Category Archives: Data Type

A framework for enhancing the fresh food retail supply chain performance: evidence from India

The paper identifies the improvement initiatives for enhancing the fresh food retail supply chain performance w.r.t. time, cost, quality, customer service and profitability. To identify the critical initiatives, a thorough analysis of the literature followed by validation in two fresh food retail supply chains were carried using a case-based approach. Data were collected by conducting the semi-structured interviews among the senior executive selected using snowball sampling technique and responses were analysed using manual coding followed by validating through a text mining software. The VantagePoint software identified the probable relationships amongst the initiatives and with the performance measures. It also clustered the initiatives and identified their strength of relationships. Cross-case syntheses of two ‘farm to fork’ models were facilitated in developing the research propositions. The study presents a comprehensive framework of initiatives comprising of supplier and customer related initiatives along with a unique and prominent initiative in food, i.e., product and process initiatives.

https://www.inderscienceonline.com/doi/abs/10.1504/IJSOM.2021.119804

Author(s): Rose Antony, Vivekanand B. Khanapuri, Karuna Jain
Organization(s): National Institute of Industrial Engineering (NITIE), Narsee Monjee Institute of Management Studies
Source: International Journal of Services and Operations Management
Year: 2021

Fat-free/lean body mass in children with insulin resistance or metabolic syndrome: a systematic review and meta-analysis (FULL-TEXT)

The current investigation aimed to examine the differences in fat-free mass /lean body mass according to the presence of insulin sensitivity/insulin resistance/glucose tolerance/metabolic syndrome in children. A systematic search was carried out in Medline/PubMed, Embase, Scopus, Web of Science, and SciELO, covering the period from each database’s respective start to 21 June 2021. Two researchers evaluated 7111 studies according to the inclusion criteria: original human studies, written in English or Spanish, evaluating fat-free mass/lean body mass in children and adolescents including both with and without insulin sensitivity/insulin resistance /glucose tolerance and metabolic syndrome and reported the differences between them in terms of fat free mass/lean body mass. The results of the studies were combined with insulin sensitivity, insulin, resistance, glucose tolerance and metabolic syndrome. The standardized mean difference (SMD) in each study was calculated and combined using the random-effects model. Heterogeneity between studies was tested using the index of heterogeneity (I2), leave-one-out sensitivity analyses were performed, and publication bias was assessed using the Egger and Begg tests. Finally, 15 studies which compared groups defined according to different glucose homeostasis criteria or metabolic syndrome out of 103 eligible studies were included in this systematic review and 12 studies in the meta-analysis. Meta-analysis showed lower fat-free mass/lean body mass percentage in participants with insulin resistance/glucose tolerance/metabolic syndrome (SMD -0.47; 95% CI, − 0.62 to − 0.32) while in mass units (kg), higher values were found in the same group (SMD, 1.01; 95% CI, 0.43 to 1.60).

For FULL-TEXT https://rdcu.be/cGBIa

Author(s): Diana Paola Córdoba-Rodríguez, Iris Iglesia, Alejandro Gomez-Bruton, Gerardo Rodríguez, José Antonio Casajús, Hernan Morales-Devia, Luis A. Moreno
Organization(s): Pontificia Universidad Javeriana, Universidad de Zaragoza, Instituto de Salud Carlos III
Source: BMC Pediatrics
Year: 2022

Scientific mapping of stem cells associated with Chagas disease : A bibliometric analysis

The objective is to map the scientific publications of research involving stem cells associated with Chagas disease. We used bibliometric and social network analysis techniques to analyze scientific data collected in the Web of Science. Most of the articles were published in 2014 and 2015. The organizations and authors with the largest number of publications and research collaborations are located in america, specifically in Brazil and the United States, which are responsible for 62% of all publications. FIOCRUZ, UFRJ, and Hospital São Rafael together account for approximately 55% of the studies related to stem cells associated with Chagas disease. Most of the studies focus on developing new strategies for treating Chagas disease using stem cells. This suggests that the research agenda in this area is still under development, highlighting the importance of continuing to pursue existing research avenues and expanding the range of strategies for the treatment of the disease.

https://doi.org/10.1080/09737766.2021.1977094

Author(s): Jânio Rodrigo de Jesus Santos, Carlos Augusto Francisco de Jesus, Cláudio Damasceno Pinto
Organization(s): Fundação Oswaldo Cruz-Fiocruz
Source: Journal of Scientometrics and Information Management
Year: 2021

A text mining-based approach for the evaluation of patenting trends on nanomaterials

