scholarly journals A Text Mining Based Map of Engineering Design: Topics and their Trajectories Over Time

2019 ◽  
Vol 1 (1) ◽  
pp. 2765-2774 ◽  
Author(s):  
Filippo Chiarello ◽  
Nicola Melluso ◽  
Andrea Bonaccorsi ◽  
Gualtiero Fantoni
Keyword(s):  
Text Mining ◽  
Engineering Design ◽  
State Of The Art ◽  
Topic Modelling ◽  
Specific Topic ◽  
Scientific Publications ◽  
New Knowledge ◽  
New Research ◽  
Large Corpus ◽  
Over Time

AbstractThe Engineering Design field is growing fast and so is growing the number of sub-fields that are bringing value to researchers that are working in this context. From psychology to neurosciences, from mathematics to machine learning, everyday scholars and practitioners produce new knowledge of potential interest for designers.This leads to complications in the researchers’ aims who want to quickly and easily find literature on a specific topic among a large number of scientific publications or want to effectively position a new research.In the present paper, we address this problem by using state of the art text mining techniques on a large corpus of Engineering Design related documents. In particular, a topic modelling technique is applied to all the papers published in the ICED proceedings from 2003 to 2017 (3,129 documents) in order to find the main subtopics of Engineering Design. Finally, we analyzed the trends of these topics over time, to give a bird-eye view of how the Engineering Design field is evolving.The results offer a clear and bottom-up picture of what Engineering design is and how the interest of researchers in different topics has changed over time.

Mapping the field of knowledge management: a bibliometric analysis using R

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rayees Farooq
Keyword(s):  
Knowledge Management ◽  
Bibliometric Analysis ◽  
Digital Libraries ◽  
Analytical Techniques ◽  
R Package ◽  
Scientific Publications ◽  
Content Type ◽  
Number Of Publications ◽  
New Research ◽  
Over Time

Purpose The purpose of this study is to offer the bibliometric analysis of the Journal of Knowledge Management (JKM) to understand how the literature has developed over time. Design/methodology/approach This study used bibliometric approaches to analyze a sample of 669 studies from 1997 to 2021. This study focused on performance analysis and scientific mapping of articles using the R package. Findings The results indicate that the number of publications during the period has significantly increased which shows a growing interest of researchers in the JKM. This study highlights new emerging themes such as change management, change readiness, product innovation and digital libraries which uncover exciting avenues for new research opportunities. USA and UK were the most productive countries in terms of the number of citations followed by few European countries including Spain, Finland, Germany and Sweden. However, it is worth noting that India was the most productive country in the emerging economies. Practical implications This study will act as a guide for researchers of various fields to evaluate the development of scientific publications in a particular theme over time, especially for those who are in the field of knowledge management (KM). Originality/value This study aims to accomplish the systematic bibliometric analysis of the JKM for more than two decades, providing useful insights into the key developments in the field of KM. This study is more rigorous and comprehensive in terms of the analytical techniques used.

Intersection of Resilience and COVID-19: Structural Topic Modelling and Word Embeddings from Reddit Titles (Preprint)

2021 ◽  
Author(s):  
Alejandro Garcia-Rudolph ◽  
Blanca Cegarra ◽  
Joan Sauri ◽  
John D. Kelleher ◽  
Katryna Cisek ◽  
...  
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
R Package ◽  
Topic Modelling ◽  
Word Embeddings ◽  
Scientific Publications ◽  
Self Presentation ◽  
Semantic Coherence ◽  
Preliminary Validation ◽  
Media Platform

