Eurasia Journal of Science and Technology
Eurasia Journal of Science and Technology (EJST) (Print ISSN: 2663-1024; Online ISSN: 2663-1016) is an international, peer-reviewed, open access journal published by Upubscience Publisher House. It covers a broad range of topics in science and technology, including engineering, computer science, environmental science, and applied physics. The journal publishes original research articles, reviews, and short communications.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
TOWARDS FOUNDATION MODELS FOR LIDAR SEMANTIC SEGMENTATION IN AUTONOMOUS DRIVING
LiDAR semantic segmentation (LSS), which assigns semantic labels to each point in a 3D scan, is a core perception task in autonomous driving. Over the past decade, fully supervised methods have achieved remarkable progress, with benchmark performance on SemanticKITTI improving from 14.6 mIoU in 2017 to over 75 mIoU in recent state-of-the-art models. Despite these advances, conventional supervised paradigms remain constrained by three fundamental limitations: dependence on large-scale dense annotations, restricted closed-set semantic understanding, and limited robustness under domain shifts and adverse environments.Recent advances in foundation models—including vision-language models, self-supervised pretraining frameworks, and segmentation foundation models—have opened a new direction for LiDAR perception by enabling transferable, label-efficient, and open-vocabulary 3D understanding. Motivated by this paradigm shift, this survey provides a systematic review of LiDAR semantic segmentation from supervised learning to foundation-model-driven approaches. We organize existing methods into five representative paradigms: cross-modal 2D-to-3D feature distillation, Segment Anything Model (SAM)-guided segmentation, open-vocabulary vision-language learning, LiDAR-specific self-supervised pretraining, and generalized 3D foundation models.Beyond taxonomy and benchmark comparison on SemanticKITTI and nuScenes, we further examine practical deployment factors—including inference latency, edge-device efficiency, robustness in adverse weather, and multimodal sensor fusion—that remain insufficiently captured by standard evaluation protocols. Finally, we identify six open research challenges and argue that the field is undergoing a fundamental transition: from adapting 2D foundation priors to developing native 3D LiDAR foundation models for autonomous driving.
Copyright (c) 2026 YiFan Zhao; ZiWei Huang https://creativecommons.org/licenses/by-nc/4.0
POST-TREATMENT STRATEGY: MOLECULAR SIEVE-CONFINED BIFUNCTIONAL METAL-SITE CATALYSTS FOR HYDROGEN PRODUCTION FROM FORMIC ACID DECOMPOSITION
Formic acid, as an important liquid organic hydrogen carrier, is considered a highly promising chemical medium for hydrogen storage due to its high hydrogen storage density, safety, non-toxicity, and ease of storage and transportation. It can also catalytically decompose to release hydrogen under mild conditions. However, traditional supported noble metal catalysts face issues such as metal nanoparticle migration, agglomeration, and leaching during the reaction process, leading to reduced catalyst activity and instability. To address these challenges, this thesis utilizes the microporous confinement effect of molecular sieves to design and prepare a series of Pd-based catalysts confined within molecular sieves. The catalytic performance, kinetic behavior, and cycle stability of these catalysts were systematically investigated. Various characterization techniques were used to elucidate the relationship between catalyst structure and performance. Using a commercial Beta molecular sieve as the carrier, acidic sites were removed through aluminophosphoric acid treatment. Then, highly dispersed Pd nanoclusters confined within the molecular sieve pores were successfully synthesized using a combination of ligand protection and hydrogen reduction after calcination. A second metal, Ce, was subsequently introduced to create the PdCe-Beta-DeAL-C-H bimetallic catalyst. Characterization results showed that aluminophosphoric acid treatment and the addition of Ce effectively improved the dispersion and stability of the Pd particles. The Pd nanoparticles had a small size of 2–5 nm and were uniformly distributed on the surface of the carrier and near its pores. Ce was in close contact with Pd, and the interfacial interactions altered the surface electronic structure of Pd, thereby promoting the formation and stabilization of metallic Pd⁰. Catalytic tests demonstrated that the optimal Pd₁.₀Ce₀.₂-Beta-DeAL-C-H catalyst achieved a conversion rate of 3199 molH₂·molPd⁻¹·h⁻¹ at 60 °C during formic acid decomposition. This value was approximately 127% higher than that of the single-metal catalyst. Additionally, the catalyst exhibited excellent cycle stability: after five cycles, its conversion rate remained above 1800 molH₂·molPd⁻¹·h⁻¹. Kinetic studies revealed that the apparent activation energy for formic acid decomposition over this catalyst was 55.93 kJ·mol⁻¹, significantly lower than that of the catalyst without Ce (84.5 kJ·mol⁻¹). Moreover, the catalyst performed well in the reaction of CO₂ hydrogenation to formic acid, with a reaction rate of 62 molformate·molPd⁻¹·h⁻¹. This study demonstrates that the incorporation of Ce through post-treatment effectively enhances the performance of Pd-based catalysts for formic acid decomposition. It provides valuable insights for the design of efficient bimetallic catalysts.