Technological developments in nanomaterials can be tracked using patent indicators. However, the traditional International Patent Classification indicators cannot be considered conclusive, since nanotechnology is not easily defined as a field of research as well as there are different types of nanomaterials not well delineated into hierarchical codes. Therefore, text mining approaches can be used to enhance patent analysis and provide insightful trends to support research and development, competitive intelligence, and policy making. This study aims at proposing a method to classify nanomaterials into main types and mapping technological developments using an advanced text mining-based method to compile patent indicators. Patent records were provided by Derwent Innovations Index database, which indexes an enhanced bibliographic data of patents filed worldwide. A comparison between the IPC indicators and those developed here by text mining is presented. We concluded that the proposed method provides useful outcomes for decision-making, technological forecasting, and material selection process.

https://doi.org/10.1007/s11051-021-05304-3

Author(s): Douglas Henrique Milanez, Leandro Innocentini Lopes de Faria, Daniel Rodrigo Leiva
Organization(s): Federal University of São Carlos
Source: Journal of Nanoparticle Research
Year: 2021

Exploring New Approaches to Understanding Innovation Ecosystems

A firm’s connections into its ecosystem influences its ability to innovate. Much research on innovation ecosystems has examined high technology firms and locations and has used interview, survey, or science and technology data methods. Our study focuses on a resource-based ecosystem—agri-food in a medium-sized region—and explores a novel method using media sources to identify ecosystem links. We use this method to capture the innovation ecosystem around two plant-based protein firms and a conventional food processor in Winnipeg, Canada. We extract organisational actors from the full text of business and news articles, link co-occurring actors in social networks, and use modularity partitioning to detect communities in these networks. Our results show that the focal agri-food firms vary in their ecosystem associations, with little duplication in the actor organisations across the different firms’ networks. The plant-based protein firm networks had a greater innovation orientation than was noticeable in the established food producer’s network, particularly with industry and civic association intermediaries, government, and other agricultural companies. Insights from using the method and implications of the findings are discussed.

https://doi.org/10.1080/09537325.2021.1972965

Author(s): Jan Youtie, Robert Ward, Philip Shapira, R. Sandra Schillo, E. Louise Earl
Organization(s): Georgia Institute of Technology, University of Ottawa
Source: Technology Analysis & Strategic Management
Year: 2021

The effect of competitive public funding on scientific output: A comparison between China and the EU

Public funding is believed to play an important role in the development of science and technology. However, whether public funding and, in particular, competitive funding from public agencies actually helps to increase scientific output (i.e. publications) remains a matter of debate. By analysing a dataset of co-publications between China and the EU and a dataset of joint project collaborations in European Framework Programs for Research and Innovation [FP7 and Horizon 2020 (H2020)], we investigate whether different public funding agencies’ competitive assets have different impact on the volume of publication output. Our results support the hypotheses that competitively funded research output varies by funding sources, so that a high level of funding does not necessarily lead to high scientific output. Our results show that FP7/H2020 funded projects do not have a positive contribution to the output of joint publications between China and the EU. Interestingly, cooperation in the form of jointly writing proposals to these EU programmes, especially when they are not granted by the European Commission, can contribute significantly to joint scientific publications in a later stage. This applies in particular to cases where funding from China is involved. Our findings highlight the key role that funding agencies play in influencing research behaviour. Our results indicate that Chinese funding triggers a high number of publications, whereas research funded by the EU does so to a much lower extent, arguably due to the EU’s strong focus on social impact and its funding schemes as tools to promote European integration.

https://doi.org/10.1093/reseval/rvaa023

Author(s): Lili Wang, Xianwen Wang, Fredrik Niclas Piro, Niels J Philipsen
Organization(s): Maastricht University, Dalian University of Technology, Nordic Institute for Studies in Innovation, Erasmus University Rotterdam
Source: Research Evaluation
Year: 2020

Mining Technological Innovation Talents Based on Patent Index using t-SNE Algorithms*: Take the Field of Intelligent Robot as an Example

The purpose of this paper is to effectively evaluate the innovation ability and classification of technical talents in the intelligent robot field, and to be able to carry out adaptive learning and mining technical innovation talents according to the real-time change data corresponding to different indicators. Taking inventor’s patent information retrieved and cleaned from DI database as research object, it constructs the evaluation index system of technological innovation talents. It reduces the dimension of the index and cluster automatically, shows the visual effect, and mines the similar technical innovation talents through t-SNE algorithm. For a large number of patent information data, machine learning algorithm improves the traditional recognition method. According to inventor similarity, automatic classification is realized. Combined with DWPI manual code mining, the corresponding innovators and members of the technical team in the intelligent robot technology field were found. According to the results of visual dimensional reduction, the specific inventors can be traced. Machine learning algorithm t-SNE can reduce dimension and analysis clustering. It breaks the limitations of artificial statistics, deals with the larger order of magnitude data, and analyzes data timely, accurate and intuitive.

