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)


Paper Submission 

Announcements

 

Submission open for Volume 49, Issue 2, February, 2022.

Dear Authors,
Please submit your manuscripts through our Online Submission System or directly to the Chief -Editor's e-mail editorial-office@jonuns.com.
Deadline:  January 20, 2022.
Journal of Hunan University Natural Sciences is an international, peer-reviewed open access journal on all aspects of natural sciences published monthly online.
Manuscripts are peer-reviewed. The first decision is given to authors about 20-30 days after submission; acceptance for publication after revisions is done within seven days.
Aims
Journal of Hunan University Natural Sciences provides an advanced forum on all aspects of natural sciences. It publishes reviews, research papers, and communications. We aim to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that everyone can reproduce the results. Electronic files and software regarding the full details of the calculation or experimental procedure can be deposited as supplementary electronic material if unable to be published in a normal way.
Scope
The journal covers physics, chemistry, engineering, environmental, earth sciences and biology.
Sections:
•    Biosciences and Bioengineering;
•    Computer and Information Science;
•    Chemistry;
•    Earth-Aerospace-Marine Science;
•    Electrical and Electronic Engineering;
•    Education;
•    Engineering;
•    Energy;
•    Environmental Sciences;
•    Economy;
•    Finance;
•    Materials Science;
•    Mathematics;
•    Medicine;
•    Neurosciences ;
•    Physics;
•    Pharmaceuticals.

The authors should prepare the articles strictly according to the template. Please check the link http://jonuns.com/docs/template.doc.
Each article should have no more than six authors.
All articles published in are published in full open access. In order to provide free access to readers, and to cover the costs of peer review, copyediting, typesetting, long-term archiving, and journal management, an article processing charge (APC) of EUR 430 applies to papers accepted after peer review.
Submitted papers should be well formatted and use good English. Authors may use our English editing service (EUR 50-70) prior to publication or during author revisions. The articles that native English speakers do not edit are not allowed for publication.
The journal publishes articles in English or Chinese.
Articles published in the Journal of Hunan University Natural Sciences will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution License (CC BY). The copyright is retained by the author(s).
Posted: 2021-12-13 More...
 
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Last Research Articles

With the continuous efforts to implement several smart cities, several challenges face these initiatives at the global level, the most prominent of which is data privacy. There is a lack of research on the factors that affect the perception of individuals' privacy, such as the risk of privacy, data sensitivity, privacy Awareness. In addition, it is not clear what those factors are, and they could swing people's intention to adopt smart services. Concerns about data privacy are categorized based on data activities to unauthorized retrieval, unauthorized use, unauthorized access, unauthorized sharing, insecure storage, and insecure transmission. Each of these issues might lead to a personal data breach and expose the data to be compromised, especially in the case of healthcare data. Therefore, this study aims to identify factors that affect the adoption of smart city healthcare services and subsequently propose a generic adoption model to focus on data and information privacy in this model, especially in health care. This model is developed based on two ways to extract the factors. First: the theories used are the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Privacy calculus theory (PCT). Secondly: extract some factors from the literature review and studies related to this research. The model is expected will help to obtain the acceptance, adoption of smart city services and the extent of their impact on data and information privacy from the perspective of individuals and fill gaps. It also can be used in countries similar to Oman, such as in the Arabian Gulf countries.

 

Keywords: smart city, healthcare services, privacy, privacy concern, technology acceptance model.

Abdullah Aslam Alzawamri, Hairoladenan Kasim, Moamin Mahmoud
2022-01-22
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In higher education, digital technologies completely change teaching and learning, with the rapid technological development compounding the problem. Because of the extensive usage of technology among today's college and university students, higher education institutions worldwide have recognized the need to use it in teaching and learning for certain reasons. Over the last two decades, studies have speculated on the beneficial and negative effects of students' constant interaction with technology in distance education. This study intended to investigate the effect of digital technology and educational outcomes through the mediating effect of student engagement. The convenience sampling approach was used to obtain 378 students and staff data. In addition, this study analyzed the data using the Partial Least Square Structural Equation Model (PLS-SEM). The study's findings indicated that digital technology and student engagement positively and significantly affect educational outcomes in distance education. In addition, the results revealed that digital technology indirectly influences educational outcomes through the mediation effect of student engagement. This study concluded that to comprehend the changes occurring in higher education. More attention should be devoted to establishing policies and strategies to increase student engagement in digital technologies in distance education of higher education contexts. From a distance education viewpoint, the current review investigated the determinants of educational outcomes through the mediating effect of student engagement. These findings have significant implications as distance education becomes a basic delivery mode of study in the coming decades. The methodological approach used in this study and the digital technology in distance education greatly improve current literature in the educational sector.

