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.
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
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
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
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
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
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
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
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
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
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
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
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
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
INDUSTRIAL AND SUPPLY CHAIN COORDINATION AND SUSTAINABLE DEVELOPMENT IN WESTERN CHINA UNDER THE DIGITAL ECONOMY
With the rapid development of the digital economy, big data, cloud computing, the Internet of Things and artificial intelligence have driven a new round of industrial transformation. In this process, data has gradually become the core driving factor, not only reconfiguring the collaborative mechanism between the industrial chain and the supply chain, but also promoting the sustainable development of the overall operation. This study, based on the background of the development of the digital economy, focuses on the western region of China and explores how regional industrial chains and supply chains can coordinate and interact to achieve sustainable economic development. Starting from the enabling mechanism of the digital economy, this article deeply analyzes the internal logic of the "dual-chain" synergy and its specific impact on regional sustainable development. This article expands the analytical framework of the collaboration between industrial chains and supply chains at the theoretical level and the mechanism of digital economy empowering the linkage and driving sustainable development of industrial chains and supply chains. Further, based on the current development status of industrial chains in the western region and the challenges and current situation faced by the development of the digital economy, corresponding suggestions are put forward. This research further enriches the relevant theories on digital economy and regional sustainable development, providing a reference basis for enhancing the economic and social development level of the western region with the aid of digital technology.
Copyright (c) 2025 LingYan Sun 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
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
EXTENSION OF NEW MODEL WITH GOOD APPROXIMATION BASED ON RECURRENCE RELATION
By using novel recurrence relation, some models with good approximation are constructed, which include three aspects: extension of curve of degree n, extension of surface of degree n over rectangular domain. First, based on a novel recurrence relation, we define a Quasi-Bernstein-basis of degree n with multiple parameters, which includes the classical Bernstein basis of degree n as a special case and has similar properties with the classic Bernstein basis. And the definition, properties, comer cutting algorithm, adjustable effect and quantification of approximation of related curves are discussed in detail. Next, tased on Quasi-Bernstein-basis, we develop a tensor product surface with multiple parameters over rectangular domain, and discuss continuity of Quasi-Bézier-surface at length. Compared with the existing methods, the proposed models keep can beautiful properties of classical method and the multiple parameters introduced in these models can flexibly adjust shape of the generated model and possess good approximation.
Copyright (c) 2025 PeiPei Ji; JunHui Yang https://creativecommons.org/licenses/by-nc/4.0
GROUP PHARMACOKINETICS OF FLUCONAZOLE IN PATIENTS WITH SEVERE INFECTION
Objective: The aim was to investigate the population pharmacokinetics (PopPK) model of Vorticamole (VRC), identify significant covariates and corresponding dose optimization strategies in the existing clinical patient VRC PopPK model, and explore the characteristics of the existing VRC PopPK model. Methods: We searched the PubMed database for clinical PopPK studies of VRC using the nonlinear mixed-effects method from the establishment of the database to May 2025, and screened the relevant references. Results: A total of 29 studies that met the inclusion criteria were finally included. One-compartment and two-compartment models were reported in 21 and 7 studies, respectively, as the best models to describe the PopPK of VRC, and one study used a three-compartment model. More than 40 covariates were included in the screening, with the most common covariates being CYP2C19 phenotype and body weight, and ALB being a secondary covariate. The typical volume of distribution in adult and pediatric patients was similar, but the estimated clearance in pediatric patients was significantly higher than that in adult patients, and the estimated bioavailability in pediatric patients was significantly lower than that in adult patients. The typical values of voriconazole clearance and total apparent volume of distribution exhibit substantial variability, ranging from 2.29–7.35 L/h and 76–194 L, respectively. Twenty-four studies used the exponential model as the basic model to describe the inter-individual variation. Only three studies used external methods to evaluate the model. Conclusion: First, this paper emphasizes the broadness and variability of the estimated PopPK parameters of VRC and provides the covariates that affect the clearance and apparent volume of distribution in such patients. Second, external evaluation of PopPK models should be conducted, and the predictive performance of various models should be further compared to improve the extrapolation ability of the models. In addition, it is recommended to conduct Monte Carlo simulations based on the significant covariates derived from different patient groups, and to supplement PopPK models to guide the differences in clinical efficacy between dose and traditional empirical dose, track the individualized medication effects of patients guided by PopPK models, and conduct supplementary trials based on actual clinical efficacy to facilitate the more reasonable application of models in clinical practice.
Copyright (c) 2025 Lu Yao; Wei Bu; Min Luo; Liu Shi; HongBo Xu; WenMei Liang; Yan Chen; Tao Chen; Bao Fu; Lei Gong https://creativecommons.org/licenses/by-nc/4.0
THE CORRELATION BETWEEN LIFESTYLE AND PREVALENCE OF HIP JOINT DISEASE IN PLATEAU AREAS
This study systematically analyzed the relationship between lifestyle habits and the incidence of hip joint disease in high-altitude areas, focusing specifically on the mechanisms by which factors such as low oxygen levels, strong ultraviolet radiation, traditional infant swaddling methods, and unique walking patterns influence hip joint health. This study provides a scientific basis for the prevention and control of hip joint disease in high-altitude areas.
