International Core Journal of Engineering
International Core Journal of Engineering is an international journal to promote all fields of Engineering, it considers original and timely contributions containing new results in mechanical engineering, electrical engineering, civil engineering, chemical engineering, etc. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Research on Tree Carbon Storage Estimation Method based on Point Cloud and Voxel Modeling
Since global climate change has become more pronounced, accurate estimation of forest carbon stocks has become a vital part of carbon cycle research and carbon neutrality strategies. However, it is well known that traditional methods for estimating tree carbon stocks rely mainly on empirical models and manual measurements, both of which have inherent problems of low accuracy and low efficiency. Therefore, recent developments in 3D laser scanning offer promising solutions.since LiDAR technology has provided new and effective methods for precise forest structure measurement, this paper makes a very clear and logical use of high-density point cloud data acquired by the Oslight Intelligent R8 backpack SLAM laser scanning system for vegetation on the campus of Liaoning University of Science and Technology, and then applies a voxelization modeling approach to compute tree volumes and estimate carbon stocks.Using Lidar360 software for individual tree segmentation, parameter extraction, and volume calculation, the results were systematically compared with those from MATLAB voxelization algorithms, and it was clearly and convincingly shown that a voxel resolution of 5 cm gives the most accurate tree volume measurements. Therefore, the paper naturally leads into discussing the advantages of voxelization algorithms in representing complex tree structures while maintaining computational efficiency, making it an excellent foundation for a high-precision, automated method of urban forest carbon sink monitoring. It also provides a solid technical reference for high-precision carbon stock estimation from point cloud data.
Point Cloud; Voxelization; Tree Carbon Storage; SLAM Laser Scanning; Lidar360.
Copyright (c) 2026 International Core Journal of Engineering https://creativecommons.org/licenses/by-nc/4.0
Optimization Design of Steam Turbine Rotors based on Thermo-Structural Coupling
This study focused on the original model of a first-stage shrouded steam turbine blade and investigated its mechanical behavior under a multiphysics environment through prestressed modal analysis, transient thermo-mechanical coupling analysis, and long-term static creep analysis. The results showed that the system exhibited nodal-diameter vibration, with a first-order natural frequency of 588.88 Hz, satisfying the requirements for dynamic stability. Under steady-state conditions, the maximum equivalent stress reached 345.09 MPa and was mainly located in the shroud connection region. After service for 6.8×108 s, the equivalent creep strain at the blade root accumulated to 0.19073 mm. This study identified the structural strength bottlenecks and deformation characteristics of the blade, providing a reference for the safe service of this type of turbine blade.
Steam Turbine Blade; Original Model; Prestressed Modal Analysis; Thermo-Mechanical Coupling; Static Creep; Stress Concentration; Service Safety.
An Optimization Model for a Two-Echelon Remanufacturing Reverse Supply Chain with Carbon Emission Reduction Consideration
To address the high level of carbon emissions in the transportation stage of remanufacturing reverse supply chains, this study develops a two-echelon network optimization model that incorporates multiple transportation modes. Two mixed-integer linear programming (MILP) models are formulated with the objectives of minimizing total cost and carbon emissions, respectively. A case study based on the Yangtze River Delta region is conducted to compare road transportation with multimodal transportation. The results indicate that under a single road transportation mode, the cost-minimization and emission-minimization objectives are aligned. In contrast, multimodal transportation achieves approximately a 48.9% reduction in carbon emissions with only a marginal increase in total cost. These findings demonstrate that multimodal transport provides an effective pathway for low-carbon optimization in remanufacturing supply chains.
Remanufacturing Reverse Supply Chain; Multimodal Transportation; Carbon Emissions; Network Optimization.
A Review of Optimization Methods for Sheet Metal Forming Processes
Sheet metal forming is a core manufacturing process in aerospace, automotive, and marine industries, where optimization of process parameters directly impacts product quality and production costs. This paper systematically reviews research progress in optimizing sheet metal forming process parameters, examining the current application status of traditional optimization methods, numerical simulation techniques, and intelligent optimization algorithms in this field. It focuses on exploring the principles and characteristics of typical optimization techniques such as finite element simulation, response surface methodology, genetic algorithms, neural networks, and multi-objective optimization. Furthermore, it provides an outlook on cutting-edge research directions like online monitoring and real-time control. Research indicates that intelligent optimization methods integrating digital twins, artificial intelligence, and big data will become a significant development trend in sheet metal forming process parameter optimization.
Sheet Metal Forming; Process Parameter Optimization; Numerical Simulation; Intelligent Algorithm; Multi-Objective Optimization.
