An API in JAVA Which Render Ease at Programming for Developers

2021 ◽  
Vol 1 (4) ◽  
pp. 27-31
Author(s):  
Bhuvan Agarwal ◽  
Soumyajeet Bhattacharjee ◽  
Sima Kar ◽  
Madhurima Saha ◽  
Vijay Kumar ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Open Source ◽  
Application Programming Interface ◽  
Development Environment ◽  
Application Programming ◽  
The Right ◽  
Almost All ◽  
Time And Space Complexity ◽  
Tree Traversals ◽  
Programming Interface

Abstract – Based on the concept of Application programming interface (API).This project comprises of a package named "algokit" which contains several algorithms based on the category of searching, sorting, dynamic programming, tree traversals and swapping. Keeping in mind that different algorithms from the same category have its own benefit in time and space complexity, This project covers almost all the algorithms known and available from each category. This would give the user several options to choose the right algorithm for its code.An user just requires to import the package named AlgoKit and call the functions inside it for a smooth programming experience. One of the prime objectives of this project is to build a kit that serves the purpose of reducing the number of lines of code and also reduce the time taken to run the same code elsewhere. It is platform independent and can be used in any open source Java development environment.

HITRAN2020: An overview of what to expect

2020 ◽  
Author(s):  
Robert Hargreaves ◽  
Iouli Gordon ◽  
Laurence Rothman ◽  
Robab Hashemi ◽  
Ekaterina Karlovets ◽  
...  
Keyword(s):  
Application Programming Interface ◽  
Line Profiles ◽  
Hitran Database ◽  
Individual Molecule ◽  
Application Programming ◽  
Vibrational Bands ◽  
Atmospheric Radiative Transfer ◽  
Almost All ◽  
Programming Interface ◽  
Scientific Collaborations

<p>The HITRAN database is an integral component of numerous atmospheric radiative transfer models and it is therefore essential that the database contains the most appropriate up-to-date spectroscopic parameters. To this end, the HITRAN2020 database is scheduled to be released at the end of this year.  The compilation of this edition (as is the tradition for the HITRAN database) exemplifies the efficiency and necessity of worldwide scientific collaborations. It is a titanic effort of experimentalists, theoreticians and atmospheric scientists, who measure, calculate and validate the HITRAN data.</p><p>The HITRAN line-by-line lists for almost all 49 molecules have been updated in comparison to HITRAN2016 (Gordon et al., 2017), the previous compilation. The extent of these updates depend on the molecule, but range from small adjustments for a few lines of an individual molecule to complete replacements of line lists and the introduction of new isotopologues. Many new vibrational bands have been added to the database, thereby extending the spectral coverage and completeness of the datasets. In addition the accuracy of the parameters for major atmospheric absorbers has been substantially increased, often featuring sub-percent uncertainties.</p><p>Furthermore, the amount of parameters has also been significantly increased. For example, HITRAN2020 will now incorporate non-Voigt line profiles for many gases, broadening by water vapour (Tan et al., 2019), as well as updated collision induced absorption sets (Karman et al., 2019). The HITRAN2020 edition will continue taking advantage of the new structure and interface available at www.hitran.org (Hill et al., 2016) and the HITRAN Application Programming Interface (Kochanov et al., 2016).</p><p>This talk will provide a summary of these updates, emphasizing details of some of the most important or drastic improvements.</p><p><strong>References:</strong></p><p>Gordon, I.E., .et al., (2017), <em>JQSRT</em> <strong>203</strong>, 3–69.  (doi:10.1016/j.jqsrt.2017.06.038)</p><p>Hill, C., et al., (2016), <em>JQSRT</em> <strong>177</strong>, 4–14.  (doi:10.1016/j.jqsrt.2015.12.012)</p><p>Karman, T., et al. (2019), <em>Icarus</em> <strong>328</strong>, 160–175.  (doi:10.1016/j.icarus.2019.02.034)</p><p>Kochanov, R.V., et al.,( 2016), <em>JQSRT</em> <strong>177</strong>, 15–30.  (doi:10.1016/j.jqsrt.2016.03.005)</p><p>Tan, Y., et al., (2019),<em> J. Geophys. Res. Atmos.</em> <strong>124</strong>, 11580-11594. (doi:10.1029/2019JD030929)</p><p> </p>

Python Earth Engine API as a new open-source ecosphere for characterizing offshore hydrocarbon seeps and spills

2021 ◽  
Vol 40 (1) ◽  
pp. 35-44
Author(s):  
Whitney Trainor-Guitton ◽  
Leo Turon ◽  
Dominique Dubucq
Keyword(s):  
Open Source ◽  
Application Programming Interface ◽  
Google Earth ◽  
Hydrocarbon Detection ◽  
Detection Algorithms ◽  
Hydrocarbon Seeps ◽  
Oil Seeps ◽  
Application Programming ◽  
Calculated Probability ◽  
Programming Interface

