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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 04 )
06 Jun 2026
Day
Hour
Min
Sec
Publish On
( 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-24-05-2026-995
Title : An AI-Driven Framework for Personalized Study Material Recommendation in Digital Learning Environments
Abstract : The rapid evolution of Educational Technology (EdTech) has necessitated the development of intelligent systems capable of tailoring learning experiences to individual student needs. This paper presents the design, architecture, and implementation of a Personalized Study Material Recommendation System embedded within a web-based digital classroom platform called the SMS Digital Classroom. Central to this system is the Student Progress and Performance Analysis AI (SPPAAI), an intelligent analytical engine that monitors student performance across tests, quizzes, and assignments, computes efficiency scores based on difficulty-weighted question categories, and recommends targeted study materials to address identified academic weaknesses. The platform employs a modern full-stack web architecture utilizing React, Vite, Express.js, MongoDB Atlas, Socket.IO, and JWT-based authentication. The system also incorporates Retrieval-Augmented Generation (RAG) methodology to enhance the quality and relevance of recommended study content. This work addresses key challenges in contemporary education, including unequal access to learning resources, lack of adaptive feedback mechanisms, and inefficient progress tracking. The paper outlines the three-phase research lifecycle, technical components, system workflow, mentor feedback, and future development directions..
Full article
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-17-05-2026-989
Title : DW-MOS: A Lightweight UNet Pruning Framework via BatchNorm Scaling Factors
Abstract : The computational complexity and memory requirements of architectures like UNet make it difficult to deploy deep learning models for medical picture segmentation efficiently. In this work, we propose a lightweight structured pruning approach that leverages the scaling factor (γ) from Batch Normalization layers as a proxy for activation strength to guide filter importance. The traditional pruning techniques rely on activation maps or gradients where expensive forward passes are required which can increase memory overhead. Our method enables pruning during or after training without external profiling. Inspired by Network Slimming defined by Liu et al. (2017), we integrate γ-based ranking with L1-norm, precomputed FLOPs, and sensitivity metrics into a unified Dynamic Weighted Multi-Objective Scoring (DW-MOS-lite) framework. Using a custom UNet trained on lung CT scans, our approach maintains a high Dice coefficient of 90.49% while pruning almost 50% of parameters. It is therefore perfect for edge-based, real-time medical applications. The results demonstrate that Batch Norm γ is a highly efficient and interpretable activation proxy for structured pruning..
Full article
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-15-05-2026-988
Title : Correlation-Guided Dynamic Multi-objective Structure Pruning for Efficient UNet Inference
Abstract : Deep convolutional neural networks such as UNet have achieved remarkable performance in medical image segmentation; however, these models suffer from high computational complexity and large parameter counts. Pruning is one of the most widely used model compression techniques to address this limitation. The proposed work created a Dynamic Weighted Multi-Objective Scoring (DW-MOS) framework. It consists of correlation-aware structured pruning for an efficient UNet compression that evaluates filter importance by jointly considering weight magnitude, computational cost (FLOPs), layer sensitivity, and inter-filter redundancy. Filter redundancy is explicitly quantified using correlation-based analysis. It helps to remove highly correlated, less informative filters and preserve diverse and complementary features. Also aggregated DW-MOS score helps to apply structured filter pruning in a layer-consistent manner. It also adjusts batch normalization parameters and subsequent convolutional layers to maintain architectural integrity. The Linear interpolation is applied to ensure stable feature alignment after pruning and the pruned network is fine-tuned to recover potential performance loss. Experimental results demonstrate that the dynamic adaptive strategy achieves effective pruning by yielding a 1.16× compression ratio, a 1.21× inference speedup, and a Dice score of 90.98% to detect infection in the lung region of a COVID-19 patient. Hence, the proposed pruning framework is also maintaining clinical relevance..
Full article
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-05-05-2026-916
Title : IntelliCore AI-Melody: An NLP-Enabled Hybrid Music Recommendation System Based on Emotion, Age, and Cultural Context
Abstract :

The multimodal approach uses the heterogeneous sources of data to promote perception, inference, and decision-making in intelligent systems. A multimodal framework does not just use one channel, like text, audio or facial appearance, but combines the corresponding streams of information and reaches more reliable and context-sensitive predictions. The present study is a multi-modal emotion recognition-based and age filtering-based developed advanced music recommendation system, which incorporates real-time multimodal emotion recognition and age filtering to deliver very personalized song recommendations by Indian/Bollywood genre. The system uses a hybrid machine learning design with an integration of Face-API.js to analyze facial expression with up to 87 percent recognition accuracy and a DistilRoBERTa transformer-based sentiment classifier to analyze text-based inputs with 85-90 percent accuracy. In addition to providing a more personalized approach to music, a selected dataset of 600 Bollywood music tracks is enriched with psychoacoustic data (valence, energy, tempo, and danceability) and mapped to affect-to-audio features with a weighted scoring model. It allows narrowing the gap between perceived emotional states and musical qualities, thus making the recommendation more relevant in the dynamic real-world context. Experimental measurements affirm good performance of the system with end-to-end response times held down to less than 2 seconds and face-processing throughput of 2530 FPS. While doing the Practical implementation a user study of around 50 participants was done in two weeks with a mean satisfaction score of 8.5/10, an 83 percent recommendation intention rate, and 72 percent repeat engagement. On the whole, it can be concluded that hybrid multimodal emotion analysis, demographic adaptation, and cultural relevance are important to further the development of next-generation music recommender systems that can provide reliable, affect-sensitive, and human-centered interaction experiences.

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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-05-05-2026-915
Title : Engineering a Comprehensive Database System for Medical Supply Inventory and Monitoring in Rural Healthcare Environments
Abstract :

Over time, customer demands and service requirements evolve, making it essential for service industries to adapt to new technologies. This underscores the importance of upgrading existing systems and processes. This study focused on evaluating the current inventory and monitoring system for medical supplies in various municipalities and developing an improved system that delivers more accurate and efficient results. Through thorough analysis, the researcher designed a user-friendly, efficient, and more accurate system. The recording and encoding of beneficiary information have become paperless and faster compared to the old system. The study not only enhanced the system but also improved the services provided by rural health centers.

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