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
58
, Issue
03
)
16 May 2026
Day
Hour
Min
Sec
Day
Hour
Min
Sec
Publish On
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Vol
58
, Issue
03
)
31 May 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-31-10-2024-775
Abstract : Egyptian government has particularly focused on real estate development during the past five years to support the economy by selling lands in new cities. However, the sector is unstable due to several ineffective policies, regulations, and procedures. Noticeably, achieving sustainable development in new cities often faces challenges regarding land provision and effective governance. Moreover, the processes to regulate and control the land provision process are inefficient and ineffectual. Many researchers associate the existing ineffective land provision policies in Egyptian new towns with the absence of real estate market monitoring, capturing the accelerating demand for land and the rapid change in land uses .The importance of real estate monitoring is being raised due to the lack of a clear strategy for the governance of the real estate sector in new cities, accompanied with no clear mechanisms to manage supply and demand and the sustainability of the land provision process .Thus, purpose of this research is to investigate how a proposed Smart Real-Estate Observatory (SREO) might help in addressing these challenges, particularly highlighting its role in addressing the drawbacks and consequences resulting from the absence of real estate monitoring in new cities, in addition to the absence of governing frameworks for a sustainable development of the real estate market. Globally, there are many concerns regarding the use and employment of smart and systematic technologies to support decision-making via models that monitors, analyzes, and controls real estate data, adopt methodologies based on digital indicators, and implement technologies such as Blockchain technology and IOT. The paper, therefore, argues that there is an urgent need to move towards the governance of land demand, as well as studying the factors affecting supply and demand through the establishment of a (SREO) that helps decision-makers to optimally utilize land uses, and induces proper governance of the real estate market in new cities in Egypt..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-25-10-2024-773
Abstract : The Degradation process model was found to be appropriate and fixed over the two bio-object frameworks: (i) Comparison among Low and High BMI, WC, OC consumption levels of Parous and Nulliparous women (ii) Profile of 17β-estradiol and progesterone against upper, middle and lower tertile. The author verified the results provided by the medical field and found that there is no significant connection between performance of Reliability function of Degradation process fixed for 17β-estradiol and progesterone and parity of the women. The monotonically increasing curves depicts that the Reliability function of Degradation process fixed for 17β-estradiol and progesterone over parous, nulliparous women have maximum performances over the women having high BMI (>25 Kg/m2) and who were consuming oral contraceptives more than 3 years. But interestingly we observed the inverted results over daily salivary observation against lower, middle and upper tertile as lower tertile with less than 10 ten years interval serves upper bound and upper tertile with 13.5 years’ time interval serves lower bound of middle tertile with interval 10 – 13.5 years..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-15-10-2024-771
Abstract : Over the last couple of decades, virtual and augmented reality technologies have been widely used to minimize the problems affecting public safety and emergency services. The paper outlines a technology that would let first responders navigate their route through built environments by using AR devices attached to their helmets and visors, which would be helpful during natural disasters and severe fires. This development has enabled the capability to see and hear through smoke, fire, debris, adverse weather, and other obscuring materials; navigation; real-time sensor data related to the environment and dangerous situations; and the ability to see through smoke. It may also provide users with audio and visual commands in a disaster, information on the availability of shelter, ways of evacuation, and what to do in an emergency using head-mounted displays and personal digital assistants [2]. Deep learning approaches, such as CNNs and SLAM techniques, are crucial in improving the graphics generated by augmented reality systems, thereby enhancing the end-user experiences. The SLAM technique tracks the positions and orientations of views in an artificial environment and supplies the geometric position for the augmented reality system. It, therefore, allows the system to draw the surroundings three-dimensionally. The CNN algorithm detects and perceives the objects within an environment..
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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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