Chinese Science Bulletin

Submission Deadline ( Vol 71 , Issue 06 )

24 Jun 2026
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Publish On ( Vol 71 , Issue 06 )

30 Jun 2026

Chinese Science Bulletin

Chinese 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.

Scopus Index(2026)

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Indexed By

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AIM AND SCOPE

Chinese Science Bulletin

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

Chinese Science Bulletin

  • CSB-12-05-2024-1311

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  • Chinese Science Bulletin

Transforming people sector performance in Pakistan: A conceptual framework

The present paper explores the complex relationships among transformational leadership, digital transformation, work-life balance, employee performance, and motivation. Motivated by the changing dynamics of the modern workplace, this conceptual study seeks to understand the synergies between these factors. The research addresses key questions: How do transformational leadership, digital transformation, and work-life balance impact employee performance? Does employee motivation mediate these relationships? The study posits that these factors positively influence performance, with motivation as

  • CSB-11-05-2024-1310

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  • Chinese Science Bulletin

Feature Descriptors and Deep Learning to Extract Features for Building Monocular VO from TQU-SLAM Benchmark Dataset

Enriching data to train Visual SLAM and VO construction models using deep learning (DL) is an urgent problem today in computer vision. DL requires a large amount of data to train the model, and more data with many different contextual and conditional conditions will create a more accurate Visual SLAM and VO construction model. In this paper, we introduce the TQU-SLAM benchmark dataset, which includes 160,631 RGB-D frame pairs. It was collected from the corridors of three interconnected buildings with a length of about 230m. The ground truth data of the TQU-SLAM benchmark dataset was prepared m

  • CSB-08-05-2024-1306

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  • Chinese Science Bulletin

Trajectory Planning Based on Bézier Curve and Q-Network

This work provides a novel use of trajectory planning in agricultural harvesting, with a particular emphasis on harvesting path optimization utilizing Bézier curve navigation and reinforcement learning. The goal of the agent is to learn adaptive trajectories that take into account geographical limits and obstacle-rich situations in the agricultural setting, where efficient harvesting is critical. The suggested method makes use of a Q-network to steer the harvesting machinery along rounded Bézier curves. It then dynamically modifies the pathways to go around obstructions, maximize yield colle

  • CSB-08-05-2024-1305

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  • Chinese Science Bulletin

SOLVING POLYNOMIAL PROGRAMS VIA CONVEX QUADRATIC REFORMULATION WITH ITERATIVE FACTORIZATION METHOD

The paper provides a mechanism for reformulating polynomial program using quadratic equations, achieved by an iterative factorization process. Our approach involves reformulating a polynomial programme using quadratic equations. This is done by taking into account the precise range of auxiliary variables and lowering their dimension. This method uses iterative factorization on monomials with a degree greater than two and the widest range to make a quadratic programme with the smallest range of auxiliary variables. Furthermore, the quadratic program is convex with an α-under estimator to obtai

  • CSB-08-05-2024-1304

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  • Chinese Science Bulletin

Framework for The Nature of Science Pedagogical Content Knowledge: A Malaysian Case-based study

Despite the well-documented importance of NOS in achieving scientific literacy, a significant number of teachers encounter challenges when attempting to effectively integrate NOS principles into their pedagogical practices. This study delved into the complex landscape of Nature of Science Pedagogical Content Knowledge (NOS PCK) utilizing a two-pronged approach, comprising a systematic literature review and in-depth case studies. Through this comprehensive investigation, four key elements emerged as determinants of teachers’ NOS PCK, shedding light on the intricate relationship between concep