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
02
)
02 Mar 2026
Day
Hour
Min
Sec
Day
Hour
Min
Sec
Publish On
(
Vol
58
, Issue
01
)
28 Feb 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-20-02-2026-915
Abstract : Future 6G physical layers need to work together to improve peak power, spectrum confinement, reliability, energy, and latency when channels are not stationary and service slices are not the same. Traditional approaches for reducing PAPR and shaping spectra, such as PTS/SLM, μ-law companding, and fixed orthogonal precoding, work to some extent but are not very reliable across different types of waveforms (UFMC, OTFS) and power-domain multiplexing (NOMA). To suggest a single, AI-driven signal-shaping architecture that combines a hybrid orthogonal precoding bank (DFT/DST/SRC/ZC) with a probabilistic deep compander (PDC) and selective/clustered companding (SCC). A learning controller, such as Deep Reinforcement Learning (PPO) or a Cultural-History Optimization Algorithm (CHOA), changes the precoding choice, companding profile, cluster aggressiveness, NOMA split, besides delay–Doppler grid (for OTFS) to minimize a multi-objective loss over PAPR, out-of-band (OOB) emissions, EVM/BER, energy per bit, and latency. During exploration, safety shields make sure that power-amplifier headroom, spectrum masks, and latency budgets are followed. Synthetic but reasonable results show that improvements are consistent across waveforms. When CCDF is 10⁻³, PAPR goes down from 11.8 dB (OFDM) besides 10.6 dB (UFMC) to 6.9 dB (Proposed-UFMC) and 6.5 dB (Proposed-OTFS). The power outside of the band (OOB) goes up to −44.5 dBm. For 16-QAM, BER at 10 dB drops to 9.8×10⁻³ (Proposed-UFMC) and 7.5×10⁻³ (Proposed-OTFS). The framework has good energy-latency trade-offs: 0.62 mJ/bit at 1.3 ms (URLLC slice) and 0.45 mJ/bit at 3.8 ms (Green slice). Ablations show how each module works: taking off AI controller lowers PAPR to 8.3 dB and OOB to −39.1 dBm. These results indicate that suggested stack is a feasible 6G-level solution that integrates waveform diversity adaptive, safety-conscious optimization..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-17-02-2026-913
Abstract : The fast growth in the scale of large-scale data systems in sectors like cybersecurity, finance, healthcare, and industrial surveillance has enhanced the necessity of powerful anomaly detecting methods that can operate without indicated data. This paper explores the use of unsupervised machine learning to detect anomalies in high-dimensional and large-scale unhomogeneous data. Four exemplary algorithms, namely: Isolation Forest, One-Class Support Vector Machine (OC-SVM), Local Outlier Factor (LOF) and Autoencoder neural networks were evaluated systematically on several large-scale datasets with network traffic, transactional and sensor records. The experimental findings proved that deep learning-based Autoencoders had a top overall detection performance with an average precision of 93.6, recall of 90.8, F1-score of 92.2, and AUC of 0.96, which showed they were most effective in identifying non-linear patterns involving complex physiological behavior. The Isolation Forest was also particularly well performing obtaining an F1-score of 89.8% with much lower training time (71 seconds on 500k records) and much lower detection latency (1.8 ms per instance), which was similar to real time applications. Conversely, LOF displayed lower scalability and performance breakdown in a high-dimensional environment as well as OC-SVM. The proposed framework is competitive or better with other related work of recent interest and, therefore, can be compared with other comparable studies. On the whole, this study can contribute to practical understanding of the choice of unsupervised techniques of anomaly detection regarding the scale of a system, the nature of collected data, and the limitations of its operation..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-16-01-2026-903
Abstract : We construct the triple difference sequence spaces Γ^3 (p∇^3 q)=(Γ^3 )_(p∇^3 q), where p=(θ_uvw^mnk ) is an infinite three-dimensional matrix of Padovan numbers θ_mnk defined by
(θ_uvw^mnk )={■(θ_mnk/(θ_(u+5,v+5,w+5)-2),&m≤u,n≤v,k≤w,@0,&m>u,n>v,k>w)┤
and ∇_q^3 is a q-difference operator of third order. We obtain some inclusion relations, topological properties, Schauder basis and α-,β- and γ- duals of the newly defined space. We examine some geometric properties.
We introduce and study some basic properties of rough I_λ-statistical convergent
of weight g(A),where g:N^3→[0,∞) is a function satisfying g(m,n,k)→∞ and g(m,n,k)↛0 as m,n,k→∞ of triple sequence of Padovan q-difference matrix, where A represent the RH-regular matrix and also prove the Korovkin approximation theorem by using the notion of weighted A-statistical convergence of weight g(A) limits of a triple sequence of Padovan q-difference matrix and also have proved Korovkin approximation theorem by using the notion of weight g(A) limits of a triple sequence space of Padovan q-difference matrix. All the results will certainly motivate the young researchers..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-17-12-2025-894
Abstract : Recent breakthroughs in deep learning have contributed to this area of glyph classification, script recognition, and even multi-lingual Optical Character Recognition. Several deep learning architectures-from the conventional Convolutional Neural Network to the recent Vision Transformer and even their combination-have been proposed in the past for performing the mentioned tasks. Each of these deep learning architectures has varying strengths and weaknesses w.r.t. accuracy, complexity, scalability, and applicability. This paper presents a clear, comparative study of recent deep learning-based architectures on existing standard benchmarks like EMNIST, Omniglot, and multi-lingual script datasets. The study has brought forth the relevance of different architectures with respect to accuracy, complexity of calculations, execution speed, and practical applicability in applications related to a few-shot learning problem and edge computing..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-04-12-2025-891
Abstract : In the e-commerce industry, where keeping current customers is frequently more cost-effective than recruiting new ones, customer churn is a major problem. In order to improve the precision and interpretability of churn prediction, this paper presents proposed hybrid framework that blends probabilistic modeling, deep learning, and stacked ensemble learning. SMOTE and ADASYN oversampling approaches are used to correct class imbalance in a real-world e-commerce dataset that includes demographic, transactional, and behavioral data. While deep learning models like CNN, RNN, and FCNN showed better recall and validation accuracy, with CNN reaching up to 83% following Keras-Tuner optimization, traditional models like Random Forest and Logistic Regression only managed baseline accuracies of about 79%. Individual models exceeded by 2.2% to 5.3%, a model that is proposed, used Gradient Boosting, CatBoost, XGBoost and SVM with Logistic Regression as a meta-classifier, reached 96% of high test accuracy post-ADASYN. Hidden Markov Models (HMMs) with 2, 4, and 6 latent states were used to further examine its churn probabilities that reflected the temporal dynamics of consumer behavior. HMM framework's ability to track dynamic states and identify churn-prone pathways supported the Individualized risk assessments and focused retention efforts. The suggested proposed model framework provides a scalable and interpretable solution, for real-world churn management, with excellent predictive performance and useful insights..
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