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.
Design of Lightweight Composite Thermal Insulation Structural Components for Civil Air Defense Projects
This study involves a lightweight composite insulation structural component for civil air defense construction, aiming to solve the problems of alignment accuracy control and leakage prevention at the board joints during the installation of insulation boards. This structural component includes a shell, positioning module, sensing module, anti-misalignment module, closure module and leak-proof module. It assists in positioning and installing adjacent insulation boards and senses their alignment in real time. When the insulation board is misaligned, the anti-misalignment module automatically blocks the screw holes and forcibly interrupts the installation process. Fixation can only be carried out when the alignment meets the standard and the anti-misalignment piece is detached from the through hole, thereby ensuring the positional accuracy after installation. In addition, the structural components adopt the TG mortise and tenon type butt joint method. Compared with the flat head butt joint, it can effectively slow down the seepage of liquid through the board joints. Combined with the anti-leakage module, it can absorb the already seeped liquid, maximizing the delay of liquid erosion on the insulation system and extending the service life of the entire system. The present invention provides a reliable solution for the rapid, precise and durable installation of insulation structures in civil air defense projects and other construction fields by integrating positioning, error-proofing and leak-proofing functions.
Civil Air Defense Project; Energy Conservation and Emission Reduction; Insulation Board; Foolproof and Leak-Proof; Installation Accuracy.
Copyright (c) 2026 International Core Journal of Engineering https://creativecommons.org/licenses/by-nc/4.0
Hourly Heat Load Prediction for District Heating Systems based on Long Short-Term Memory Network
In conjunction with the rapid progress in intelligent heating technology, heat load-based prediction and on-demand heating have been considered as the most important factor for the realization of optimized regulation in a centralized system. The prediction of heat load in heating systems will be useful for the optimization of automatic control and green heating. But traditionally applied methods are less effective and vulnerable to noise, hence they cannot meet the demands of industrial practice. Recently, deep learning techniques have been intensively researched and used in order to extract meaningful features from the data and construct models. LSTM model has proven superior compared to other approaches in terms of performance. In this regard, the purpose of this study is to use a deep learning method of Long Short-Term Memory Network for the accurate heat load prediction in DHSs in order to reduce the waste of resources.
Heat Load Prediction; DHSs; Deep Learning; LSTM Model.
Esterified Starch-derived Hard Carbon Anodes as High Initial Coulombic Efficiency Anode Material for Sodium-ion Batteries
The widespread adoption of sodium-ion batteries (SIBs) depends heavily on the development of affordable and renewable hard carbon (HC) anodes. While existing HC synthesis methods often rely on complex chemical modifications to boost electrochemical properties, these complicated processes hinder large-scale industrial application. To address this issue, our study presents a cost-effective and green HC material derived from an esterified cassava starch precursor. We comprehensively evaluated how this specific precursor influences the material's microstructural evolution, surface oxygen functionalities, and over all Na-ion storage mechanisms. During electrochemical evaluation, the optimized MA-SCHC-20 anode delivered an impressive reversible capacity of 356.5 mAhg-1 at 0.1 C and achieved a remarkable initial coulombic efficiency (ICE) of 90.2%. Even at a high rate of 5 C, it maintained a robust capacity of 195.7 mAhg-1. Furthermore, multi-scan-rate cyclic voltammetry was employed to elucidate the underlying sodium storage kinetics.
Sodium-ion Battery; Hard Carbon; Starch; Malay Anhydride.
Synergistic Mechanisms of Carbonated Bayer Red Mud in Enhancing Chloride Binding Capacity of Cement-Based Materials
Bayer red mud (BRM), a solid waste, can be effectively treated through carbonation to decrease its alkalinity and improve its reactivity.This study introduces carbonated Bayer red mud (CBRM) as a supplementary cementitious material into the cement system, systematically revealing its influence mechanism on chloride ion (Cl-) binding behavior and performance optimization pathways in marine environments. The evolution of phase composition and microstructural characteristics during the reaction process of the composite cementitious material system (OPC-CBRM) was analyzed using XRD, TG/DTG, and scanning electron microscopy (SEM).The results showed that compared to ordinary Portland cement paste, the composite cement paste incorporating 30% CBRM exhibited increases of 40% and 68% in total bound chloride and chemically bound chloride contents, respectively, while the physically bound chloride content decreased by 12%.OPC-CBRM generates Friedel's salt (AFm-Cl) efficiently through the release of reactive Al³+ (secondary pozzolanic reaction) and the conversion of calcium carbonate to monocarbonate aluminate (AFm-CO3), followed by Cl-/CO32- ion exchange. The Freundlich model demonstrates a higher goodness-of-fit than the Langmuir model for describing the relationship between free chloride and bound chloride.The presence of Mg2+ and Ca2+ promotes the decomposition of AFt to form gibbsite. Specifically, Ca2+ increases chemically bound chloride content by 24%, while Mg2+ increases it by 13%. Sulfate attack reduces chemically bound chloride content: compared with Na2SO4 attack, MgSO4 attack results in a 61% reduction, and CaSO4 attack leads to a 60% reduction. It should be noted that CBRM incorporation reduces the compressive strength of cement-based materials, with strength decreasing progressively as CBRM content increases, primarily attributed to reduced C-S-H formation, retarded hydration by Na2O, and insufficient filler effect of CaCO3. This study provides theoretical foundation for red mud resource utilization and the design of highly durable marine cementitious materials, achieving the dual objectives of "solid waste management for corrosion control" and "carbon sequestration, " which is of significant importance for extending the service life of marine engineering structures.