BACKGROUND Topic modeling and word embeddings’ studies of Twitter data related to COVID-19 are being extensively reported. Another social media platform that experienced a tremendous increase in new users and posts due to COVID-19 was Reddit, offering a much less explored alternative, especially the submissions’ titles, due to their format (≤ 300 characters) and content rules. The positivity of self-presentation on social media has an influence on both the quantity and quality of reactions (upvotes) from other social media contacts. OBJECTIVE 1) Expand on the concept of resilience identifying possible related topics considering their number of upvotes and its closest terms and 2) Associate specific emotions obtained from the state-of-the-art literature to their closest terms in order to relate such emotions to experienced situations. METHODS Reddit data were collected from pushshift.io, with the pushshiftr R package, data cleaning and preprocessing was performed using quanteda, tidyverse, tidytext R packages. A word2vec model (W2V) was trained using submissions’ titles, preliminary validation was performed using a subset of Mikolov’s analogies and a COVID-19 glossary. The W2V model was trained with the wordVectors R package. Main topics (represented as sets of words) using the number of upvotes as covariate were extracted using structural topic modelling (STM) with the spectral methos using the stm R package. Topics validation was performed using semantic coherence and exclusivity. Clusters were assessed using Dunn index. RESULTS We collected all 374,421 titles submitted by 104,351 different redditors to the r/Coronavirus subreddit between January 20th 2020 and 14th May 2021. We trained W2V and identified more than 20 valid analogies (e.g. doctor – hospital + teacher = school). We further validated W2V with representative terms extracted from a COVID-19 glossary, all closest terms retrieved by W2V were verified using state of the art publications. STM retrieved 20 topics (with 20 words each) ordered by their number of upvotes, we run W2V in a representative topic (addressing vaccines) and we used two terms as seeds leading to other related terms (represented using cluster analysis) that we validated using scientific publications. STM did not retrieve any topic containing the term “resilience”, it hardly appeared (less than 0.02%) in all titles. Nevertheless we identified several closest terms (e.g. wellbeing, roadmap) and combined terms (e.g. resilience and elderly, resilience and indigenous) as well as specific emotions that W2V related to lived experiences (e.g. the emotion of gratitude associated to applauses and balconies). CONCLUSIONS We applied for the first time the combination of STM and a word2vec model trained with a relatively small Coronavirus dataset of Reddit titles, leading to immediate and accurate terms that can be used to expand our knowledge on topics associated to the pandemic (e.g. vaccines) or specific aspects such as resilience.

scholarly journals Applying a text mining framework to the extraction of numerical parameters from scientific literature in the biotechnology domain

2013 ◽  
Vol 1 (1) ◽  
pp. 1-8 ◽  
Author(s):  
André Santos ◽  
Regina Nogueira ◽  
Anália Lourenço
Keyword(s):  
Text Mining ◽  
Scientific Literature ◽  
Scientific Publications ◽  
Text Documents ◽  
Manual Curation ◽  
Physico Chemical ◽  
Process Wastewater ◽  
And Performance ◽  
The Stability ◽  
New Research

Scientific publications are the main vehicle to disseminate information in the field of biotechnology for wastewater treatment. Indeed, the new research paradigms and the application of high-throughput technologies have increased the rate of publication considerably. The problem is that manual curation becomes harder, prone-to-errors and time-consuming, leading to a probable loss of information and inefficient knowledge acquisition. As a result, research outputs are hardly reaching engineers, hampering the calibration of mathematical models used to optimize the stability and performance of biotechnological systems. In this context, we have developed a data curation workflow, based on text mining techniques, to extract numerical parameters from scientific literature, and applied it to the biotechnology domain. A workflow was built to process wastewater-related articles with the main goal of identifying physico-chemical parameters mentioned in the text. This work describes the implementation of the workflow, identifies achievements and current limitations in the overall process, and presents the results obtained for a corpus of 50 full-text documents.

Investment Traits and Intelligence in Adulthood

2013 ◽  
Vol 34 (2) ◽  
pp. 82-89 ◽  
Author(s):  
Sophie von Stumm
Keyword(s):  
Individual Differences ◽  
Personality Traits ◽  
Fluid Intelligence ◽  
Need For Cognition ◽  
Openness To Experience ◽  
Intelligence Tests ◽  
Crystallized Intelligence ◽  
New Knowledge ◽  
The Relationship ◽  
Over Time

Intelligence-as-knowledge in adulthood is influenced by individual differences in intelligence-as-process (i.e., fluid intelligence) and in personality traits that determine when, where, and how people invest their intelligence over time. Here, the relationship between two investment traits (i.e., Openness to Experience and Need for Cognition), intelligence-as-process and intelligence-as-knowledge, as assessed by a battery of crystallized intelligence tests and a new knowledge measure, was examined. The results showed that (1) both investment traits were positively associated with intelligence-as-knowledge; (2) this effect was stronger for Openness to Experience than for Need for Cognition; and (3) associations between investment and intelligence-as-knowledge reduced when adjusting for intelligence-as-process but remained mostly significant.