Copyright (c) 2026 LeBin Mi; Ning Wang; Tao Cai https://creativecommons.org/licenses/by-nc/4.0
PREPARATION OF BIOCHAR FROM WASTE NUTSHELLS AND ITS APPLICATION IN NITROGEN-CONTAINING WASTEWATER TREATMENT
Due to their unique pore structures and physicochemical properties, agricultural nutshell wastes serve as ideal precursors for the preparation of biomass biochar. However, traditional nutshell-derived biochars exhibit limitations in adsorption selectivity and capacity, showing poor removal efficiency for nitrate and nitrite nitrogen from rural domestic wastewater. In this study, walnut shells were used as feedstock to prepare pristine biochar via carbonization and physical activation. Subsequently, the biochar was modified through oscillatory impregnation with acid, alkali, and Fe³⁺ loading. The effects of modification conditions, adsorbent dosage, and adsorption time on the removal efficiency of the target nitrogen pollutants were investigated. The results indicated that the biochar achieved the optimal removal efficiency for the target nitrogen pollutants under the following conditions: a carbonization temperature of 700°C, Fe³⁺ modification, an Fe³⁺ loading ratio of 20%, an adsorbent dosage of 2.0 g, and an adsorption time of 60 min. Featuring simple operation and controllable costs, this process achieves the dual goals of “high-value utilization of waste” and “water pollution control”.
Copyright (c) 2026 Bin Han; JingYue Bao; Hao Li; HaiTao Liu; YanWen Guan https://creativecommons.org/licenses/by-nc/4.0
A NUMERICAL STUDY ON THE LOWER BOUNDS OF THE CLASSICAL AND REFINED HEISENBERG UNCERTAINTY PRINCIPLES
In Fourier analysis and quantum mechanics, the Heisenberg uncertainty principle stands as a fundamental result. Its classical version provides a product lower bound for the variances of position and frequency. More recently, for vector-valued functions, Dang, Deng and Qian introduced an improved inequality featuring an extra covariance term derived from the current density. The present work performs a one-dimensional numerical comparison of the two lower bounds using four test functions: a pure Gaussian, an asymmetric sum of two Gaussians, a linear chirp, and a vector-valued signal with spatially separated orthogonal components. For the Gaussian, the covariance term vanishes up to machine precision, making the two bounds identical. For the asymmetric sum, the refined bound exceeds the classical one by about three percent. The chirp signal yields a dramatically larger refined bound, approximately sixteen times above the classical one. For the vector-valued case with each component a real Gaussian, the refined bound is about eight times higher. These numerical outcomes confirm the validity of the refined inequality and highlight the sensitivity of the covariance term to phase structure and vectorial coupling.
Copyright (c) 2026 LuHao Yang; QiZhi Zhang; XueXin Li https://creativecommons.org/licenses/by-nc/4.0
BIOMECHANICAL ANALYSIS OF UPPER LIMB REHABILITATION DEVICE FOR GUIDING MOVEMENTS SELECTION
Purpose: The selection of appropriate rehabilitation movements is critical for the effective design of upper limb rehabilitation devices. Conventional approaches primarily rely on mechanical degrees of freedom, often neglecting quantitative biomechanical evaluation, which may lead to mismatches between device motion and human physiological characteristics. This study proposes a biomechanics-driven framework to guide rehabilitation movement selection. Methods: Four representative upper limb rehabilitation movements were selected based on the Brunnstrom recovery stages. Joint range of motion (ROM) data were collected from ten healthy subjects using clinical measurement methods. These kinematic data were imported into the OpenSim musculoskeletal model (DAS3) to simulate muscle forces of the deltoid, biceps brachii, and triceps brachii. Root mean square (RMS) values of muscle forces were calculated and statistically analyzed using one-way ANOVA and Tukey’s HSD post-hoc tests. Results: Significant differences in muscle activation were observed among the four movements. Horizontal adduction/abduction and flexion/extension exhibited significantly higher RMS muscle forces, particularly in the deltoid and triceps (p < 0.05). These movements demonstrated both higher peak forces and sustained muscle activation levels. Conclusion: The findings indicate that rehabilitation movements with higher biomechanical activation potential can be effectively identified through musculoskeletal simulation. Horizontal adduction/abduction and flexion/extension are recommended as optimal guiding movements for upper limb rehabilitation device design. This study provides a quantitative and physiologically grounded approach to improve the compatibility between rehabilitation devices and human motion.
Copyright (c) 2026 HaoYu Han; Li Feng https://creativecommons.org/licenses/by-nc/4.0
ENZYME-FREE COLOURIMETRIC SENSOR BASED ON ZIRCONIUM-BASED MOF FOR THE DETECTION OF MALATHION
To address the issues of enzyme inactivation, complex procedures, and dependence on large-scale equipment in traditional organophosphorus pesticide detection, we developed an enzyme-free colourimetric sensor employing Hemin@UiO-66 composite nanomaterials for the rapid and accurate detection of malathion. The UiO-66 metal-organic framework was synthesised using the solvothermal method, while the Hemin@UiO-66nanoenzyme was produced through post-modification with hemin chloride (Hemin). The structural characteristics of the material were analysed using Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS).Thisnanoenzyme demonstrated remarkable peroxidase-like activity, effectively catalysing the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to blue-coloured oxidised TMB (oxTMB) in the presence of hydrogen peroxide (H₂O₂). Malathion interacts with the active sites of the material via Zr-O-P coordination, which significantly inhibits the catalytic activity of thenanoenzyme. As a result, the colour of the system gradually diminished with increasing malathion concentration. Under optimised conditions, a strong linear correlation was established between the change in absorbance of the sensor and malathion concentration, spanning from 5 to 270 ng/mL, with a detection limit as low as 4.23 ng/mL (3σ) and a detection time of 20 minutes. This method exhibited excellent selectivity, stability, repeatability, and anti-interference capabilities. The recovery rates of malathion in spiked real samples ranged from 95.3% to 104.7%, accompanied by a relative standard deviation (RSD) of less than 4.2%. By eliminating the need for biological enzymes, this sensor facilitates straightforward operation and rapid response, thereby providing an innovative approach for the on-site rapid detection of malathion residues in food.
