarching narrative. Yet while the idea of narrative convergence has long figured in conspiracy theory research, empirical evidence has been scarce. The present article aims to address this gap by means of an investigation of an archive containing over 470,000 conspiracy-related Instagram posts from 2020. Given the size and conceptual complexity of the dataset, the paper introduces a 'digital hermeneutics' approach, which combines data science methods with qualitative interpretation and theorization. Operating across three levels of observation (hashtag analysis, text analysis, and image analysis) we identify patterns of convergence among different conspiracy narratives (including anti-vax, QAnon, anti-5G, and 'The Great Reset') over the year 2020 as well as the apparent role of protagonists and antagonists (notably Donald Trump and Bill Gates) in creating connections. In interpreting these findings we focus on the concept of 'the Deep State' as a bridge between various conspiracist narratives, which seems to cut diagonally across political ideologies.

The UK’s Online Safety Bill: The day we took a stand against serious online harms or the day we lost our freedoms to platforms and the state?

This paper discusses the UK’s Online Safety Bill, which is intended to protect vulnerable individuals online, although at the risk of promoting surveillance techniques and mandating proactive content removal by platforms. It analyses how the Bill, a very ambitious project, tries to safeguard vulnerable people through means which could be easily abused, and asks whether the risk of abuse that could affect everyone is worth the protection of a minority of online users. Recently demonstrated authoritarian approaches to solving the COVID-19 crisis make this concern palpable. The paper concludes by saying that once we take a path, it will be difficult to walk it back. © 2022, Henry Stewart Publications. All rights reserved.

Techno-economic assessment of manufacturing process in small scale industry to evaluate energy saving potential

Energy is an essential input for smooth functioning of manufacturing industry. Small scale industries employ a significant portion of unskilled labor in India. With ever increasing competition, these industries are forced to reduce their production cost. However, these industries lack the adequate expertise about monitoring the energy usage and consequently derive the benefits of reduction in energy consumption on production cost. Cost reduction through energy saving avenues becomes particularly important after facing the impact of COVID-19 pandemic on survival and growth of such industries. This paper aims to provide a practical, grass root level perspective about utilization of energy audit as an effective energy conservation tool. This tool gives the plant management a factual idea about technical and economic feasibility of various Energy Conservation Measures (ECMs) suggested, in quantitative terms. This paper puts forth the outcomes of energy audit conducted in a small-scale industry engaged in the manufacturing of non – ferrous flat and shape wires, high precision rolling mills, slab caster etc. Energy audit was focused on energy savings in electrical as well as thermal systems. Energy audit study revealed that this small scale manufacturing plant can save 6226 kW h of electrical energy per annum, which is total saving potential of 22.82% of the plant's annual energy consumption. Suggested ECMs exhibit an average payback period of 4 months which is considerably good. © 2022

The need for a financial sector legal standard to support the NIST Cybersecurity Framework

The National Institute of Standards and Technology (NIST) published the NIST Framework for Improving Critical Infrastructure Cybersecurity of 2014, followed by an updated version in 2017. The Framework, which was developed as a joint effort between the U.S. Federal Government and the private sector, serves only as a guideline and is not mandated by any legal authority. Currently, adoption of the Framework is voluntary. The Financial Sector, one of sixteen Department of Homeland Security critical infrastructure sectors, should be incentivized to adopt the framework, based on inconsistency and accountability of best practices implementation across the sector. Global cyber attack opportunists used the 2020 COVID-19 pandemic to exploit cybersecurity vulnerabilities and gaps in the U.S. Financial Sector. The NIST Cybersecurity Framework provides guidelines for strengthening cybersecurity and identifies areas of potential cyber attack impacts. This paper is a summary of the author's published 2021 doctoral dissertation, which includes research and analysis of reported Financial Sector risks, failures and impacts due to weak or lack of cybersecurity controls. The study also provides analysis of success stories of Financial Sector and other entities which have adopted the NIST Cybersecurity Framework. Dr. Goodwin is a Senior Member of IEEE. © 2022 IEEE.

Modeling and Simulation of Household Appliances Power Consumption

Featured ApplicationThe method presented in this paper serves to predict the consumption of household appliances by modeling their behavior and by simulating accordingly.The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this paper proposed a method of predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov–Smirnov Test and Pearson’s X2 test). To test the veracity of the evaluations, first, a set of random values was simulated for each hour, and their respective averages were calculated. These were compared with the averages of the real values for each hour. With the exception of HVAC during working days, great results were obtained. For the refrigerator, the maximum error was 3.91%, while for the lighting, it was 4.27%. At the point of consumption, the accuracy was even higher, with an error of 1.17% for the dryer while for the washing machine and dishwasher, their minimum errors were less than 1%. The error results confirm that the applied methodology is perfectly acceptable for modeling household appliance consumption and consequently predicting it. However, these consumptions can be only extrapolated to dwellings with similar surface areas and habitats.

