Scientometric and Patentometric Analyses to Determine the Knowledge Landscape in Innovative Technologies: the Case of 3D Bioprinting

This research proposes an innovative data model to determine the landscape of emerging technologies. It is based on a competitive technology intelligence methodology that incorporates the assessment of scientific publications and patent analysis production, and is further supported by experts' feedback. It enables the definition of the growth rate of scientific and technological output in terms of the top countries, institutions and journals producing knowledge within the field as well as the identification of main areas of research and development by analyzing the International Patent Classification codes including keyword clusterization and co-occurrence of patent assignees and patent codes. This model was applied to the evolving domain of 3D bioprinting. Scientific documents from the Scopus and Web of Science data-bases, along with patents from 27 authorities and 140 countries, were retrieved. In total, 4782 scientific publications and 706 patents were identified from 2000 to mid-2016. The number of scientific documents published and patents in the last five years showed an annual average growth of 20% and 40%, respectively. Results indicate that the most prolific nations and institutions publishing on 3D bioprinting are the USA and China, including the Massachusetts Institute of Technology (USA), Nanyang Technological University (Singapore) and Tsinghua University (China), respectively. Biomaterials and Biofabrication are the predominant journals. The most prolific patenting countries are China and the USA; while Organovo Holdings Inc. (USA) and Tsinghua University (China) are the institutions leading. International Patent Classification codes reveal that most 3D bioprinting inventions intended for medical purposes apply porous or cellular materials or biologically active materials. Knowledge clusters and expert drivers indicate that there is a research focus on tissue engineering including the fabrication of organs, bioinks and new 3D bioprinting systems. Our model offers a guide to researchers to understand the knowledge production of pioneering technologies, in this case 3D bioprinting.This work was funded by Tecnologico de Monterrey through the Escuela de Ingenieria y Ciencias and also supported by a grant from the National Council for Science and Technology (CONACYT), Mexico (Grant number:261683).The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript

Green energy: identifying development trends in society using Twitter data mining to make strategic decisions

This study analyzes Twitter’s contribution to green energy. More than 200,000 global tweets sent during 2020 containing the terms “green energy” OR “greenenergy” were analyzed. The tweets were captured by web scraping and processed using algorithms and techniques for the analysis of massive datasets from social networks. In particular, relationships between users (through mentions) were determined according to the Louvain multilevel algorithm to identify communities and analyze global (density and centralization) and node-level (centrality) metrics. Subsequently, the content of the conversation was subject to semantic analysis (co-occurrence of the most relevant words), hashtag analysis (frequency analysis), and sentiment analysis (using the Vader model). The results reveal nine main communities and their leaders, as well as three main topics of conversation and the emotional state of the digital discussion. The main communities revolve around politics, socioeconomic issues, and environmental activism, while the conversations, which have developed mostly in positive terms, focus on green energy sources and storage, being aligned with the main communities identified, i.e., on political, socioeconomic, and climate change issues. Although most of the conversations have been about socioeconomic issues, the presence of leading company accounts was minor. The main aim of this work is to take the first steps toward an innovative competitive intelligence methodology to study and determine trends within different scientific fields or technologies in society that will enable strategic decisions to be made

TeknoAssistant : a domain specific tech mining approach for technical problem-solving support

This paper presents TeknoAssistant, a domain-specific tech mining method for building a problem-solution conceptual network aimed at helping technicians from a particular field to find alternative tools and pathways to implement when confronted with a problem. We evaluate our approach using Natural Language Processing field, and propose a 2-g text mining process adapted for analyzing scientific publications. We rely on a combination of custom indicators with Stanford OpenIE SAO extractor to build a Bernoulli Naive Bayes classifier which is trained by using domain-specific vocabulary provided by the TeknoAssistant user. The 2-g contained in the abstracts of a scientific publication dataset are classified in either "problem", "solution" or "none" categories, and a problem-solution network is built, based on the co-occurrence of problems and solutions in the abstracts. We propose a combination of clustering technique, visualization and Social Network Analysis indicators for guiding a hypothetical user in a domain-specific problem solving process

A Bibliometric Analysis in Industry 4.0 and Advanced Manufacturing: What about the Sustainable Supply Chain?