Copyright (c) 2026 Rui Liu; WenJia Chen; JunXian Li; Run Wang; Liang Dong https://creativecommons.org/licenses/by-nc/4.0
AUTOMATIC SLEEP STAGE CLASSIFICATION USING MULTI-PHYSIOLOGICAL SIGNALS BASED ON PCBSLEEPNET
Automatic sleep staging, a key technology in sleep medicine, faces feature selection challenges due to multi-modal physiological signal heterogeneity. This study establishes a framework to evaluate bioelectrical signals' impacts on deep neural network feature representation, aiming to identify optimal integration paradigms.We propose a multi-branch hybrid neural network with: 1) a three-channel parallel convolutional module incorporating cascaded residual and dilated convolutions for time-frequency feature extraction, and 2) bidirectional long short-term memory (BiLSTM) layers for temporal dependency modeling. We systematically analyzed multi-channel electroencephalogram (EEG), single-channel physiological signals, and their combinations covering both single-channel and multi-channel configurations of EEG, electrooculogram (EOG), and electromyogram (EMG). Results demonstrate that six-channel EEG configurations outperformed single/dual-channel implementations. In the single- and dual-channel EEG settings, the C3-M2 and C4-M1 + C3-M2 channels exhibited superior performance. EEG maintained superior classification capability over EOG/EMG. The tri-modal fusion (EEG+EOG+EMG) achieved peak performance (accuracy: 89.5%, Cohen’s κ: 0.85). This demonstrates EEG's central role in sleep staging, while EOG/EMG synergistically enhance model performance. The findings reveal heterogeneous bioelectric signal interactions in deep neural networks, providing foundations for optimizing signal fusion strategies and model architectures.
Copyright (c) 2026 YuLi Yang; Ying Liu; HaoWei Zhang https://creativecommons.org/licenses/by-nc/4.0
NON-DESTRUCTIVE TESTING METHOD FOR THE WIRE ROPE WITH DIFFERENT SURFACE WEAR
Steel wire ropes, as key load-bearing components, suffer from significant wear and broken wire damage during their service period, which poses a serious threat to operational safety. Addressing the issues such as the single excitation method in traditional eddy current detection methods, the large noise interference from the phase wave, and the low accuracy of damage quantitative identification, this paper proposes a new non-destructive testing method that combines a dual-level excitation structure with signal processing. Firstly, a dual-level excitation structure composed of three asymmetric magnetic poles on the left, middle, and right sides was designed. By using differentiated permanent magnet grades (N35/N52/N52) and a 60 mm magnetic pole spacing, two independent intervals with significantly different excitation intensities (up to 7.7%) were formed within the steel wire rope, effectively reducing mutual interference. Subsequently, through finite element simulation analysis, the influence of magnetic pole parameters on the excitation effect was investigated, and a signal processing method based on offset superposition and sliding average filtering was proposed to effectively suppress periodic phase wave noise. Experimental results show that this method established a high-precision cubic polynomial quantitative model between the number of broken wires and the peak-to-peak value of the eddy current signal (coefficient of determination R² = 0.995), and compared with existing detection devices, the error rate of broken wire identification was significantly reduced from 51.3% to 13.3%. This research provides an effective technical means for the precise quantitative identification of wire rope wear and broken wire damage, especially composite damage.
Copyright (c) 2026 LiShuo Wu; Kun Huang; GongNing Li; XiangDong Chang; LiQun Xiong; Zhou Zhou https://creativecommons.org/licenses/by-nc/4.0
EXPERIMENTAL DATA FITTING AND INFLUENCING FACTORS ANALYSIS OF TEA WITHERING
With the continuous progress of tea factory intelligence and modernization, the construction of tea withering mode plays the important role on the product quality in the process of tea withering. It is very important to analyze the construction of tea withering model and the analysis of influencing factors for the improvement of product quality in the process of tea withering. In this paper, the weight of each factor of tea withering on the trend and time of water loss and weight loss of the whole withering was successfully analyzed by means of multiple regression analysis. When withering to 50 % weight loss, the weight of each factor from large to small is temperature, humidity, tea green layer number, tea green density. At the same time, multiple regression analysis was used to predict the trend of withering water loss and weight loss under different conditions. The model data fitting can better control the withering time, and has positive significance for the post-mortem investigation of the withering process.