Copyright (c) 2025 JianCheng Li; Peng Zhou; KaiZhao Pang; JiRong Li 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
LATERAL FORCE OF MIXING DRUM IN CONCRETE MIXER TRUCK BASED ON EDEM
Concrete demonstrates fluid properties, which results in specific dynamic behaviors during the transit of concrete mixer trucks. A defining operational trait of these vehicles is that their Concrete Mixing Drum sustain continuous rotation while the trucks are in motion. When a concrete mixer truck makes a turn, the concrete inside the Concrete Mixing Drum undergoes load migration—a phenomenon primarily induced by the combined effects of the concrete’s intrinsic fluidity and the centrifugal force generated by the Concrete Mixing Drum’s rotation. This load migration is often accompanied by dynamic impacts that exert forces on the Concrete Mixing Drum and the vehicle’s chassis. Notably, when the truck’s speed increases slightly, the aforementioned load migration and dynamic impacts further deteriorate the vehicle’s roll stability. Once the roll stability drops below a critical level, the risk of the truck encountering a rollover accident rises significantly, potentially endangering road safety, property, and human lives. To address this pressing issue, the present study focuses on investigating the Lateral Force generated during the concrete mixing process, as these forces are key factors contributing to the compromised roll stability of the vehicle. To achieve this research goal, EDEM software—a specialized tool for discrete element method simulations—was utilized to conduct numerical simulations of the concrete mixing process inside the Concrete Mixing Drum. The simulations were designed to cover two distinct Filling Ratio conditions of the Concrete Mixing Drum: one with a Filling Ratio of 1:1, corresponding to a total mass of concrete and aggregates of 10,000 kg, and the other with a Filling Ratio of 1:1.2, where the total mass of concrete and aggregates amounts to 12,000 kg. Additionally, three different Rotational Speed of the Concrete Mixing Drum—2 rpm (revolutions per minute), 5 rpm, and 10 rpm—were incorporated into the simulations to examine their respective influences on the generation and magnitude of Lateral Force. By analyzing the simulation data under these varied conditions, the study aims to gain insights into how Filling Ratio and Concrete Mixing Drum Rotational Speed affect Lateral Force characteristics, thereby providing a theoretical foundation for enhancing the roll stability of concrete mixer trucks and mitigating the risk of rollover accidents.
Copyright (c) 2025 YuHuan Gao https://creativecommons.org/licenses/by-nc/4.0
A RETRIEVAL-AUGMENTED GENERATION FRAMEWORK FOR EXPLAINABLE ACADEMIC PAPER QUALITY ASSESSMENT
With the exponential growth of global scholarly output, traditional academic paper evaluation methods face significant challenges in reliability, consistency, and scalability. Peer review processes suffer from low inter-rater agreement and lengthy decision times, while bibliometric approaches systematically disadvantage emerging fields. To address these systemic limitations, this study proposes a novel evaluation framework leveraging Retrieval-Augmented Generation (RAG) architecture and large language models (LLMs). The framework implements a four-dimensional assessment mechanism—analyzing research questions, methodologies, results, and conclusions—supported by contextual knowledge retrieval and explainable judgment generation. Experimental validation demonstrates the superiority of the RAG-based approach over both human experts and conventional machine learning baselines, achieving an F1-score of 0.77 at the quartile level. Additionally, the system provides transparent evaluative judgments supported by comparable evidence from prior literature. This work contributes to advancing scholarly communication by offering a scalable, explainable, and reliable alternative to existing evaluation paradigms.
Copyright (c) 2025 WeiJing Zhu; RunTao Ren; Wei Xie; CenYing Yang 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
BASED ON UNITY3D, TWO-PLAYER COOPERATION CAPTURES THE DESIGN AND IMPLEMENTATION OF THE GOBLIN GAME "INK WALKER"
This paper takes Ink Walker, a Chinese-style two-player cooperative goblin-catching game developed based on the Unity3D engine, as the research object. Combining practical experience from Game Jam competitions, it explores the design and implementation process of the game. The research centers on three core goals: the visual design of Chinese-style elements, the innovation of two-player collaboration mechanisms, and the cross-platform technical implementation. It unfolds from aspects such as artistic concept design (including the setting of world view and plot, the visual design of characters, goblins, scenes and UI), technical indicators and research methods (covering technical foundations and development toolchains, the technical implementation of core gameplay, system architecture and performance optimization), and performance optimization strategies. By integrating Chinese-style culture with game design, innovating two-player collaboration mechanisms, utilizing Unity3D to realize cross-platform development and conduct performance optimization, it provides technical solutions and creative paradigms for the development of similar Chinese-style games, and has certain theoretical and practical significance.
