scholarly journals How Do Travel Costs Shape Collaboration?

2020 ◽  
Vol 66 (8) ◽  
pp. 3340-3360 ◽  
Author(s):  
Christian Catalini ◽  
Christian Fons-Rosen ◽  
Patrick Gaulé
Keyword(s):  
Collaborative Research ◽  
Low Cost ◽  
Scientific Publications ◽  
Data Set ◽  
Knowledge And Skills ◽  
Travel Costs ◽  
Local Environments ◽  
Development Teams ◽  
Specialized Equipment ◽  
Different Levels

We develop a simple theoretical framework for thinking about how geographic frictions, and in particular travel costs, shape scientists’ collaboration decisions and the types of projects that are developed locally versus over distance. We then take advantage of a quasi-experiment—the introduction of new routes by a low-cost airline—to test the predictions of the theory. Results show that travel costs constitute an important friction to collaboration: after a low-cost airline enters, the number of collaborations increases between 0.3 and 1.1 times, a result that is robust to multiple falsification tests and causal in nature. The reduction in geographic frictions is particularly beneficial for high-quality scientists that are otherwise embedded in worse local environments. Consistent with the theory, lower travel costs also endogenously change the types of projects scientists engage in at different levels of distance. After the shock, we observe an increase in higher-quality and novel projects, as well as projects that take advantage of complementary knowledge and skills between subfields, and that rely on specialized equipment. We test the generalizability of our findings from chemistry to a broader data set of scientific publications and to a different field where specialized equipment is less likely to be relevant, mathematics. Last, we discuss implications for the formation of collaborative research and development teams over distance. This paper was accepted by Toby Stuart, entrepreneurship and innovation.

scholarly journals Publishing at Any Cost? The Need for the Improvement of the Quality of Scholarly Publications

2020 ◽  
Vol 9 (3) ◽  
pp. 214
Author(s):  
Maria José Sá ◽  
Carlos Miguel Ferreira ◽  
Ana Isabel Santos ◽  
Sandro Serpa
Keyword(s):  
Open Access ◽  
Low Cost ◽  
Critical Element ◽  
Academic Institutions ◽  
Scientific Publications ◽  
Scholarly Publications ◽  
Open Access Journals ◽  
Open Review ◽  
Different Levels

At a time of great dynamism among publishers of scientific publications, with the inevitability of Open Access and the ease of publishing online at low cost, it is possible to find publications with different levels of scientific respectability. In this context, the improvement of the quality of scholarly publications emerges as a critical element for publishers, authors and academic institutions, as well as for society in general. This opinion piece discusses Open Access journals with different levels of quality, focusing on the following quality-promoting measures: blacklists, author’s preparation, and institutional prevention. The analysis allows concluding that the open review will be one of the key elements in the process of clarification and promotion of the level of quality and consequent scientific respectability of each of the Journals, of the thousands currently existing, a number that is likely to increase.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals Biodegradable Chitosan Films with ZnO Nanoparticles Synthesized Using Food Industry By-Products—Production and Characterization

Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 646
Author(s):  
Victor Gomes Lauriano Souza ◽  
Marta M. Alves ◽  
Catarina F. Santos ◽  
Isabel A. C. Ribeiro ◽  
Carolina Rodrigues ◽  
...  
Keyword(s):  
Zinc Oxide Nanoparticles ◽  
Food Industry ◽  
Low Cost ◽  
Young Modulus ◽  
Barrier Properties ◽  
Zno Nps ◽  
Elongation At Break ◽  
Chitosan Films ◽  
By Products ◽  
Different Levels

This work aimed to produce bionanocomposites of chitosan incorporated with zinc oxide nanoparticles (ZnO NPs) synthesized using food industry by-products and to characterize them. Such nanoparticles are highlighted due to their low cost, antimicrobial activity, accessibility, and sustainability synthesis. Four different levels of ZnO NPs (0, 0.5, 1.0, and 2.0% w/w of chitosan) were tested, and the bionanocomposites were characterized in terms of their hydrophobicity, mechanical, optical, and barrier properties. Overall, the incorporation of ZnO NPs changed the composites from brittle to ductile, with enhanced elongation at break and reduced Young Modulus and tensile strength. Thus, ZnO NPs acted as plasticizer, turning the films more flexible, due to the presence of organic compounds on the NPs. This also favored permeability of oxygen and of water vapor, but the good barrier properties were maintained. Optical properties did not change statistically with the ZnO NPs incorporation. Thus, the characterization presented in this paper may contribute to support a decision on the choice of the material’s final application.