Copyright (c) 2026 ZhenHua Shao; GuoShi Li; JiaLiang Ye; JiaCheng Ye; YuQin Liu; MeiXue Lin; PengFu Ye https://creativecommons.org/licenses/by-nc/4.0
WATER DAMAGE RESISTANCE OF LOW-TEMPERATURE MODIFIED ASPHALT MIXTURE
This paper analyzes the influence law of water damage on the strength and stability of low-temperature modified asphalt mixture, and discusses the action mechanism of raw material characteristics, mix proportion design, construction technology and environmental factors on water stability. The research shows that the water damage of low-temperature modified asphalt mixture is mainly caused by asphalt film peeling, aggregate softening and internal structure damage due to water intrusion; the addition of modified components such as rubber powder and polymer resin can significantly improve the water damage resistance of the mixture; optimizing the asphalt-aggregate ratio, improving the aggregate gradation and adopting reasonable construction temperature control measures can effectively reduce the risk of water damage.
Low-temperature modified asphalt mixture
Copyright (c) 2026 XiaoPeng Tong https://creativecommons.org/licenses/by-nc/4.0
HYBRID FEATURE-ENHANCED 4D GAUSSIAN SPLATTING FOR DYNAMIC SCENE RECONSTRUCTION
Real-time modeling of dynamic scenes is a pivotal challenge in computer vision and graphics. Methods employing canonical space deformation with 3D Gaussians have achieved compelling speed, but a fundamental limitation persists: their feature representation often fails to capture the complex interplay of spatial, temporal, and multi-scale information in dynamic settings. This paper introduces a hybrid feature enhancement framework that systematically addresses this core issue. Our key idea is to forge a powerful and adaptive feature representation through the synergistic co-design of three modules: a Spatial Relation Module that explicitly encodes geometric context, a Dynamic Feature Adapter that employs gating for temporal conditioning, and a Multi-scale Integration Module that dynamically aggregates features across scales. The primary contribution of our work is this unified architecture designed to robustly enhance the feature backbone of deformation-based dynamic Gaussian representations. Extensive experiments on major benchmarks, including D-NeRF and HyperNeRF, demonstrate that our framework consistently elevates reconstruction quality, achieving superior performance over state-of-the-art methods on key metrics like PSNR and SSIM, thereby validating its general effectiveness.
Copyright (c) 2026 Sha Li; WanXiang Qin https://creativecommons.org/licenses/by-nc/4.0
DESIGN AND IMPLEMENTATION OF A KA-BAND SILICON-BASED 3D-HETEROGENEOUS-INTEGRATED 8-CHANNEL TRANSMIT MICROSYSTEM
This paper presents the design and implementaion of a Ka-band Silicon-Based 3D-Heterogeneous-Integrated 8-channel transmit microsystem. By introducing capacitive and inductive transition structures in the 3D passive interconnections of the microsystem, up to 40GHz broadband impedance matching is realized. Meanwhile, a three-layer Cu-Sn bonded silicon interposer architecture and its corresponding fabrication process are developed for the implementation of the two stacked sub-modules. And sub-modules stacking and fan-out of ball grid array (BGA) pins are achieved using solders and solder balls with differentiated temperature gradients. The fabricated 8-channel transmit microsystem features 1 input port and 8 output ports, with an operating frequency band over 27–32 GHz, a saturated output power of 23 dBm, an overall dimensions of 9.1 mm × 8.5 mm × 3.25 mm, and a weight of less than 0.1 g per channel.
Copyright (c) 2026 Ming Wei https://creativecommons.org/licenses/by-nc/4.0
CAN CONSUMER’S IDENTITY MANAGEMENT AFFECT PLATFORM’S BEHAVIOR-BASED PERSONALIZED PRICING?
With the development of big data and platform economy, platforms can easily gather consumer information through first-period purchase and exercise personalized pricing. When consumers realize it, they may engage in identity management to bypass the platform’s attempt to price discriminate. Therefore, it is significant to clarify the impact of identity management on platform’s behavior-based price discrimination, and whether platforms continue to compete consumers if they realize that consumers may engage in identity management. We introduce the identity management into the standard framework of behavior-based price discrimination, then we analyze and compare equilibrium results with and without identity management. The main findings are as the follows: (i) when consumers are active in identity management, the competition between platforms is intensified in the information collection stage, but it is softened in the information application stage, and platforms can exercise perfect price discrimination to consumers. (ii) The consumer identity management leads to lower consumer surplus and higher social welfare. When consumers become farsighted, platforms can be better off. (iii) platforms still have incentives to compete with consumers and gather consumer information, even though platforms predict that consumers are active in the identity management. (iv) Compared to one-sided market, the consumer identity management in two-sided market makes platforms more willing to implement perfect price discrimination.
Copyright (c) 2026 KaiHua Bao; ZhiQiang Zhang; Ting Zhang https://creativecommons.org/licenses/by-nc/4.0
MATHEMATICAL MODELING AND HIGH-ORDER FINITE DIFFERENCE METHODS FOR SPATIAL FRACTIONAL FITZHUGH-NAGUMO EQUATIONS
Mathematical modeling of the spatial fractional-order FitzHugh-Nagumo equations provides a critical framework for describing the propagation of electrical potentials in heterogeneous cardiac tissues, a problem that continues to attract significant research attention. Due to the geometrically irregular cross-sections of cardiac tissue, numerical solutions on conventional regular or approximately irregular domains remain limited, and analytical solutions are generally unavailable. In this study, we employ a high-order finite difference method in space coupled with a fourth-order Runge-Kutta scheme in time to solve this nonlinear fractional-order system. Numerical experiments are conducted to validate the high-order convergence and computational stability of the proposed numerical approach.