Carbonation; Bayer Red Mud; Chloride Binding; Friedel's Salt; Marine Engineering Materials.
Finite Element Analysis and Structural Performance Research on Chassis Frame of Crawler Bamboo Transport Vehicle
Because most of the bamboo forests in China are distributed in hilly areas, the terrain conditions are complex, and the transfer of bamboo skidding is difficult. In view of the above problems, a forest multi-functional power platform was developed. In order to ensure the safety of the whole chassis frame structure design of the power platform and understand the vibration characteristics of the whole chassis frame, the chassis frame of the tracked transport vehicle is taken as the research object. Firstly, the three-dimensional model of the chassis frame is established in SolidWorks software. Based on HyperMesh, the finite element model of the frame is established. Combined with two typical working conditions of full load and half load, the static and modal analysis is carried out by OptiStruct. The stress and deformation distribution of the frame under different working conditions are obtained. The results show that the maximum stresses of the frame under full load and half load conditions are 172.6 MPa and 206.1 MPa, respectively, which are less than the yield limit of the material, and the maximum deformation meets the allowable requirements. The modal analysis results show that the natural frequency of the frame does not coincide with the excitation frequency of the road surface and the engine, and there is no resonance risk. The results show that the frame structure meets the requirements of strength and stiffness.
Track Transport Vehicle; Chassis Frame; Finite Element Analysis; Modal Analysis.
Design of Electronic Scale based on ESP32 Single-chip Microcomputer
This paper presents a smart electronic scale design scheme based on the ESP32 microcontroller. The solution integrates high-precision load cells, ESP32 microcontroller, display modules, and vibration motor technology to achieve intelligent weighing, data transmission, and processing capabilities. The paper elaborates on system architecture design, hardware implementation, and software development, followed by system assembly and testing. Experimental results demonstrate the high feasibility and practicality of this smart scale design. The developed solution offers advantages including high accuracy, user-friendly operation, comprehensive functionality, and strong scalability, effectively meeting diverse user requirements and enhancing quality of life.
ESP32; Smart Electronic Scale; High Precision; Intelligentization.
Research Progress on Safe Utilization and Remediation Technologies of Cadmium-Contaminated Farmland Soil
Soil cadmium (Cd) pollution has become a prominent environmental problem restricting the sustainable development of agriculture and food security in China. Under the background of scarce cultivated land resources, the safe utilization technologies such as in situ immobilization, screening of low-accumulation crops and combined regulation are adopted for moderately and lightly Cd-contaminated farmland, which is the core path to realize "production while remediation". This paper systematically sorts out the current situation, sources, ecological and health hazards of Cd pollution in farmland soils in China, reviews the principles, characteristics and application progress of four remediation technologies including physical, chemical, biological and agronomic regulation, focuses on the research results of immobilizer development, screening of low-Cd-accumulation wheat varieties and their combined remediation, analyzes the shortcomings in the research of Cd-contaminated alkaline farmland in northern China, and takes Jiaozuo area as an example to illustrate the regional research status. Finally, it prospects the construction of efficient green immobilization materials, precise matching of varieties and immobilizers, long-term mechanism in field and regional technical system, aiming to provide theoretical reference for the safe utilization and scientific management of Cd-contaminated farmland.
Farmland Soil; Cadmium Pollution; In Situ Immobilization; Low-Accumulation Wheat; Combined Remediation; Safe Utilization.
Optimization of Parameters for Copper Electroplating in Through-Glass Vias
Through-Glass Via (TGV) technology has become a research hotspot in advanced electronic packaging due to its potential for high-density interconnections on glass substrates. However, the insulating nature and low adhesion of glass surfaces pose challenges to the uniform deposition of copper coatings. This study systematically optimizes the process parameters for copper electroplating in TGVs, with a focus on the effects of voltage, temperature, and sample movement speed on coating quality under single-step and multi-step plating conditions. In the experiments, vias were fabricated using picosecond laser drilling combined with wet etching, and direct current electrodeposition was performed after forming a seed layer via electroless copper plating. The results indicate that a lower voltage contributes to a smoother coating surface but requires a longer plating time; an appropriate temperature (30°C) enhances deposition efficiency; and excessively fast sample movement speed reduces filling integrity. Multi-step electroplating (e.g., 0.4 V followed by 1 V) significantly shortens the plating time while maintaining filling quality, thereby improving efficiency. The optimized process achieves void-free, high-adhesion copper-filled TGV structures, providing a feasible technical pathway for the application of glass substrates in high-frequency, high-density packaging.
