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Enriching and analyzing small citation networks: A case study on transistor’s history
Abstract
The development of modern electronics was to a large extent related to the advent and popularization of bipolar junction technology. The present work applies science of science concepts and methodologies in order to develop a relatively systematic, quantitative study of the development of electronics from a bipolar-junction perspective. First, we searched the adopted dataset (Microsoft Academic Graph) for entries related to “bipolar junction transistor”. Community detection was then applied in order to derive sub-areas, which were tentatively labeled into 10 overall groups. This modular graph was then studied from several perspectives, taking into account topological measurements as well as time evolution. A number of interesting results are reported, including a good level of thematic coherence within each identified area, as well as the identification of distinct periods over time including the onset and coming of age of bipolar junction technology and related areas. A particularly surprising result was the verification of a particularly stable interrelationship between the identified areas along time.
... For example, in [24] the authors analyzed two subareas of chemistry and found that some communities are potentially more related to the chemical methods while others to applications. In another work, the authors investigated a way to enrich citation networks that ended up being too small by expanding the core set of papers based on related cited materials [25]. ...
... In this study, we considered the Infomap method [29], which has been employed in related applications of the science of science studies [18,30]. This approach was also used in [24,25] for community detection before determining the respective labels. Even given the relative advantages of using Infomap in the addressed type of problems, noise and lack of citations can still affect the quality of the detected communities. ...
Citation networks can reveal much important information regarding the development of science and the relationship between different areas of knowledge. Frequently, citation networks are created using articles acquired from a set of relevant keywords or queries. Here, we study the robustness of citation networks communities with regards to the keywords that were used for collecting the respective articles. A perturbation approach is proposed, in which the influence of missing keywords on the community structure of citation networks is quantified. In addition, the relationship between keywords and the community structure of citation networks is studied using networks generated from a simple model. We find that, owing to its highly modular structure, the community structure of citation networks tends to be preserved even when many relevant keywords are left out. Furthermore, the proposed model can reflect the impact of missing keywords on different situations.
... For example, in [24] the authors analyzed two subareas of chemistry and found that some communities are potentially more related to the chemical methods while others to applications. In another work, the authors investigated a way to enrich citation networks that ended up being too small by expanding the core set of papers based on related cited materials [25]. ...
... In this study, we considered the Infomap method [29], which has been employed in related applications of the science of science studies [18,30]. This approach was also used in [24,25] for community detection before determining the respective labels. Even given the relative advantages of using Infomap in the addressed type of problems, noise and lack of citations can still affect the quality of the detected communities. ...
Citation networks can reveal many important information regarding the development of science and the relationship between different areas of knowledge. Thus, many studies have analyzed the topological properties of such networks. Frequently, citation networks are created using articles acquired from a set of relevant keywords or queries. Here, we study the robustness of citation networks with regards to the keywords that were used for collecting the respective articles. A perturbation approach is proposed, in which the influence of missing keywords on the topology and community structure of citation networks is quantified. In addition, the relationship between keywords and the community structure of citation networks is studied using networks generated from a simple model. We find that, owing to its highly modular structure, the community structure of citation networks tends to be preserved even when many relevant keywords are left out. Furthermore, the proposed model can reflect the impact of missing keywords on different situations.
... The first two represent the cities of São Carlos-SP, Brazil, and Oldenburg, Germany [55]. We also considered citation networks obtained from the Microsoft Academic Graph [56], referring to the areas of Transistors [57] and Capillary electrophoresis-mass spectrometry (CE-MS) [58]. The third type regards social networks, namely a sub-sample of Facebook [59] and a sub-sample of follower-following Twitter users related to the Anti-austerity movement in Spain Twitter [60]. ...
In this paper, we present the implementation of a Python library to calculate symmetry, accessibility, and generalized accessibility, measures that are used to characterize the topological properties of complex network nodes. The software is written as a Python C extension and provides both a fast implementation and user friendly API. In order to illustrate the use of the implemented resources, we present some applications. First, we show that accessibility can grasp information regarding the borders of the networks. Then, we illustrate that in a specific geographical network, the four considered measures are relatively little correlated among themselves, with some exceptions. To conclude, we compared the implemented measures and found that for a specific set of real-world networks covering different types, the symmetry measurements tended to yield more distinct characterizations.