The fourth-order explicit Runge-Kutta method
Copyright (c) 2025 WenYe Jiang; QiZhi Zhang; Yu Li https://creativecommons.org/licenses/by-nc/4.0
AN INTEGRATED MULTI-SOURCE INFORMATION FUSION APPROACH FOR ENTERPRISE USER PORTRAIT CONSTRUCTION IN TECHNOLOGICAL DEMAND IDENTIFICATION
In the context of the innovation-driven development paradigm, the accurate identification of enterprise technological demands has become a core theoretical and practical issue in the field of technology transfer. Traditional demand identification paradigms are constrained by single-dimensional text analysis, leading to the lack of a systematic understanding of technological demands. To address this gap, this study proposes an integrated multi-source information fusion approach for enterprise user portrait construction oriented to technological demand identification. This approach systematically clarifies the connotation of enterprise technological demand portraits, defines the multi-dimensional information sources of portrait construction based on methodological logic, establishes a feature extraction system based on the dual dimensions of explicit and potential demands, and explores the mechanism of technological demand reconstruction through multi-source information fusion. This study enriches the methodological system of user portrait and technological demand identification, and provides technical guidance for breaking the information asymmetry in technology transfer.
Multi-source information fusion approach
Copyright (c) 2025 HongYu Su https://creativecommons.org/licenses/by-nc/4.0
OPTIMAL CROP PLANTING STRATEGIES BASED ON INTEGER LINEAR PROGRAMMING AND MONTE CARLO MODELS
This study investigates optimal crop planting strategies for a mountainous village in North China during 2024–2030 to maximize profits. Two optimization models were developed for different market scenarios. First, assuming stable market and production parameters, an integer linear programming model is developed to maximize profits while incorporating core agricultural constraints: field type compatibility, crop rotation to avoid replanting, and legume rotation. This model solves for optimal planting schemes under two scenarios: (1) surplus production leading to unsold waste, and (2) surplus sales at a 50% discount. Second, to address uncertainties and potential risks in projected sales volume, yield per mu, planting costs, and sales prices over the coming years, a Monte Carlo model was introduced for analysis. This method simulates fluctuations in parameters (e.g., annual sales growth rate of 5%–10%, yield variation of ±10% per mu) through random sampling, calculates maximum average returns, and thereby determines the optimal planting area allocation scheme with risk resilience in uncertain environments.
Copyright (c) 2025 CongCong Wang; YuLu Ding; XuZhe Chen; WeiHao Zhou; TianLe Cheng https://creativecommons.org/licenses/by-nc/4.0
DEEP LEARNING IN STROKE REHABILITATION COMPENSATORY MOVEMENT DETECTION: FROM LABORATORY BENCHMARKS TO CLINICAL REALITY
Stroke rehabilitation is going through some pretty major changes right now. We're seeing a gradual move away from those subjective clinical scales toward more objective, automated assessments—though it's not happening as fast as some people hoped. Traditional methods like the Fugl-Meyer Assessment (FMA) are still considered the gold standard in clinics, but they've got some real problems. They're time-consuming, different raters often give different scores, and they tend to hit ceiling effects that miss subtle improvements patients are making.This review looks at important studies from 2016 to 2025. We trace how automated detection systems evolved from those lab-based depth sensors to RGB cameras you can use at home and wearable IMUs. We compare different sensing technologies and look at public datasets. There's also this "sim-to-real" gap issue - basically, models trained on healthy actors often don't work well with real patients, which is a big problem. The algorithms have changed a lot too, going from traditional machine learning to foundation models. Clinical metrics have also expanded beyond simple detection to things like severity grading.At the end, we talk about what this all means for tele-rehabilitation and why getting clinicians to actually adopt these technologies is still really challenging. We suggest a roadmap that focuses on privacy-preserving collaboration and long-term validation studies, though honestly implementing all of this won't be easy.
Copyright (c) 2026 Xiao Xu; HaoNan Qin; LanShu Zhou https://creativecommons.org/licenses/by-nc/4.0
RAIL DAMAGE DETECTION SYSTEM BASED ON FUSION OF PLANAR CAPACITIVE SENSING AND VISION
This research addresses the specific and crucial application scenario of railway track damage detection by designing and implementing a comprehensive intelligent detection system solution. At the hardware architecture level, the system meticulously selects the high-performance STM32F103 microcontroller as the core control unit. This unit offers fast processing speed, low power consumption, and high reliability, laying a solid foundation for the system's stable operation. Centered around this core control unit, the system integrates various functional modules, including high-precision detection modules like planar capacitive sensors for real-time and accurate collection of track status data. Simultaneously, the OpenMV visual processing module is utilized to achieve precise machine vision recognition of track surface defects, significantly enhancing the intuitiveness and accuracy of detection. Additionally, the system is equipped with a motor drive module to enable precise movement of the detection device. The system not only features excellent hardware configuration but also possesses powerful data processing capabilities, enabling real-time and efficient analysis and processing of the collected massive data, thereby significantly improving detection accuracy and timeliness. In terms of material selection, the system innovatively adopts new energy-saving and environmentally friendly materials, which not only substantially reduce the overall energy consumption of the system but also effectively extend the equipment's service life, markedly enhancing its durability. This system integrates numerous advantages such as efficient detection, energy saving, environmental protection, and low-cost operation, demonstrating broad application prospects and significant market promotion potential in the field of railway operation and maintenance. It is expected to provide a solid and powerful guarantee for the safe and stable operation of railways.
