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This paper describes SHAPE TAPE™, a thin array of fiber optic curvature sensors laminated on a ribbon substrate, arranged to sense bend and twist. The resulting signals are used to build a three dimensional computer model containing six degree of freedom position and orientation information for any location along the ribbon. The tape can be used to derive dynamic or static shape information from objects to which it is attached or scanned over. This is particularly useful where attachment is only partial, since shape tape “knows where it is” relative to a starting location. Measurements can be performed where cameras cannot see, without the use of magnetic fields. Applications include simulation, film animation, computer aided design, robotics, biomechanics, and crash testing.

Examines the Internet’s potential for becoming an information highway. Defines the information highway and the Internet. Describes the characteristics of an information highway. The concept of an information highway puts some requirements on the infrastructure. The users of an information highway, who are located in the residential and business environment, have their share of requirements as well, but they focus on the requirements put forward by the services or applications they will use on an information highway. Checks whether the Internet has implemented these properties and how, or if work is going on to develop them. The framework for this discussion is the TCP/IP reference model. Places some emphasis on the IP next generation protocol, IP version 6 (IPv6). Provides an overview showing all the properties with an indication of how well the posed requirements are met. Concludes that the Internet certainly has potential for becoming an information highway.

The present article's primary purpose is the topic modeling of the global coronavirus publications in the last 50 years.

The present study is applied research that has been conducted using text mining. The statistical population is the coronavirus publications that have been collected from the Web of Science Core Collection (1970–2020). The main keywords were extracted from the Medical Subject Heading browser to design the search strategy. Latent Dirichlet allocation and Python programming language were applied to analyze the data and implement the text mining algorithms of topic modeling.

The findings indicated that the SARS, science, protein, MERS, veterinary, cell, human, RNA, medicine and virology are the most important keywords in the global coronavirus publications. Also, eight important topics were identified in the global coronavirus publications by implementing the topic modeling algorithm. The highest number of publications were respectively on the following topics: “structure and proteomics,” “Cell signaling and immune response,” “clinical presentation and detection,” “Gene sequence and genomics,” “Diagnosis tests,” “vaccine and immune response and outbreak,” “Epidemiology and Transmission” and “gastrointestinal tissue.”

The originality of this article can be considered in three ways. First, text mining and Latent Dirichlet allocation were applied to analyzing coronavirus literature for the first time. Second, coronavirus is mentioned as a hot topic of research. Finally, in addition to the retrospective approaches to 50 years of data collection and analysis, the results can be exploited with prospective approaches to strategic planning and macro-policymaking.