Grid Planning Considering the Development Stages of Distributed PV and Electric Vehicles
Under the "dual carbon" goal, distributed photovoltaics (DPV) and electric vehicles (EV) have developed rapidly in China and have now become an important part of the power system. Their impact on the distribution grid, including voltage distribution, load characteristics, power flow and equipment utilisation, there are significant differences in different stages of development. This study considers the process of transforming distributed photovoltaics from "spontanetary self-use, surplus electricity on the Internet" to high penetration rate reverse power flow, and the evolution of electric vehicles from disorderly charging to intelligent coordinated charging and finally developing into V2G mode. On this basis, a multi-stage power grid planning framework is proposed, which includes the generation of uncertain scenarios, coordinated "network-source-load-storage" optimisation model and elastic network structures suitable for different penetration rates. The method was tested on the 33-node power distribution system and the improved 123-node feeder system. The results showed that compared with traditional static planning, the method reduced the whole life cycle cost by about 15-28%, alleviated the problems of voltage over-limit and equipment overload, and postponed the investment in power grid upgrade by 2-4 years. In addition, the sensitivity analysis of the participation rate of electric vehicles, the cost of energy storage and the elasticity of time-shring electricity prices is also carried out.
Grid Planning; Distributed Photovoltaics; Electric Vehicles; Development Stages; V2G; Coordinated Optimization; Life-cycle Cost.
Key Technologies for Online Joint Optimization of Transmission-Distribution Coordination for Reactive Power Reserve Requirements in New Power Systems
With the rapid development of high-proportion renewable energy power systems, reactive power and voltage regulation faces multiple challenges including intensified source-load bilateral uncertainty and deepened coupling between transmission and distribution networks. The traditional independent optimization mode for transmission and distribution networks can hardly satisfy the requirements of global voltage stability and economic operation. This paper proposes a transmission-distribution collaborative online joint optimization method for reactive power reserve demand in new-type power systems. First, the spatio-temporal distribution characteristics of reactive power reserve demand under high renewable energy penetration are analyzed, and a reactive power reserve demand assessment model considering voltage stability margin (VSM) constraints is established. Second, a transmission-distribution collaborative multi-objective joint optimization model is constructed, taking system active power loss, reactive power reserve cost, voltage deviation, and voltage stability margin penalty as optimization objectives. Then, the alternating direction method of multipliers (ADMM) is adopted to achieve decomposition-coordination solving between transmission and distribution networks, meeting the computational efficiency requirements for online applications. Finally, case studies are carried out based on a coupled test system consisting of a modified IEEE 30-bus transmission network and an IEEE 33-bus distribution network. The results demonstrate that compared with independent optimization strategies, the proposed method can reduce network losses by 21.6%, improve voltage stability margin by 83.3%, and the computing time is only 20.8% of that of centralized optimization, verifying the effectiveness and practicality of the method.
New-type Power System; Reactive Power Reserve; Transmission-distribution Collaboration; Online Optimization; Alternating Direction Method of Multipliers.
Efficacy and Mechanism of Temperature-Regulated ECT-MFC for Heavy Metal Removal from Mine Water
Nowadays sustainable development has gained widespread attention, making people pay more attention to water pollution problems. However, fast industrial development makes water pollution increasingly serious, especially heavy metal pollution in water. Traditional wastewater treatment methods cost much to operate and often produce secondary pollution with poor removal results. Microbial fuel cells can recycle electricity while cleaning sewage, so they have obvious comprehensive advantages. We applied a modified eight-chamber ECT-MFC to treat composite heavy metal wastewater in this test, and the device maintained stable operation and great treatment performance. Temperature controls microbial metabolism and electrode reaction efficiency, which largely changes the overall removal effect. Test results show low temperatures strongly inhibit system reactions; high temperatures raise the initial removal speed but damage microbial activity and cut treatment efficiency after long-term running. The study confirmed that the optimal synergistic effect of reactions occurs under medium-temperature conditions (30°C), and these findings can provide a reference for temperature control in practical equipment applications.By comparing all test working conditions, medium temperature provides the optimum reaction synergy and counts as the best running temperature for the reactor, and all conclusions from this research can offer practical support for field temperature control of such devices.
Microbial Fuel Cell; Eight-chamber ECT-MFC; Heavy Metal Wastewater; Temperature Regulation; Wastewater Treatment.
Key Structural Design of a Downhole Variable-Angle Hollow-Shaft Turbine Generator
To address the insufficient wide-flow adaptability, limited structural integration, and restricted tool passability of downhole turbine power generation systems, a downhole variable-angle hollow-shaft turbine generator system is proposed. The system consists of a guide vane, a variable-angle turbine, a hypoid gear adjustment mechanism, and a hollow-shaft outer-rotor permanent magnet generator. Based on blade design theory, velocity triangle theory, and isentropic flow theory, the key parameters of the guide vane and turbine are determined, and 14 attack-angle models ranging from 0° to 90° are established. A hypoid gear mechanism is designed to realize synchronous angle adjustment of multiple blades, and a hollow-shaft outer-rotor permanent magnet generator is developed to achieve compact integration of the turbine, transmission mechanism, and power generation unit, providing a reference for the optimization and engineering application of downhole turbine power generation devices.