Copyright (c) 2025 YuYang Xia; ZhongFu Liu https://creativecommons.org/licenses/by-nc/4.0
FATIGUE LIFE ANALYSIS OF METRO AXLEBOX BEARINGS BASED ON A VEHICLE-BEARING COUPLED DYNAMICS MODEL
Considering the nonlinear characteristics of tapered roller bearings, a coupled dynamic model of a metro vehicle and axle box bearings is established. Based on actual track geometry and train operational schedules, the dynamic force on the axle box bearings are simulated and analyzed under full working conditions, including traction, braking, negotiation of curves with different radii, and straight-line running. The simulation results indicate that compared to traditional vehicle dynamics models, the differences in bearing dynamic force predicted by this coupled model are mainly concentrated above 100 Hz. According to the train operational loads and track characteristics, a full life-cycle dynamic load spectrum for the axle box bearings is constructed. Using this load spectrum and a rolling contact finite element model of the bearing, the dynamic stress spectra at critical nodes of each bearing component are analyzed, followed by an assessment of their fatigue life. The computational results show that, compared to traditional L-P method-based fatigue life calculations using rated dynamic force derived from axle weights, the proposed method—which evaluates bearing fatigue life based on actual application conditions—aligns more closely with reality. This approach can provide more accurate data support for the residual life assessment and maintenance strategy formulation of metro axle box bearings.
Copyright (c) 2026 YiFan Yang; LinHua Fan; ZhengGang Lu https://creativecommons.org/licenses/by-nc/4.0
INTELLIGENT FOLLOWING CAR BASED ON ULTRASONIC POSITIONING
Aiming at the problem of blind area of safety monitoring in electric power operation site, an intelligent following car based on ultrasonic positioning is designed in this paper. The target coordinates are calculated by dual-chip ranging to achieve ± 10 cm accuracy tracking, supporting a maximum identification distance of 10 meters and a dynamic response of 1.5 m/s. The system adopts STM32F103RBT6 main control board, integrates ultrasonic sensor and wireless communication module, and combines with base-station and tag positioning mechanism to solve the problem of monitoring failure caused by manual intervention lag of traditional monitoring equipment. The test shows that the car can last for 4 hours under the condition of slope ≤ 15 ° and load 30 kg, which effectively improves the efficiency of operation safety management and control.
Copyright (c) 2025 WenFang Yang; BuSheng Luo; Guang Luo https://creativecommons.org/licenses/by-nc/4.0
OPTIMAL PLANTING STRATEGY BASED ON GOAL PROGRAMMING
In order to maximize the income of agriculture, this paper established linear programming models for the two cases of more than half selling and more than half selling with the constraints of plot area, continuous cropping restrictions, legume crop rotation, planting plots should not be dispersed and crop planting adaptability restrictions. Then it was solved by genetic algorithm, and the optimization results were divided into four categories according to the category of crops: grain, vegetables, edible fungi and beans. The uncertainty risk is measured by calculating the standard deviation of the expected sales volume, yield per mu, planting cost and sales price of crops, and the objective function of minimizing the risk is determined by the weighted sum of the four risk variances. Then, under the condition that more than half price is sold, a double objective optimization model with the objective function of maximizing income and minimizing risk is established, which is solved by NSGA-II algorithm The substitution and complementarity are incorporated into the risk model through the risk weighting mechanism, and the objective function is updated. Then, the Pareto frontier graph is generated by using NSGA-II algorithm. Compared with the results of problem 2, it is found that after considering the substitution and complementarity of agricultural products, the optimal solution set becomes more replaceable, more selective, and more extensive search space.
Copyright (c) 2025 Yang Rong; ShunYu Li; YuXin Wang https://creativecommons.org/licenses/by-nc/4.0
RESEARCH PROGRESS ON HIGH-NITROGEN AUSTENITIC STAINLESS STEEL
Advancements in metallurgical technology, coupled with the ever-growing industrial demand, have continuously driven the development of new materials. High-nitrogen austenitic stainless steel (HNASS), a resource-saving material, has been extensively investigated and rapidly developed owing to its high strength, high plasticity, and excellent corrosion resistance. This paper chronologically outlines the development of Cr–Mn–N series HNASS, which has evolved primarily through nickel-saving high-manganese austenitic stainless steel (such as AISI 200 series stainless steel), low-carbon molybdenum-containing austenitic stainless steel (such as AL6XN), and nickel-free HNASS (such as P900, developed by the VSG company of Germany). The development of HNASS is constrained by advancements in smelting technology. This paper outlines the current smelting methods for high-nitrogen steel, including atmospheric pressure smelting and high-pressure smelting, and summarizes the issues encountered during the smelting process. Welding technology is another key factor limiting the industrial application of HNASS. Extensive research has been conducted to reduce the number of welding defects in high-nitrogen steel. By optimizing welding processes and improving welding filler materials, welding joints with good performance can be obtained. However, welding technology for HNASS with universal applicability and industrialization potential still warrants further research.
