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
(
Vol
58
, Issue
01
)
26 Jan 2026
Day
Hour
Min
Sec
Day
Hour
Min
Sec
Publish On
(
Vol
58
, Issue
01
)
31 Jan 2026
Scopus Indexed (2026)
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-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..
Full article
Full article
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..
Full article
Full article
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..
Full article
Full article
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..
Full article
Full article
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-20-09-2024-759
Abstract : The identification of emotions has become a significant research area in the current societal landscape, owing to its numerous applications. Facial expression recognition (FER) is one of them and is crucial in conveying emotional information about people. In recent years, the popularity of facial emotion recognition has grown, driven by the advancement of artificial intelligence. Usually, face pre-processing, including facial detection and alignment, contributes to the increased complexity of the facial emotion recognition (FER) classification process. However, in this paper, we simplify the process by concurrently addressing identification, recognition, and classification. Initially, the CK Plus dataset underwent manual annotation. Subsequently, facial expressions were analyzed using an endto-end Facial Emotion Recognition (FER) network named FER using YOLOv8. The architecture of FER using YOLO is derived from YOLOv8. The proposed model exhibits exceptional accuracy on the CK Plus and HFER datasets, with experimental findings revealing high detection performance (mAP50) ranging from 0.974 to 0.989 on the test datasets. Our method ensures both high accuracy and swift inference for Facial Emotion Recognition, demonstrated through real-time image testing with accurate results. Additionally, the model is validated for realtime FER using spontaneous images from a camera, showcasing its robust performance in dynamic scenarios..
Full article
Full article