Through-Glass Via (TGV); Copper Electroplating; Multi-step Electroplating.
Geotechnical Evaluation and Foundation Scheme Research for Soft Soil Foundation Engineering Reconstruction and Expansion Project in Guangzhou Urban Area
To address core geotechnical challenges in reconstruction projects within soft soil areas of the Pearl River Delta, this study employs the Guangzhou Urban Area Campus A Basketball Court Expansion Project as a case study. Through integrated survey methods combining drilling, in-situ testing, and laboratory experiments, we systematically investigated the site's engineering geological conditions, mechanical properties of soil layers, groundwater distribution patterns, and types of adverse geological phenomena. The findings reveal that the site represents a typical alluvial-originated non-uniform foundation in the Pearl River Delta, consisting of artificial fill layers, flow-plastic to soft plastic silt layers, loose fine sand layers, and weathered chalky siltstone formations. The silt layers exhibit high compressibility and low strength characteristics, while the fine sand layers demonstrate severe liquefaction potential. Considering a seismic fortification intensity of 7 degrees, the site is classified as structurally vulnerable for buildings. Geological assessments indicate that groundwater poses minimal corrosion risks to concrete structures, while overground soil layers exhibit moderate corrosive effects. Although the site demonstrates good geological stability suitable for construction, critical mitigation measures are required to prevent soft soil subsidence, sand liquefaction, uneven settlement, and pile foundation-induced disturbances to existing structures. The study recommends rotary drilling/boring cast-in-place piles as the primary foundation method, proposes targeted mitigation strategies, and provides technical references for similar soft soil reconstruction projects in Guangzhou urban areas.
Detailed Geotechnical Investigation; Soft Soil Foundation; Sand Liquefaction; Foundation Stability; Pile Foundation Design.
The Influence of Different Manganese-iron Ratios on the Friction and Wear Properties of Al-10Mg2Si Alloy
The study investigated the effect of different manganese-to-iron ratios on the friction and wear performance of Al-10Mg2Si alloy. The results showed that the as-cast α-Al in the Al-10Mg2Si alloy without manganese and iron phases was relatively coarse, and its eutectic Mg2Si morphology was mostly in the form of long needles and needle-like flakes. When the manganese-to-iron ratio was 0.5, the manganese-iron phases were polygonal and fine granular. As the manganese-to-iron ratio continued to increase, the morphology of the manganese-iron phases became polygonal and nearly spherical, with particle sizes of about 10-12μm, showing a clear coarsening phenomenon. Under the conditions of a load of 40N, a rotation speed of 200r/min, and a wear time of 30min, the alloy not treated with ultrasonic had the least wear scar volume when the manganese-to-iron ratio was 0.5, which was 2.635mm³, showing the best wear resistance relatively, and the worn surface exhibited abrasive wear accompanied by slight adhesive wear.
Al-10Mg2Si alloy; Varying Manganese-To-Iron Ratios; Friction and Wear Performance.
Time-Frequency Feature Analysis of Human Motion Attitude in IMU-based Pedestrian Dead Reckoning
Aiming at the core bottlenecks restricting the improvement of positioning accuracy in existing Pedestrian Dead Reckoning (PDR) systems,including the separation between positioning parameter solution and human attitude recognition,the failure to establish a quantitative relationship between attitude features and positioning errors,and the imperfection of the PDR-oriented attitude feature system,this study carries out a time-frequency feature analysis of human motion attitude based on the Inertial Measurement Unit (IMU).In this research,three high-performance IMUs were used to collect triaxial acceleration and angular velocity data of six typical human motions with the sensor mounted on the chest.On this basis,sliding window filtering was determined as the optimal preprocessing scheme with its parameters optimized,a comprehensive time-frequency domain feature system was constructed,and the characteristics of the feature set were verified through multi-subject experiments.The results show that the proposed feature set can clearly distinguish different motion types and reveal the correlation law of time-frequency features of the six typical attitudes.This work provides technical support for high-precision PDR positioning,and has important reference value for the application of self-contained positioning with wearable inertial sensing technology.
Human Attitude Recognition; Pedestrian Dead Reckoning; Inertial Measurement Unit; Feature Extraction; Attitude Recognition; fTime-Frequency Domain Analysis.