10.1109/ICAICA50127.2020.9182541

Author(s): Ning Zhao, Guohui Yang, Yang Cao
Organization(s): Harbin Institute of Technology
Source: 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)
Year: 2020

Neglected tropical diseases in Brazil: lack of correlation between disease burden, research funding and output

Objectives
To assess the correlation between the burden of seven priority neglected tropical diseases (NTDs) included in the Brazilian National Agenda of Priorities in Health Research – tuberculosis, Chagas disease, leprosy, malaria, leishmaniasis, dengue and schistosomiasis – and their respective research funding and output.

Methods
This retrospective review obtained data on disease burden from the Global Burden of Disease Study and funding data from open access sources. Publications were retrieved from Scopus and SciELO, and characterised according to the type of research conducted. Correlation between funding, research output and burden was assessed by comparing the ‘expected’ and ‘observed’ values for funding and publications relative to the proportional burden for each disease.

Results
There was an emphasis in basic biomedical research (average 30% of publications) and a shortage of health policy and systems (average 7%) and social sciences research (average 3%). Research output and funding were poorly correlated with disease burden. Tuberculosis, Chagas disease and schistosomiasis accounted for more than 75% of total NTD‐related DALYs, but accounted for only 34% of publications. Leprosy, leishmaniasis and malaria, together, received 49% of NTD‐related funding despite being responsible for only 9% of DALYs.

Conclusions
The analysis evidenced a lack of correlation between disease burden, research output and government funding for priority NTDs in Brazil. Our findings highlight the importance of monitoring health needs, research investments and outputs to inform policy and optimise the uptake of evidence for action, particularly in developing countries, where resources are scarce and the research capacity is limited. The results contribute to health policy by highlighting the need for improving coordination of scientific activities and public health needs for effective impact.

https://doi.org/10.1111/tmi.13478

Author(s): Bruna de Paula Fonseca, Priscila Costa Albuquerque, Fabio Zicker
Organization(s): Oswaldo Cruz Foundation (Fiocruz)
Source: Tropical Medicine & International Health
Year: 2020

Do national funding organizations properly address the diseases with the highest burden? – Observations from China and the UK (Full-Text)

Recent years have witnessed an incipient shift in science policy from a focus mainly on academic excellence to a focus that also takes into account “societal impact”. This shift raises the question as to whether medical research has given proper attention to the diseases imposing the greatest burden on society. Therefore, with the aim of identifying correlations between research funding priorities and public demand in health, we examine grants issued by the major medical research funding bodies of China and the UK during the decade 2006-2017 and compare the focus of their funded projects with the diseases that carry the highest burden of death, risk, or loss of health. The results indicate that the funding decisions of both nations do correspond to the illnesses with the highest health impact on their citizens. For both regions, the greatest health concerns surround non-communicable diseases, and neoplasms and cardiovascular disease in particular. In China, national health priorities have remained focused on these illnesses for the benefit of its own population, whereas the UK has funded a wider variety of research, extending to projects with impacts outside its borders to some developing countries. Additionally, despite an increased incidence of mental illness and HIV/AIDs in China, there is evidence that less priority has been given to these conditions. Both of these health areas seem to require more attention from China’s national funding agencies and the society in general. Methodologically, this study can serve as an example of how to conduct analyses related to public health issues by combining informetric methods and data with data and tools from other fields, thereby inspiring other scientometrics studies.

For FULL-TEXT download at DOI: 10.31219/osf.io/ckpf8

Author(s): Lin Zhang, Wenjing ZHAO, Jianhua Liu, Gunnar Sivertsen, Ying HUANG
Organization(s): Wuhan University, KU Leuven, Beijing Wanfang Data Ltd., Nordic Institute for Studies in Innovation Research and Education (NIFU)
Source: SocArXiv
Year: 2020

Robotic Bureaucracy and Administrative Burden: What Are the Effects of Universities’ Computer Automated Research Grants Management Systems?

Our paper seeks to understand effects of computerized approaches to university research grants and contracts management, especially impacts on administrative burden. Ours is a multi-method paper, including interviews with academic researchers but focuses chiefly on participant-observer research, using hundreds of our own emails from two projects located at two different universities. We find that robotic emails have complex effects and that their utility pertains to researchers’ familiarity with the systems and compliance requirements, the clarity of administrative requests, the extent and location of staff support, and the interaction of personal work habits with system requirements. We provide suggestions for improving automated research administration.

https://doi.org/10.1016/j.respol.2020.103980

Author(s): Barry Bozeman, Jan Youtie, Jiwon Jung
Organization(s): Arizona State University, Georgia Institute of Technology
Source: Research Policy
Year: 2020