Big bibliographic datasets hold promise for revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Yet, hosting proprietary and open big bibliographic datasets poses significant difficulties for libraries, both large and small. Libraries face significant barriers to hosting such assets, including cost and expertise, which has limited their ability to provide stewardship for big datasets, and thus has hampered researchers' access to them. What is needed is a solution to address the libraries' and researchers’ joint needs. This article outlines the theoretical framework that underpins the Collaborative Archive and Data Research Environment project. We recommend a shared cloud-based infrastructure to address this need built on five pillars: 1) C ommunity–a community of libraries and industry partners who support and maintain the platform and a community of researchers who use it; 2) A ccess–the sharing platform should be accessible and affordable to both proprietary data customers and the general public; 3) D ata-Centric–the platform is optimized for efficient and high-quality bibliographic data services, satisfying diverse data needs; 4) R eproducibility–the platform should be designed to foster and encourage reproducible research; 5) E mpowerment—the platform should empower researchers to perform big data analytics on the hosted datasets. In this article, we describe the many facets of the problem faced by American academic libraries and researchers wanting to work with big datasets. We propose a practical solution based on the five pillars: The Collaborative Archive and Data Research Environment. Finally, we address potential barriers to implementing this solution and strategies for overcoming them.
Argument
As historian Henning Schmidgen notes, the scientific study of the nervous system would have been “unthinkable” without the industrialization of communication in the 1830s. Historians have investigated extensively the way nerve physiologists have borrowed concepts and tools from the field of communications, particularly regarding the nineteenth-century work of figures like Helmholtz and in the American Cold War Era. The following focuses specifically on the interwar research of the Cambridge physiologist Edgar Douglas Adrian, and on the technology that led to his Nobel-Prize-winning research, the thermionic vacuum tube. Many countries used the vacuum tube during the war for the purpose of amplifying and intercepting coded messages. These events provided a context for Adrian's evolving understanding of the nerve fiber in the 1920s. In particular, they provide the background for Adrian's transition around 1926 to describing the nerve impulse in terms of “information,” “messages,” “signals,” or even “codes,” and for translating the basic principles of the nerve, such as the all-or-none principle and adaptation, into such an “informational” context. The following also places Adrian's research in the broader context of the changing relationship between science and technology, and between physics and physiology, in the first few decades of the twentieth century.
Technological evolution has been compared to biological evolution by many authors over the last two centuries. As a parallel experiment of innovation involving economic, historical, and social components, artifacts define a universe of evolving properties that displays episodes of diversification and extinction. Here, we critically review previous work comparing the two types of evolution. Like biological evolution, technological evolution is driven by descent with variation and selection, and includes tinkering, convergence, and contingency. At the same time, there are essential differences that make the two types of evolution quite distinct. Major distinctions are illustrated by current specific examples, including the evolution of cornets and the historical dynamics of information technologies. Due to their fast and rich development, the later provide a unique opportunity to study technological evolution at all scales with unprecedented resolution. Despite the presence of patterns suggesting convergent trends between man‐made systems end biological ones, they provide examples of planned design that have no equivalent with natural evolution.
Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.
In recent years, there has been a trend towards a science and technology policy that is applied in nature. In times of growing public discontent about high taxation and budget deficits, even basic science needs to document industry relevance. Counting patents apparently related to basic research activities through citations would be one way of measuring the relevance of basic research to industry. This has become especially interesting since Narin et al. [Narin, F., Hamilton, K.S., Olivastro, D., 1995. Linkage between agency supported research and patented industrial technology. Research Evaluation 5 (3), 183–187.] observed an increasing linkage between US technology and public science. The results of Narin et al. indicate a growing relationship between science and technology in a very general way. This idea of an increasingly science-based technology might convey the impression that there is direct knowledge-transfer taking place that is reflected in citations to scientific research papers in patents. A study of front pages of patents in the field of nanoscale technologies suggests that citation linkages hardly represent a direct link between cited paper and citing patent.