Design of a Small Pure Electric Tracked Tillage Machine
To address the challenges of complex terrain, fragmented plots, varying slopes, and low levels of agricultural mechanization in hilly and mountainous areas of my country, a small, all-electric tracked tillage machine suitable for unmanned aerial vehicle (UAV) lifting and transportation was designed. Based on the characteristics of the hilly and mountainous operating environment, design requirements for the machine in terms of slope stability, passability, lightweight design, and intelligent operation were proposed. Through comparative analysis of the walking system and rotary tillage system structures, a triangular tracked walking device was selected to improve the machine's adhesion and adaptability to complex terrain. The selection and calculation of track parameters, drive motor, and reducer were completed, and the power of the rotary tillage motor and battery capacity were rationally matched. Based on this, the finite element method was used to model and simulate the vehicle frame structure. The model was reasonably simplified and shell element meshing was adopted to improve computational efficiency and accuracy. The results show that the tillage machine, while meeting lightweight requirements, possesses good slope stability and obstacle-crossing ability, effectively adapting to the complex operating environment of hilly and mountainous areas. This research provides a reference for the electrification and intelligent development of agricultural machinery equipment in hilly and mountainous areas.
Hilly and Mountainous Areas; Pure Electric Tillage Machine; Tracked Chassis; Structural Design.
Phosphoric Acid-assisted Structural Engineering of Wheat Straw-derived Hard Carbon for High-performance Sodium-ion Batteries
This article uses agricultural waste straw as a precursor, first employing phosphoric acid pretreatment to regulate its lignocellulosic structure, and prepared phosphoric acid-treated straw hard carbon (PLHC-P). Structural characterization indicates that PLHC possesses a relatively high number of surface oxygen-containing functional groups and defects, with sodium storage primarily occurring through surface adsorption. Phosphoric acid pretreatment effectively deconstructs the dense lignocellulosic structure of straw, creating a rich porous structure through etching, expanding the interlayer spacing of carbon, and introducing phosphorus doping. After further compositing with asphalt, a stable carbon layer is formed, effectively buffering volume changes, further expanding interlayer spacing, increasing closed pore volume, and forming a denser carbon structure. Electrochemical test results show: PLHC has a reversible capacity of 230 mAh/g at a current density of 20 mA/g; PLHC-P has a capacity of 280 mAh/g under the same conditions, and at a high current density of 300 mA/g, PLHC-P maintains a capacity of 200 mAh/g after 800 cycles.
Sodium-ion Battery; Wheat Straw; Anode Material.
Research on Liquid Cooling Pipeline Flow Mechanism based on Optimization of Battery Module Heat Dissipation Efficiency
Optimizing heat dissipation efficiency is a core challenge in the thermal management systems of new energy batteries and a key issue for ensuring the safety and stable performance of these batteries. With the increasing adoption of high-power fast-charging technology, current cooling solutions struggle to effectively handle the transient high thermal loads of battery packs, leading to intensified risks of local overheating and thermal runaway. Therefore, this study achieves efficient heat dissipation by integrating a design layout with threaded pipe structures. This paper introduces a power battery compartment cooling device based on composite synergistic cooling technology. The device realizes precise temperature control of the battery compartment through the combined effect of phase change material buffering, enhanced heat transfer via threaded liquid cooling pipes, and dynamic adjustment with variable-frequency energy-saving fans. This study not only improves battery cooling efficiency but also extends battery lifespan, reduces the risk of thermal runaway, and provides theoretical support for optimizing thermal management systems in new energy batteries.
Liquid Cooling System; Fluid Circulation; Air Cooling; Piping; Numerical Simulation.
A Knowledge Distillation-Based Lightweight Task Offloading Algorithm for Mobile Edge Computing
To address the issues of complex models, high inference latency, and difficulties in deploying traditional deep reinforcement learning offloading strategies on resource-constrained devices in mobile edge computing, this paper proposes a lightweight D3QN computation offloading method based on knowledge distillation. This method constructs a teacher-student network architecture, transferring the decision-making capabilities of the teacher model to a lightweight student network via offline knowledge distillation. Concurrently, an adaptive exploration strategy incorporating heuristic rules is designed to accelerate convergence, whilst LSTM temporal awareness and a dynamic energy consumption normalisation mechanism are introduced to optimise QoE. Experimental results demonstrate that, at a compression ratio of 0.4, the number of parameters in the student model is reduced by 77.61%, inference latency is reduced by 26.32%, and QoE reaches 829.07, approaching the performance of the teacher model, thereby providing an efficient and feasible lightweight solution for edge intelligence offloading.
Mobile Edge Computing; Task Offloading; Deep Reinforcement Learning; Knowledge Distillation; Lightweight Models; D3QN.
Design of Additively Manufactured Biomimetic Goat Horn-inspired Lattice Structures and Simulation Study on Their Compressive Mechanical Properties
To address the limitations in load-bearing and energy absorption of traditional lattice structures, a bio-inspired conical spiral lattice structure was designed based on the impact resistance of goat horns. Numerical simulations were employed to compare its quasi-static compressive properties with the BCC structure and investigate the effects of relative density (20%–50%). The results indicate that the initial peak stress and specific energy absorption of the bio-inspired structure increased by 205.7% and 171.2%, respectively, compared to the BCC structure. With the increase of relative density, the structural strength exhibits non-linear growth, while the failure mode transitions from ductile yielding to brittle collapse. Comprehensive analysis suggests that a relative density of 40% offers the optimal balance between strength and energy absorption stability.
