scholarly journals ‘SleepCycles’ package for R - A free software tool for the detection of sleep cycles from sleep staging

2020 ◽  
Author(s):  
Christine Blume ◽  
Christian Cajochen
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Tool ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Fine Grained ◽  
Equal Duration ◽  
Open Source Software Package ◽  
Sleep Microstructure ◽  
Sleep Cycles

The detection of sleep cycles in human sleep data (i.e. polysomnographically assessed sleep stages) enables fine-grained analyses of ultradian variations in sleep microstructure (e.g. sleep spindles, and arousals), or other amplitude- and frequency-specific electroencephalographic features during sleep. While many laboratories have software that is used internally, reproducibility requires the availability of open source software. Therefore, we here introduce the ‘SleepCycles’ package for R, an open-source software package that identifies sleep cycles and their respective (non-) rapid eye movement ([N]REM) periods from sleep staging data. Additionally, each (N)REM period is subdivided into parts of equal duration, which may be useful for further fine-grained analyses. The detection criteria are, with some adaptations, largely based on criteria originally proposed by Feinberg and Floyd (1979). The latest version of the package can be downloaded from the Comprehensive R Archives Network (CRAN).•The package ‘SleepCycles’ for R allows to identify sleep cycles and their respective NREM and REM from sleep staging results.•Besides the cycle detection, NREM and REM are also split into parts of equal duration (percentiles) thereby allowing for a better temporal resolution across the night and temporal alignment of sleep cycles with different durations among different night recordings.

scholarly journals Integration of enabling methods for the automated flow preparation of piperazine-2-carboxamide

2014 ◽  
Vol 10 ◽  
pp. 641-652 ◽  
Author(s):  
Richard J Ingham ◽  
Claudio Battilocchio ◽  
Joel M Hawkins ◽  
Steven V Ley
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Package ◽  
Flow Chemistry ◽  
Raspberry Pi ◽  
Step Flow ◽  
Simultaneous Control ◽  
Open Source Software Package

Here we describe the use of a new open-source software package and a Raspberry Pi® computer for the simultaneous control of multiple flow chemistry devices and its application to a machine-assisted, multi-step flow preparation of pyrazine-2-carboxamide – a component of Rifater®, used in the treatment of tuberculosis – and its reduced derivative piperazine-2-carboxamide.

scholarly journals An open source software package for primality testing of numbers of the form p2^n+1, with no constraints on the relative sizes of p and 2^n

2018 ◽  
Author(s):  
Tejas R. Rao
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Package ◽  
Primality Testing ◽  
Fermat Number ◽  
Open Source Software Package ◽  
Sierpinski Numbers ◽  
Efficient Software

We develop an efficient software package to test for the primality of p2^n+1, p prime and p>2^n. This aids in the determination of large, non-Sierpinski numbers p, for prime p, and in cryptography. It furthermore uniquely allows for the computation of the smallest n such that p2^n+1 is prime when p is large. We compute primes of this form for the first one million primes p and find four primes of the form above 1000 digits. The software may also be used to test whether p2^n+1 divides a generalized fermat number base 3.

scholarly journals Cantera: an open source software tool for integrating complex thermo-chemistry into device-level simulations

2020 ◽  
Author(s):  
Richard West ◽  
Robert Kee ◽  
Kyle Niemeyer ◽  
Steven C. DeCaluwe ◽  
C. Goldsmith ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Tool

scholarly journals An uncertainty-based protocol for the setup and measurement of soot/black carbon emissions from gas flares using sky-LOSA

2020 ◽  
Author(s):  
Bradley M. Conrad ◽  
Matthew R. Johnson
Keyword(s):  
Open Source ◽  
Black Carbon ◽  
Open Source Software ◽  
Carbon Emissions ◽  
Climate Models ◽  
Optical Measurement ◽  
Measurement Data ◽  
Software Tool ◽  
Measurement Protocol ◽  
Measurement Uncertainties