High-nitrogen austenitic stainless steel
Copyright (c) 2026 YanYan Hong; XiaoPing Huang; Di Lu https://creativecommons.org/licenses/by-nc/4.0
A CONTROL-DRIVEN DATA ASSET CLASSIFICATION METHOD FROM A RIGHTS PERSPECTIVE
Against the backdrop of the digital economy, the process of data assetization is accelerating. However, in operational links such as registration, utilization, and transaction, institutional data rights have failed to be translated into enforceable implementation mechanisms, leading to ambiguous rights, responsibilities, and inefficient circulation. From a rights management perspective, this paper proposes a tripartite classification framework for data assets: identification data resolves issues of attribution, raw material data regulates usage based on clarified ownership, and tool data further enables the distribution of benefits. This framework integrates the national policy of "Tripartite Rights Separation", analyzes the distinct rights characteristics of different data types, and designs differentiated governance pathways. It aims to provide institutional support for data assetization operations and foster the efficient functioning of the data factor market.
Copyright (c) 2025 DaXing Chen; Kun Meng; YuChen Zhao; QiYuan Wang https://creativecommons.org/licenses/by-nc/4.0
NEURAL-NETWORK-ASSISTED MODEL PREDICTIVE CONTROL FOR ACTUATORS IN NUCLEAR POWER PLANT DRIVE SYSTEMS
This study develops a unified dynamic modeling framework for nuclear-grade actuators, such as fans, pumps, and valves, critical for reactor cooling, feedwater regulation, and ventilation. These actuators exhibit nonlinearities, cross-coupling, and operational constraints, challenging conventional controllers to maintain performance and safety under fluctuating conditions. To address this, a neural-network-assisted model predictive control (NN-MPC) architecture is proposed. The framework incorporates a mask-based input encoding strategy to represent all actuators within a single model, and a fully differentiable neural network is trained to approximate actuator dynamics. A bias-compensation mechanism is introduced to address multi-step prediction drift caused by process variations and aging. The resulting nonlinear MPC integrates actuator constraints, output limitations, rate restrictions, and real-time optimization, ensuring millisecond-level performance required in nuclear applications. The implementation workflow includes real-time execution analysis, anomaly detection, safety fallback logic, and hardware-in-the-loop validation. Experimental results demonstrate that the NN-MPC framework provides accurate modeling, high-performance control, and compliance with nuclear safety requirements. This research offers a robust pathway for advancing intelligent, data-driven control systems in next-generation nuclear power plants.
Copyright (c) 2025 Yuan Zhang; YanKun Li; Chao Si https://creativecommons.org/licenses/by-nc/4.0
ULTRAFINE PALLADIUM NANOPARTICLES SUPPORTED ON THE MFI ZEOLITE FOR METHANE COMBUSTION REACRION
The environmental impact of methane's incomplete combustion underscores the importance of catalytic oxidation, a process in which palladium (Pd) nanoparticles serve as a highly active catalyst despite their tendency to aggregate during synthesis and operation due to inherent high surface energy. Zeolites, as nanoporous materials with ordered micropore structures and excellent thermal stability, are considered ideal supports for stabilizing noble metal nanoparticles. Herein, MFI-type zeolite nanosheets rich in silanol defects were used as supports to prepare finely dispersed Pd nanocatalysts (Pd/SP-S-1 and Pd/SP-ZSM-5) via incipient wetness impregnation. The resulting catalysts exhibited excellent activity toward methane combustion. The T 90 (temperature for 90% methane conversion) value of Pd/SP-S-1 was 459 °C, which is lower than that of the catalyst supported on conventional S-1 zeolite. Introducing acidity further enhanced the catalytic performance, lowering the T 90 of Pd/SP-ZSM-5 to 426 °C. Moreover, the acidic zeolite framework improved the stability of Pd nanoparticles. The Pd/SP-ZSM-5 catalyst maintained stable activity for nearly 50 h at 430 °C. This work highlights the potential of zeolite-confined Pd catalysts for methane combustion and provides insights into designing synergistic catalytic systems combining metallic and acidic sites.
