Submission Deadline ( Vol 70 , Issue 10 )
16 Oct 2025Publish On ( Vol 70 , Issue 10 )
31 Oct 2025Chinese Science Bulletin (ISSN:0023-074X) and (E-ISSN:2095-9419) is a monthly peer-reviewed scopus indexed journal originally from 1963 to 1964, from 1980 to 1984, 1989, from 2015 to Present. The publisher of the journal is Editorial Office of Journal of Science China Press.The journal welcomes all kind of research/review/abstract papers regarding Multidisciplinary subjects.
AIM AND SCOPE
1.Agricultural Science/Agricultural Engineering
2.Electrical Engineering and Telecommunication Section
3.Computer Science and Engineering
4.Civil and Architectural Engineering Section
5.Mechanical and Materials Engineering Section
6.Chemical Engineering Section
7.Food Engineering Section
8.Physics Section
9.Mathematics Section
10.Accounting and finance
11.Economics
12.Management
13.Social science
14.Earth science
15.Law
16.Linguistics
17.Biological science
18.Environmental science
19.Material science
20.zoology
21.Fishery and Science
22.Psychology
23.International Business
24.HRM
25.Marketing
26.History
27.Public health
28.Botany
ALL PUBLISH JOURNAL HERE
CSB-05-06-2024-1344
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Chinese Science Bulletin
Internet is not just about connecting computers or browsing. Now internet took a step ahead and it is evolving into Internet of Things. Infrastructure, devices, smart objects and even physical environment are getting connected. If we combine Internet of Things with Artificial Internet then it will maximize the potential of both technologies. The combination of Internet of Things and Cyber physical system with data science could bring about the next “smart digital revolution“. Internet of Things allows business to gather continuous data on various physical activities offering valuable insig
CSB-05-06-2024-1343
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Chinese Science Bulletin
For the manufacturing sector to become economically viable, innovation and adaptation with new technologies are essential. Sustainable manufacturing is already being achieved through the application of machine learning and other AI techniques. To assist with this analysis, we used UCINET and NVivo 12 software along with databases such as Web of Science and SCOPUS. One attractive discovery was that after Industry 4.0 started, the United States published a greater number of articles and there was an upsurge in interest in this subject. Current developments in advanced robotics have been greatly
CSB-05-06-2024-1342
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Chinese Science Bulletin
The outbreak of the COVID-19 pandemic has presented significant challenges to global healthcare systems, necessitating a timely and accurate detection of COVID-19 cases. In this research paper, the authors have proposed an adapted Detrac model (a novel deep learning-based approach) designed to classify input images into three distinct categories: pneumonia, COVID, normal) called DeepEnTraCT for COVID-19 detection using chest X-ray images. This innovation aims to improve the precision and efficiency of COVID-19 detection models by incorporating advanced techniques in feature extraction, selecti
CSB-04-06-2024-1341
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Chinese Science Bulletin
In modern computing environments characterized by high variability and complex workloads, traditional load balancing algorithms such as Round Robin and Least Connections are often lower in effectively distributing tasks and maintaining optimal performance. This paper presents a Hybrid- Machine Learning (Hybrid-ML) load balancing algorithm that combines the strengths of Fastest Response Load Balancing (FRLB) and Priority-based Load Balancing (PBLB) with advanced machine learning (ML) techniques. The Hybrid-ML algorithm leverages real-time data to predict optimal server allocations, dynamically
CSB-29-05-2024-1336
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Chinese Science Bulletin
Covid 19 pandemic has become the world's worst public health challenge in 2020. To control the viral infection, major changes to the existing educational systems were implemented. This study primarily aimed to investigate the student’s level of academic performance and determined significant differences in Science, Technology and Society (STS) exposed to modular, limited face-to-face and full face-to-face learning modalities. Quantitative analysis was used to assess the level of student's academic performance in STS. Analysis of variance (ANOVA) was used to test the significance differences