The Python Earth Engine application programming interface (API) provides a new open-source ecosphere for testing hydrocarbon detection algorithms on large volumes of images curated with the Google Earth Engine. We specifically demonstrate the Python Earth Engine API by calculating three hydrocarbon indices: fluorescence, rotation absorption, and normalized fluorescence. The Python Earth Engine API provides an ideal environment for testing these indices with varied oil seeps and spills by (1) removing barriers of proprietary software formats and (2) providing an extensive library of data analysis tools (e.g., Pandas and Seaborn) and classification algorithms (e.g., Scikit-learn and TensorFlow). Our results demonstrate end-member cases in which fluorescence and normalized fluorescence indices of seawater and oil are statistically similar and different. As expected, predictive classification is more effective and the calculated probability of oil is more accurate for scenarios in which seawater and oil are well separated in the fluorescence space.

scholarly journals Pengembangan Sistem Informasi Registrasi Mahasiswa Baru dengan Metode Analisis Gugus Kendali Mutu

2021 ◽  
Vol 10 (3) ◽  
pp. 191
Author(s):  
Santo Wijaya ◽  
Marta H.R.S.R. Sari ◽  
Adian Wihariono Putera
Keyword(s):  
Open Source ◽  
Application Programming Interface ◽  
Web Based ◽  
Application Programming ◽  
Programming Interface

Pendidikan sebagai industri produk dan jasa berbasis ilmu pengetahuan dan keterampilan menghadapi persaingan yang semakin kompetitif dengan banyaknya institusi baik dalam dan luar negeri yang operasional di Indonesia. Untuk meningkatkan daya saing, maka utilisasi teknologi informasi khususnya di era revolusi industri 4.0 menjadi kunci penting. Penelitian ini bertujuan untuk mengembangkan Sistem Informasi Registrasi Mahasiswa Baru (SIRMB) menggunakan kerangka open-source web-based application serta integrasinya dengan teknologi Application Programming Interface (API) Bank BNI menjadikan layanan administrasi yang terotomasi. Proses identifikasi masalah sampai perancangan solusi SIRMB menggunakan analisis gugus kendali mutu (QCC) dengan pendekatan metode Plan-Do-Check-Action (PDCA) sehingga menjamin perbaikan yang berkesinambungan. Penelitian ini berkontribusi terhadap perbaikan 76.9% terhadap proses kerja dengan eliminasi proses kerja manual registrasi mahasiswa baru, sehingga memberikan peningkatan kualitas layanan dan peningkatan produktivitas secara keseluruhan.

scholarly journals Arangopipe, a tool for machine learning meta-data management

Data Science ◽  
2021 ◽  
pp. 1-15
Author(s):  
Jörg Schad ◽  
Rajiv Sambasivan ◽  
Christopher Woodward
Keyword(s):  
Machine Learning ◽  
Life Cycle ◽  
Open Source ◽  
Data Model ◽  
Application Programming Interface ◽  
Learning Models ◽  
Essential Components ◽  
Application Programming ◽  
Programming Interface ◽  
Machine Learning Models

Experimenting with different models, documenting results and findings, and repeating these tasks are day-to-day activities for machine learning engineers and data scientists. There is a need to keep control of the machine-learning pipeline and its metadata. This allows users to iterate quickly through experiments and retrieve key findings and observations from historical activity. This is the need that Arangopipe serves. Arangopipe is an open-source tool that provides a data model that captures the essential components of any machine learning life cycle. Arangopipe provides an application programming interface that permits machine-learning engineers to record the details of the salient steps in building their machine learning models. The components of the data model and an overview of the application programming interface is provided. Illustrative examples of basic and advanced machine learning workflows are provided. Arangopipe is not only useful for users involved in developing machine learning models but also useful for users deploying and maintaining them.

An Approach to Facebook Post Analytics Using Python and Advance Open Source Tools

2018 ◽  
Vol 6 (6) ◽  
pp. 78
Author(s):  
Amit Sharma
Keyword(s):  
Open Source ◽  
Long Range ◽  
Data Extraction ◽  
Data Gathering ◽  
Application Programming Interface ◽  
Informal Communication ◽  
Standard Document ◽  
Kinship Networks ◽  
Application Programming ◽  
Programming Interface

The paper portrays the utilization of tools for data gathering and extraction that permits researchers to fare data in standard document groups from various areas of the facebook long range informal communication benefit. Kinship networks, gatherings, and pages can subsequently be breaking down quantitatively and subjectively with respect to demographical, post-demographical, and social qualities. The paper gives a review over expository headings opened up by the data made accessible, talks about stage particular parts of data extraction through the official Application Programming Interface, and quickly connects with the troublesome moral contemplations connected to this sort of research.