Civility, Trust, and Responding to Echo Chambers

Due to the rampant epistemic distrust present in echo chamber members towards outsiders, responding to echo chambers with civil debate is unlikely to lead to any agreement or compromise. Moreover, a civil response may contribute to the echo chamber's inflated sense of epistemic status, which is precisely what needs to be dismantled or diminished if agreement/compromise is to be made possible. When responding to particularly dangerous and resistant echo chambers, a moderately uncivil response may be warranted.

Bi-objective Problem of Material-based GreenHouse Gas Emission and Costs by Global Supply Chain Network Disruption across TPP countries during COVID-19

A global supply chain is affected by many factors, such as disruption, customs duty, economic partnership agreements, and requirements for the reduction of GreenHouse Gas (GHG) emission. COVID-19 has caused a negative impact on the global supply chain. A sustainable supply chain needs to reduce GHG emission. However, GHG emission varies across countries because of the energy mix. Therefore, the impact of disruption and different GHG emission levels should be considered in a global supply chain design. Moreover, a global supply chain is influenced by customs duty and Trans-Pacific Partnership, which is a free trade agreement. This study models and formulates a global supply chain network including disruption, material-based GHG emission, customs duty, and TPP to minimize the total cost and total GHG emission by integer programming with ϵ constraint. The result of the numerical experiments indicates that the nationalization of a factory in a developed country is effective in reducing GHG emission to avoid disruption in the middle GHG emission country in a global supply chain. Additionally, it is found that there is no trade-off relationship between disruption effect and reducing total GHG emission, Though, there is a trade-off relationship between total cost and total GHG emission in the experiment. © 2021 IEEE.

Essentially Surplus: The Struggle for the California Domestic Worker Bill of Rights (AB 241) and the Afterlife of Reproductive Slavery

Zarate talks about the struggle for the California Domestic Worker Bill of Rights (AB 241) and the afterlife of reproductive slavery. The COVID-19 pandemic has introduced the U.S. population to the essential worker. This is a novel category of worker designated by the state as essential to the functioning of the country's vital infrastructure. Their occupations range from professional roles in healthcare and IT to low-wage jobs like store clerks, meatpackers, and farmworkers. In California, from where I write, 14 percent of the state's recognized essential workforce lives in poverty, and although many of these workers serve as a literal lifeline during the pandemic, many have had to go without protective equipment and basic worker protections like access to sick days and health benefits. This article offers an account of the relationship between "essential" and the category of labor by examining how reproductivity, as the foundation to all work, life, and sociality, is, as Kalindi Vora argues, rendered into racialized surplus, a condition of devaluation and disposability premised on the extraction and exhaustion of life.

Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories

Sentiment analysis has recently become increasingly important with a massive increase in online content. It is associated with the analysis of textual data generated by social media that can be easily accessed, obtained, and analyzed. With the emergence of COVID-19, most published studies related to COVID-19’s conspiracy theories were surveys on the people's sentiments and opinions and studied the impact of the pandemic on their lives. Just a few studies utilized sentiment analysis of social media using a machine learning approach. These studies focused more on sentiment analysis of Twitter tweets in the English language and did not pay more attention to other languages such as Arabic. This study proposes a machine learning model to analyze the Arabic tweets from Twitter. In this model, we apply Word2Vec for word embedding which formed the main source of features. Two pretrained continuous bag-of-words (CBOW) models are investigated, and Naïve Bayes was used as a baseline classifier. Several single-based and ensemble-based machine learning classifiers have been used with and without SMOTE (synthetic minority oversampling technique). The experimental results show that applying word embedding with an ensemble and SMOTE achieved good improvement on average of F1 score compared to the baseline classifier and other classifiers (single-based and ensemble-based) without SMOTE.

CONTRACTS AND THE COVID-19 VIRUS PANDEMIC

[...]as often noted by U.S. President Donald Trump and others, United States commerce is, for the most part, at a standstill throughout the pandemic period. [...]the use of "Stay at Home" or "Stay at Work" lockdown orders and other strict restrictions is a postconstitutional, or more modern interpretation by judicial review, is often controversial. [...]the exposure of the American population to the current COVID-19 pandemic is not a unique circumstance in the nation's history, nor is its impact on the state of contracts in commercial transactions unique. ( 1.Revolutionary war The Constitution's contract clause was enacted in the aftermath of the American Revolution (1775-1781) to prohibit states, federal government and other debtors from reneging on contracts and resultant "IOU" debts issued by states, their agents and militias during times of conflict.8 Indeed, one of the earliest Supreme Court cases dealt with such an attempt by the State of Georgia to breach a contract in the case of Chisholm v. Georgia (1793).9 Chisholm was the executor for a South Carolina merchant, Farquhar, who had supplied provisions to the Georgia Militia fighting during the Revolutionary War.10 When it came time to pay for the provisions, Georgia refused claiming "sovereign immunity.