Abstract. Gas flaring is an important source of atmospheric soot/black carbon, especially in sensitive Arctic regions. However, emissions have traditionally been challenging to measure and remain poorly characterized, confounding international reporting requirements and adding uncertainty to climate models. The sky-LOSA optical measurement technique has emerged as a powerful means to quantify flare black carbon emissions in the field, but broader adoption has been hampered by the complexity of its deployment, where decisions during setup in the field can have profound, non-linear impacts on achievable measurement uncertainties. To address this challenge, this paper presents a prescriptive measurement protocol and associated open-source software tool that simplifies acquisition of sky-LOSA data in the field. Leveraging a comprehensive Monte Carlo-based General Uncertainty Analysis (GUA) to predict measurement uncertainties over the entire breadth of possible measurement conditions, general heuristics are identified to guide a sky-LOSA user toward optimal data collection. These are further extended in the open-source software utility, SetupSkyLOSA, which interprets the GUA results to provide detailed guidance for any specific combination of location, date/time, and flare, plume, and ambient conditions. Finally, a case study of a sky-LOSA measurement at an oil and gas facility in Mexico is used to demonstrate the utility of the software tool, where potentially small region(s) of optimal instrument setup are easily and quickly identified. It is hoped that this work will help increase the accessibility of the sky-LOSA technique and ultimately the availability of field measurement data for flare black carbon emissions.

Prospects of Open Source Software for Maximizing the User Expectations in Heterogeneous Network

2021 ◽  
pp. 257-271
Author(s):  
Pushpa Singh ◽  
Rajeev Agrawal
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Research Work ◽  
Software Tool ◽  
Knn Classifier ◽  
User Expectations

This article focuses on the prospects of open source software and tools for maximizing the user expectations in heterogeneous networks. The open source software Python is used as a software tool in this research work for implementing machine learning technique for the categorization of the types of user in a heterogeneous network (HN). The KNN classifier available in Python defines the type of user category in real time to predict the available users in a particular category for maximizing profit for a business organization.

scholarly journals Nine axes of merit for technosignature searches

2019 ◽  
Vol 19 (3) ◽  
pp. 237-243 ◽  
Author(s):  
Sofia Z. Sheikh
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Search Strategy ◽  
Software Tool ◽  
Advantages And Disadvantages ◽  
History Of

AbstractIt can be difficult to develop an effective and balanced search strategy in SETI, especially from a funding perspective, given the diverse methodologies and myriad orthogonal proposals for the best technosignatures. Here I propose a framework to compare the relative advantages and disadvantages of various proposed technosignatures based on nine ‘axes of merit’. This framework was first developed at the NASA Technosignatures Workshop in Houston in 2018 and published in that report. I give the definition and rationale behind the nine axes as well as the history of each axis in the SETI and technosignature literature. These axes are then applied to three classes of technosignature searches as an illustration of their use. An open-source software tool is available to allow technosignature researchers to make their own version of the figure.

scholarly journals Development and Evaluation of an Open-Source Software Package “CGITA” for Quantifying Tumor Heterogeneity with Molecular Images

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Hua Dean Fang ◽  
Chien-Yu Lin ◽  
Meng-Jung Shih ◽  
Hung-Ming Wang ◽  
Tsung-Ying Ho ◽  
...  
Keyword(s):  
Open Source ◽  
Texture Analysis ◽  
Open Source Software ◽  
Software Package ◽  
Tumor Heterogeneity ◽  
Locally Advanced ◽  
Image Texture ◽  
Pixel Intensity ◽  
Open Source Software Package ◽  
Molecular Images

Background. The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research community as a free, open-source project.Methods. With a user-friendly graphical interface, CGITA provides users with an easy way to compute more than seventy heterogeneity indices. To test and demonstrate the usefulness of CGITA, we used a small cohort of eighteen locally advanced oral cavity (ORC) cancer patients treated with definitive radiotherapies.Results. In our case study of ORC data, we found that more than ten of the current implemented heterogeneity indices outperformed SUVmeanfor outcome prediction in the ROC analysis with a higher area under curve (AUC). Heterogeneity indices provide a better area under the curve up to 0.9 than the SUVmeanand TLG (0.6 and 0.52, resp.).Conclusions. CGITA is a free and open-source software package to quantify tumor heterogeneity from molecular images. CGITA is available for free for academic use athttp://code.google.com/p/cgita.

Sign in / Sign up

Export Citation Format

Share Document