In the past several years studies have started to appear comparing the accuracies of various science mapping approaches. These studies primarily compare the cluster solutions resulting from different similarity approaches, and give varying results. In this study we compare the accuracies of cluster solutions of a large corpus of 2,153,769 recent articles from the biomedical literature (2004–2008) using four similarity approaches: co-citation analysis, bibliographic coupling, direct citation, and a bibliographic coupling-based citation-text hybrid approach. Each of the four approaches can be considered a way to represent the research front in biomedicine, and each is able to successfully cluster over 92% of the corpus. Accuracies are compared using two metrics—within-cluster textual coherence as defined by the Jensen-Shannon divergence, and a concentration measure based on the grant-to-article linkages indexed in MEDLINE. Of the three pure citation-based approaches, bibliographic coupling slightly outperforms co-citation analysis using both accuracy measures; direct citation is the least accurate mapping approach by far. The hybrid approach improves upon the bibliographic coupling results in all respects. We consider the results of this study to be robust given the very large size of the corpus, and the specificity of the accuracy measures used. © 2010 Wiley Periodicals, Inc.
A Family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced. These measures define centrality in terms of the degree to which a point falls on the shortest path between others and therefore has a potential for control of communication. They may be used to index centrality in any large or small network of symmetrical relations, whether connected or unconnected.
In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation Reports of the Science Citation Index and the Social Sciences Citation Index 2004, and then also in relation to local citation environments which can be considered as proxies of specialties and disciplines. Betweenness centrality is shown to be an indicator of the interdisciplinarity of journals, but only in local citation environments and after normalization because otherwise the influence of degree centrality (size) overshadows the betweenness-centrality measure. The indicator is applied to a variety of citation environments, including policy-relevant ones like biotechnology and nanotechnology.
Change is a fundamental ingredient of interaction patterns in biology, technology, the economy, and science itself: Interactions within and between organisms change; transportation patterns by air, land, and sea all change; the global financial flow changes; and the frontiers of scientific research change. Networks and clustering methods have become important tools to comprehend instances of these large-scale structures, but without methods to distinguish between real trends and noisy data, these approaches are not useful for studying how networks change. Only if we can assign significance to the partitioning of single networks can we distinguish meaningful structural changes from random fluctuations. Here we show that bootstrap resampling accompanied by significance clustering provides a solution to this problem. To connect changing structures with the changing function of networks, we highlight and summarize the significant structural changes with alluvial diagrams and realize de Solla Price's vision of mapping change in science: studying the citation pattern between about 7000 scientific journals over the past decade, we find that neuroscience has transformed from an interdisciplinary specialty to a mature and stand-alone discipline.
The web of relations linking technological innovation can be fairly described in terms of patent citations. The resulting patent citation network provides a picture of the large-scale organization of innovations and its time evolution. Here we study the patterns of change of patents registered by the U.S. Patent and Trademark Office. We show that the scaling behavior exhibited by this network is consistent with a preferential attachment mechanism together with a Weibull-shaped aging term. Such an attachment kernel is shared by scientific citation networks, thus indicating a universal type of mechanism linking ideas and designs and their evolution. The implications for evolutionary theory of innovation are discussed.
Fifty years ago, in November 1947, John Bardeen and Walter
Brattain discovered the transistor on the fourth floor of Building 1 at
Bell Labs in Murray Hill, NJ. Fifty years later, the authors are still
working with silicon but it is a very different silicon effort.
Currently with the silicon optical bench they are trying to integrate
optical components the way transistors have been over the last 50 years.