Lattice Structure; Bio-inspired Design; Relative Density; Mechanical Properties.
Functionalized Graphene Promoted Photocured Waterborne Acrylic/Polyaniline Conductive Anticorrosion Coatings
Poor compatibility between graphene and polymer matrices impedes composite coatings from simultaneously meeting bipolar‑plate performance requirements for conductivity and corrosion resistance. To address this, a two‑step graphene functionalization was developed that retains high electrical conductivity while enabling the graphene to participate in the photocuring reaction and thereby strengthen interfacial bonding with the resin. A ternary composite containing 71wt% functionalized graphene (FG) and 11.8wt% polyaniline (PANI) exhibited a high electrical conductivity of 124.9 S·cm-1 and a low interfacial contact resistance (ICR) of 16.18 mΩ·cm2. Increasing the crosslink density between FG and the waterborne acrylic resin (WAR) reduced the corrosion current density (Icorr) to 0.56 μA·cm-2. Property characterization indicates that PANI performs a dual role in the composite: it cooperates with FG to form continuous conductive pathways and fills coating voids while passivating the substrate, thereby enhancing barrier performance.
Functionalized Graphene; Photocuring; Polyaniline; Composite Coating; Electrical Conductivity; Corrosion Protection.
Hyperspectral Response Characteristics and Quantitative Prediction of Moisture Content in Different Iron Ore Samples
The moisture content of iron ore is a key parameter affecting beneficiation efficiency, transport costs and subsequent smelting processes. Although the traditional drying and weighing method yields accurate results, it has drawbacks such as being time-consuming and causing sample damage. Accordingly, this study investigates methods for predicting the moisture content of iron ore using hyperspectral technology, using a total of nine representative iron ore samples (H1–H9) from Anshan-type magnetite in China, limonite in Australia and hematite in Brazil, covering the sub-types of limonite, magnetite and hematite. Under conditions where grade and particle size were kept consistent, different moisture content gradients were established, and spectral data in the 350–2400 nm wavelength range were acquired using a FieldSpec 4 field spectrometer. Outliers were removed using Pearson correlation analysis, followed by a comparison of 11 classical pre-processing methods and their 1–3-step cascaded combinations. The top 15 feature bands were selected using analysis of variance (ANOVA). Based on this, a mean fusion model combining partial least squares regression (PLSR) and random forest (RF) was constructed. The results indicate that moisture content is generally negatively correlated with spectral reflectance, with distinct absorption features appearing near 1440 nm and 1920 nm; the optimal pre-processing method combinations vary across different mineral samples; and the characteristic bands are primarily concentrated near wavelengths of 360 nm, 750 nm, 1140 nm, 1402 nm, 1880 nm and 2490 nm. The PLSR-RF fusion models for the H1, H2, H3, H6, H7 and H8 all achieved an R² of over 0.94 for the training set, over 0.83 for the test set, and an overall R² of over 0.91, with the H8 sample exhibiting the highest R² across the entire dataset (0.9747). Compared with existing studies, this method demonstrates a significant advantage in predictive accuracy, providing a viable solution for the rapid, non-destructive detection of moisture content in iron ore.
Hyperspectral Technology; Moisture Content; Spectral Pre-Processing; Hybrid Predictive Model; Iron Ore.
Intelligent Detection of Northern Shaanxi Red Fuji Apples based on Improved YOLOv8 Model
Detection of Northern Shaanxi Red Fuji apples in natural orchards faces challenges such as branch-leaf occlusion, fruit overlapping, illumination variation, and scale differences, leading to decreased detection accuracy and localization stability of the model. To address these issues, YOLOv8s was employed as the baseline model, with improvements made from three aspects: small object detection layer, attention mechanism, and bounding box regression loss. Specifically, a P2 detection layer, Coordinate Attention, EIoU, and WIoU were introduced. Multiple ablation experiments were conducted under unified training conditions, and best.pt was adopted for re-validation to avoid deviations caused by early stopping. Experimental results demonstrate that among the improved schemes, YOLOv8s+EIoU achieved the optimal performance, with mAP50-95 increased to 69.73%, representing a 0.21 percentage point improvement over the baseline, which verifies the optimization effect of EIoU on bounding box regression. This study provides technical references for intelligent apple recognition, automated harvesting, and orchard management.
Northern Shaanxi Red Fuji Apples; YOLOv8; Object Detection; EIoU; Smart Agriculture.