scholarly journals Mask R-CNN Based C. Elegans Detection with a DIY Microscope

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 257
Author(s):  
Sebastian Fudickar ◽  
Eike Jannik Nustede ◽  
Eike Dreyer ◽  
Julia Bornhorst
Keyword(s):  
Cell Biology ◽  
High Throughput Screening ◽  
Low Cost ◽  
Image Acquisition ◽  
Rapid Development ◽  
Model Organism ◽  
Large Data ◽  
Data Set ◽  
C Elegans ◽  
Do It Yourself

Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a well-suited model to study toxicity with identified toxic compounds closely matching those observed in mammals. For phenotypic screening, especially the worm number and the locomotion are of central importance. Traditional methods such as human counting or analyzing high-resolution microscope images are time-consuming and rather low throughput. The article explores the feasibility of low-cost, low-resolution do-it-yourself microscopes for image acquisition and automated evaluation by deep learning methods to reduce cost and allow high-throughput screening strategies. An image acquisition system is proposed within these constraints and used to create a large data-set of whole Petri dishes containing C. elegans. By utilizing the object detection framework Mask R-CNN, the nematodes are located, classified, and their contours predicted. The system has a precision of 0.96 and a recall of 0.956, resulting in an F1-Score of 0.958. Considering only correctly located C. elegans with an [email protected] IoU, the system achieved an average precision of 0.902 and a corresponding F1 Score of 0.906.

Web-based tools for collaborative research

2017 ◽  
Vol 34 (8) ◽  
pp. 8-19
Author(s):  
Stacy Brody
Keyword(s):  
Collaborative Research ◽  
Design Methodology ◽  
Research Collaboration ◽  
Low Cost ◽  
Research Process ◽  
Web Based ◽  
Content Type ◽  
Collaborative Tools ◽  
Start Ups ◽  
Practical Implications

Purpose The purpose of this paper is to profile various types of Web-based tools to facilitate research collaboration within and across institutions. Design/methodology/approach Various Web-based tools were tested by the author. Additionally, tutorial videos and guides were reviewed. Findings There are various free and low-cost tools available to assist in the collaborative research process, and librarians are well-positioned to facilitate their usage. Practical implications Librarians and researchers will learn about various types of tools available at free or at low cost to fulfill needs of the collaborative research process. Social implications As the tools highlighted are either free or of low cost, they are also valuable to start-ups and can be recommended for entrepreneurs. Originality/value As the realm of Web-based collaborative tools continues to evolve, the options must be continually revisited and reviewed for currency.

scholarly journals Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket colposcope

2018 ◽  
Author(s):  
Mercy Nyamewaa Asiedu ◽  
Anish Simhal ◽  
Usamah Chaudhary ◽  
Jenna L. Mueller ◽  
Christopher T. Lam ◽  
...  
Keyword(s):  
Acetic Acid ◽  
Visual Inspection ◽  
Point Of Care ◽  
Low Cost ◽  
Intraepithelial Neoplasia ◽  
Support Vector ◽  
Machine Model ◽  
Data Set ◽  
Automatic Feature Extraction ◽  
Lugol’S Iodine

AbstractGoalIn this work, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol’s iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance.MethodsWe developed algorithms to pre-process pathology-labeled cervigrams and to extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol’s iodine, and a combination of the two contrasts.ResultsThe proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, 63% accuracy).ConclusionThe results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol’s iodine images may provide unbiased automation of cervigrams.SignificanceThis would enable automated, expert-level diagnosis of cervical pre-cancer at the point-of-care.

scholarly journals A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications

2020 ◽  
Vol 34 (08) ◽  
pp. 13369-13381
Author(s):  
Shivashankar Subramanian ◽  
Ioana Baldini ◽  
Sushma Ravichandran ◽  
Dmitriy A. Katz-Rogozhnikov ◽  
Karthikeyan Natesan Ramamurthy ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Generic Drugs ◽  
Low Cost ◽  
Processing System ◽  
Drug Repurposing ◽  
Cancer Type ◽  
Entity Extraction ◽  
Scientific Publications