Downhole Turbine Power Generation; Variable-Angle Turbine; Hollow Shaft; Hypoid Gear; Outer-Rotor Permanent Magnet Generato.
Data-Driven Development of Deep Eutectic Solvents: A Review of Machine Learning in Property Prediction and Rational Design
Deep Eutectic Solvents (DESs) represent a novel class of emerging green solvents formed through hydrogen-bonding interactions between hydrogen bond donors (HBDs) and hydrogen bond acceptors (HBAs). Boasting core advantages such as facile synthesis, low cost, environmental benignity, and exceptional structural and functional designability, DESs have demonstrated substantial application value and industrial potential across various sectors, including natural product extraction, biomass valorization, hydrometallurgy, acidic gas capture, and electrochemical energy storage. However, several fundamental scientific and technical bottlenecks-including the combinatorial explosion of the chemical design space arising from vast HBD/HBA combinations, the elusive structure-property relationships between microscopic intermolecular interactions and macroscopic physicochemical properties, and the challenges in synergistically optimizing multiple performance indicators in complex application scenarios-have rendered traditional "trial-and-error" R&D models inadequate for the targeted rational design of DESs, thereby significantly impeding their large-scale industrialization. As a data-driven intelligent research paradigm, machine learning (ML) provides a transformative technical route to address these challenges by leveraging its robust capabilities in high-dimensional feature mining, complex non-linear relationship fitting, and global optimization. This review systematically delineates the latest domestic and international advancements in ML-enabled DES research. First, it elucidates the core principles and mainstream methodologies for molecular descriptor selection and high-quality dataset construction in DES property prediction. Second, it summarizes model architectures, algorithmic optimizations, and predictive performance of ML regarding the thermophysical properties as well as chemical and biological activities of DESs. Subsequently, it highlights technical implementations and application outcomes of ML in the intelligent prediction of DES formation, application-oriented reverse design, and multi-objective optimization of extraction and separation processes. Finally, the review provides an in-depth analysis of the core challenges currently facing the field-such as the lack of standardized datasets, insufficient model interpretability, and weak mechanistic insights-while offering perspectives on future research directions. This review aims to provide a systematic theoretical framework and research roadmap for the intelligent rational design, precise property modulation, and industrial application of DESs, facilitating the paradigm shift in green solvent development from traditional empirical trial-and-error toward a data-driven intelligent paradigm.
Deep Eutectic Solvents (DES); Machine Learning; Property Prediction; Inverse Design; Multi-objective Optimization; Data-driven.
Simulation Analysis of Axle Load Transfer During Actual Traction and Braking
Driven by the Belt and Road Initiative, the demand for efficient transport has promoted the development of multimodal transport, particularly road-rail intermodal transport, to overcome the drawbacks of traditional railway freight. This paper investigates the axle load transfer problem of a specific type of road-rail combined vehicle during operation. Unlike existing studies that mostly employ idealized or simplified traction/braking conditions, this study innovatively introduces real traction and braking curves into the dynamic model, thereby accurately reproducing the actual operating conditions of the train. Through simulating various empty and loaded vehicle marshalling conditions, the axle load transfer characteristics under different train formations are systematically analyzed. On the basis of exploring the fundamental mechanisms inducing axle load transfer, three targeted improvement schemes are put forward. Numerical simulations are carried out for these schemes and compared with the original design, so as to obtain the quantitative values and variation laws of axle load transfer under different conditions. The findings can provide important theoretical data and engineering references for the structural design, performance optimization and manufacturing of road-rail combined vehicles.
Road–rail Vehicle; Actual Traction Braking; Axle Load Transfer; Coupler Buffer Device.