A UAV Ejection Device Integrating Thermal and Compressed Air Coupled Launch
Aiming at the rapid take-off demand of small and medium-sized UAVs under restricted site conditions, a composite ejection device combining compressed air and gunpowder is proposed. The device combines the transient high-pressure characteristics of gunpowder gas with the controllable release characteristics of compressed air to achieve efficient acceleration of UAVs within a short stroke. Based on the LS-DYNA platform, a multi-physics field coupling numerical model including structure, gas and gas source is established, and the dynamic response characteristics under pure compressed air ejection and composite ejection are compared and analyzed. The simulation results show that under the same stroke condition, the maximum velocity and total system energy of the composite ejection scheme are significantly improved, with the maximum velocity increased by about 46% and the total system energy increased by about 5.9 times. Meanwhile, the structural stress of the UAV is lower than the allowable value of the material. The research results provide a theoretical basis for the engineering design and parameter optimization of composite energy UAV ejection devices.
UAV Ejection; Compressed Air; Gunpowder Pressurization; Multi-Physics Coupling; Numerical Simulation.
Spot Recognition in Tongue Images based on ResNet50
Tongue diagnosis plays a crucial role in traditional Chinese medicine (TCM) by offering valuable insights for clinical syndrome differentiation and health assessment through tongue image analysis. Among these features, tongue spots, as a common abnormal manifestation, may indicate underlying pathological changes. Nevertheless, manual tongue diagnosis in TCM heavily relies on subjective visual observation, posing challenges in standardization. To tackle this issue, our study introduces an automated method for recognizing tongue spots using deep residual networks and transfer learning. Leveraging a real clinical dataset of 5,371 tongue images, we preprocessed the images, applied data augmentation, and fine-tuned a ResNet50 model pretrained on ImageNet for binary classification of tongue spot features. We employed five-fold cross-validation to assess the proposed approach. The results demonstrated that ResNet50 achieved an average accuracy of 97.48% (±0.53%) and a macro-F1 score of 53.84% (±6.64%), surpassing the ResNet18 baseline model. These findings suggest that deep residual learning effectively captures distinctive features from tongue images, showing promise for automated tongue feature recognition. Our study offers practical insights for the advancement of intelligent TCM-assisted diagnostic systems and may pave the way for further investigations into detailed tongue image analysis.
Tongue Image Recogition; Deep Learning Residual Network; Transfer Learning.
Synthesis and Magneto-Optical Performance of a Terbium-Based Hybrid Glass
Rare-earth-doped glasses have attracted sustained interest for magneto-optical applications because they combine high transparency with appreciable Verdet constants. However, conventional inorganic magneto-optical glasses typically require melt processing above 1000 °C and often rely on high contents of costly heavy rare-earth components, both of which increase fabrication complexity and cost. In this work, a desolvation strategy was used to prepare a terbium-based hybrid glass at low temperature. The obtained Tb-based glass shows optical transmittance approaching 80%, a Verdet constant of -22 rad/(T·m) at 405 nm, and a glass-transition temperature near 42 °C. Although the relatively low Tg limits thermal durability, the combination of transparency, compositional homogeneity, and field-linear Faraday rotation demonstrates the potential of rare-earth hybrid glasses for compact photonic and sensing applications.
Magneto-Optical; Tb-Based Glass; Rare-earth-doped Glasses.
BIM-Based Low-Carbon Design and Operation & Maintenance Practices for the Full Life Cycle of Buildings in the Jidong Region
Against the backdrop of green and low-carbon development in the construction industry, the Jidong region-as a major industrial hub in northern China-has made the management of carbon emissions across the entire building lifecycle a key focus of industry development. Drawing on research conducted under the Undergraduate Innovation and Entrepreneurship Training Program, this paper examines typical buildings in the Jidong region and deeply integrates BIM technology into the entire lifecycle of architectural design, construction, and operations and maintenance. By leveraging the information integration, visualization analysis, and simulation optimization capabilities of BIM models, this study explores low-carbon implementation pathways for each stage of the building lifecycle. It proposes BIM-based low-carbon application solutions tailored to the regional characteristics of the Jidong region, providing technical references and practical insights for achieving the "dual carbon" goals in regional construction.
BIM Technology; Building Lifecycle; Low-carbon Design; Low-carbon Operation and Maintenance; Jidong Region.
From Dye-Sensitized to Perovskite: The Evolution and Promise of Perovskite Photovoltaic Technology
The last thirty years have witnessed tremendous advances in photovoltaic technology. Photovoltaic technology has allowed for more effective and affordable solar energy conversion systems. The authors will follow the path of photovoltaic technology from the early days of the first generation, silicon based solar cells, to dye sensitized solar cells (DSSC) to the emergence of perovskite solar cells (PSC). The authors have identified that there are numerous solar cells that have achieved efficiencies greater than 25% via power conversion efficiency (PCE) metrics. The authors have performed a systematic literature review to identify key milestones, innovative mechanisms and materials advancements that contributed to the transition from one generation of photovoltaic technology to another. The authors found that the major advancements that enabled the emergence of PSC included replacing liquid electrolytes with solid state hole conductor, perovskite is no longer simply a photosensitizer but rather an ambipolar thin film absorber and adopting inorganic hole transport layers including nickel oxide. While there has been significant development in the field, concerns remain regarding the potential for toxicity from lead and the long term durability of these systems. The authors conclude by providing a comprehensive historical account of the field and identifying areas where future research can be directed toward creating commercially viable and environmentally friendly PSCs.
