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 56 , Issue 02 )
02 Mar 2024
Day
Hour
Min
Sec
Publish On
( Vol 56 , Issue 01 )
29 Feb 2024
Scopus Indexed (2024)

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 :

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-20-10-2022-361

Abstract : Precision agriculture analysis is a development fact in big data mining for predicting weather data for future recommendation agricluture. Especially in agricultural development, the spatial data is more difficult to predict right information because of more dimension due to non-relation feature analysis leads classification prediction problems. To resolve this, we propose a Forecasting weather prediction model based On Mutual Invariance Feature Selection Model (MIFSM) with intent of Successive Weather Influence Rate (WSIR) depended feature prediction and classified with Subset Spread Spectral Deep Neural Network (S3-DNN). Further predicting right features based on relevant features estimation using successive weather influence rate (SWIF) is estimated. Initially the proposed system collects the geo spatial weather data and process into feature selection using Mutual In variance Feature selection model. The features gets estimated using Successive Weather Influence Rate (SWIF) to make mean weightage along with marginal values observed from dataset rainfall, temperature, humidity etc. the estimated weight is patterned using Spatial Harvest Successive Rate (SHSR) to make ordered ranking. Further the selected features is trained into Soft Max Logical Activation Function (SMLAF) to get tuned neural network Using Convolution Neural Network (CCN). The classifier get trained with SMLAF to process input features make categorize the data into recommend and non-recommend fields based on this weather which is for recommendation for agricultural resources. The proposed system produce best recommendation performance as well than previous system..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-18-10-2022-360

Abstract : Improving the operational performance of wastewater treatment plants can be effectively approached by means of model simulation. GPS-X model was used in this study. Calibration and validation of the model were carried out, with various sensitive parameters subject to modification, with results found within the prescribed parameters for R and RMSE. Sensitivity analysis then indicated that the most important factor for reducing nitrogen and phosphorous concentrations was the readily biodegradable fraction; thus, the IR, RAS ratio, DO, and WAS flows were reduced from 3% to 1%, from 100 to 20%, from 3.5 to 2 mg/L, and from 3,500 to 1,000 m3/d, respectively, producing an optimization that saved 688.4 Kw.h in energy and gave a sludge reduction of 32%. These results showed that an IR percentage of 3% is not appropriate. Decreased rbCOD thus necessitates a chemical upgrade, which was implemented in this case by means of adding an external carbon source, represented by acetic acid, propionic acid, methanol, and glycerol, with good results. These additions led to improvements in terms of reduced TN and TP by suitable ratios. The best external carbon source was thus determined to be methanol, while glycerol was less effective than the others. The process of pre-denitrification was compared with the post-denitrification process by means of the addition of methanol as an external carbon source, which gave good results for the reduction of TN in the post-denitrification process, by up to 80%; however, the effect on other pollutants was to increase concentrations..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-17-10-2022-359

Abstract : We introduce the new class quasi -(ζ,η)- normal operators. Composition operators, Weighted composition operators, Composite multiplication operators of Quasi –(ζ,η)- normal and their adjoints on L^2 (ℷ) are described..
Full article
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-17-10-2022-358

Abstract : Present a newborn class of an operator (℘≀,ϱ)-Paranormal and (℘≀,ϱ)-*-Paranormal Composition, Weighted Composition and Composite Multiplication operators on L^2 space are characterized. Also characterized (℘,≀,ϱ)-paranormal and (℘,≀,ϱ)-*-paranormal operators on Hardy space..
Full article
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-15-10-2022-355

Abstract : In today's modern world, the Internet is highly crucial. A larger number of wired and wireless sensors characterize the Internet of Things (IoT). These sensors are networked devices that create massive amounts of data, reaction time, latency, and security vulnerabilities. These problems create delay in decision making in some situation which is very extreme in case of Healthcare, Smart vehicles, Industry 4.0 etc., the data moving from the sensor devices have to reach the cloud and from there it has to reach back to receivers. Therefore, in order deal with such huge data we use fog computing, a well-known distributed architecture. Fog computing works with the aim to enhance the processing, intelligence, and accumulation of data closer to the Edge devices. The proposed framework helps in reduce latency as we place a Fog node device between the cloud and the edge device where data is generated and sent to the cloud and retrieved from the cloud. The framework is designed in such way that the different sensor devices can be placed on it and collects the sensor data from them. To test the framework functioning, without the fognode and with the fognode, comparing the latency with packet transfer rate from sensor devices to the cloud and vice versa. In this paper, we are considering two different case studies to test the proposed functioning of the framework with the following case studies i) Related to medical are like diabetes and cardiovascular illnesses are considered for prediction based on patient health records and ii) Vehicle theft where the owner get an alert of the theft of the vehicle within few seconds of the theft. In Medical case study initially, patient health data is acquired and stored using Sensor devices. A group of patient health records is first subjected to the unique rule-based clustering technique. Finally, we consider diabetes and cardiovascular diseases in our study. To test the performance of the suggested task, a rigorous experiment and study using healthcare data were conducted. Various Machine Learning (ML) algorithms are applied to patient health information, and the prediction result, as well as the accuracy of each algorithm, is generated. The findings show that the proposed work accurately predicts cardiac and diabetes problems, identifies which algorithm has the highest accuracy among them, and reduces burden on cloud. Devices having inbuilt sensors used in this work are pulse oximeter, smart watch, glucometer and thermometer. In Vehicle theft case study, there are numerous anti-theft solutions for smart vehicles based on GSM and cloud computing. The previous models have excessive latency and bandwidth. Fog computing is a creative new concept that combines existing solutions with GPS and GSM. IoT development for a fog- based vehicle anti-theft technology that uses a mobile application to pinpoint the exact position of a stolen vehicle. Low latency, low bandwidth, fast responsiveness, and high security are all advantages of fog computing. Thus with the use of the above proposed framework can reduce the latency and bandwidth in these applications and helps to avoid the delay decision making. Further this framework can be used to analyze and improve the performance and consumption of power in smart cities, smart homes and smart appliances..
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