Forecasting the Spreading of Technologies in Research Communities

Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution

Forecasting Technology Migrations by means of the Technology-Topic Framework

Technologies such as algorithms, applications and formats usually originate in the context of a specific research area and then spread to several other fields, sometimes with transformative effects. However, this can be a slow and inefficient process, since it not easy for researchers to be aware of all interesting approaches produced by unfamiliar research communities. We address this issue by introducing the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model and machine learning to forecast the propagation of technologies across research areas. The aim is to foster the knowledge flow by suggesting to scholars technologies that may become relevant to their research field. The system was evaluated on a manually curated set of 1,118 technologies in Semantic Web and Artificial Intelligence and the results of the evaluation confirmed the validity of our approach

Foreign direct investment and spillovers through workers' mobility.

We analyze a model where a multinational fir can use a superior technology in a foreign subsidiary only after training a local worker. Technological spillovers from foreign direct investment arise when this worker is later hired by a local firm Pecuniary spillovers arise when the foreign affiliat pays the trained worker a higher wage to prevent her from moving to a local competitor. We study conditions under which these spillovers occur. We also show that the multinational fir might fin it optimal to export instead of investing abroad to avoid dissipation of its intangible assets or the payment of a higher wage to the trained worker.Multinational corporations; Externalities; Spillovers; Training; Labor movility;

What causes the fragmentation of debris streams in TDEs?

A tidal disruption event (TDE) occurs when a star passes too close to a supermassive black hole and gets torn apart by its gravitational tidal field. After the disruption, the stellar debris form an expanding gaseous stream. The morphology and evolution of this stream is particularly interesting as it ultimately determines the observational properties of the event itself. In this work we perform 3D hydrodynamical simulations of the TDE of a star modelled as a polytropic sphere of index {\gamma} = 5/3, and study the gravitational stability of the resulting gas stream. We provide an analytical solution for the evolution of the stream in the bound, unbound and marginally bound case, that allows us to describe the stream properties and analyse the time-scales of the physical processes involved, applying a formalism developed in star formation context. Our results are that, when fragmentation occurs, it is fueled by the failure of pressure in supporting the gas against its self-gravity. We also show that a stability criterion that includes also the stream gas pressure proves to be far more accurate than one that only considers the black hole tidal forces, giving analytical predictions of the time evolution of the various forces associated to the stream. Our results point out that fragmentation occurs on timescales longer compared with the observational windows of these events and is thus not expected to give rise to significant observational features.Comment: 12 pages,15 figures, accepted for publication in MNRA

2100 AI: Reflections on the mechanisation of scientific discovery

The pace of research is nowadays extremely intensive, with datasets and publications being published at an unprecedented rate. In this context data science, artificial intelligence, machine learning and big data analytics are providing researchers with new automatic techniques which not only help them to manage this flow of information but are also able to identify automatically interesting patterns and insights in this vast sea of information. However, the emergence of mechanised scientific discovery is likely to dramatically change the way we do science, thus introducing and amplifying serious societal implications on the role of researchers themselves, which need to be analysed thoroughly

Classifying Research Papers with the Computer Science Ontology

Ontologies of research areas are important tools for characterising, exploring and analysing the research landscape. We recently released the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 26K topics and 226K semantic relationships. CSO currently powers several tools adopted by the Springer Nature editorial team and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. As an effort to encourage the usage of CSO, we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedbacks at different levels of the ontology. In this paper, we present the CSO Classifier, an application for automatically classifying academic papers according to the rich taxonomy of topics from CSO. The aim is to facilitate the adoption of CSO across the various communities engaged with scholarly data and to foster the development of new applications based on this knowledge base

Modulating Calcitonin Fibrillogenesis AN ANTIPARALLEL α-HELICAL DIMER INHIBITS FIBRILLATION OF SALMON CALCITONIN

We have investigated the prefibrillar state of salmon (s) and human (h) calcitonin (CT). Size exclusion chromatography at pH 3.3 and 7.4 indicates that sCT is present in solution as a dimer, whereas hCT elutes as a monomer at pH 3.3 and as monomer-dimer at pH 7.4. Guanidine hydrochloride unfolding experiments show that dimerization is stabilized by hydrophobic interactions. We investigated the dimeric structure by multidimensional nuclear magnetic resonance spectroscopy and calculations by using an sCT mutant (LAsCT) in which Pro23 and Arg24 were substituted for Leu23 and Ala24. As indicated by the Leu9–Tyr27 and Leu12–Leu19 contacts, the mutated hormone forms a head-to-tail dimer whose basic unit is an α-helix in the region Leu12–Tyr22. The solution behavior of LAsCT is identical to that of sCT, so the dimeric structure can safely be extended to sCT: we believe that such a structure inhibits fibril maturation in sCT. No stable dimer was observed for hCT, which we attributed to the absence of a defined helical structure. However, we suggest that intermolecular collisions of short ordered regions (for example, a sequence of turns) in hCT favors intermolecular contacts, and specific orientation can be obtained through hydrogen bond formation involving Tyr12, Phe16, and Phe19, with the aromatic ring acting as an acceptor. Taken together, our results indicate that hCT fibrillation can be reduced by favoring a helical dimer, obtainable by replacing the three aromatic amino acids with leucines

Nuclear magnetic resonance-based metabolomics in respiratory medicine

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