Driving Factors of Polarization on Twitter During Protests Against COVID-19 Mitigation Measures in Vienna

We conduct the analysis of the Twitter discourse related to the anti-lockdown and anti-vaccination protests during the so-called 4th wave of COVID-19 infections in Austria (particularly in Vienna). We focus on predicting users' protest activity by leveraging machine learning methods and individual driving factors such as language features of users supporting/opposing Corona protests. For evaluation of our methods we utilize novel datasets, collected from discussions about a series of protests on Twitter (40488 tweets related to 20.11.2021;7639 from 15.01.2022 – the two biggest protests as well as 192 from 22.01.2022;8412 from 11.12.2021;3945 from 11.02.2022). We clustered users via the Louvain community detection algorithm on a retweet network into pro- and anti-protest classes. We show that the number of users engaged in the discourse and the share of users classified as pro-protest are decreasing with time. We have created language-based classifiers for single tweets of the two protest sides – random forest, neural networks and a regression-based approach. To gain insights into language-related differences between clusters we also investigated variable importance for a word-list-based modeling approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Media Coverage and Public Perception of Distance Learning During the COVID-19 Pandemic: A Topic Modeling Approach Based on BERTopic

The current study proposes a topic modeling approach for analyzing press coverage and public perception of distance learning during the COVID-19 pandemic. We evaluate the applicability of a novel approach for neural topic modeling based on transformer-based language models. Our methodology is tested empirically on a large sample of news and discussions in various social and news media platforms to derive valuable insights on press coverage and public perception of COVID-19 impact on the education system in Bulgaria. The study outlines key advantages of using BERTopic in analyzing big data. Our work contributes to the body of literature devoted on value creation through big data and text analytics utilization in the public sector. © 2022 IEEE.

Measuring, Modeling, and Forecasting the Mental Wealth of Nations.

The COVID-19 pandemic has exposed the deep links and fragility of economic, health and social systems. Discussions of reconstruction include renewed interest in moving beyond GDP and recognizing "human capital", "brain capital", "mental capital", and "wellbeing" as assets fundamental to economic reimagining, productivity, and prosperity. This paper describes how the conceptualization of Mental Wealth provides an important framing for measuring and shaping social and economic renewal to underpin healthy, productive, resilient, and thriving communities. We propose a transdisciplinary application of systems modeling to forecast a nation's Mental Wealth and understand the extent to which policy-mediated changes in economic, social, and health sectors could enhance collective mental health and wellbeing, social cohesion, and national prosperity. Specifically, simulation will allow comparison of the projected impacts of a range of cross-sector strategies (education sector, mental health system, labor market, and macroeconomic reforms) on GDP and national Mental Wealth, and provide decision support capability for future investments and actions to foster Mental Wealth. Finally, this paper introduces the Mental Wealth Initiative that is harnessing complex systems science to examine the interrelationships between social, commercial, and structural determinants of mental health and wellbeing, and working to empirically challenge the notion that fostering universal social prosperity is at odds with economic and commercial interests.

What is in the palliative care 'syringe'? A systems perspective.

The diffusion of palliative care has been rapid, yet uncertainty remains regarding palliative care's "active ingredients." The National Consensus Project Guidelines for Quality Palliative Care identified eight domains of palliative care. Despite these identified domains, when pressed to describe the specific maneuvers used in clinical encounters, palliative care providers acknowledge that "it's complex." The field of systems has been used to explain complexity across many different types of systems. Specifically, engineering systems develop a representation of a system that helps manage complexity to help humans better understand the system. Our goal was to develop a system model of what palliative care providers do such that the elements of the model can be described concretely and sequentially, aggregated to describe the high-level domains currently described by palliative care, and connected to the complexity described by providers and the literature. Our study design combined methodological elements from both qualitative research and systems engineering modeling. The model drew on participant observation and debriefing semi-structured interviews with interdisciplinary palliative care team members by a systems engineer. The setting was an interdisciplinary palliative care service in a US rural academic medical center. In the developed system model, we identified 59 functions provided to patients, families, non-palliative care provider(s), and palliative care provider(s). The high-level functions related to measurement, decision-making, and treatment address up to 8 states of an individual, including an overall holistic state, physical state, psychological state, spiritual state, cultural state, personal environment state, and clinical environment state. In contrast to previously described expert consensus domain-based descriptions of palliative care, this model more directly connects palliative care provider functions to emergent behaviors that may explain system-level mechanisms of action for palliative care. Thus, a systems modeling approach provides insights into the challenges surrounding the recurring question of what is in the palliative care "syringe."

Agent-based Modeling as a Tool for Predicting the Spatial-temporal Diffusion of the COVID-19 Pandemic

The article presents the original methodology of using agent-based modeling (ABM) for the numerical simulations of the COVID-19 pandemic's development. The proposed solution makes it possible to analyze changes in the number of cases both in space and time. The devised methodology enables considering spatial conditions in terms of population distribution, such as places of work, rest, or residence, and uses multi-agent modeling to consider spatial interactions. Numerical simulations utilize the spatial and demographic data in GIS databases and the GAMA environment that enables the parameterization of the epidemiological model. Testing the developed methodology on a test area also allowed for checking the effects of a potential decrease or increase in social restrictions numerically. The simulations performed show a high correlation between the level of social distancing and the number of COVID-19 cases. © 2021 IEEE.

Reconfigurable Production Line Design Method to Ensure High Efficiency in Both Steady and Emergency Situations

Manufacturers faced severe conditions due to frequent natural disasters and spread of the COVID-19 virus need to continue the production even in any emergency situations. We proposed a reconfigurable production line composed of resources that include line workers and multipurpose equipment. Skilled workers can handle complicated tasks quickly in unexpected situation but require high production cost after transition to steady situation. Equipment takes time temporarily to launch and adjust a task but can stabilize the quality regularly. This paper describes a production line design method for maximizing production efficiency in both steady and emergency situations by modeling the capabilities of resources. © 2021 The Japan Society of Mechanical Engineers.