Earth's magnetic field spatial-temporal changes in relation to the SARS-CoV-2 virus spreading

The results of a study on the possible connection between the spread of the SARS-CoV-2 virus and the Earth's magnetic field based on the analysis of a large array digital data for 95 countries of the world are presented. The dependence of the spatial SARS-CoV-2 virus spread on the magnitude of the BIGRF Earth's main magnetic field modular induction values was established. The maximum diseases number occurs in countries that are located in regions with reduced (25. 0-30. 0 μT) and increased (48. 0-55. 0 μT) values, with a higher correlation for the first case. The spatial dependence of the SARS-CoV-2 virus spreading on geomagnetic field dynamics over the past 70 years was revealed. The maximum diseases number refers to the areas with maximum changes in it, both in decrease direction (up to - 6500 nT) and increase (up to 2500 nT), with a more significant correlation for countries located in regions with increased geomagnetic field. © 2022 EAGE. All Rights Reserved.

Analysis of Digital Data Consumption of Video Streaming Platforms During COVID-19

In order to determine the trend of studies on the factors that generate the consumption of paid video streaming platforms during the COVID-19 pandemic, a systematic review of scientific literature was conducted. To search for the information, the Scopus database and the Concytec repository, Alicia, were consulted. The keywords "streaming”, "platform”, "media”, "COVID-19”, "Netflix”, "video” and "pandemic” were used. Sources were located in three languages. The data analysis allowed dividing the information into five categories: background on the positioning of streaming platforms, audience behaviour, consumption drivers, cases related to Netflix and platforms in times of confinement. It is concluded that during the pandemic, people mutated their mode of digital consumption, becoming more dependent, which has been capitalised on by streaming platforms that, taking advantage of habits, adaptability, and consumption trends, and responding with innovation, have increased users, in a distribution of the sector in which Netflix, thanks to its own strategies, is the leader. These reviewed factors move a consumer marketplace uphill, creating loyalty among previous audiences and tempting new ones, which could even overcome the pandemic period. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

‘I've left enough data': Relations between people and data and the production of surveillance

Exploring emergent relations between data-producing individuals and their data products, this study aims to contribute to the ongoing scholarly discussion on agencies in data practices. It focuses on shifts in surveillance structure in the era of Big Data, in which the individual becomes both a subject and an object in the production of data surveillance. Drawing on the concept of the ‘dividual', the study analyses data practices for a tracing system invented by the South Korean government during the COVID-19 pandemic, with findings from field research conducted with 11 research participants in various urban sites in Seoul. Highlighting how the tracing system positioned surveillance ‘in the hands of citizens', the study exposes the complexities of the relations that the participants formed with the data they produced, and how they reflexively reappropriated their practices through alterations and deflections on the basis of their tacit knowledge and imaginaries concerning digital data and their constituent positions in the knowledge production system. The resultant expression of surveillance was directly shaped by the evolving relationship between the producers (participants) and products (digital data). The study proposes that an intersectional focus on surveillance and critical data studies, with close attention to ordinary people's relations with data, has the capacity to inquire into the politics of data more fully. © The Author(s) 2023.

Producing Generative Digital Data Objects: An Empirical Study on COVID-19 Data Flows in Online Communities

Digital data objects on viruses have played a pivotal role in the fight against COVID-19, leading to healthcare innovation such as new diagnostics, vaccines, and societal intervention strategies. To effectively achieve this, scientists access viral data from online communities (OCs). The social-interactionist view on generativity, however, has put little emphasis on data. We argue that generativity on data depends on the number of data instances, data timeliness, and completeness of data classes. We integrated and analyzed eight OCs containing SARS-CoV-2 nucleotide sequences to explore how community structures influence generativity, revealing considerable differences between OCs. By assessing provided data classes from user perspectives, we found that generativity was limited in two important ways: When required data classes were either insufficiently collected or not made available by OC providers. Our findings highlight that OC providers control generativity of data objects and provide guidance for scientists selecting OCs for their research. © 2022 IEEE Computer Society. All rights reserved.

Introduction to the Special Track on Artificial Intelligence and COVID-19

The human race is facing one of the most meaningful public health emergencies in the modern era caused by the COVID-19 pandemic. This pandemic introduced various challenges, from lock-downs with significant economic costs to fundamentally altering the way of life for many people around the world. The battle to understand and control the virus is still at its early stages yet meaningful insights have already been made. The uncertainty of why some patients are infected and experience severe symptoms, while others are infected but asymptomatic, and others are not infected at all, makes managing this pandemic very challenging. Furthermore, the development of treatments and vaccines relies on knowledge generated from an ever evolving and expanding information space. Given the availability of digital data in the modern era, artificial intelligence (AI) is a meaningful tool for addressing the various challenges introduced by this unexpected pandemic. Some of the challenges include: outbreak prediction, risk modeling including infection and symptom development, testing strategy optimization, drug development, treatment repurposing, vaccine development, and others. © 2023 AI Access Foundation. All rights reserved.

Improving Text Clustering Using a New Technique for Selecting Trustworthy Content in Social Networks

Today’s information society has led to the emergence of a large number of applications that generate and consume digital data. Many of these applications are based on social networks, and therefore their information often comes in the form of unstructured text. This text from social media also tends to contain a high level of noise and untrustworthy content. Therefore, having systems capable of dealing with it efficiently is a very relevant issue. In order to verify the trustworthiness of the social media content, it is necessary to analyse and explore social media data by using text mining techniques. One of the most widespread techniques in the field of text mining is text clustering, that allows us to automatically group similar documents into categories. Text clustering is very sensitive to the presence of noise and so in this paper we propose a pre-processing pipeline based on word embedding that allows selecting trustworthy content and discarding noise in a way that improves clustering results. To validate the proposed pipeline, a real use case is provided on a Twitter dataset related to COVID-19. © 2022, Springer Nature Switzerland AG.

Learning to Learn HVAC Failures: Layering ML Experiments in the Absence of Ground Truth

Passenger comfort systems such as Heating, Ventilation, and Air-Conditioning units (HVACs) usually lack the data monitoring quality enjoyed by mission-critical systems in trains. But climate change, in addition to the high ventilation standards enforced by authorities due to the COVID pandemic, have increased the importance of HVACs worldwide. We propose a machine learning (ML) approach to the challenge of failure detection from incomplete data, consisting of two steps: 1. human-annotation bootstrapping, on a fraction of temperature data, to detect ongoing functional loss and build an artificial ground truth (AGT);2. failure prediction from digital-data, using the AGT to train an ML model based on failure diagnose codes to foretell functional loss. We exercise our approach in trains of Dutch Railways, showing its implementation, ML-predictive capabilities (the ML model for the AGT can detect HVAC malfunctions online), limitations (we could not foretell failures from our digital data), and discussing its application to other assets. © 2022, Springer Nature Switzerland AG.