Journal of Hunan University Natural Sciences

The Journal of Hunan University Natural Sciences is the leading Chinese academic journal that publishes articles in all areas of natural sciences. The Journal is meant to serve as a means of communication and discussion of important issues related to science and scientific activities. The Journal publishes only original articles in English which have international importance. In addition to full-length research articles, the Journal publishes review articles. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications.
Articles for the Journal are peer-reviewed by third-party reviewers who are selected from among specialists in the subject matter of peer-reviewed materials.
The Journal of Hunan University Natural Sciences is a kind of forum for discussing issues and problems facing science and scholars, as well as an effective means of interaction between the members of the academic community. The Journal of Hunan University Natural Sciences is read bya large number of scholars, and the circulation of the journal is constantly growing.
The Journal of Hunan University Natural Sciences publishes special issues on various and relevant topics of interest to the scientific community.
The Journal of Hunan University Natural Sciences is indexed by Web of Science, Scopus, Current Contents, Geobase and Chemical Abstracts.
Articles containing fundamental or applied scientific results in all areas of the natural sciences are accepted for consideration.
The Editorial Board of the Journal of Hunan University Natural Sciences is composed of 25 members and is chaired by Academician Chen Zhengqing. Editor-in-chief is Prof. Yi Weijian.
Frequency of publication: monthly
ISSN: 1674-2974
Access to all articles on the website is open, does not require registration or payment.
Journal articles are licensed under the CC BY 4.0 Creative Commons Attribution 4.0 License.
The Journal of Hunan University Natural Sciences takes care of maintaining electronic versions of articles. Data safety is ensured by backing up digital data in accordance with internal regulations. Logical and physical data migration is also provided. Cloud technologies are applied.
For further information, please contact:
E-mail: editorial-office@jonuns.com
Address: Lushan Road (S), Yuelu District, Changsha, Hunan Province, Zip Code: 410082 (Editorial Department of Journal)
Announcements
Submission open for Volume 52, Issue 7, July, 2025 |
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Dear Authors, Deadline: July 25, 2025. |
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Posted: 2025-07-03 | More... |
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Last Research Articles
Accurately distinguishing Indonesian banknote denominations remains challenging for older adults and people with visual impairments, especially after the recent release of a redesigned currency. To address this gap, we propose an intelligent recognition system based on deep transfer learning with systematic hyperparameter optimization. Our approach fine-tunes a pre-trained ResNet-50 backbone while simultaneously calibrating the learning rate, batch size, and training epochs via an am-optimized grid search. Extensive experiments across multiple convolutional neural-network architectures confirmed that the ResNet-50 model, trained with a learning rate of 0.0001, batch sizes 20 and 15 epochs, achieved state-of-the-art performance: 99.29 % accuracy, 99.32 % precision, 99.29 % recall, and 99.29 % F1-score. These findings demonstrate that carefully tuned transfer-learning pipelines can deliver near-perfect classification of the new Indonesian banknote series while retaining strong generalization to previously unseen images, thereby offering a practical assistive tool for the visually impaired and elderly.
Keywords: Deep transfer learning; ResNet-50; Convolutional neural networks; Indonesian banknote recognition; Hyper-parameter optimization; Assistive computer vision.
Yuni Yamasari, Bagas Ahmad Sadewa, Anita Qoiriah, Ervin Yohannes, Ricky Eka Putra, Tohari Ahmad
2025-07-15
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This study examines subjectivity and polarity in multilingual WhatsApp messages exchanged during the employees’ union election at the Mehran University of Engineering and Technology (MUET), Khairpur campus. As social-media platforms generate vast volumes of unstructured multilingual text, sentiment analysis an intersection of natural language processing (NLP) and machine learning (ML) offers a rigorous means of extracting collective opinion. We introduce a novel analytical framework for code-switched discourse in Sindhi and Urdu, thereby contributing to both multilingual NLP research and electoral studies. A corpus of WhatsApp messages reflecting anticipated election outcomes was collected, cleaned, and annotated. Five supervised classifiers—feed-forward neural networks, nonlinear support vector machines, random forests, decision trees, and k-nearest neighbours were trained to detect sentiment polarity and subjectivity. Across all models, Candidate 1 received markedly more favourable sentiment, posting an average positive-sentiment score of 61.48 % versus 37.11 % for Candidate 2. Candidate 1 likewise exhibited superior aggregate performance metrics (mean accuracy = 89.92 %, recall = 86.52 %, precision = 85.12 %, F1-score = 85.81 %). These findings demonstrate the feasibility of fine-grained sentiment mining in under-resourced languages and yield actionable insights for campaign strategists. Future research will enhance classification accuracy through advanced deep-learning architectures and will track dynamic sentiment patterns across multiple social-media platforms throughout the electoral cycle.