Design of an Environmental Monitoring System for Additive Manufacturing Workshops based on Two-Level Data Fusion
As an important element in the framework of intelligent manufacturing, additive manufacture works in the complex and changing workshop environment. These environments will bring great changes and instability to the data measured by every single sensor. And the traditional evaluation ways by one parameter only is limited by the one-dimensional view of it, which is not enough to get the precision that we need for the environment grade in the real time control application. In this paper, we solve these problems by giving an environmental monitoring system for the additive manufacture workshop, which is based on the dual level data fusion architecture of Kalman filtering and Bayesian networks. The system implementation is using an STM32 micro controller, a USR-EG828 edge gateway, embedded system program, Python development environment and the Modbus communication protocol. The results of experiments show that after the first fusion stage, the temperature data has greatly decreased the fluctuation, and the data of integration is more like the actual conditions. The estimated environmental parameters are very close to the real measured ones. In particular, the mean absolute error (MAE), root mean square error (RMSE) and maximum absolute error (MAXAE) for the temperature are decreased from 0.855 °C, 0.9776 °C, and 1.7543 °C (with one sensor only) to 0.373 °C, 0.2985 °C and 0.6127 °C, respectively. Similar improvements on the noise suppression and the measure precision can also be seen for the humidity, TVOC, and PM2.5 data. The second level data fusion gets perfect accuracy on the classification of the environmental grades for all ten validation samples. In summary, the introduced framework can help to make very accurate monitoring of many parameters and a complete assessment of the environmental grades in the manufacturing environment, which can largely improve the reliability of evaluating the ambient conditions. This research provides a practical technical way for the management of environmental factors in the additive manufacturing facilities.
Two-level Data Fusion; Kalman Filtering; Bayesian Network; Additive Manufacturing Workshop; Environmental Monitoring System.
Teaching Reform and Practice of the AI-Empowered Model in Web Front-End Development Courses
The deep integration of artificial intelligence technology with new engineering education provides a brand-new path for the transformation of teaching models in computer-related courses in universities. In response to the problems existing in the traditional Web front-end development technology courses, we have constructed an AI-driven teaching reform model. This method first systematically reconfigures the teaching content through five dimensions (interest, problem, project, integration, and ideological education), and then designs a multi-dimensional guiding teaching system of “interest-driven, problem-driven orientation, project practice, interdisciplinary integration, ideological and political education”, integrating artificial intelligence technology throughout the entire classroom teaching and complementing it with a diversified process assessment system, aiming to improve the course quality and enhance students’ professional abilities. Teaching practice shows that this method effectively compensates for the shortcomings of traditional teaching, significantly improving students’ classroom participation, code development ability, and autonomous learning literacy, providing a replicable practical framework and theoretical reference for Web front-end and similar computer courses in the context of new engineering education.
AI; Web Front-end; New Engineering Education.
Prediction and Interpretability Analysis of Deep Eutectic Solvent Viscosity based on a Stacking Ensemble Model
Deep eutectic solvents (DES) are a new type of green solvent with significant application potential in the selective leaching of zinc-containing solid waste via hydrometallurgy. However, traditional design methods rely heavily on trial-and-error experimentation, resulting in low R&D efficiency, and the core physical property (viscosity) that determines mass transfer and reaction kinetics in the system is difficult to estimate accurately. To address this industry challenge, this paper proposes a high-precision viscosity prediction and micro-mechanism analysis framework that integrates multi-dimensional cross-feature engineering, the Whale Optimization Algorithm (WOA), and Stacking ensemble learning. Based on a rigorously cleaned, multi-source standardized dataset, the model employs Extreme Gradient Boosting (XGBoost) and Random Forest (RF)-both globally optimized via WOA-as base learners, combined with a Linear Regression (LR) meta-learner through a two-stage deep integration. Extrapolation evaluation on an independent test set demonstrates that this two-layer architecture effectively overcomes the generalization bottleneck in complex sample chemical spaces, achieving an excellent accuracy with a coefficient of determination ( ) of 0.8620 and an average absolute relative deviation (AARD) as low as 9.88%, with overall performance significantly outperforming various single-baseline models.Furthermore, this study introduced game-theoretic SHAP analysis, successfully breaking through the “black-box” barrier of deep ensemble models. The research quantitatively confirmed that the drastic changes in DES viscosity essentially stem from strong electrostatic coupling and a dense hydrogen-bond cross-linking network formed after component mixing.The positive synergistic interaction between high steric hindrance in the mixed space and strong electrostatic attraction constitutes the underlying mechanism driving the macroscopic viscosity jump, while the “coupled resonance” of multiple microscopic features at low temperatures serves as the fundamental driving force behind the sharp rise in hydrodynamic viscosity.This study not only provides a breakthrough data-driven paradigm for estimating the physical properties of complex chemical systems, but also offers solid quantitative theoretical guidance for the targeted reverse design of low-viscosity, high-performance green DES solvents for industrial zinc extraction through a multidimensional, mechanism-transparent analysis.
Deep Eutectic Solvents (DESs); Viscosity Prediction; Stacking Ensemble Learning; Whale Optimization Algorithm (WOA); SHAP Interpretability Analysis; Hydrometallurgy.
