scholarly journals Power Quality: Scientific Collaboration Networks and Research Trends

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2067 ◽  
Author(s):  
Francisco Montoya ◽  
Raul Baños ◽  
Alfredo Alcayde ◽  
Maria Montoya ◽  
Francisco Manzano-Agugliaro
Keyword(s):  
Power Quality ◽  
Scientific Collaboration ◽  
Research Field ◽  
Research Trends ◽  
Collaboration Network ◽  
Collaboration Networks ◽  
Different Types ◽  
Voltage Frequency ◽  
Advanced Model ◽  
Proper Operation

Power quality is a research field related to the proper operation of devices and technological equipment in industry, service, and domestic activities. The level of power quality is determined by variations in voltage, frequency, and waveforms with respect to reference values. These variations correspond to different types of disturbances, including power fluctuations, interruptions, and transients. Several studies have been focused on analysing power quality issues. However, there is a lack of studies on the analysis of both the trending topics and the scientific collaboration network underlying the field of power quality. To address these aspects, an advanced model is used to retrieve data from publications related to power quality and analyse this information using a graph visualisation software and statistical tools. The results suggest that research interests are mainly focused on the analysis of power quality problems and mitigation techniques. Furthermore, they are observed important collaboration networks between researchers within and across countries.

scholarly journals Multi-hop assortativities for network classification

2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne
Keyword(s):  
Machine Learning ◽  
Scientific Collaboration ◽  
State Of The Art ◽  
Medical Engineering ◽  
Research Field ◽  
Classification Task ◽  
Collaboration Network ◽  
Structural Patterns ◽  
Art Methods

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

scholarly journals Uma Análise da Produção Técnica e de Colégios Invisíveis com Ênfase nas Patentes Brasileiras

2021 ◽  
Author(s):  
Thiago Magela Rodrigues Dias ◽  
João Vitor de Melo Machado ◽  
Patrícia Mascarenhas Dias
Keyword(s):  
Social Network ◽  
Scientific Collaboration ◽  
Temporal Analysis ◽  
Collaboration Network ◽  
Collaboration Networks ◽  
Collaboration Process ◽  
Network Metrics ◽  
Technical Products ◽  
Identification And Characterization

Analyzes of scientific collaboration networks have been extensively explored in research from different areas of knowledge, in view of their ability to identify how groups of researchers have carried out their work collectively. Such studies make it possible to identify how collaboration between individuals occurs through analyzes based on social network metrics. In this context, new studies have been proposed in order to analyze collaboration in the development of technical products, with data on patents being studied in most studies. This type of analysis is relevant because it makes it possible to understand the collaboration process in the proposal of new inventions. In this work, initially a general characterization of the group of individuals analyzed is presented, and afterwards, a global and temporal analysis of the collaboration network is performed in the proposal of patents of Brazilian individuals with curricula registered in the Lattes Platform. For that, all the patents registered in the curricula of these individuals were used for the identification and characterization of the collaboration networks. As a result, it is possible to see how collaboration in the proposed inventions of the analyzed set has been intensified over the years, with an emphasis on the institutions and areas of expertise of each inventor.

Homophily versus preferential attachment: Evolutionary mechanisms of scientific collaboration networks

2014 ◽  
Vol 25 (05) ◽  
pp. 1440014 ◽  
Author(s):  
Zhen-Zhen Wang ◽  
Jonathan J. H. Zhu
Keyword(s):  
Scientific Collaboration ◽  
Preferential Attachment ◽  
Network Evolution ◽  
Collaboration Network ◽  
Supporting Evidence ◽  
Collaboration Networks ◽  
Evolutionary Mechanisms ◽  
Active Choice ◽  
Scholarly Collaboration ◽  
Over Time

Homophily and preferential attachment are among the most recognized mechanisms of network evolution. Instead of examining the two mechanisms separately, this study considers them jointly in a scholarly collaboration network. Specifically, when a new scholar enters a field, how does he/she choose the first collaborator from the pool of available scholars? We find that new scholars tend to collaborate with someone who works in the same institution (which is called constrained acceptance), shares similar specialty interests (active choice), or has already worked with many collaborators (random action). We view constrained acceptance and active choice as supporting evidence for homophily (because similarity is attractive) and random action as supporting evidence for preferential attachment (because popularity is attractive). As such, both homophily and preferential attachment affect the evolution of collaboration networks. Furthermore, the influences vary over time with random action, constrained acceptance, and active choice taking turns to act the dominant force at the beginning, middle and later phases of the evolution process, respectively.