scholarly journals KOFFI and Anabel 2.0—a new binding kinetics database and its integration in an open-source binding analysis software

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Leo William Norval ◽  
Stefan Daniel Krämer ◽  
Mingjie Gao ◽  
Tobias Herz ◽  
Jianyu Li ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Application Programming Interface ◽  
Extraction Methods ◽  
Binding Kinetics ◽  
Binding Data ◽  
Meta Information ◽  
Application Programming ◽  
Kinetics Of ◽  
Programming Interface

Abstract The kinetics of featured interactions (KOFFI) database is a novel tool and resource for binding kinetics data from biomolecular interactions. While binding kinetics data are abundant in literature, finding valuable information is a laborious task. We used text extraction methods to store binding rates (association, dissociation) as well as corresponding meta-information (e.g. methods, devices) in a novel database. To date, over 270 articles were manually curated and binding data on over 1705 interactions was collected and stored in the (KOFFI) database. Moreover, the KOFFI database application programming interface was implemented in Anabel (open-source software for the analysis of binding interactions), enabling users to directly compare their own binding data analyses with related experiments described in the database.

scholarly journals AGI (Artificial General Intelligence): Peluang Indonesia Melompat Jauh ke Depan

2018 ◽  
Vol 1 (2) ◽  
pp. 1-11
Author(s):  
Johanes B. Bunyamin
Keyword(s):  
Open Source ◽  
Application Programming Interface ◽  
General Intelligence ◽  
Artificial General Intelligence ◽  
Application Programming ◽  
Programming Interface

AGI (Artificial General Intelligence) adalah kecerdasan buatan yang setara dengan kecerdasan manusia, sehingga semua pekerjaan intelektual manusia bisa digantikan oleh perangkat AGI. Nilai komersialnya akan sangat tinggi di masa yang akan datang. Devisa yang diperoleh dari ekspor perangkat AGI bisa mencapai 10 triliun dolar per tahun. Indonesia memiliki peluang mengembangkan teknologi AGI dan mengkomersialkannya, sebab sampai saat ini belum ada satu ahli pun yang mengetahui cara kerja otak manusia secara lengkap yang bisa dijabarkan ke dalam algoritma AGI. Jadi, Indonesia tidak ketinggalan. Berdasarkan perkiraan paling optimis, AGI akan tercipta pada tahun 2029. Bila Indonesia menganggarkan dana untuk penelitian dan pengembangan AGI sebesar Rp 90 miliar total untuk 10 tahun, ada kemungkinan Indonesia berhasil menciptakan AGI. Biaya ini relatif kecil sebab strateginya adalah memanfaatkan sebanyak mungkin teknologi yang sudah dipublikasikan baik berupa open source maupun API (Application Programming Interface), dan menjadwalkan eksperimen sesuai dengan penurunan harga perangkat keras. Dalam makalah ini penulis mengusulkan Model Kecerdasan Berbasis Konsep atau CBIM (Concept Based Intelligence Model), yang dapat digunakan untuk menciptakan AGI. Model ini menggunakan pendekatan berdasarkan cara kerja otak manusia bukan hanya dari segi struktural otak melainkan juga pembelajaran dan perkembangannya secara evolusi. Secara teori, model ini masuk akal (plausible) karena bisa memberikan jawaban yang memuaskan terhadap masalah-masalah utama dalam menciptakan AGI. Namun, model ini hanya terbukti benar setelah dilakukan pengembangan. Karena kompleksitasnya dalam tingkat detail, diperkirakan butuh waktu 10 tahun untuk menyelesaikan pengembangannya, dimana targetnya adalah mendidik AGI sampai tingkat sarjana.  Keberhasilan pengembangan AGI memiliki nilai yang sangat strategis. Sebagaimana diilustrasikan dalam makalah ini, AGI bisa dimanfaatkan untuk mencerdaskan kehidupan bangsa, sesuai amanah UUD 45. Kemajuan akan mengalami percepatan di segala bidang termasuk di bidang ekonomi, kesehatan, pendidikan, budaya, sains, teknologi, dan militer, sehingga Indonesia akan menjadi negara termaju. Mengingat nilai strategisnya, sebaiknya pemerintah segera memulai proyek AGI.

scholarly journals Tibanna: software for scalable execution of portable pipelines on the cloud

2018 ◽  
Author(s):  
Soohyun Lee ◽  
Jeremy Johnson ◽  
Carl Vitzthum ◽  
Koray Kırlı ◽  
Burak H. Alver ◽  
...  
Keyword(s):  
Web Services ◽  
Open Source ◽  
Open Source Software ◽  
Source Code ◽  
Software Tool ◽  
Application Programming Interface ◽  
Description Language ◽  
Amazon Web Services ◽  
Application Programming ◽  
Programming Interface

