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
03
)
03 Apr 2026
Day
Hour
Min
Sec
Day
Hour
Min
Sec
Publish On
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Vol
58
, Issue
02
)
31 Mar 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-23-03-2026-934
Abstract : Cotton is one of the most important cash crops in the world, and many plant diseases have a big effect on how much it grows. To protect food and economic security, it is important to find and treat cotton plant diseases as soon as possible. Machine learning (ML) and deep learning (DL) methods have been widely used in the last few years to automatically find plant diseases using pictures of leaves. Nonetheless, current studies present findings derived from varying experimental conditions, datasets, and evaluation metrics, complicating direct comparisons. The agricultural productivity and the textile industry are at a high risk due to cotton plant diseases. These diseases should be detected early and when it is necessary as this will make the loss minimal and may lead to better yield in crops. This paper will involve a detailed comparative performance evaluation of deep learning and conventional machine learning algorithms in the detection of cotton plant diseases. Deep learning networks, such as ResNet18 and Swin Transformer, are tested in comparison with the traditional machine learning algorithms, such as Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), and Gaussian Naive Bayes. The experiment is done on a publicly available dataset of a cotton disease. Findings indicate that Transformer-based models are much better than CNN and traditional methods because they can capture global contextual relationships. The Swin Transformer has the most accurate and ROC-AUC and is thus suitable when faced with complex plant disease classification problems..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-07-03-2026-925
Abstract : While developing Generative and Training Language Models, there is a problem called "hallucinations", which refers to generative language models producing responses that seem reasonable given a context but are actually factually incorrect. In this paper, we present a new Optimised Retrieval Augmented Generation Pipeline that seeks to eliminate the hallucination problem, by dynamically grounding responses to external, retrievable knowledge. We have developed a system that leverages the following: (1) LangChain for orchestration; (2) LangGraph for workflow management; (3) Crew AI for tier-3 agent-to- agent coordination; (4) ChromaDB, a vector data base; and (5) the Gemini API for Generative outputs. Key architectural distinctions include: Intelligent Semantic Chunking; Adaptive Retrieval Strategies; and Structured Multi-Stage Prompts, and we back those distinctions up with a rigorous evaluation framework. Our experimental evaluation demonstrates a statistically significant improvement over our baseline LLMs, with a factual accuracy score of 93.4% compared to 76.2% for the baseline; a decrease in identification of "hallucinations" from 18.3% of all responses to under 4.1% of all responses; and a citation accuracy score of 96.2% of all responses produced by this system in the shortest time frame (3.4 seconds). The modular architecture of this system will allow for effortless adaptation across different domains, by simply updating the knowledge base, without having to retrain any models. This work establishes a practical scalable framework for safely deploying factually grounded, trustworthy generative AI models, and provides an example of how organisations with limited resources in machine learning can easily access sophisticated RAG capabilities..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-01-03-2026-921
Abstract : In critical situations such as medical emergencies, domestic violence, or fire incidents, people, particularly those with speech or physical impairments, may not be able to seek help by conventional means. This work presents a gesture-based real-time emergency alert system that uses AI-driven technologies integrated within an edge-cloud architecture. The system runs on a lightweight edge device, such as a Raspberry Pi, and captures live video to detect predefined emergency gestures using a computer vision-based approach with landmark extraction and gesture classification. Upon confident detection, the system triggers automated alerts through SMS or voice calls using a cloud back-end hosted on AWS. To improve reliability, it incorporates confidence smoothing, gesture conflict resolution, and filtering mechanisms to reduce false positives. The proposed framework offers a scalable, low-cost and accessible enhancement to existing emergency response systems..
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-27-02-2026-920
Abstract : With the surge in digital imagery across law, healthcare, and media, ensuring authenticity and tamper-proof evidence has become a critical concern. The current survey offers an in-depth overview of how blockchain technology can strengthen digital image forensics (DIF), which exploit weaknesses involving unauthentic changes, metadata tampering, and ineffective chain of custody. The paper starts by a brief description of the issues of the traditional DIF systems, especially in distributed systems and IoT-based systems. It then categories the blockchain-integrated frameworks like LEChain, FIF-IoT, BB-RICP, and BlockShield by focusing on their architectures, encryption mechanisms, models of consensus, and performance results. These systems demonstrate the ability to store safely, cryptographically verify, query at low latency and manage evidence that is in real time. One of the assessments compared was a comparative analysis, showing such measures as throughput, tamper detection, and energy efficiency, wherein blockchain has application in increasing forensic integrity and traceability. These applications reach into law enforcement, surveillance, and legal trials, with blockchain enhancing the admissibility and provenance of evidence. In spite of these developments, the paper cites remaining issues on scalability, privacy, interoperability, and incorporation of AI. And the future directions include quantum-resistant cryptography, AI-driven decision model, cross-chain compatibility, and mobile-supported forensic solutions to design secure and legally compliant forensic infrastructures..
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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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