Optimizing Road Networks in Small and Medium-sized Cities: A Multidimensional Trade-off between Efficiency and Environmental Sustainability
The unreasonable structure of the road network has become a significant bottleneck restricting the sustainable development of transportation systems in small - medium cities. This paper is based on road network grading theory and integrates a multi-dimensional performance evaluation framework to address the common problem of the "inverted pyramid" structure and its environmental impacts. We first establish a theoretical road network model that follows a pyramid structure. In order to reconcile the contradiction between public transit priority and travel efficiency, we put forward a new type of functional segmentation strategy of the arterial road system, which can divide it into "passenger-oriented" and "freight-oriented" corridors. A set of 12 second-order indicators is included under the first-order dimensions of structure, efficiency, equity, resilience, and environmental impact. The weights of these indicators are quantified using an improved hierarchical CRITIC objective weighting method. We simulate and analyse three road network density scenarios (a base case of 6.8 km/km2, a plan density of 12.8 km/km2, and a high density benchmark of 21.1 km/km2) for a virtual case city that has been calibrated with empirical data. The results show a significant deviation between environmental and efficiency objectives. The planned densification scenario (12.8 km/km2) achieves the best overall performance (score: 0.819), lower than 15.2% compared to the baseline. However, this situation also leads to an increase in noise, and corresponding noise reduction measures need to be taken. Pursuing excessive density (21.1 km/km2) leads to decreased efficiency of resource utilization and intensified noise pollution. This study provides a systematic decision-support tool and the theoretical basis for road network optimization design, public transportation priority, and reducing environmental externality effects of small- to medium-sized cities.
Road Network Hierarchy; Multi-Criteria Evaluation; Environmental Externalities; CRITIC Weighting; Transit Priority; Small and Medium- Sized Cities; Transportation Carbon Emissions; Traffic Noise.
Hydrocarbon Accumulation Conditions and Main Controlling Factors of Intracratonic Carbonate Rocks in Russia
Research objectives and scope: Taking intracratonic carbonate rocks in Block S as the research object, this paper aims to clarify the complex hydrocarbon accumulation rules, define the core controlling factors and hydrocarbon migration-accumulation mechanisms, and fill the research gaps in relevant fields. Research methods: Based on drilling, seismic and core test data, integrated seismic-geological interpretation, tectonic-sequence evolution restoration and accumulation element coupling analysis were adopted to systematically analyze source-reservoir-cap assemblage characteristics and hydrocarbon migration-accumulation laws. Research results and conclusions: An efficient hydrocarbon accumulation system consisting of high-quality hydrocarbon source rocks, high-energy reef-beach reservoirs and thick gypsum-salt cap rocks was identified. A dual-mode migration pathway network dominated by inherited paleo-uplifts as well as faults and unconformities was developed. Hydrocarbon accumulation was jointly controlled by the three-dimensional coupling of paleo-uplifts, reef-beach reservoirs and source-connected faults, and the corresponding hydrocarbon accumulation model was established. Innovation and application value: The established model further improves the accumulation theory of intracratonic carbonate reservoirs, provides important analogical references for the exploration of similar reservoirs in Tarim Basin, Sichuan Basin and other basins, and has important guiding significance for oil and gas exploration practice.
Intracratonic Carbonate Rock; Inherited Paleo-uplift; Dual-mode Migration and Accumulation; Reef-beach Reservoir; Block S.
Application of Distributed Photovoltaic Power Generation Systems on Rural Roofs in Rural Tourism
Comprehensive rural revitalization represents a central goal of China's "Three Rural" work. In the process of promoting rural revitalization, it is necessary to balance the dual requirements of economic development and ecological environmental protection. Rural tourism can effectively revitalize idle rural resources, drive the development of rural industries, and help farmers increase their income. The construction of distributed photovoltaic (PV) power generation systems on rural rooftops can effectively solve problems such as energy pollution and electricity shortages in rural areas. This paper discusses the application of rural rooftop distributed PV power generation systems in rural tourism, analyzes the application value and practical pathways of this model, and provides a reference for sustainable rural development.
Distributed Photovoltaic Generation Systems; Rural Tourism; Rural Rooftops.
Research on Manipulator Path Planning based on Improved RRT*
Aiming at the drawbacks of traditional RRT* algorithm in 3D path planning for manipulators, such as blind random sampling, low search efficiency and redundant initial paths, an improved RRT* path planning method integrating goal-biased sampling, adaptive step-size expansion, path pruning and B-spline smoothing is proposed. Firstly, goal-biased sampling is adopted to drive the search tree to expand toward the target area and avoid random exploration in irrelevant regions. Secondly, an adaptive step size is introduced on the basis of target guidance to further reduce redundant search. Finally, path pruning and B-spline smoothing are applied to optimize path compactness and trajectory continuity. A 3D static obstacle environment is established on MATLAB, and 100 repeated simulation experiments are conducted on RRT, RRT* and the improved RRT* algorithm. The experimental results show that the success rate of the improved algorithm reaches 100% in all three scenarios. In complex environments, its average planning time is 0.039727 s, which is lower than 0.122270 s of RRT and 1.201800 s of RRT*. Meanwhile, the average number of iterations and tree nodes are also significantly reduced. Ablation experiments verify that goal-biased sampling is the main factor for the improvement of search efficiency; the adaptive step size further enhances the expansion efficiency based on target guidance, and path pruning and smoothing effectively improve the quality of the final path.