Copyright (c) 2026 Tao Cai; Ning Wang; Yue Sun https://creativecommons.org/licenses/by-nc/4.0
LIFE ATTITUDE PROFILES AMONG CHINESE UNIVERSITY STUDENTS: A SURVEY STUDY OF POSITIVE, LYING-FLAT, BUDDHA-LIKE, AND NEGATIVE MINDSETS
Introduction: Amid the massification of higher education and rapid social transformation in China, university students face intensified academic competition, uncertain mobility prospects, and heightened psychosocial stress. These conditions make “attitudes toward life” a consequential construct for understanding how students evaluate life circumstances and translate such evaluations into coping and action tendencies. However, existing studies often examine positive functioning and distress-related outcomes separately, with limited integration of co-existing orientations such as engagement, withdrawal, and goal attenuation. Objectives: This study aims to develop a context-sensitive Life Attitude Scale (LAS) and to map the overall pattern and subgroup differences of college students’ life attitudes across four co-existing orientations: positive mindset, lying-flat mindset, Buddha-like mindset, and negative mindset. Methods: A cross-sectional, on-site paper-and-pencil survey was administered to students from nine colleges at a university in Guangzhou, yielding 287 valid responses (effective rate 89.69%). The LAS comprised 19 items across four subscales (5, 5, 5, and 4 items, respectively) rated on a 5-point Likert-type scale; survey data were analyzed in SPSS 27.0 with psychometric screening and nonparametric group comparisons, supplemented by interview materials for contextual interpretation. Results: Descriptive results showed that the positive mindset was highest (M = 4.093), while the lying-flat mindset (M = 2.419) and the negative mindset (M = 2.190) remained comparatively low; the Buddha-like mindset was near the midpoint (M = 2.987). Kruskal-Wallis tests indicated statistically significant differences across selected background variables, including discipline-related variation in positive mindset, as well as gender- and family-background-related variation in low-desire orientations and negative mindset. Discussion: The findings depict an overall positive life orientation among contemporary Chinese university students, alongside meaningful heterogeneity in withdrawal-style and low-desire coping patterns. The four-dimensional LAS provides a measurement basis for differentiated educational guidance and mental health promotion. Future research should extend validation to multi-site samples and longitudinal designs, and further test mechanism pathways linking structural stressors, relative deprivation, and life-attitude configurations.
Copyright (c) 2025 Liu Yang https://creativecommons.org/licenses/by-nc/4.0
THE FORMATION MECHANISM OF EMERGENCY RESPONSE CAPACITY IN MAJOR EMERGENCIES AT THE GRASS-ROOTS LEVEL
The emergency response capacity of major emergencies at the grass-roots level includes both potential emergency response capacity elements and emergency response capacity realization mechanisms. The emergency response capability realization mechanism is not only the mechanism for potential emergency response capability elements to be transformed into real emergency response capability, but also the environmental conditions and institutional guarantee for the formation of emergency response capability. In the formation process of emergency response capacity for major emergencies at the grassroots level, potential emergency response capacity elements, emergency response capacity realization mechanism and real emergency response capacity elements interact and influence each other to form a complete emergency response capacity. Therefore, this study will start from the analysis of the role of emergency response capacity realization mechanism on the transformation of potential emergency response capacity into real emergency response capacity, and explain the mechanism of emergency response capacity formation in major emergencies at the grassroots level.
Copyright (c) 2025 ZiXuan Liu; HaoLin Tian https://creativecommons.org/licenses/by-nc/4.0
UNSUPERVISED SEGMENTATION OF DEFORMING 3D MESHES VIA DEFORMATION-AWARE GRAPH CUTS
We propose an unsupervised method for segmenting deforming 3D mesh sequences using a deformation-aware graph cut. Our approach constructs a spatiotemporal graph and formulates segmentation as a minimum s-t cut problem. Unary costs, derived from per-vertex deformation energy, separate near-rigid parts from deforming regions, while pairwise costs enforce spatial smoothness. By iteratively applying max-flow/min-cut, the algorithm greedily extracts coherent parts without supervision. Results show the automatic partitioning of complex animations into meaningful, temporally-consistent components, validating our approach.
Copyright (c) 2025 Yu Su https://creativecommons.org/licenses/by-nc/4.0
OPTIMAL CROP PLANTING STRATEGY BASED ON PARTICLE SWARM ALGORITHM
With the development of rural economy, optimizing planting strategy is an important research topic in organic planting industry. At present, most experts are not clear about the binding conditions for crop planting. This paper searched for data on agricultural products and local soil conditions in Hutang Town, Changzhou City, Jiangsu Province, and identified data on local cropping strategies for 2023. Based on the above data and the ideas of linear programming and multi-objective programming, the planting strategy optimization model under multiple constraints was established by determining planting risk factors and other indicators to seek the optimal crop planting strategy with maximum returns, and particle swarm optimization algorithm was used to solve the model. Finally, the optimal planting strategy under certain conditions and uncertain factors is obtained, and the complex problems arising from the optimal crop planting strategy are solved.
Copyright (c) 2025 TianYou Zuo; LiangYing Ji; YuSa Liu https://creativecommons.org/licenses/by-nc/4.0
ROBUST MULTI-OBJECTIVE CROP PLANNING IN MOUNTAINOUS NORTHERN CHINA VIA LP–NSGA-II–MONTE CARLO
This paper addresses the optimization of crop planting strategies for a mountainous rural village in northern China, characterized by diverse land resources and strict crop rotation requirements. A comprehensive mathematical modeling framework is developed, incorporating linear programming, genetic algorithms (NSGA-II), and Monte Carlo simulation to address multi-objective optimization and uncertainty in agricultural production. The study considers multiple practical constraints, including land type suitability, crop rotation, legume planting frequency, and limitations on greenhouse cultivation. The results reveal that the proposed models can effectively generate robust, adaptive cropping plans that maximize economic returns while reducing planting risks under both stable and fluctuating market conditions. The integration of Monte Carlo simulation enables the model to account for yield, price, and cost uncertainty, providing decision-makers with reliable strategies for risk management. The findings offer theoretical and practical guidance for agricultural planners and farmers, and the modeling framework has strong scalability for application in diverse environments.
Copyright (c) 2025 ChiHo Thong; Jun Ren; ShuHao Liu https://creativecommons.org/licenses/by-nc/4.0