scholarly journals Automatic segmentation and classification of seven-segment display digits on auroral images

2016 ◽  
Vol 5 (2) ◽  
pp. 305-314 ◽  
Author(s):  
Tuomas Savolainen ◽  
Daniel Keith Whiter ◽  
Noora Partamies
Keyword(s):  
Automatic Segmentation ◽  
Scientific Information ◽  
Spherical Mirror ◽  
Automatic Method ◽  
Time Period ◽  
Film Images ◽  
Fully Automatic ◽  
Night Sky

Abstract. In this paper we describe a new and fully automatic method for segmenting and classifying digits in seven-segment displays. The method is applied to a dataset consisting of about 7 million auroral all-sky images taken during the time period of 1973–1997 at camera stations centred around Sodankylä observatory in northern Finland. In each image there is a clock display for the date and time together with the reflection of the whole night sky through a spherical mirror. The digitised film images of the night sky contain valuable scientific information but are impractical to use without an automatic method for extracting the date–time from the display. We describe the implementation and the results of such a method in detail in this paper.

scholarly journals Automatic segmentation and classification of seven-segment display digits on auroral images

2016 ◽  
Author(s):  
T. Savolainen ◽  
D. K. Whiter ◽  
N. Partamies
Keyword(s):  
Automatic Segmentation ◽  
Scientific Information ◽  
Spherical Mirror ◽  
Automatic Method ◽  
Data Set ◽  
Time Period ◽  
Film Images ◽  
Fully Automatic ◽  
Night Sky

Abstract. In this paper we describe a new and fully automatic method for segmenting and classifying digits in seven-segment displays. The method is applied to a data set consisting of about 7 million auroral all-sky images taken during the time period of 1973–1997 at camera stations centered around Sodankylä observatory in Northern Finland. In each image there is a clock display for the date and time together with the reflection of the whole night sky through a spherical mirror. The digitised film images of the night sky contain valuable scientific information, but are impractical to use without an automatic method for extracting the date-time from the display. We describe the implementation and the results of such a method in detail in this paper.

scholarly journals Fully automatic classification of breast cancer microarray images

2016 ◽  
Vol 3 (2) ◽  
pp. 348-359 ◽  
Author(s):  
Nastaran Dehghan Khalilabad ◽  
Hamid Hassanpour ◽  
Mohammad Reza Abbaszadegan
Keyword(s):  
Breast Cancer ◽  
Automatic Classification ◽  
Fully Automatic

Fully automatic segmentation of sinonasal cavity and pharyngeal airway based on convolutional neural networks

2021 ◽  
Vol 159 (6) ◽  
pp. 824-835.e1
Author(s):  
Rosalia Leonardi ◽  
Antonino Lo Giudice ◽  
Marco Farronato ◽  
Vincenzo Ronsivalle ◽  
Silvia Allegrini ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Pharyngeal Airway ◽  
Fully Automatic

Long-term Safety and Efficacy of Mexiletine in Myotonic Dystrophy Types 1 and 2

2021 ◽  
pp. 10.1212/CPJ.0000000000001073
Author(s):  
Christina Mousele ◽  
Emma Matthews ◽  
Robert Pitceathly ◽  
Michael Hanna ◽  
Susan McDonald ◽  
...  
Keyword(s):  
Myotonic Dystrophy ◽  
Sodium Channels ◽  
Genetic Disorders ◽  
Clinical Feature ◽  
Efficacy And Safety ◽  
Voltage Gated Sodium Channels ◽  
Time Period ◽  
Mean Time

AbstractBackground:Myotonic dystrophy types 1 and 2 are progressive multisystem genetic disorders, whose core clinical feature is myotonia. Mexiletine, an antagonist of voltage-gated sodium channels, is a recommended anti-myotonic agent in the non-dystrophic myotonias, but its use in myotonic dystrophy is limited due to lack of data regarding its long-term efficacy and safety profile.Methods:To address this issue, this study retrospectively evaluated patients with myotonic dystrophy receiving mexiletine over a mean time-period of 32.9 months (range 0.1 to 216 months).Results:This study demonstrated that 96% of patients reported some improvement in myotonia symptoms with mexiletine treatment. No clinically relevant cardiac adverse events were associated with the long-term use of mexiletine.Conclusions:These findings support that mexiletine is both safe and effective when used long-term in myotonic dystrophy.Classification of Evidence:This study provides class IV evidence that mexiletine is a well-tolerated and effective treatment for myotonic dystrophy types 1 and 2.

scholarly journals The COVID-19 Infodemic: The complex task of elevating signal and eliminating noise

2021 ◽  
Author(s):  
Tejas Desai ◽  
Arvind Conjeevaram
Keyword(s):  
Healthcare Workers ◽  
Scientific Information ◽  
Healthcare Providers ◽  
Application Programming Interface ◽  
Publication Date ◽  
Time Period ◽  
Application Programming ◽  
Impossible Task ◽  
Visual Basic Application ◽  
Programming Interface

