Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Gongcheng Kexue Yu Jishu/Advanced Engineering Science (ISSN: 2096-3246) is a bi-monthly peer-reviewed international Journal. Gongcheng Kexue Yu Jishu/Advanced Engineering Science was originally formed in 1969 and the journal came under scopus by 2017 to now. The journal is published by editorial department of Journal of Sichuan University. We publish every scope of engineering, Mathematics, physics.
Submission Deadline
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Vol
57
, Issue
11
)
04 Dec 2025
Day
Hour
Min
Sec
Day
Hour
Min
Sec
Publish On
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Vol
57
, Issue
10
)
30 Nov 2025
Scopus Indexed (2025)
Aim and Scope
Gongcheng Kexue Yu Jishu/Advanced Engineering Science (ISSN: 20963246) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to:
Agricultural science and engineering Section:
Horticulture,
Agriculture,
Soil Science,
Agronomy,
Biology,
Economics,
Biotechnology,
Agricultural chemistry,
Soil,
development in plants,
aromatic plants,
subtropical fruits,
Green house construction,
Growth,
Horticultural therapy,
Entomology,
Medicinal,
Weed management in horticultural crops,
plant Analysis,
Tropical,
Food Engineering,
Venereal diseases,
nutrient management,
vegetables,
Ophthalmology,
Otorhinolaryngology,
Internal Medicine,
General Surgery,
Soil fertility,
Plant pathology,
Temperate vegetables,
Psychiatry,
Radiology,
Pulmonary Medicine,
Dermatology,
Organic farming,
Production technology of fruits,
Apiculture,
Plant breeding,
Molecular breeding,
Recombinant technology,
Plant tissue culture,
Ornamental horticulture,
Nursery techniques,
Seed Technology,
plantation crops,
Food science and processing,
cropping system,
Agricultural Microbiology,
environmental technology,
Microbial,
Soil and climatic factors,
Crop physiology,
Plant breeding,
Electrical Engineering and Telecommunication Section:
Electrical Engineering,
Telecommunication Engineering,
Electro-mechanical System Engineering,
Biological Biosystem Engineering,
Integrated Engineering,
Electronic Engineering,
Hardware-software co-design and interfacing,
Semiconductor chip,
Peripheral equipments,
Nanotechnology,
Advanced control theories and applications,
Machine design and optimization ,
Turbines micro-turbines,
FACTS devices ,
Insulation systems ,
Power quality ,
High voltage engineering,
Electrical actuators ,
Energy optimization ,
Electric drives ,
Electrical machines,
HVDC transmission,
Power electronics.
Computer Science Section :
Software Engineering,
Data Security ,
Computer Vision ,
Image Processing,
Cryptography,
Computer Networking,
Database system and Management,
Data mining, Big Data, Robotics ,
Parallel and distributed processing ,
Artificial Intelligence ,
Natural language processing ,
Neural Networking, Distributed Systems ,
Fuzzy logic, Advance programming,
Machine learning,
Internet & the Web,
Information Technology ,
Computer architecture,
Virtual vision and virtual simulations,
Operating systems,
Cryptosystems and data compression,
Security and privacy,
Algorithms,
Sensors and ad-hoc networks,
Graph theory,
Pattern/image recognition,
Neural networks.
Civil and architectural engineering :
Architectural Drawing,
Architectural Style,
Architectural Theory,
Biomechanics,
Building Materials,
Coastal Engineering,
Construction Engineering,
Control Engineering,
Earthquake Engineering,
Environmental Engineering,
Geotechnical Engineering,
Materials Engineering,
Municipal Or Urban Engineering,
Organic Architecture,
Sociology of Architecture,
Structural Engineering,
Surveying,
Transportation Engineering.