So, silicon technology is still progressing. When considering the
invention of the transistor, the authors note that the work of Bardeen
and Brattain was really a discovery not an invention. At the time they
discovered transistor action, they were investigating the nature of
surface states and ways to reduce their presence. It was only later that
things really became clear as to what was going on. Fifty years later
that discovery is celebrated, and the authors present a brief history of
the major events and the key people involved
The understanding of how science works can contribute to making scientific development more effective. In this paper, we report an analysis of the organization and the interconnection between unbalanced areas of study. More specifically, we considered two important subareas in analytical chemistry, namely capillary electrophoresis (CE) and mass spectrometry (MS). These areas are particularly interesting because MS is employed in a bigger range of applications than CE. Consequently, these different portions of papers can interfere in the quality of the searches for papers devoted to CE–MS. Here, we considered a citation network in which the nodes and connections represent papers and citations, respectively. The network clusters were detected by employing the Infomap algorithm. By considering the clusters and the respective abstracts, the subjects were identified. Interesting results were found, including a marked separation between some clusters of articles devoted to instrumentation techniques and applications, which was quantified from the abstract contents. For instance, the most well-defined community was assigned as CE (Instru.), with 73.8% of the papers having abstracts that include the word “electrophoresis” and not the word “mass”. However, the papers that describe CE–MS did not lead to a well-defined cluster. In order to better understand the organization of the citation network, we considered a multi-scale analysis, in which we used the information regarding sub-clusters. Firstly, we analyzed the sub-cluster that contains the first article devoted to the coupling between CE and MS, whose subject was found to be a good representation of its sub-cluster. The second analysis was about the sub-cluster of a seminal paper known to be the first that dealt with protein analysis by using CE–MS and a similar result was obtained. By considering the proposed methodologies, our paper can contribute to researchers working with similar scenarios, since it shows that a given subject can be spread on many clusters of the network, therefore, lead to better literature reviews.
How well integrated are theoretically and application oriented works in Physics currently? This interesting question, which has several relevant implications, has been approached mostly in a more subjective way. Recent concepts and methods from network science are used in the current work in order to develop a more principled, quantitative and objective approach to quantifying the integration and centrality of more theoretical/applied journals within the APS journals database, represented as a directed and undirected citation network. The results suggest a level of integration between more theoretical and applied journals, which are also characterized by remarkably similar centralities in the network.
In this paper we describe a new release of a Web scale entity graph that serves as the backbone of Microsoft Academic Service (MAS), a major production effort with a broadened scope to the namesake vertical search engine that has been publicly available since 2008 as a research prototype. At the core of MAS is a heterogeneous entity graph comprised of six types of entities that model the scholarly activities: field of study, author, institution, paper, venue, and event. In addition to obtaining these entities from the publisher feeds as in the previous effort, we in this version include data mining results from the Web index and an in-house knowledge base from Bing, a major commercial search engine. As a result of the Bing integration, the new MAS graph sees significant increase in size, with fresh information streaming in automatically following their discoveries by the search engine. In addition, the rich entity relations included in the knowledge base provide additional signals to disambiguate and enrich the entities within and beyond the academic domain. The number of papers indexed by MAS, for instance, has grown from low tens of millions to 83 million while maintaining an above 95% accuracy based on test data sets derived from academic activities at Microsoft Research. Based on the data set, we demonstrate two scenarios in this work: a knowledge driven, highly interactive dialog that seamlessly combines reactive search and proactive suggestion experience, and a proactive heterogeneous entity recommendation.
The use of science to understand its own structure is becoming popular, but
understanding the organization of knowledge areas is still limited because some
patterns are only discoverable with proper computational treatment of
large-scale datasets. In this paper, we introduce a network-based methodology
combined with text analytics to construct the taxonomy of science fields. The
methodology is illustrated with application to two topics: complex networks
(CN) and photonic crystals (PC). We built citation networks using data from the
Web of Science and used a community detection algorithm for partitioning to
obtain science maps of the fields considered. We also created an importance
index for text analytics in order to obtain keywords that define the
communities. A dendrogram of the relatedness among the subtopics was also
obtained. Among the interesting patterns that emerged from the analysis, we
highlight the identification of two well-defined communities in PC area, which
is consistent with the known existence of two distinct communities of
researchers in the area: telecommunication engineers and physicists. With the
methodology, it was also possible to assess the interdisciplinary and time
evolution of subtopics defined by the keywords. The automatic tools described
here are potentially useful not only to provide an overview of scientific areas
but also to assist scientists in performing systematic research on a specific
topic.