Scientific collaboration networks and firm innovation: the contingent impact of a dynamic environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Yang ◽  
Jing Zhang ◽  
Deming Zeng
Keyword(s):  
Scientific Collaboration ◽  
Negative Binomial ◽  
Dynamic Environment ◽  
Collaboration Network ◽  
High Tech ◽  
Market Dynamics ◽  
Collaboration Networks ◽  
Firm Innovation ◽  
Content Type ◽  
The Impact

PurposeThe environment in high-tech industries is highly dynamic, and after COVID-19, it has become even more unpredictable. Hence, it has become critical for firms to develop strategies to cope with a highly dynamic environment. This paper aims to analyze how the impact of the scientific collaboration networks with URIs (universities and research institutes) on firm innovation performance is contingent on technological and market dynamics.Design/methodology/approachUsing a sample of 174 Chinese firms in the new-energy vehicle industry during 2004–2015, the authors applied a random-effects negative binomial modeling approach to model these relationships.FindingsA broad and strong scientific collaboration network promotes firm innovation network effects are contingent on technological and market dynamics. While technological dynamics strengthen the effect market dynamics weaken it due to the different purposes of collaboration for firms and URIs.Practical implicationsFirms should adjust the structure of scientific collaboration networks with URIs when facing different environments. The government should encourage firms to jointly research with diverse URIs and play an active role in stabilizing market environments.Originality/valueThis study contributes to the academic debate on university-industry scientific collaborations. Applying the temporary competitive advantage (TCA) framework, we provide nuances to the literature that studies the factors that condition the effects of networks. This study also adds to the research on firm scientific collaboration networks by measuring networks based on the coauthorship between firms and URIs.

scholarly journals A Survey on Blockchain-Based Search Engines

2021 ◽  
Vol 11 (15) ◽  
pp. 7063
Author(s):  
Esmaeel Rezaee ◽  
Ali Mohammad Saghiri ◽  
Agostino Forestiero
Keyword(s):  
Search Engines ◽  
Research Field ◽  
The Internet ◽  
Context Aware ◽  
Search System ◽  
Search Results ◽  
Related Information ◽  
Research Article ◽  
Different Types ◽  
Search History

With the increasing growth of different types of data, search engines have become an essential tool on the Internet. Every day, billions of queries are run through few search engines with several privacy violations and monopoly problems. The blockchain, as a trending technology applied in various fields, including banking, IoT, education, etc., can be a beneficial alternative. Blockchain-based search engines, unlike monopolistic ones, do not have centralized controls. With a blockchain-based search system, no company can lay claims to user’s data or access search history and other related information. All these data will be encrypted and stored on a blockchain. Valuing users’ searches and paying them in return is another advantage of a blockchain-based search engine. Additionally, in smart environments, as a trending research field, blockchain-based search engines can provide context-aware and privacy-preserved search results. According to our research, few efforts have been made to develop blockchain use, which include studies generally in the early stages and few white papers. To the best of our knowledge, no research article has been published in this regard thus far. In this paper, a survey on blockchain-based search engines is provided. Additionally, we state that the blockchain is an essential paradigm for the search ecosystem by describing the advantages.

Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction

2021 ◽  
Vol 15 (3) ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Feng Xia ◽  
Jian Wu ◽  
Zhiguo Gong ◽  
Hanghang Tong ◽  
...  
Keyword(s):  
Scientific Collaboration ◽  
Early Stage ◽  
Collaboration Network ◽  
Vector Representation ◽  
Network Embedding ◽  
Machine Learning Methods ◽  
Academic Networks ◽  
Special Relationships ◽  
Real World Datasets ◽  
Vector Representations

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining.

scholarly journals A Review of Climate-Smart Agriculture Research and Applications in Africa

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1255
Author(s):  
Paul M. Barasa ◽  
Christina M. Botai ◽  
Joel O. Botai ◽  
Tafadzwanashe Mabhaudhi
Keyword(s):  
Climate Change ◽  
Research Field ◽  
Successful Implementation ◽  
Collaboration Networks ◽  
African Countries ◽  
Government Programs ◽  
Agricultural Adaptation ◽  
Strategic Plans ◽  
Smart Agriculture ◽  
Support Decision Making

Funders and governments are promoting climate-smart agriculture (CSA) as key to agricultural adaptation under climate change in Africa. However, with its progressions still at the policy level and framework description, there is a need to understand the current developments and activities conducted within the CSA research field. We conducted a scientific mapping and analyses of CSA research studies in Africa to understand the (i) thematic trends, (ii) developments, (iii) nature of collaboration networks, and (iv) general narratives supporting the adoption and application of CSA in Africa. Results show that several African countries had endorsed CSA as an approach to addressing agricultural productivity challenges, supporting adaptation strategies, and building resilience to climate change. However, a majority do not have national Climate-Smart Agriculture Investment Plans (CSAIPs). Additionally, CSA research in Africa is still developing, with only a few countries dominating the research outputs. For a successful implementation of CSA, a framework provided by the CSAIPs must be established to guide the processes. This will provide a framework to guide the integration of government programs, policies, and strategic plans by combining other inputs from stakeholders to support decision making and implementation of CSA.

Scientific collaboration networks

Physics Today ◽  
2018 ◽  
Vol 71 (2) ◽  
pp. 72-72
Author(s):  
Richard J. Fitzgerald
Keyword(s):  
Scientific Collaboration ◽  
Collaboration Networks
Sign in / Sign up

Export Citation Format

Share Document