3D Path Planning; Improved RRT*; Goal-Oriented Sampling; Manipulator; Path Optimization.
Research on Fine-Tuning Large Language Models for Teaching Assistance in Data Science based on LoRA/QLoRA
This paper compares LoRA and QLoRA for structured teaching assistance tasks in data science education under limited computing resources. A dataset with three tasks was built from student questionnaire data. A post-processing pipeline and evaluation metrics were designed for structured output. Experiments show that LoRA is better in training efficiency, while QLoRA performs better in JSON validity, field coverage, structure quality, and citation generation. Overall, QLoRA is more suitable for structured teaching assistance tasks in resource-constrained settings.
Parameter-Efficient Fine-Tuning; Teaching Assistance; LoRA; QloRA.
An Improved Rolling Bearing Fault Diagnosis Method based on Enhanced Feature Extraction and Regularized SVM
Rolling bearing fault diagnosis is critical for ensuring the safety and reliability of rotating machinery. This paper proposes an improved fault diagnosis method that addresses the overfitting problem commonly observed in traditional approaches. The method employs a 9-dimensional enhanced feature vector combining time-domain statistical features (RMS, kurtosis, crest factor, skewness) with envelope spectrum features (BPFO, BPFI, BSF amplitudes, band energy ratio, spectral entropy). A data augmentation strategy using Gaussian noise perturbation is introduced to increase training sample diversity. The RBF-SVM classifier is regularized with optimized parameters (C=1, γ=0.1) to prevent overfitting. Validated on the CWRU bearing dataset with multiple damage severities, the proposed method achieves 98.5% accuracy under strict temporal-split validation with a train-test gap of only 1.5%, compared to 87.0% for the baseline model. The results demonstrate that enhanced features combined with proper regularization effectively resolve overfitting while maintaining high diagnostic performance.
Rolling Bearing; Fault Diagnosis; Feature Extraction; Support Vector Machine; Regularization; Overfitting; CWRU Dataset.
Category Home Online Custom One-click Quotation System and Method
Aiming at the problems of low quotation efficiency, poor real-time price, isolated front-end and back-end data, high artificial dependence, and non-standard pricing in traditional online customization of home furnishings, this paper proposes an online customization one-click quotation system and implementation method based on product configuration theory, multi-dimensional linkage constraint model, real-time intelligent pricing model, and ERP order state machine model. The system consists of four modules : commodity price basic database, quotation front-end page, one-click quotation operation module and ERP order processing module. Through parameter standardization, configuration linkage, pricing formulation and order integration, the user-side one-click real-time quotation and enterprise-side order automatic transfer production are realized. The simulation results show that the average quotation time of the system is reduced from 145 s to less than 1.5 s, the quotation accuracy rate is more than 99.3 %, and the order review and production scheduling efficiency are improved by 82 %. It effectively solves the problem of online quotation and offline production coordination in door industry customization, significantly improves user experience and enterprise operation efficiency, and provides technical reference for mass customization in home building materials industry.
Category Home; Online Customization; One-click Quotation; Product Configuration Theory; ERP Integration.
Mechanism Design and Simulation Analysis of an Underactuated Hexapod Robot
To reduce the control complexity and algorithm development difficulty of conventional hexapod robots (which require 18 servo motors), a leg mechanism for a hexapod robot based on the underactuated principle was designed using SolidWorks. This design omits the ankle joint servo, instead using a single servo combined with a tendon-driven transmission system to actuate both the knee joint and ankle joint in a coupled manner, while a torsion spring provides the restoring torque required for leg lifting. A kinematic model of the leg was established based on the D-H parameter method, and both forward and inverse kinematic analyses of the foot-end effector were performed. The desired foot-end trajectory was obtained through MATLAB simulation. Dynamic simulation analysis of the mechanical leg mechanism was carried out using ADAMS, which verified the correctness and feasibility of the above underactuated structural design and theoretical model, providing a theoretical basis for the development of a physical prototype of the underactuated hexapod robot.
Underactuated; Hexapod Robot; Kinematic Model; ADAMS Analysis.
Research Progress on the Permeability of Flexible Sealing Layer in Artificial Cavern for Compressed Air Energy Storage
Aiming at the permeability of flexible sealing layer in artificial cavern for compressed air energy storage, the engineering application cases of flexible materials as sealing layers are sorted out. The current research progress in experimental research, theoretical analysis and numerical simulation in this field is expounded, and the advantages and disadvantages of each research method are analyzed. On this basis, the future research direction in this field is prospected. It is believed that in the future, key variables such as air pressure, temperature, tension state and cycle times should be comprehensively considered in the penetration test to reveal the penetration law of flexible materials. At the same time, the penetration evolution model is constructed and numerical calculation is introduced to further explore the multi-field coupling response characteristics of flexible sealing layer.