Perovskite Solar Cells; Dye-Sensitized Solar Cells; Photovoltaic Technology; Power Conversion Efficiency; Hole Transport Layer; Solid-State Electrolyte.
Research on Assistant Decision-making System for Reversible Quality Deviation Recovery in Long-process Steelmaking based on DBSCAN-LightGBM and IPORF
To address the challenges of undetectable quality deviations and the lack of quantitative evaluation of downstream compensation capability in long-process steelmaking, this paper proposes an active collaborative control method based on DBSCAN-LightGBM and an Improved Parrot Optimization algorithm combined with Ordinal Regression Random Forest (IPORF). First, multi-source heterogeneous data from L2, MES, and LIMS are integrated, and anomaly diagnosis and restoration are performed using the Isolation Forest algorithm under metallurgical mechanism constraints, resulting in a high-dimensional structured dataset comprising 12,458 heats. Second, the DBSCAN algorithm is employed to automatically mine and label deviation patterns without prior labels, and a classification model is constructed based on LightGBM, achieving a weighted F1-score of 0.945 for deviation identification. Furthermore, the IPORF model is proposed to quantify the compensation capability of downstream processes for upstream deviations. Hyperparameters of the Ordinal Regression Random Forest are optimized using the Improved Parrot Optimization algorithm, yielding a model accuracy of 94.5% and a mean absolute error of 0.08. Experimental results demonstrate that the proposed method effectively enables closed-loop management from deviation perception to strategy generation, providing theoretical support and engineering demonstration for intelligent quality governance in steel manufacturing processes.
Ong-Process Steelmaking; DBSCAN-LightGBM; IPORF.
Robust Multi-Sensor Fusion for Dynamic Robotic Grasping Using Fuzzy Adaptive Extended Kalman Filter
Precise target localization and tracking are fundamental prerequisites for robotic arm grasping tasks in dynamic environments. Traditional single-sensor systems, such as depth cameras, are susceptible to environmental interference, particularly under varying illumination and complex backgrounds. To solve this problem, this paper proposes a novel lightweight multi-sensor fusion framework combining 2D LiDAR and depth camera data using a Fuzzy Adaptive Extended Kalman Filter (FA-EKF). Unlike standard EKF methods that use a fixed observation noise covariance matrix , the proposed FA-EKF utilizes a fuzzy logic controller to dynamically adjust the noise covariance based on real-time measurement innovations. This allows the system to robustly handle non-linearities, sensor degradation, and time-varying noise. In the visual perception module, an improved YOLOv8 equipped with a Convolutional Block Attention Module (CBAM) is utilized to enhance feature extraction. Meanwhile, the LiDAR module extracts cylindrical targets using a robust RANSAC-based geometric fitting algorithm rather than traditional least-squares. Experimental validations performed on an AUBO-i5 robotic arm tracking a hovering coaxial drone demonstrate that the proposed FA-EKF method reduces the Root Mean Square Error (RMSE) to 0.0038m, and achieves a grasping success rate of 96% even under low-light conditions. The proposed system offers a real-time and highly robust solution for dynamic robotic grasping.
Multi-Sensor Fusion; Fuzzy Logic; Extended Kalman Filter; Robotic Grasping; 2D LiDAR; RANSAC.
Research Progress on Artificial Intelligence Empowered Digitization of Tongue Diagnosis in Traditional Chinese Medicine
Tongue diagnosis, as the core content of inspection in Traditional Chinese Medicine (TCM), is a specific embodiment of the TCM diagnostic thinking of “observing the exterior to infer the interior” and has an irreplaceable value in TCM syndrome differentiation and treatment. However, traditional tongue diagnosis has inherent bottlenecks such as strong subjectivity, difficulty in quantification, and low inheritance efficiency, which restrict the modernization of TCM. With the breakthrough progress of artificial intelligence (AI) technology in fields such as computer vision, deep learning, and multimodal fusion, it has provided core technical support for solving the problems of “subjectivity and fuzziness” in tongue diagnosis and realizing the digitalization, objectivity, and intelligence of tongue image diagnosis. This paper systematically reviews the research progress of AI empowering the digitalization of tongue diagnosis, focuses on elaborating the evolution of core technologies such as tongue image preprocessing, intelligent feature recognition, and multimodal fusion, summarizes its clinical application practices in core scenarios including disease auxiliary diagnosis, health status identification, and dynamic efficacy monitoring, and deeply analyzes the current bottlenecks such as insufficient data standardization, lack of model interpretability, superficial integration of TCM theory and AI technology, and limited clinical promotion. In-depth analysis of these current situations and challenges is of great value for promoting the continuous deepening and application of AI in the digital diagnosis of TCM tongue image, and helps to facilitate the inheritance, innovation and modernization of TCM.