Copyright (c) 2025 Yue Zhou https://creativecommons.org/licenses/by-nc/4.0
LNCRNA-GASS: A PROSPECTIVE BIOMARKER FOR SEPSIS THERAPY
Sepsis, characterized by life-threatening organ impairment triggered by aberrant host reaction to infection, continues to pose a global health challenge with consistently elevated rates of morbidity and mortality. Long non-coding RNAs (lncRNAs) have emerged as crucial modulators in various biological processes, including the development of sepsis. Among these molecules, lncRNA-GASS has attracted growing interest as a promising biomarker of sepsis. This review compiles the current understanding of lncRNA-GASS, covering its regulatory mechanisms in sepsis, its connections to sepsis-induced organ damage, and its potential as a diagnostic/prognostic biomarker for sepsis treatment.
Copyright (c) 2025 Dan Yang; JianQuan Li https://creativecommons.org/licenses/by-nc/4.0
THE LOCATION OF COLD CHAIN LOGISTICS FOR AGRICULTURAL PRODUCTS FOR THE"FIRST KILOMETRE"
With the accelerated development of China's agricultural modernisation, the importance of cold chain logistics for agricultural products has become increasingly prominent. However, problems such as insufficient cold chain coverage and lack of pre-cooling links have led to high spoilage rates and high logistics costs for fresh agricultural products. Taking Jiangsu Province as an example, this study constructs a two-stage cold chain logistics and distribution centre site selection model that takes economic, environmental and social factors into account. In the first stage, this study selects 12 indicators in four dimensions, namely, economic development, traffic congestion, total logistics demand, and green and low-carbon level, and uses the entropy weight method to quantitatively assess the logistics level of 13 cities in Jiangsu Province. By calculating the logistics level score of each city, the spatial economic distance between cities is defined, and the K-means clustering algorithm is combined to divide Jiangsu Province into regions. The clustering results show that the clustering method based on spatial economic distance can more scientifically reflect the economic connection and logistics demand among cities, which provides an important basis for the subsequent site selection decision. In the second stage, the study uses the centre of gravity method to select the optimal distribution centre location within each clustering region. The centre of gravity method determines the optimal location of the distribution centre by calculating the weighted average location of each demand point within the region with the objective of minimising transport costs. Ultimately, by comparing the two methods based on spatial economic distance clustering and traditional geographic distance clustering, the results show that the clustering method based on spatial economic distance significantly reduces the transport cost. The total transport cost is reduced from $16,356.33 to $14,156.00, which is about 13.45%. This result not only verifies the validity and practicability of the constructed model, but also provides a scientific basis for the location of cold chain logistics distribution centre in Jiangsu Province, which helps to improve the operational efficiency and economic benefits of cold chain logistics.
Copyright (c) 2025 YiTao Yin; YiFei Liao; LeTong Ji; ZiFan Wang https://creativecommons.org/licenses/by-nc/4.0
EFFECT OF THE LIGHT QUANTUM EFFICIENCY ON THE SYNCHRONOUS PHOTO CATALYTIC ABATEMENT OF SO2–NOX BINARY GAS
Photocatalytic decontamination has emerged as a highly promising technology for air pollutant control and energy conversion. In this work, a semiconductor photocatalyst was synthesized via a hydrothermal method, featuring nano-sized TiO 2 doped with nickel (Ni), zirconium (Zr), and nonmetallic nitrogen (N). The kinetic process of synchronous abatement of the SO 2 -NO x binary gas system was systematically investigated in a self-designed photocatalytic reactor. From the perspective of quantum optics, a light quantum efficiency model was established to elaborate the photocatalytic degradation mechanism, accompanied by a detailed analysis of the synergistic effects of multiple influencing factors on the interaction between incident light and pollutants. Experimental results revealed that flue gas concentrations exert significant impacts on contaminant removal efficiencies. Notably, the experimental data showed excellent consistency with the calculated results across a wide range of operational conditions.
Simultaneous desulfurization and denitrification
Copyright (c) 2025 Kuo Yao; JingLi Gao; Yasser M. A. Mohamed; YingHui Han https://creativecommons.org/licenses/by-nc/4.0
SUSTAINABLE CROP PLANNING IN A MOUNTAINOUS VILLAGE USING LINEAR PROGRAMMING
This study focuses on optimizing crop planning in a mountainous village in North China, where the cold climate permits only one growing season per year. The village’s arable land is distributed across 34 plots of various types, including flat drylands, terraces, slopes, and irrigated fields. A linear programming model is developed to maximize planting revenue, considering crop rotation requirements, land suitability, planting costs, yields, and market prices. To simulate future conditions, crop prices in 2030 are modeled using normally distributed random numbers. The model sets two sales scenarios: (1) surplus production beyond expected sales cannot be sold, resulting in waste; and (2) surplus is sold at 50% of the 2023 market price. To address uncertainties in yield, cost, price, and demand, as well as potential planting risks, a particle swarm optimization algorithm is employed with random factors to simulate changing conditions. Results show that the village can achieve annual revenues between 8 to 9 million RMB under both deterministic and stochastic scenarios. The optimized strategies satisfy agronomic and environmental constraints and offer scientific support for sustainable agricultural development in resource-limited mountainous regions.
Linear programming particle swarm algorithm
Copyright (c) 2025 Tong Chen https://creativecommons.org/licenses/by-nc/4.0