Mechanical and Materials Engineering :
kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.Chemical Engineering :
Chemical engineering fundamentals,
Physical, Theoretical and Computational Chemistry,
Chemical engineering educational challenges and development,
Chemical reaction engineering,
Chemical engineering equipment design and process design,
Thermodynamics,
Catalysis & reaction engineering,
Particulate systems,
Rheology,
Multifase flows,
Interfacial & colloidal phenomena,
Transport phenomena in porous/granular media,
Membranes and membrane science,
Crystallization, distillation, absorption and extraction,
Ionic liquids/electrolyte solutions.
Food Engineering :
Food science,
Food engineering,
Food microbiology,
Food packaging,
Food preservation,
Food technology,
Aseptic processing,
Food fortification,
Food rheology,
Dietary supplement,
Food safety,
Food chemistry.
Physics Section:
Astrophysics,
Atomic and molecular physics,
Biophysics,
Chemical physics,
Civil engineering,
Cluster physics,
Computational physics,
Condensed matter,
Cosmology,
Device physics,
Fluid dynamics,
Geophysics,
High energy particle physics,
Laser,
Mechanical engineering,
Medical physics,
Nanotechnology,
Nonlinear science,
Nuclear physics,
Optics,
Photonics,
Plasma and fluid physics,
Quantum physics,
Robotics,
Soft matter and polymers.
Mathematics Section:
Actuarial science,
Algebra,
Algebraic geometry,
Analysis and advanced calculus,
Approximation theory,
Boundry layer theory,
Calculus of variations,
Combinatorics,
Complex analysis,
Continuum mechanics,
Cryptography,
Demography,
Differential equations,
Differential geometry,
Dynamical systems,
Econometrics,
Fluid mechanics,
Functional analysis,
Game theory,
General topology,
Geometry,
Graph theory,
Group theory,
Industrial mathematics,
Information theory,
Integral transforms and integral equations,
Lie algebras,
Logic,
Magnetohydrodynamics,
Mathematical analysis.
Latest Journals
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-15-10-2024-770
Abstract : "Mind Harbor: Navigating Wellness Together - with AI integration" is a research initiative aimed to propose an application to address mental health challenges faced by university students in Oman. This study recognizes the complex elements influencing students' mental health and focuses on those who are experiencing anxiety and depression. Sometimes social situations, insecurity, shyness or awkwardness make students reluctant to talk about their mental health problems. This may lead to significant mental and physical health issues, as well as negatively impact academic performances which is a major concern for university, parents and the society. Through this suggested application's user-friendly interface, students will be able to express their concerns in a safe and non-judgmental environment. The inputs collected through the interface will then be sent to an AI-powered LLM that is intended to offer customized recommendations and solutions to assist students in overcoming obstacles. This research aims to provide a comprehensive and easily accessible support system for managing mental health challenges of students by combining the capabilities of artificial intelligence with human-computer interaction. The research aims to create a customized interface to be integrated with Large Language Model (LLM) which will provide recommendations and interventions depending on the particular requirements and circumstances of every learner. The core of the application lies in its user-friendly interface, designed to facilitate easy and confidential communication between students and the system. Students can input their concerns, which are then processed by an AI-powered Large Language Model (LLM). This research intends to improve the delivery of mental health support in learning environments by merging technology with human-centered design concepts that are sympathetic. Students can access a virtual harbor called Mind Harbor, where their voices are heard, their problems are validated, and solutions provided..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-11-10-2024-768
Abstract : This study aims to assess the reliability of crop production under diverse environmental conditions and utilizes regression modeling for Foxtail millets growth prediction, a focal point of this investigation. Machine learning is an emerging field in agricultural research, particularly in the analysis and forecasting of Foxtail millets growth yields. The process of crop production is impacted by a multitude of factors such as the number of days to flowering, maturity period, plant height, and fodder yield, among others. In this research, machine learning techniques, particularly linear regression, have been employed to forecast Foxtail millets yield. Linear regression was chosen due to its effectiveness as a predictive model, demonstrating a notably higher accuracy for this dataset in comparison to alternative models. Complex datasets that pose challenges for conventional analysis methods can be effectively decoded using machine learning strategies, uncovering valuable underlying patterns automatically. This enables informed decision-making processes by revealing unseen knowledge and