Interdisciplinary knowledge combinations and emerging technological topics: Implications for reducing uncertainties in research evaluation

2020 ◽  
Author(s):  
Seokbeom Kwon ◽  
Jan Youtie ◽  
Alan L Porter
Keyword(s):  
Drug Delivery ◽  
Synthetic Biology ◽  
Interdisciplinary Research ◽  
Autonomous Vehicles ◽  
Research Evaluation ◽  
Scientific Publications ◽  
Interdisciplinary Knowledge ◽  
Over Time

Abstract This article puts forth a new indicator of emerging technological topics as a tool for addressing challenges inherent in the evaluation of interdisciplinary research. We present this indicator and test its relationship with interdisciplinary and atypical research combinations. We perform this test by using metadata of scientific publications in three domains with different interdisciplinarity challenges: Nano-Enabled Drug Delivery, Synthetic Biology, and Autonomous Vehicles. Our analysis supports the connection between technological emergence and interdisciplinarity and atypicality in knowledge combinations. We further find that the contributions of interdisciplinary and atypical knowledge combinations to addressing emerging technological topics increase or stay constant over time. Implications for policymakers and contributions to the literature on interdisciplinarity and evaluation are provided.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

scholarly journals C3VFC: A Method for Tracing and Quantification of Microglia in 3D Temporal Images

2021 ◽  
Vol 11 (13) ◽  
pp. 6078
Author(s):  
Tiffany T. Ly ◽  
Jie Wang ◽  
Kanchan Bisht ◽  
Ukpong Eyo ◽  
Scott T. Acton
Keyword(s):  
Vector Field ◽  
Critical Points ◽  
State Of The Art ◽  
Temporal Data ◽  
Entire Structure ◽  
Tracing Algorithm ◽  
Vector Field Convolution ◽  
Multiple Cells ◽  
Tracing Method ◽  
Over Time

Automatic glia reconstruction is essential for the dynamic analysis of microglia motility and morphology, notably so in research on neurodegenerative diseases. In this paper, we propose an automatic 3D tracing algorithm called C3VFC that uses vector field convolution to find the critical points along the centerline of an object and trace paths that traverse back to the soma of every cell in an image. The solution provides detection and labeling of multiple cells in an image over time, leading to multi-object reconstruction. The reconstruction results can be used to extract bioinformatics from temporal data in different settings. The C3VFC reconstruction results found up to a 53% improvement on the next best performing state-of-the-art tracing method. C3VFC achieved the highest accuracy scores, in relation to the baseline results, in four of the five different measures: Entire structure average, the average bi-directional entire structure average, the different structure average, and the percentage of different structures.

Leven met water

2019 ◽  
Vol 4 (4) ◽  
pp. 265-268
Author(s):  
MICHEL LASCARIS
Keyword(s):  
The Netherlands ◽  
Cultural Heritage ◽  
Climate Adaptation ◽  
Adaptation Strategies ◽  
Digital Map ◽  
Digital Maps ◽  
Pumping Stations ◽  
New Challenges ◽  
New Research ◽  
Over Time

Living with water. The Dijkenkaart of the Netherlands De Cultural Heritage Agency made an interesting digital map (in GIS) of all the dikes in the Netherlands. This was possible by using existing digital maps, but new research was necessary to make this general overview. There was discussion about the dating of dikes, because dikes can be of medieval origin, but were adjusted over time. Besides dikes, researchers find GIS and historical information on poldermills, kolks, reclamations and pumping stations. That is why this map is called ‘Living with water’, because this information can help addressing new challenges in climate adaptation strategies dealing with water. Everyone can take a look, or download the map in GIS, on www.cultureelerfgoed.nl/onderwerpen/bronnen-en-kaarten/overzicht/levenmet-water-kaart.

Engineering Design: A Perspective on U.K. and Swiss Developments

1990 ◽  
Author(s):  
W. Ernst Eder
Keyword(s):  
United Kingdom ◽  
Engineering Design ◽  
State Of The Art ◽  
International Group ◽  
The United Kingdom ◽  
Current State

Abstract Historic developments, and the current state of the art are surveyed in thought, theory, methods and methodology, and education for and about engineering design. This survey covers two particular and disparate regions, the United Kingdom, and Switzerland, the latter including the activities of an international group centered in Zürich known as WDK — Workshop Design-Konstruktion.

A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Information Retrieval ◽  
Network Analysis ◽  
Text Mining ◽  
Engineering Design ◽  
Large Scale ◽  
Semantic Network ◽  
Document Retrieval ◽  
Design Information ◽  
Improve Design ◽  
Correlation Degree

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

Sign in / Sign up

Export Citation Format

Share Document