Branly and the telecommunications industry are discussed. Edouard Branly, during the early years of his career was elected one ballot ahead of Marie Curie and was successful scientist. He himself denied being one of the fathers of radio communications; he is more accurately the discoverer of a physical effect (the Branly effect) that gave him the opportunity to devise an efficient wave sensor (the coherer) that permitted the invention of radio. Branly had devised a first circuit to create sparks using the Wimshurt machine, and then a second very simple circuit: a Daniel battery, a galvanometer, and a metallic disk all wired together in series. Branly called the detecting device as the tube filled in with metallic particles as a radio conductor. In 1906 Braun replaced the coherer by a crystal of Galena used as a rectifier. Branly died in 1940 at the age of 96.
Many networked systems, including physical, biological, social, and technological networks, appear to contain ``communities'' -- groups of nodes within which connections are dense, but between which they are sparser. The ability to find such communities in an automated fashion could be of considerable use. Communities in a web graph for instance might correspond to sets of web sites dealing with related topics, while communities in a biochemical network or an electronic circuit might correspond to functional units of some kind. We present a number of new methods for community discovery, including methods based on ``betweenness'' measures and methods based on modularity optimization. We also give examples of applications of these methods to both computer-generated and real-world network data, and show how our techniques can be used to shed light on the sometimes dauntingly complex structure of networked systems.
In the highly competitive world, there has been a concomitant increase in the need for the research and planning methodology, which can perform an advanced assessment of technological opportunities and an early perception of threats and possibilities of the emerging technology according to the nation’s economic and social status.
This research is aiming to provide indicators and visualization methods to measure the latest research trend and aspect underlying scientific and technological documents to researchers and policy planners using “co-word analysis”. Information Security field is a highly prospective market value. In this paper, we presented an analysis Information Security.
Co-word analysis was employed to reveal patterns and trends in the Information Security fields by measuring the association strength of terms representatives of relevant publications or other texts produced in the Information Security field. Data were collected from SCI and the critical keywords could be extracted from the author keywords. These extracted keywords were further standardized. In order to trace the dynamic changes in the Information Security field, we presented a variety of technology mapping. The results showed that the Information Security field has some established research theme and also rapidly transforms to embrace new themes.
In this paper, we compared structures of the citation network of scientific publications with those of patents, and discussed the differences between them. A case study was performed in a solar cell to develop a method of detecting gaps between science and technology. Scientific research has tended to be more basic, especially in terms of cell design, whereas patents have focused on more applied technology used in solar cell modules. Of the major citation clusters of scientific publications, only two, namely silicon and compound solar cells, corresponded semantically with patent clusters. Conversely, there were no patent clusters corresponding to the other two scientific research fronts, namely dye-sensitized and polymer solar cells. These research areas could be regarded as opportunities for industrial commercialization because scientific activities exist but not technological applications. Our results could offer an intellectual basis for discovering potential opportunities for industrial commercialization.
This report describes the results of automatic processing of a large number of scientific papers according to a rigorously defined criterion of coupling. The population of papers under study was ordered into groups that satisfy the stated criterion of interrelation. An examination of the papers that constitute the groups shows a high degree of logical correlation.
There is a rich literature on how science and technology are related to each other. Patent citation analysis is amongst the
most frequently used to tool to track the strengths of links. In this paper we explore the relationship between patent citations
and citation impact in nanoscience. Our observations indicate that patent-cited papers perform better in terms of standard
bibliometric indicators than comparable publications that are not linked to technology in this way. More specifically, we
found that articles cited in patents are more likely to be cited also by other papers. The share of highly cited papers is
the most striking result. Instead of the average of 4% of all papers, 13.8% of the papers cited once or twice in patents fall
into this category and even 23.5% of the papers more frequently cited in patents receive citation rates far above the standard.
Our analyses further demonstrate the presence and the relevance of bandwagon effects driving the development of science and
technology.
Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties, with the aim to apply centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of twenty years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness, betweenness, degree and PageRank) for authors in this network. We find out that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking, and suggest that centrality measures can be useful indicators for impact analysis. Comment: 17 pages, 4 figures
Many real-world networks are so large that we must simplify their structure before we can extract useful information about the systems they represent. As the tools for doing these simplifications proliferate within the network literature, researchers would benefit from some guidelines about which of the so-called community detection algorithms are most appropriate for the structures they are studying and the questions they are asking. Here we show that different methods highlight different aspects of a network's structure and that the the sort of information that we seek to extract about the system must guide us in our decision. For example, many community detection algorithms, including the popular modularity maximization approach, infer module assignments from an underlying model of the network formation process. However, we are not always as interested in how a system's network structure was formed, as we are in how a network's extant structure influences the system's behavior. To see how structure influences current behavior, we will recognize that links in a network induce movement across the network and result in system-wide interdependence. In doing so, we explicitly acknowledge that most networks carry flow. To highlight and simplify the network structure with respect to this flow, we use the map equation. We present an intuitive derivation of this flow-based and information-theoretic method and provide an interactive on-line application that anyone can use to explore the mechanics of the map equation. We also describe an algorithm and provide source code to efficiently decompose large weighted and directed networks based on the map equation. Comment: 9 pages and 3 figures, corrected typos. For associated Flash application, see http://www.tp.umu.se/~rosvall/livemod/mapequation/
Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
The stimulation of technological development by scientific discovery is a widely accepted premise. Somewhat less recognized is the inverse relationship, the stimulation of science by technology. Examples of the latter are the advances in thermodynamics following the development of the steam engine by Watt; and the advancement of the understanding of thermionic emission into gases, by Langmuir, following his own invention of the nitrogen-filled electric bulb.In this paper, we discuss several examples of contributions to science at IBM Research which were generated by our technological effort. These are of two types. The first one comprises scientific investigation stimulated by the need to understand some technological problems. Examples are the ‘two-dimensional electron gas’ investigations inspired by the desire to understand the physical properties of the planar field-effect transistors; or contributions to the theory of magnetism stimulated by the advent of ‘magnetic bubbles’ as a promising storage medium. The second type of stimulation of science by technology is the creation of new tools for scientific investigation. Examples are advances in electron-beam microscopy, originating from a search for improved microfabrication techniques; or the development of tunable lasers, coming out of a search for new devices, not necessarily with a computer orientation, and finding a wide range of applications in physics, chemistry and biology.Beyond enumerating several examples such as the above, we have made an attempt to identify factors contributing to the cross-fertilization of science and technology. We propose three as among the most important ones: strong interactions among scientists and technologists; novelty of the technology involved; and initiative coupled with a searching attitude for underlying principes and phenomena on the part of the researchers involved.
The relationship between industry and universities
- Liévana
The audion: a new receiver for wireless telegraphy
- De Forest
Resistance measurement of a resistor in a bipolar junction transistor (BJT)-based power stage
- S Agarwal
- R Singh
Two terminal drive of bipolar junction transistor (BJT) of a light emitting diode (LED)-based bulb
- S Maru
- J L Melanson
- R Singh
- E King
- T Rengachari
- R Zanbaghi
- A K Dutta
Compensating for a reverse recovery time period of a bipolar junction transistor (BJT) in switch-mode operation of a light-emitting diode (LED)-based bulb
- J L Melanson
- T Rengachari
- S Maru
- R Zanbaghi
- F Azrai
- R Singh
Detection and control mechanism for tail current in a bipolar junction transistor (BJT)-based power stage
- R Zanbaghi
Switch-mode drive sensing of reverse recovery in bipolar junction transistor (BJT)-based power converters
- J L Melanson
- R Zanbaghi
- T Rengachari
- P Drakshapalli
- R Singh
- A K Dutta
Single pin control of bipolar junction transistor (BJT)-based power stage
- R Zanbaghi
- J L Melanson
- J Choi