Artificial Intelligence; Tongue Diagnosis; Digitalization; Current Situation.
Rapid urbanization and population growth have led to a sharp increase in municipal solid waste, bringing severe challenges to environmental sustainability and public health. Effective waste classification and recycling are critical to alleviating these problems. However, traditional manual sorting is labor-intensive, inefficient, and prone to human error. In recent years, computer vision and deep learning have provided promising solutions for automated waste recognition. Object detection models can identify garbage and generate bounding boxes, but they struggle to capture the irregular shapes of waste, which is crucial for robotic grasping and automated sorting systems. To address this limitation, this paper explores the application of YOLOv8-seg, a state-of-the-art one-stage instance segmentation algorithm. It enables accurate garbage recognition and pixel-level segmentation in complex environments. We trained and evaluated the YOLOv8-seg model on the public TACO (Trash Annotations in Context) dataset. The model can simultaneously perform waste classification and generate instance masks. The network architecture-especially the C2f module and decoupled segmentation head-is analyzed in detail to clarify its feature extraction and mask generation mechanisms. Experimental results show that YOLOv8-seg achieves a strong balance between accuracy and inference speed, outperforming traditional two-stage models such as Mask R-CNN in real-time scenarios. Specifically, the model achieves a mask mAP@50 of 82.6% while maintaining a high frame rate suitable for edge deployment. This work provides a solid technical foundation for building intelligent waste-sorting robots and supports the development of automated environmental management.
Garbage Recognition; Instance Segmentation; YOLOv8-seg; Deep Learning; TACO Dataset; Computer Vision.
RIS-Assisted Heterogeneous Collaborative Secure Offloading for Multi-Server MEC Systems based on HATD3
To address physical-layer eavesdropping threats and heterogeneous hybrid decision complexity in Reconfigurable Intelligent Surface (RIS)-assisted multi-server Multi-access Edge Computing (MEC) systems, this paper proposes a heterogeneous collaborative secure offloading method based on HATD3. First, a secure offloading model involving user equipment (UE), edge servers, RIS, and an eavesdropper is established for a multi-server MEC system under communication, computation, energy consumption, and secrecy constraints. The resulting problem is then formulated as a Decentralized Partially Observable Markov Decision Process (DEC-POMDP) for heterogeneous multi-agent coordination. Then, to handle the hybrid action space composed of continuous power control, discrete server selection, and RIS resource allocation, a Heterogeneous-Agent Twin Delayed Deep Deterministic Policy Gradient (HATD3)-based heterogeneous action decoupling and mapping mechanism is developed. Simulation results demonstrate that, compared with MATD3 and the no-RIS baseline, the proposed method achieves superior secure offloading performance in complex wireless environments, maintaining the secrecy rate around 7.0 Mbps, reducing the security violation rate to below 10%, and achieving a better trade-off among latency, energy consumption, and security.
Multi-access Edge Computing; Secure Computation Offloading; Resource Allocation; Heterogeneous Multi-Agent Deep Reinforcement Learning; Physical Layer Security; Reconfigurable Intelligent Surface.
Efficient Removal of PFCs from Fluoropolymer Production Wastewater Using Functionalized MCM-41 Adsorbents
Perfluorinated compounds (PFCs), especially perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), are persistent organic pollutants of increasing concern because of their extensive use in fluoropolymer production and their environmental persistence. In this work, mesoporous MCM-41 was synthesized and further functionalized by post-grafting to obtain amino-modified MCM-41 (MCM-41-NH₂) and fluorinated MCM-41 (MCM-41-3F-Cl). Their adsorption performances toward representative PFCs were systematically evaluated through structural characterization, adsorption isotherm analysis, regeneration tests, and treatment of real fluoropolymer emulsions. The results showed that surface functionalization significantly improved the adsorption performance of MCM-41. Structural characterization confirmed that the mesoporous framework was retained after modification, while the introduced functional groups effectively altered the surface properties of the adsorbents. Both functionalized materials exhibited high adsorption capacities toward PFOA, with maximum values of approximately 380-390 mg/g. In addition, high removal efficiencies were obtained for PFOS and PFBA. Among the tested materials, MCM-41-3F-Cl showed the most balanced overall performance, including good regeneration stability over five adsorption–desorption cycles and effective treatment of real fluoropolymer emulsions. The removal efficiency of PFOA remained above 90%, and the residual concentration was reduced to below 25 μg/L. These results indicate that functionalized MCM-41, especially MCM-41-3F-Cl, is a promising adsorbent for the treatment of PFC-containing fluoropolymer wastewater.
Perfluorinated Compounds; MCM-41; Surface Functionalization; Adsorption; Fluoropolymer Wastewater.