More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of these drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs. In many cases, there is already evidence of efficacy for cancer, but trying to manually extract such evidence from the scientific literature is intractable. In this emerging applications paper, we introduce a system to automate non-cancer generic drug evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language processing techniques and plan to treat them as baseline approaches for future improvement of individual components.

scholarly journals Organoleptic, physiochemical and microbiological qualities of dried fish, tengra (Mystus tengara) with different levels of salt and turmeric powder

2018 ◽  
Author(s):  
J. K. Jakhar ◽  
H. K. Vardia ◽  
Neelmani Chandravanshi ◽  
Rohit Kumar Painkra ◽  
Shabir Mir ◽  
...  
Keyword(s):  
Low Cost ◽  
Salt Content ◽  
Plate Count ◽  
Human Consumption ◽  
Sensory Assessment ◽  
Salted Fish ◽  
Hot Air ◽  
Dried Fish ◽  
Aerobic Plate Count ◽  
Different Levels

Fresh tengra fish (Mystus tengara) samples were collected from fish market, Kawardha and different levels of salt and turmeric powder were added (0% salt and 0% turmeric powder, T0; 2% salt with 0.2% turmeric, T1; 4% salt with 0.2% turmeric, T2; 8% salt with 0% turmeric powder, T3 and 12% salt with 0% turmeric powder, T4). The processed and salted fish were dried in hot air oven at 60°C for 20 hours. Carcass yield (%), salt content (%), pH and moisture content of cured fish were respectively 39.06 - 43.87%, 3.15 - 4.59%, 6.52- 6.90 and 4.91 – 6.84 %. The sensory assessment showed that treatment T2 had the highest score for texture 5.70; appearance 8.30, odor 8.02 and taste 8.05 while T0 and T1 had least sensory scores. Aerobic plate count of various treatments were found significantly different (p Lass Than 0.05) with the lowest in treatment T4 (3.3 x 103 cfu/g) followed by treatment T2 (3.7 x 103 cfu/g) and highest in treatment T0 (5.4 x 103 cfu/g). Tengra fish cured with 4% salt and 0.2% turmeric powder (T2) found to be the best in yield, microbial load and sensory attributes. The dry salted fish processed with low level of salt and turmeric powder are best for human consumption, particularly for the patients of blood pressure and diabetes.Therefore, preparation of dried products from low-cost fish will help in increasing the employment opportunities and also reduce post-harvest losses.

A Novel Autonomous Inspection System of USV for Submarine Buried Pipeline

2019 ◽  
Vol 53 (3) ◽  
pp. 90-95
Author(s):  
Lei Gao ◽  
Hai-Tao Gu ◽  
Hong-li Xu
Keyword(s):  
Conventional Method ◽  
Low Cost ◽  
Quadratic Functions ◽  
Inspection System ◽  
Submarine Pipeline ◽  
Data Set ◽  
Surface Vessels ◽  
Submarine Buried Pipeline ◽  
Embedded Algorithms ◽  
Subbottom Profiling

AbstractThe conventional method of surveying utilizing manned vessels requires a large investment of labor-intensive and time-consuming efforts. With the phenomenal progress of unmanned surface vessels (USVs), they have become a useful tool for surveyors and engineers who have been seeking a more productive and low-cost method as an alternative. This paper depicts a novel design of USVs for autonomous detection and recognition of buried submarine pipeline. The design adopted a parametric subbottom profiling system with embedded algorithms for path planning, autonomous obstacle avoidance, and autonomous pipeline recognition and navigation. The pipeline detection is based on the analysis of quadratic functions generated by the subbottom data set. Compared to the conventional method, the use of USVs equipped with subbottom profiling system turns out to be more useful and efficient for accurate detections of submarine pipeline.

scholarly journals Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 62 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Frank R. Dunshea ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Food Industry ◽  
Emerging Technologies ◽  
Low Cost ◽  
Consumer Preference ◽  
Beverage Industry ◽  
Wide Range ◽  
Alcoholic Drinks ◽  
Different Levels

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

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