Keywords: multilingual sentiment analysis; code-switching; WhatsApp; natural language processing; machine learning; Sindhi; Urdu; electoral behaviour.
Imitaz Ali Solangi, Javed Ahmed Mahar, Ghulam Ali Mallah, Zulfiqar Ali Solangi
2025-07-14
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The purpose of the article is to develop and validate a hybrid biomedical signal monitoring system that integrates edge-computing capabilities with Microsoft Azure cloud services. The article describes a new edge-and-cloud architecture based on the EmotiBit wearable sensor and a Raspberry Pi gateway, enabling continuous acquisition, local buffering, and scalable cloud synchronization of multi-modal biometric signals. Using hardware evaluation (signal accuracy, power consumption, wireless connectivity), Azure Blob Storage, Azure SQL Database, Power BI dashboards, and Azure Stream Analytics, the authors demonstrate a seamless pipeline for real-time visualization and post-hoc analysis of electrocardiogram (ECG), photoplethysmogram (PPG), galvanic skin response (GSR), and additional vital signs. We illustrate the proposed system by conducting a rigorous battery performance analysis under three workloads continuous sensing alone, sensing with local storage, and combined local plus cloud upload and comparing operational endurance across battery capacities ranging from 1,200 mAh (≈12 h runtime) to 6,000 mAh (≈45 h runtime).
Keywords: EmotiBit; Raspberry Pi; Microsoft Azure; Edge Computing; Internet of Things (IoT); Real-time Monitoring; Telemedicine.
Olguer Morales, Giovanny Tarazona, Robinson Jiménez-Moreno
2025-07-12
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The Sine–Cosine method has emerged as a robust analytical approach to derive solitary wave solutions for nonlinear dispersive partial differential equations. In this work, we systematically employ this technique to the Camassa–Holm hierarchy, encompassing the classical Camassa–Holm, Degasperis–Procesi, Fornberg–Whitham, and Fuchssteiner–Fokas–Camassa–Holm equations. Each member of the hierarchy models shallow-water wave propagation under specific integrability conditions, exhibiting rich dynamical behavior. By applying an appropriate wave transformation, we reduce the governing equations to ordinary differential equations and construct exact travelling-wave solutions in terms of trigonometric and hyperbolic functions. The solutions obtained include compacton like profiles and classical sech² and cosh² structures, with explicit expressions for wave speed and amplitude as functions of model parameters. Comparative analysis highlights the effectiveness and simplicity of the Sine–Cosine method relative to more elaborate techniques such as the Hirota bilinear formalism and the inverse scattering transform. Our contributions lie in the unified application of this method across the entire hierarchy and the presentation of a comprehensive classification of solitary wave families.
Keywords: Camassa–Holm hierarchy; Sine–Cosine method; solitary wave solutions; nonlinear dispersive equations; integrable systems; exact analytical solutions.
Felipe Pipicano, Gerardo Loaiza
2025-07-10
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The purpose of this article is to propose a novel machine learning–enhanced data-driven model predictive control (DD-ML-MPC) to improve robustness and performance under uncertainty. The article describes a DD-ML-MPC method, based on neural-network predictors trained on historical I/O data and integrated within an MPC framework, enabling more accurate forecasts and tighter constraint satisfaction when plant models are unavailable. Using numerical simulations on a MIMO LTI system corrupted by Gaussian noise, the authors compare three strategies—Model-Based MPC (MB-MPC), Data-Driven MPC (DD-MPC) and DD-ML-MPC—by assessing root mean square error, convergence time, constraint violation rate and computational overhead. The proposed DD-ML-MPC is demonstrated on a four-state, two-input, two-output plant subjected to stochastic disturbances. Our DD-ML-MPC achieves a 20 % reduction in constraint violations and a 15 % decrease in tracking error relative to MB-MPC.
Keywords: Model Predictive Control (MPC); Data-Driven Control; Machine Learning Control; Neural Network.
Chandar Kumar, Dur Muhammad Soomro, Najeeb Ur Rehman Malik
2025-07-08
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