AbstractIn Situation Report #3 and 39 days before declaring COVID-19 a pandemic, the WHO declared a -19 infodemic. The volume of coronavirus tweets was far too great for one to find accurate or reliable information. Healthcare workers were flooded with which drowned the of valuable COVID-19 information. To combat the infodemic, physicians created healthcare-specific micro-communities to share scientific information with other providers. We analyzed the content of eight physician-created communities and categorized each message in one of five domains. We coded 1) an application programming interface to download tweets and their metadata in JavaScript Object Notation and 2) a reading algorithm using visual basic application in Excel to categorize the content. We superimposed the publication date of each tweet into a timeline of key pandemic events. Finally, we created NephTwitterArchive.com to help healthcare workers find COVID-19-related signal tweets when treating patients. We collected 21071 tweets from the eight hashtags studied. Only 9051 tweets were considered signal: tweets categorized into both a domain and subdomain. There was a trend towards fewer signal tweets as the pandemic progressed, with a daily median of 22% (IQR 0-42%. The most popular subdomain in Prevention was PPE (2448 signal tweets). In Therapeutics, Hydroxychloroquine/chloroquine wwo Azithromycin and Mechanical Ventilation were the most popular subdomains. During the active Infodemic phase (Days 0 to 49), a total of 2021 searches were completed in NephTwitterArchive.com, which was a 26% increase from the same time period before the pandemic was declared (Days −50 to −1). The COVID-19 Infodemic indicates that future endeavors must be undertaken to eliminate noise and elevate signal in all aspects of scientific discourse on Twitter. In the absence of any algorithm-based strategy, healthcare providers will be left with the nearly impossible task of manually finding high-quality tweets from amongst a tidal wave of noise.

scholarly journals The works of Leningrad popular science film studio “Lennauchfilm” in the 1970s – 1980s

2021 ◽  
pp. 19-29
Author(s):  
Mariya Vladimirovna Kalenichenko
Keyword(s):  
Social Life ◽  
Popular Science ◽  
Time Period ◽  
Film Directors ◽  
Archival Documents ◽  
Art And Culture ◽  
History Of ◽  
Film Texts ◽  
Scientific Achievements

This article is dedicated to examination of works of the film directors of the Leningrad popular science film studio “Lennauchfilm” in the 1970s – 1980s. Based on the archival documents presented in the Central Archive of Literature and Art of Saint Petersburg, the author analyzes the work of the film studio: carries out classification of filmography by formal-semantic criterion, as well as determines the key processes typical to this time period. The following main trends are highlighted: natural science, technical-propagandistic, historical-revolutionary, military-patriotic, social life, history of art and culture. Special attention is given to the films that cover the topics, which have not previously been included in the field of popular science cinematography. The novelty of this research lies in classification of the thematic trends of the Leningrad film studio as an integral artistic system, as well as in comparison of the plots of popular science film texts by each direction over the two decades. As a result, the author identified the main trends, which broadened the thematic field in the work of the studio, as well as fundamentally changed the representations on the goals and tasks of popular science cinematography. The key object of popular science cinematography is being shifted during the Perestroika period. Emphasis is place not on science and technological achievements, but human and society. Film directors through their works conveyed the attitude of society towards science, raising the questions of transformation of ethics and morality in the context of scientific and technological revolution. The idea of the harm of scientific achievements and responsibility of the scholars before society is being advanced. Without any doubt, the works of the Leningrad film directors broadened the ideological-artistic range by offering the own vision of specificity of the Soviet popular science cinematography.

scholarly journals Analysis and classification of tem¬perature measurements during melting and casting of alloys using neural networks

2020 ◽  
Vol 63 (10) ◽  
pp. 856-861
Author(s):  
A. V. Fedosov ◽  
G. V. Chumachenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Human Factor ◽  
High Efficiency ◽  
Thermal Conditions ◽  
Second Stage ◽  
Three Stages ◽  
Fully Automatic ◽  
Artificial Neural

The article considers the issues of monitoring the thermal conditions of alloys melting and casting at foundries. It is noted that the least reliable method is when the measurement and fixing the temperature is assigned to the worker. On the other hand, a fully automatic approach is not always available for small foundries. In this regard, the expediency of using an automated approach is shown, in which the measurement is assigned to the worker, and the values are recorded automatically. This method assumes implementation of an algorithm for automatic classification of temperature measurements based on an end-to-end array of data obtained in the production stream. The solving of this task is divided into three stages. Preparing of raw data for classification process is provided on the first stage. On the second stage, the task of measurement classification is solved by using neural network principles. Analysis of the results of the artificial neural network has shown its high efficiency and degree of their correspondence with the actual situation on the work site. It was also noted that the application of artificial neural networks principles makes the classification process flexible, due to the ability to easily supplement the process with new parameters and neurons. The final stage is analysis of the obtained results. Correctly performed data classification provides an opportunity not only to assess compliance with technological discipline at the site, but also to improve the process of identifying the causes of casting defects. Application of the proposed approach allows us to reduce the influence of human factor in the analysis of thermal conditions of alloys melting and casting with minimal costs for melting monitoring.

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