AbstractSummaryWe introduce Tibanna, an open-source software tool for automated execution of bioinformatics pipelines on Amazon Web Services (AWS). Tibanna accepts reproducible and portable pipeline standards including Common Workflow Language (CWL), Workflow Description Language (WDL) and Docker. It adopts a strategy of isolation and optimization of individual executions, combined with a serverless scheduling approach. Pipelines are executed and monitored using local commands or the Python Application Programming Interface (API) and cloud configuration is automatically handled. Tibanna is well suited for projects with a range of computational requirements, including those with large and widely fluctuating loads. Notably, it has been used to process terabytes of data for the 4D Nucleome (4DN) Network.AvailabilitySource code is available on GitHub at https://github.com/4dn-dcic/tibanna.

Open-Source Multi-GPU-Accelerated QM/MM Simulations with AMBER and QUICK

2021 ◽  
Author(s):  
Vinicius Cruzeiro ◽  
Madushanka Manathunga ◽  
Kenneth M. Merz, Jr. ◽  
Andreas Goetz
Keyword(s):  
Open Source ◽  
Application Programming Interface ◽  
Electronic Structure Calculations ◽  
Bulk Water ◽  
Photoactive Yellow Protein ◽  
Multiple Gpus ◽  
Application Programming ◽  
Computational Kernel ◽  
Structure Calculations ◽  
Programming Interface

<div><div><div><p>The quantum mechanics/molecular mechanics (QM/MM) approach is an essential and well-established tool in computational chemistry that has been widely applied in a myriad of biomolecular problems in the literature. In this publication, we report the integration of the QUantum Interaction Computational Kernel (QUICK) program as an engine to perform electronic structure calculations in QM/MM simulations with AMBER. This integration is available through either a file-based interface (FBI) or an application programming interface (API). Since QUICK is an open-source GPU-accelerated code with multi-GPU parallelization, users can take advantage of “free of charge” GPU-acceleration in their QM/MM simulations. In this work, we discuss implementation details and give usage examples. We also investigate energy conservation in typical QM/MM simulations performed at the microcanonical ensemble. Finally, benchmark results for two representative systems, the N-methylacetamide (NMA) molecule and the photoactive yellow protein (PYP) in bulk water, show the performance of QM/MM simulations with QUICK and AMBER using a varying number of CPU cores and GPUs. Our results highlight the acceleration obtained from a single or multiple GPUs; we observed speedups of up to 38x between a single GPU vs. a single CPU core and of up to 2.6x when comparing four GPUs to a single GPU. Results also reveal speedups of up to 3.5x when the API is used instead of FBI.</p></div></div></div>

scholarly journals NaijaCovidAPI: an application programming interface for retrieval of COVID19 data from the Nigerian Center for Disease Control web platform

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1227
Author(s):  
Emmanuel Baldwin Mbaya ◽  
Babatunde Alao ◽  
Philip Ewejobi ◽  
Innocent Nwokolo ◽  
Victoria Oguntosin ◽  
...  
Keyword(s):  
Disease Control ◽  
Application Programming Interface ◽  
The Body ◽  
Development Environment ◽  
Representational State Transfer ◽  
Body Of Knowledge ◽  
State Transfer ◽  
Application Programming ◽  
Rest Api ◽  
Programming Interface

Background: In this work, a COVID19 Application Programming Interface (API) was built using the Representational State Transfer (REST) API architecture and it is designed to fetch data daily from the Nigerian Center for Disease Control (NCDC) website. Methods: The API is developed using ASP.NET Core Web API framework using C# programming language and Visual Studio 2019 as the Integrated Development Environment (IDE). The application has been deployed to Microsoft Azure as the cloud hosting platform and to successfully get new data from the NCDC website using Hangfire where a job has been scheduled to run every 12:30 pm (GMT + 1) and load the fetched data into our database. Various API Endpoints are defined to interact with the system and get data as needed, data can be fetched from a single state by name, all states on a particular day or over a range of days, etc. Results: The results from the data showed that Lagos and Abuja FCT in Nigeria were the hardest-hit states in terms of Total Confirmed cases while Lagos and Edo states had the highest death causalities with 465 and 186 as of August 2020. This analysis and many more can be easily made as a result of this API we have created that warehouses all COVID19 Data as presented by the NCDC since the first contracted case on February 29, 2020. This system was tested on the BlazeMeter platform, and it had an average of 11Hits/s with a response time of 2905milliseconds. Conclusions: The extension of NaijaCovidAPI over existing COVID19 APIs for Nigeria is the access and retrieval of previous data. Our contribution to the body of knowledge is the creation of a data hub for Nigeria's COVID-19 incidence from February 29, 2020, to date

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