 

Keywords: distance education, digital technology, educational outcomes, higher education, Partial Least Square Structural Equation Model.

Hani Jarrah, Hanene Lahiani
2022-01-22
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Over the last two decades, medical imaging examinations, and technologies together have been exponentially increased. With the increased demand for medical examinations, the demand for medical imaging experts is also increased. Manual identification and annotation of biomedical concepts tend to be rigorous and error-prone due to the varied knowledge of imaging experts. There is a critical need for automated Medical Concept Detection methods. Finding the relevant biomedical concepts present in a medical image holds the key to solve many automated clinical diagnosis problems, a machine learning pipeline for medical information retrieval, and other related issues, like creating and managing legacy or cloud-based descriptive digital repository. Appropriate mapping from biomedical image concepts into precise textual summary highly depends on the efficiency of Medical Concept Detection techniques. A novel clustering technique is presented as a complementary data preconditioning step to reach high concept detection results. The authors grouped 8767 Concept unique Identifiers (CUIs) into 970 clusters (label size decreased by 26% approximately using 97.7% images from the dataset). The main objective of this research is to examine the state-of-the-art convolution-based deep learning pre-trained and full-scale training models for the task of multi-label classification of medical concepts using medical image input. The research work evaluates the performance of transfer learning networks: InceptionV3, Xception, Dense Convolution Network (DenseNet) 121, VGG-16, and MobileNet. This work also presents one full-scale learning CNN architecture for the identification of relevant biomedical concepts that exist in medical images. Transfer learning technique using Xception model has achieved the highest F1 score of 36.29. The shallow VGG-like full-scale training architecture also has shown a promising result with an F1 score of 20.018. The obtained results reflect the significant improvement from previous experiments, offering state-of-the-art performance, with new data preconditioning precedence for highly variable and complex datasets.

 

Keywords: concept detection, concept annotation, deep learning, medical image processing, neural networks, machine learning.

Farhat Ullah Khan, Izzatdin Aziz, Nordin Zakaria
2022-01-22
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The development of type-2 diabetes mellitus (T2DM) has been linked to environmental, genetic, and epigenetic risk factors. Long non-coding RNAs (lncRNAs) play a role in the pathophysiology of complicated disorders and can also be used as diagnostic indicators. The literature strongly supports the implication of these lncRNAs in certain metabolic disorders. In this regard, one of the lncRNAs Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1) expressions is also under investigation concerning diabetes and its related complications. There are few researches on the relationship between T2DM progression and MALAT1 dysregulation, and this study was undertaken to investigate this link further and gain a better knowledge of its clinical significance in the diagnosis and prognosis of T2DM. Therefore, the purpose of this study was to assess the gene expression of lncRNA MALAT1 in healthy controls, type 2 diabetics, and T2DM patients with the presence of coronary artery disease complications. This study was the case-control one performed on blood samples followed by centrifugation to separate the buffy coat. Then total RNA was extracted from buffy coat using triazole method followed by quantifying its expression levels by quantitative polymerase chain reaction (qPCR). The results of our study showed the significant up-regulation of lncRNAs-MALAT1 expression in patients with T2DM 1.22 (3.7) and T2DM with related coronary artery disease (CAD) complications 1.95 (2.10) (p-value <0.001). These findings suggest that modulating the expression of lncRNA MALAT1 may be a future strategy for diagnosing and treating diabetes-related complications like CAD.

 

Keywords: long non-coding RNAs, metastasis associated lung adenocarcinoma transcript 1, quantitative polymerase chain reaction. 

Sadia Arif, Fouzia Shaikh, Shumaila Usman, Najia Tabassum, Faisal Memon, Aliya Irshad Sani
2022-01-22
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Human fingerprint features extraction is important for building a fingerprint classification system. The extracted features must be unique and stable by remaining unchanged for the same fingerprint with various rotation degrees. This paper will analyze the most famous methods used to extract fingerprint features. K-means, LBP, WPT, and minutiae methods will be investigated; the obtained experimental results will be analyzed and compared to raise suitable recommendations for using these methods. The process of extracting the fingerprint's characteristics is one of the important things for use in discrimination systems, which requires saving memory and accelerating the classification process to take the appropriate decision and as quickly as possible. The stability of the properties of the fingerprint image, regardless of the different rotations, will lead to the use of a single features vector for the fingerprint with its different conditions and thus treat all the rotated fingerprints as one fingerprint image. This factor will be studied in this research to recommend the experts interested in building fingerprint recognition systems.

 

Keywords: fingerprint, features, local binary pattern, K-means, wavelet packet tree, bifurcation, ridge, Euclidean distance.

Mua’ad M. Abu-Faraj, Ziad A. Alqadi, Khaled Aldebei
2022-01-22
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Journal of Hunan University Natural Sciences
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