Evidence map›Paper›PMID 39209969›Full record

ArticleScientific reports2024

Enhanced forest fire evacuation planning using real-time sensor and GPS algorithm.

Vishal Sharma, Deepali Nagpal, Suhasini Monga, Ahmad Almogren, Durgesh Srivastava, Ayman Altameem, Jaeyoung Choi

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Vishal SharmaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
Deepali NagpalChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
Suhasini MongaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India. suhasini.monga@gmail.com.
Ahmad AlmogrenDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, 11633, Riyadh, Saudi Arabia.
Durgesh SrivastavaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
Ayman AltameemDepartment of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud University, 11543, Riyadh, Saudi Arabia.
Jaeyoung ChoiSchool of Computing, Gachon University, Seongnam-si, 13120, Republic of Korea. jychoi19@gachon.ac.kr.

Funding

King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Project RSP2024R184National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) 2022R1C1C1004590
6 · The paper itself

Abstract

Forest fires are the source of countless fatalities and extreme economic repercussions. The safe evacuation of residents of an area affected by forest fires is the highest priority of local authorities, and finding the most optimal course of action has been a primary research focus for years. Previous studies over several decades have attempted to find an optimal solution using the applications of bug navigation systems, road network reconfiguration, graph traversals, swarm optimization, etc. The author, with the motivation to prevent human casualties at the time of such calamity, presents a novel study which solves the problem in nearly linear time computation, surpassing the performance standards of previous research, and accommodates the unpredictability of the spread of forest fires. This includes a proposal of an algorithm which builds upon the application of Spielman and Teng's Electrical Circuit Approach to solve for maximum flow in a network and implements this with real-time sensor and Global Positioning System input.

Identifiers

PMID39209969
PMCPMC11362607

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