Flexible Sealing Layer; Permeability; Experimental Study; Theoretical Analysis; Numerical Simulation.
Study on Hydraulic Performance of Modular Constructed Wetlands based on Baffle Optimization
This study addresses the severe clogging, challenging construction quality control, and high maintenance costs associated with traditional constructed wetlands by exploring structural optimization approaches for modular constructed wetlands (MCW). To gain an intuitive understanding of the internal flow patterns within modular constructed wetlands, a three-dimensional CFD simplified porous medium model was developed using FLUENT software. To further optimize the hydraulic efficiency of the modular wetland system, the impact of baffle arrangement on overall hydraulic performance was systematically investigated. Nine baffle configuration modes were compared in simulations, revealing that a configuration with two inner baffles represented the optimal solution: it achieved a flow field uniformity index of 0.68, an effective volume ratio of 0.815, a hydraulic efficiency of 0.792, and a fluid dispersion coefficient of merely 0.028. Increasing the number of baffles or concentrating them inward significantly degraded the flow field, resulting in a hydraulic efficiency of only 0.441 for a configuration with four inner baffles. This study validates the effectiveness of CFD-based structural parameter optimization, providing a quantitative theoretical foundation and practical technical solutions for efficient, standardized design of modular constructed wetlands.
Modular Constructed Wetlands; Numerical Simulation; Flow Field Distribution; Hydraulic Characteristics; Structural Optimization Design.
Fire Prevention Technology Practice in Syncline Structure Area based on Inhibitor Spraying and Tube Bundle Monitoring
To address the “water-gas-structure” multi-field coupling disaster encountered by the 9401 fully mechanized top-coal caving face at Hengsheng Coal Mine while passing through the Liujiazhuang syncline axis, the three-dimensional compound air leakage mechanism composed of “boreholes, overlying goaf, and surface fissures” and the associated risk of residual coal spontaneous combustion were analyzed in depth. Consequently, an active prevention and control system centered on mobile calcium chloride inhibitor spraying was constructed. A calculation model for the inhibitor dosage based on mining parameters was established, determining the optimal daily spray volume at 9.98 m³, accompanied by the implementation of a hierarchical dynamic control strategy. Combined with a beam tube monitoring system, full-process dynamic tracking of key gas components in the goaf, including O2, CH4, CO2, and CO, was conducted. Field application results indicated that during the implementation, the gas flow field in the goaf remained generally stable. The peak methane concentration was controlled below 0.55%, and carbon monoxide levels consistently remained at low background values without abnormal fluctuations. This system effectively blocked compound air leakage channels, inhibited the oxidation process of residual coal, and ensured safe mining operations under complex geological conditions.
Syncline Axis; Compound Air Leakage; Inhibitor Spraying; Beam Tube Monitoring; Prevention of Coal Spontaneous Combustion.
Study on the State Evolution and Long-Term Stability Control of Subgrades
As highway engineering in China shifts from large-scale construction to long-term maintenance, the influence of long-term subgrade stability on pavement service performance has become increasingly prominent. Current construction control practices mainly rely on stage-specific indicators such as the degree of compaction, which cannot fully capture the continuous evolution of subgrade conditions under traffic loading, moisture fluctuations, wetting–drying cycles, and freeze–thaw actions. From a life-cycle perspective, this paper analyzes the relationships among compaction state, moisture condition, and mechanical response, and discusses the effects of key parameters-including degree of compaction, porosity, water content, degree of saturation, matric suction, and resilient modulus-on subgrade performance. The study suggests that subgrade stability is jointly governed by fill material properties, compaction energy, moisture migration, particle rearrangement, and cumulative loading effects. Future evaluation should shift from single-index control based on compaction degree toward a coordinated assessment using multiple state parameters, thereby providing a basis for subgrade distress diagnosis, performance prediction, and maintenance decision-making.
Subgrade State; Long-term Stability; Degree of Compaction; Moisture Condition; Resilient Modulus.
