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Results in Engineering (RINENG)

Publisher :

Elsevier B.V.

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • Engineering
e-ISSN :

2590-1230

Issue Frequency :

Monthly

Impact Factor :

6.0

Est. Year :

2019

Mobile :

31204853911

Country :

Netherlands The

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

P2PhelpdeskUS@Elsevier.com

Journal Descriptions

Results in Engineering (RINENG) is a gold open access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of engineering. Results in Engineering accepts papers that are scientifically sound, technically correct and provide valuable new knowledge to the engineering community. Subject coverage includes all aspects of Engineering: • Biomedical Engineering and Bioengineering Applications • Chemical & Environmental • Civil, Structural & Materials • Computers, Artificial Intelligence & Machine Learning • Energy & Sustainability • Electrical & Electronics • Mechanical & Aerospace Results in Engineering welcomes two types of papers: 1. Full research papers 2. Micro-articles: very short papers, no longer than two pages. They may consist of a single, but well-described piece of information, such as: • Data and/or a plot plus a description • Description of a new method or instrumentation • Negative results • Concept or design study This article type will allow the engineering community to publish snippets of research that have not matured into a complete study and that have not found a publication home yet.


Results in Engineering (RINENG) is :

International, Peer-Reviewed, Open Access, Refereed, Engineering , Online Monthly Journal

UGC Approved, ISSN Approved: P-ISSN , E-ISSN - 2590-1230, Established in - 2019, Impact Factor - 6.0

Not Provide Crossref DOI

Indexed in Scopus, WoS, DOAJ

Not indexed in PubMed, UGC CARE

Indexing

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WoS
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DOAJ

+7 More

Publications of RINENG

Research Article
  • dott image Md. Ashikur Rahman
  • dott image December, 2024

DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model

Quorum Sensing Peptides (QSP) are small molecules crucial for microbial communication, enabling bacterial populations to coordinate behaviors such as biofilm formation and virulence. The ide...

Research Article
  • dott image Md. Ashikur Rahman
  • dott image December, 2024

DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model

Quorum Sensing Peptides (QSP) are small molecules crucial for microbial communication, enabling bacterial populations to coordinate behaviors such as biofilm formation and virulence. The ide...

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