During the last decade, different concepts, methodologies, and technologies have appeared, evolving industry toward what we know today as the fourth industrial evolution or Industry 4.0 (I4.0) and Advanced Manufacturing (AM). Based on both, Supply Chain (SC) is presented as the relevant process that sets the sustainability of manufacturing and, therefore, is defined as a key term in a sustainable approach to I4.0. However, there are no studies that analyze the evolution of science in the fields of I4.0 and AM together. In order to fill this gap, the aim of this research work is to analyze the tendencies of science research related to I4.0 and AM by conducting a bibliometric and network analysis and also to generate a new contribution through the analysis of scientific trends related to SC and Sustainable Supply Chain (SSC) within this scientific context, for the time span 2010–2019. The results show that the number of publications is growing exponentially and the most active countries are Germany and the U.S., with Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University being the most productive organization and Tecnologico de Monterrey the most collaborative. The analysis of the scientific terms allows us to conclude that the research field is in a growth phase, generating up to almost 4500 new terms in 2019

A Method for the Detection and Characterization of Technology Fronts: Analysis of the Dynamics of Technological Change in 3D Printing Technology

This paper presents a method for the identification of the "technology fronts"-core technological solutions-underlying a certain broad technology, and the characterization of their change dynamics. We propose an approach based on the Latent Dirichlet Allocation (LDA) model combined with patent data analysis and text mining techniques for the identification and dynamic characterization of the main fronts where actual technological solutions are put into practice. 3D printing technology has been selected to put our method into practice for its market emergence and multidisciplinarity. The results show two highly relevant and specialized fronts strongly related with mechanical design that evolve gradually, in our opinion acting as enabling technologies. On the other side, we detected three fronts undergoing significant changes, namely layer-by-layer multimaterial manufacturing, data processing and stereolithograpy techniques. Laser and electron-beam based technologies take shape in the latter years and show signs of becoming enabling technologies in the future. The technology fronts and data revealed by our method have been convincing to experts and coincident with many technology trends already pointed out in technical reports and scientific literature

Design and implementation of a cloud computing adoption decision tool: Generating a cloud road

Migrating to cloud computing is one of the current enterprise challenges. This technology provides a new paradigm based on "on-demand payment" for information and communication technologies. In this sense, the small and medium enterprise is supposed to be the most interested, since initial investments are avoided and the technology allows gradual implementation. However, even if the characteristics and capacities have been widely discussed, entry into the cloud is still lacking in terms of practical, real frameworks. This paper aims at filling this gap, presenting a real tool already implemented and tested, which can be used as a cloud computing adoption decision tool. This tool uses diagnosis based on specific questions to gather the required information and subsequently provide the user with valuable information to deploy the business within the cloud, specifically in the form of Software as a Service (SaaS) solutions. This information allows the decision makers to generate their particular Cloud Road. A pilot study has been carried out with enterprises at a local level with a two-fold objective: To ascertain the degree of knowledge on cloud computing and to identify the most interesting business areas and their related tools for this technology. As expected, the results show high interest and low knowledge on this subject and the tool presented aims to readdress this mismatch, insofar as possible. Copyright: © 2015 Bildosola et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This work is funded by the program INNPACTO 2011; Project: (2011–2013) Plataforma Inteligente de Gestión Empresarial Basada en Cloud Computing y la WEB 2.0; Project Reference: IPT-2011-1805-430000. The Financing Entity was: Ministerio de Economía y Competitivida

Influential factors, drivers and barriers in competitive intelligence system implementations: case study and quantitative analysis

Every organizational change project will surely find obstacles and drivers, and the start-up of a competitive intelligence (CI) system confirms the rule. This paper describes the main features of six CI projects leaded by IK4-Ideko , pointing out the challenges and support they experienced while conducting it. This analysis is complemented by quantitatively analyzing the effects of 21 factors considered to have influence on CI system implementation. Results show that CI tools act as drivers and that no factor clearly acts as a barrier for CI, even though human factors are perceived to be nearly inconsequential. The overall handiness of information and the good execution of the first steps of the project can be important factors for future studies

Reformas organizativas Versus Burocracia. “El caso de la Universidad ESTIA”. Organizational Reforms versus Bureaucracy “The Case of ESTIA University”

ISSN 2314-3738International audienc

Global scientific trends in 3D bioprinting.

<p>A summary of the publications on 3D bioprinting that are indexed in Scopus and the Web of Science according to (A) publication output by year, from 2000 to 2015; (B) the 10 most frequent affiliation countries of the authors; (C) the 10 most frequent organizational affiliations of the authors (11 institutions are reported due to a tie for tenth place); and (D) the 10 journals with the most occurrences of the search terms.</p