A qualitative–quantitative study of science mapping by different algorithms: The Polish journals landscape

2020 ◽  
pp. 016555152090273
Author(s):  
Veslava Osinska
Keyword(s):  
Clustering Algorithms ◽  
Science Mapping ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Knowledge Domains ◽  
Clustering Quality ◽  
Information Studies ◽  
Quantitative And Qualitative Methods ◽  
Local Proximity

By applying different clustering algorithms, the author strived to construct the best visual representation of scientific domains and disciplines in Poland. Journals and their disciplinary categories constituted a data set. A comparative analysis of maps was based on both qualitative and quantitative approaches. Complex patterns of eight maps were evaluated taking into account both the local proximity of disciplines and the whole structure of presented domains. Final clustering quality value was introduced and calculated in reference to the knowledge domains. The authors underlined the role of quantitative and qualitative methods in combination in the mapping evaluation. The best results were obtained with the T-distributed stochastic neighbour embedding (t-SNE) algorithm. This youngest technique may have the biggest potential for semantic information studies and in the scope of broadly understood semantic solutions.

scholarly journals Understanding the Application of Science Mapping Tools in LIS and Non-LIS Domains

2020 ◽  
Vol 4 (2) ◽  
pp. 94-108 ◽  
Author(s):  
Wen Lou ◽  
Jie Zhang ◽  
Kai Li ◽  
Jiangen He
Keyword(s):  
Knowledge Discovery ◽  
Information Science ◽  
Scholarly Communication ◽  
Scientific Field ◽  
Library And Information Science ◽  
Science Mapping ◽  
Knowledge Domains ◽  
Research Questions ◽  
Scientific Fields

AbstractAs a scientific field, scientific mapping offers a set of standardized methods and tools which can be consistently adopted by researchers in different knowledge domains to answer their own research questions. This study examined the scientific articles that applied science mapping tools (SMT) to analyze scientific domains and the citations of these application articles. To understand the roles of these application articles in scholarly communication, we analyzed 496 application articles and their citations from 14 SMT by classifying them into library and information science (LIS) and other fields (non-LIS) in terms of both publication venues and analyzed domains. In our study, we found that science mapping, a topic that is deeply situated in the LIS field, has gained increasing attention from various non-LIS scientific fields over the last few years, especially since 2012. Science mapping application studies practically grew up in LIS domain and spread to other fields. The application articles within and outside of the LIS fields played different roles in advancing the application of science mapping and knowledge discovery. Especially, we have discovered the important role of articles, which studied non-LIS domains but published in LIS journals, in advancing the application of SMTs.

scholarly journals An Optimized Approach for Image Segmentation on Mobile Devices

2021 ◽  
Vol 12 (2) ◽  
pp. 3018-3024
Author(s):  
Hemantkumar R. Turkar, Et. al.
Keyword(s):  
Global Optimization ◽  
Image Segmentation ◽  
Clustering Algorithms ◽  
Smart Devices ◽  
Qualitative And Quantitative Analysis ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Standard Data ◽  
Quad Tree ◽  
Strong Ability

Now a day’s image segmentation is widely used in many multimedia applications. We have introduced the optimized approach for image segmentation based on clustering for use on smart devices. The proposed optimized approach is based on the combination of partitioning of images using quad-tree and Ant Colony Optimization. This approach utilizes the strong ability of ACO i.e global optimization. The proposed optimized algorithm is evaluated on images of standard data set and its performance is compared with existing clustering algorithms. The qualitative and quantitative analysis has been performed to measure the efficacy of the optimized approach over conventional existing algorithms. This procedure obtains better quality results than existing clustering algorithms.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Vocational Development in Emerging Adulthood

2018 ◽  
Author(s):  
Michael W. Pratt ◽  
M. Kyle Matsuba
Keyword(s):  
Turning Point ◽  
Life Stories ◽  
Life Story ◽  
Emerging Adult ◽  
Vocational Development ◽  
Data Set ◽  
The World ◽  
Key Concepts

Chapter 6 reviews research on the topic of vocational/occupational development in relation to the McAdams and Pals tripartite personality framework of traits, goals, and life stories. Distinctions between types of motivations for the work role (as a job, career, or calling) are particularly highlighted. The authors then turn to research from the Futures Study on work motivations and their links to personality traits, identity, generativity, and the life story, drawing on analyses and quotes from the data set. To illustrate the key concepts from this vocation chapter, the authors end with a case study on Charles Darwin’s pivotal turning point, his round-the-world voyage as naturalist for the HMS Beagle. Darwin was an emerging adult in his 20s at the time, and we highlight the role of this journey as a turning point in his adult vocational development.

scholarly journals Actors, Partisan Inclination, and Emotions: An Analysis of Government Shutdown News Stories Shared on Twitter