Construction Technology and Application of Graphite-based Flexible Grounding Bodies for Wind Farms and Photovoltaic Power Stations
To fundamentally solve the challenges of grounding grid corrosion, difficult construction, and high environmental risk in highly corrosive and high-resistivity complex terrain areas for wind farms and photovoltaic (PV) stations, a complete set of construction techniques for graphite-based flexible grounding bodies is proposed and systematically summarized. This technology uses high-carbon flake graphite as the main body material, utilizes the principles of electric field dispersion and composite anti-corrosion, ensures long-term resistance reduction, and completely avoids open-flame operations in the field. This paper elaborates on the construction process flow, key operation points, materials and equipment, and quality control measures for graphite-based flexible grounding bodies. Through life-cycle cost analysis and typical engineering case verification, its technical and economic superiority is demonstrated. The results show that this method can stably maintain grounding resistance below the design value, reduce the life-cycle cost by approximately 60% compared with the traditional copper-clad steel scheme, and avoid heavy metal pollution, offering significant economic, social, and environmental benefits.
Graphite-based Flexible Grounding Body; Wind Farm; Photovoltaic Power Station; Grounding Grid Construction; Anti-corrosion and Resistance Reduction.
Dynamic Process Control based on an AI Model Cluster for Atmospheric and Vacuum Distillation
From November 2024 to August 2025, a data-driven real-time closed-loop optimization project was carried out for a 10 Mt/a atmospheric and vacuum distillation unit. The study was intended to address the strong coupling, frequent operating-mode switching, and limited long-term effectiveness of conventional optimization methods in large crude distillation systems. A process-oriented artificial intelligence (AI) model cluster was therefore developed by using full-process modular artificial neural networks (ANNs) to characterize the coupled states of the unit and reflect changes in operating conditions. On this basis, an intelligent execution system (iES) was built to translate plant-wide optimization results into dynamic control targets acceptable to the field, which were then implemented through multivariable advanced control schemes at the distributed control system (DCS) level. Industrial application shows that, within quality and safety boundaries, this method can transfer optimization solutions to the production unit in a safe and stable manner, demonstrating good industrial applicability.
Atmospheric and Vacuum Distillation; Process AI Model Cluster; Dynamic Optimization; Intelligent Control System; Expert Knowledge.
Research on the Application of Life Cycle Method in Carbon Emission Accounting of Rural Households
Against the dual background of China's "dual carbon" goal and the rural revitalization strategy, the low - carbon development of rural areas is a key link in achieving carbon emission reduction across society. This paper integrates the carbon emission accounting method of the Intergovernmental Panel on Climate Change and the life cycle assessment method to construct a nested carbon emission accounting framework of "four stages and three modules" suitable for rural households. Taking rural households in Tangshan City, Hebei Province as the research object, the study found that: the operation and use stage of rural household carbon emissions contributes the vast majority of emissions, followed by the building material production stage. Electricity consumption, embodied carbon in building materials, and emissions from small - scale breeding constitute the three core emission sources. The accounting framework constructed in this paper provides methodological support for the accurate accounting of rural household carbon emissions, and the research conclusions can provide a reference for rural low - carbon development decision - making.
Life Cycle Assessment; Rural Households; Carbon Emission Accounting; Rural Areas in Hebei.
Preparation of Co-BDC/Ti3C2Tx Composite Materials for High-performance Supercapacitor Electrodes
Ti₃C₂Tₓ MXene holds great potential for supercapacitor electrodes, yet its performance is hindered by nanosheet restacking. Herein, a Co-BDC/Ti3C2Tx composite is fabricated by in situ growth of a cobalt-based MOF on MXene nanosheets. This unique architecture synergistically integrates the high conductivity of MXene with the large surface area and rich pseudocapacitance of MOF, leading to significantly enhanced electrochemical performance. The composite electrode exhibits a superior areal capacitance of 10.31 F cm-2 at 2 mA cm-2. A solid-state asymmetric supercapacitor constructed with this composite delivers an energy density of 0.741 mW cm-2 at a power density of 0.483 mWh cm-2 and retains 89% of its initial capacitance after 5000 cycles, highlighting its remarkable cycling stability. This work presents an effective strategy for designing high-performance MXene-based electrodes for next-generation flexible energy storage devices.
Supercapacitor; Electrochemistry; Composite Materials; Energy Storage.
Research on Terahertz Band-stop Filter based on Circular Quasi-periodic Waveguide
The terahertz filtering behavior of quasi-periodic waveguide based Fibonacci sequence is investigated by finite element research method. The long-range order of quasi-periodic waveguide has achieved the functions of band-stop and narrow band filter. The waveguide generation can directly influence the number of bandgaps. Periodic processing can enable low generation waveguides to have the band-stop filtering function, too. The length ratio can be used to regulate the number of forbidden bands, operating frequency and bandwidth. The maximum bandwidth of a single bandgap is 192 GHz. The total operating frequency of a single waveguide can reach up to 281 GHz. The 8th generation waveguide also realizes narrow band filtering function with a bandwidth of 14 GHz. Quasi-periodic waveguides can provide new ideas for the design of terahertz filters.
Terahertz; Band-stop Filter; Quasi-periodic; Waveguide.