Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process.

This paper describes Dedalus, an open-source Python code for simulating partial differential equations from all areas of physics. Dedalus translates plain-text equations into efficient and parallelized solvers using global spectral methods. Here the authors detail the numerical methods enabling this translation and describe the code's design and implementation. They also illustrate its capabilities with diverse examples, including optical network dynamics, magnetized shocks in plasmas, large-scale oceanic flows, low Reynolds number flows, stellar and atmospheric waves, and diamagnetic levitation.

https://link.aps.org/pdf/10.1103/PhysRevResearch.2.023068

Dedalus: A flexible framework for numerical simulations with spectral methods

Im echten (analogen) Leben sind wir eher selten offenen Beleidigungen oder Hass ausgesetzt. Im Internet hingegen lesen wir deutlich häufiger wütende, hasserfüllte Kommentare, Snaps, Tweets und Posts gegen uns oder gegen andere. Für diesen Hass im Netz gibt es den Begriff "Hate Speech". Hate Speech ist ein Phänomen dem rund zwei Drittel (67 Prozent) der Onliner schon einmal begegnet sind, wie eine forsa-Umfrage im Auftrag der Landesanstalt für Medien NRW (LfM) zeigt.

What about Hate Speech

Since U.S. transportation is highly dependent on imported oil supply, technical options (such as the electric vehicle) are being actively pursued through a number of national research and development programs. The hybrid and electric vehicle program, which has been funded by the United States Department of Energy, intends to advance E&HV technology and to demonstrate E&HV market potential. This paper presents the research and development activities in electric transportation and the status of current projects. (Author/TRRL)

Electric vehicles in the united states

Assessing global energy poverty: An integrated approach

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

/pdf/text-mining-in-big-data-analytics-2w6xgx3q9b.pdf

Text Mining in Big Data Analytics

The focus of this paper is to bring to light the vital issue of energy poverty alleviation and how big data could improve the data collection quality and mechanism. It also explains the vicious circle of low productivity, health risk, environmental pollution and energy poverty and presents currently used energy poverty measures and alleviation policies and stresses the associated problems in application due to the underlying dynamics.

Big Data and Energy Poverty Alleviation

This article investigates the impact of big data on the actuarial sector. The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to increased data availability. The analyzed areas of this paper span from automobile insurance policy pricing, mortality and healthcare modeling to estimation of harvest-, climate- and cyber risk as well as assessment of catastrophe risk such as storms, hurricanes, tornadoes, geomagnetic events, earthquakes, floods, and fires. We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance companies. We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this area and prevailing data asymmetries, for which we identify the application of artificial intelligence, in particular machine learning techniques, as a possible solution to improve policy pricing accuracy and results.

https://www.mdpi.com/2504-2289/4/4/40/pdf

Big Data and Actuarial Science

Digitization is the emerging process in the current transformation of industry. Understanding the role and socio-economic consequences of digitalization is crucial for the way technology is being deployed in each sector. One of the affected sectors is dentistry. This study highlights the current advances and challenges in integrating and merging artificial intelligence (AI), intelligence augmentation (IA), and machine learning (ML) in dentistry. We conduct a comparative analysis to give an overview of which technology is being currently deployed and what role IA and AI will play in dentistry, as AI plays an assistive role in advancing human capabilities. We find that challenges range from AI finding its way into routine medical practice to qualitative challenges of retrieving adequate data input. Other challenges lie in the yet unanswered questions of liability in how to reduce deployment costs of new technology. Given these challenges, we provide an outlook of how future technology can be deployed in daily-life dentistry and how robots and humans will interact, given the current technology developments. The aim of this paper is to discuss the future of dentistry and whether it is AI or IA conquering the modern dentistry era.

Shaping the Future of Smart Dentistry: From Artificial Intelligence (AI) to Intelligence Augmentation (IA)

The importance of energy security for successful functioning of private companies, national economies, and the overall society should not be underestimated. Energy is a critical infrastructure for any modern society, and its reliable functioning is essential for all economic sectors and for the well-being of everybody. Uncertainty in terms of the availability of information, reliable data to make predictions and to plan for investment as well as for other actions of stakeholders at the energy markets is one of the factors, which has the highest influence on

/pdf/big-data-and-energy-security-impacts-on-private-companies-4nbqn5rbqa.pdf

Stephan Unger

Papers

Text Mining in Big Data Analytics

Big Data and Energy Poverty Alleviation

Big Data and Actuarial Science

Shaping the Future of Smart Dentistry: From Artificial Intelligence (AI) to Intelligence Augmentation (IA)

Big Data and Energy Security: Impacts on Private Companies, National Economies and Societies