2021 ◽  
Vol 7 (2) ◽  
pp. 205630512110088
Author(s):  
Colin Agur ◽  
Lanhuizi Gan
Keyword(s):  
Emotional Responses ◽  
The United States ◽  
Online Information ◽  
News Frames ◽  
Data Set ◽  
Political Issues ◽  
News Stories ◽  
News Sharing ◽  
Government Shutdown

Scholars have recognized emotion as an increasingly important element in the reception and retransmission of online information. In the United States, because of existing differences in ideology, among both audiences and producers of news stories, political issues are prone to spark considerable emotional responses online. While much research has explored emotional responses during election campaigns, this study focuses on the role of online emotion in social media posts related to day-to-day governance in between election periods. Specifically, this study takes the 2018–2019 government shutdown as its subject of investigation. The data set shows the prominence of journalistic and political figures in leading the discussion of news stories, the nuance of emotions employed in the news frames, and the choice of pro-attitudinal news sharing.

Do the Heterogeneous Determinants of Repayment Affect Differently across Borrowers of Diverse Credit Sources in Rural Assam? A Double Hurdle Approach

2021 ◽  
pp. 245513332110316
Author(s):  
Tiken Das ◽  
Pradyut Guha ◽  
Diganta Das
Keyword(s):  
Rural Areas ◽  
Probit Model ◽  
Original Data ◽  
Community Based ◽  
Data Set ◽  
Brahmaputra Valley ◽  
Double Hurdle ◽  
Instrumental Variable Probit ◽  
Instrumental Variable Probit Model

This study made an attempt to answer the question: Do the heterogeneous determinants of repayment affect the borrowers of diverse credit sources differently? The study is based on data collected from 240 households from three districts in the lower Brahmaputra valley of Assam through a carefully designed primary survey. Besides, the study uses the double hurdle approach and the instrumental variable probit model to reduce possible selection bias. It observes better repayment performance among formal borrowers, followed by semiformal borrowers, while occupation wise it is prominent among organised employees. It has been found that in general, the household characteristics, loan characteristics and location-specific characteristics significantly affect repayment performance of borrowers. However, the nature of impact of the factors influencing repayment performance is remarkably different across credit sources. It ignores the role of traditional community-based organisations in rural Assam while analysing the determinants of repayment performance. The study also recommends for ensuring productive opportunities and efficient market linkages in rural areas of Assam. The study is based on an original data set that has specially been collected to examine question that—do the heterogeneous determinants of repayment affect the borrowers of diverse credit sources differently in the lower Brahmaputra valley of Assam—which has not been studied before.

scholarly journals Selecting single cell clustering parameter values using subsampling-based robustness metrics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ryan B. Patterson-Cross ◽  
Ariel J. Levine ◽  
Vilas Menon
Keyword(s):  
Single Cell ◽  
Optimal Parameter ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Parameter Selection ◽  
Data Set ◽  
Biologically Relevant ◽  
Cell Clustering ◽  
Parameter Values ◽  
Robustness Metrics

Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

scholarly journals Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

2020 ◽  
Vol 17 (10) ◽  
pp. 3730 ◽  
Author(s):  
Sina Shaffiee Haghshenas ◽  
Behrouz Pirouz ◽  
Sami Shaffiee Haghshenas ◽  
Behzad Pirouz ◽  
Patrizia Piro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Disease ◽  
Relative Humidity ◽  
Cost Function ◽  
Selection Process ◽  
Environmental Parameters ◽  
Pso Algorithm ◽  
Data Set ◽  
De Algorithm

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

Market responsiveness: antecedents and the moderating role of external supply chain integration

2017 ◽  
Vol 32 (1) ◽  
pp. 30-45 ◽  
Author(s):  
Tuan Luu
Keyword(s):  
Supply Chain ◽  
Supply Chain Integration ◽  
Manufacturing Companies ◽  
Data Set ◽  
Content Type ◽  
Business Context ◽  
Market Responsiveness ◽  
Positive Effect ◽  
Ambidextrous Leadership

Purpose The interaction between opening and closing behaviors of ambidextrous leadership produces “change” force throughout the organization in proactive response to market forces. This research aims to assess the role of ambidextrous leadership in fostering entrepreneurial orientation (EO) and market responsiveness. The research also seeks an insight into how external supply chain integration moderates the positive effect of EO on market responsiveness. Design/methodology/approach Research data were collected from 327 meso-level managers and 517 subordinates from chemical manufacturing companies in the Vietnam business context. Findings Research findings shed light on the positive effect of ambidextrous leadership on EO, which in turn contributes to market responsiveness. The moderation role that external supply chain integration plays on the EO–market responsiveness linkage was also grounded on the data set. Originality/value Through the identification of the predictive roles of ambidextrous leadership and EO for market responsiveness, the current research indicates the convergence between leadership, EO and market responsiveness research streams.

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