patterns related to various agricultural challenges. Furthermore, machine learning facilitates the prediction of future events. During the growing season, farmers are keen on estimating their expected yield. With the continuous increase in agricultural data volume globally, this paper focuses on predicting crop yields using collected agricultural datasets. The research employs a regression analysis model to evaluate the accuracy and efficacy of predicting Foxtail millets crop yields in India. Linear regression is utilized to establish correlations between mean, variance and Foxtail millets yield. Assessing the potential millet production rate is crucial for farmers to benefit from predictive outcomes and mitigate financial losses. The research findings highlight the accuracy of Foxtail millets yield predictions using the regression model..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-11-10-2024-767
Abstract : Channel estimation is a pivotal component in determining the performance of wireless networks. Recent advancements in deep learning have significantly enhanced communication reliability and reduced computational complexity in 5G and future wireless networks. While least squares (LS) estimation remains widely used due to its simplicity and lack of requirement for prior statistical knowledge about the channel, it often suffers from relatively high estimation errors in digital communication. This paper introduces a novel channel estimation framework that leverages deep learning hybrid models to enhance the accuracy of channel estimates traditionally obtained through the least squares (LS) method. Our approach is validated using a Multiple-Input Multiple-Output (MIMO) system, incorporating a multi-path channel profile, and simulating scenarios in 5G and beyond networks under various mobility conditions characterized by Doppler effects. The system model is designed to accommodate any number of transceiver antennas, and the machine learning component is versatile, allowing the use of various neural network architectures. Numerical results show that the proposed deep learning-based channel estimation structure outperforms traditional methods commonly used in previous studies. Furthermore, our analysis indicates that bidirectional long short-term memory (LSTM) networks achieve the maximum channel estimation accuracy and the lowest bit error rate among the evaluated artificial neural network architectures..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-21-09-2024-761
Abstract : Natural fibers such as sisal fibers have shown great promise as an alternative renewable material that can be used as reinforcement in cement-based composites. One of the ways by which sisal fibers mechanical properties can be improved to enable the fibers to compete with fiber types such as steel and synthetic fibers is through fiber surface modification. In order to improve the surface properties of sisal fibers, three fiber treatment methods namely chemical treatment, thermal treatment and hybrid treatment were carried out on the sisal fibers and the effect of these treatment methods on the mechanical properties and water absorption of the sisal fibers were investigated. Tensile tests were performed on the treated sisal fibers and their tensile strength and tensile modulus were evaluated. The experimental results showed that all the treatment methods considered led to an improvement in the tensile strength, tensile modulus and water absorption of the sisal fibers. The chemical treatment, thermal treatment and hybrid treatment led to 32.9%, 17.5% and 37.4% improvement in the tensile strength respectively. Similarly, the tensile modulus was improved by 30.9%, 13.4%, and 122% for the chemical treatment, thermal treatment and hybrid treatment methods respectively. The water absorption of the treated sisal fibers was reduced by 38.3%, 34.9% and 45.8% for the chemical treatment, thermal treatment and hybrid treatment methods respectively. The results showed that fiber treatment (chemical, thermal and hybrid) methods are effective ways of improving the mechanical properties and water absorption of sisal fibers..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-21-09-2024-760
Abstract : Currently, development in the industrial sector is very advanced. In a development, heavy equipment is definitely needed. In the use of heavy equipment, it certainly requires a lubricant that serves to reduce the wear that occurs due to friction between two surfaces. Grease that is spread on the market today is a petroleum-based product that is not environmentally friendly and not abundant. The manufacture of lubricating grease in this study uses vegetable oil as the base material, which is soybean oil. Lithium as a thickener oil and molybdenum as an additive. The tests carried out in this study were melting point test, corrosion rate test and viscosity test. Based on the research that has been done, the results of soybean oil-based grease lubricants with melting point values below 150o C are obtained. Soybean oil-based grease is included in the NLGI GC standard. Soybean oil-based grease has excellent corrosion resistance..
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