bluuebunny/crossref_metadata_2025 huggingface dataset:
A review: classification and production methods of β-glucan
Authors: Deng Pan , Liu Yuehan , Wei Wenyao , Rong Wen , Nie Xinxuan , Wu Jingwen , Meng Zhengxiang , Wang Junqing , Fan Han
Published: December 2024

β-Glucan is a biologically active natural polysaccharide, which is widely used in the fields of food, cosmetics and health care supplements. In recent years, β-glucan has become a research hotspot, and its various efficacious uses have been developed one after another. Many studies have reported the optimization methods of β-glucan production and extraction, and the yield has been improved accordingly. This paper reviews the classification of β-glucan and the current production methods, classifies the β-glucan sources and connecting bonds which can be broadly classified into the categories of plants, bacteria, and fungus. It also provides a detailed review of the acid and alkali extraction method, enzyme extraction method and fermentation method. The passage provides theoretical support for future commercialization and industrialization.
Modeling Dandelion Seed Dispersion and Population Dynamics: Insights into Invasive Species Impact on Natural Environments
Authors: Li Jiawei , Li Yixiu , Rong Jingzhao
Published: December 2024

Dandelions, known for their robust growth and global distribution, possess vibrant flowers and unique seed heads, along with medicinal and edible properties. This study establishes a seed propagation model considering wind speed variations based on dandelion seed characteristics. Utilizing the Weber distribution to account for differing climatic conditions, a computer simulation depicts seed spread within one hectare. Additionally, a logistic model, adjusted for factors like natural resources and environmental conditions, characterizes dandelion population changes throughout the year. This modified logistic model considers species competitiveness, climate variations, and adjusts the inherent growth rate to create a comprehensive model for invasive species. Computer simulations comparing dandelions, French wild oats, and hard bromegrass reveal that dandelions typically avoid becoming invasive, while the latter two can negatively impact local ecosystems and economic development.
A Study of Driver behavior Based on Structural Equation Modelling in Human-Machine Hybrid Driving Environments
Authors: Chen TianLan
Published: December 2024

Driver behavior in hybrid traffic flow, consisting of both conventional and automated vehicles, is a crucial aspect of studying the balance between urban traffic supply and demand, as well as alleviating future urban traffic congestion. In view of this, this study introduces three variables of driver's attitude, subjective norms and perceived behavioural control based on the theory of planned behavior to construct a structural equation model. The study's results show that drivers' subjective norms can have a significant positive indirect effect on perceived behavioural control through the mediating variable of attitude. These findings can provide a theoretical basis for regulating driver behavior in the future human-machine hybrid driving environment.
Mining and biosynthesis strategies of sea cucumber antimicrobial peptides
Authors: Zhao Yanqiu , Yin Xin , Hao Lujiang
Published: December 2024

With the abuse of antibiotics, more and more drug-resistant strains have emerged clinically Antimicrobial peptides kill bacteria by inhibiting the synthesis of nucleic acid and protein in cell membrane and regulating cellular immune response. Because of its bactericidal mechanism, antimicrobial peptides can kill many gram-negative bacteria and gram-positive bacteria and are not easy to develop drug resistance, they are good candidates for a new generation of antibiotics to treat multidrug-resistant bacteria Sea cucumber is one of the excellent sources of antimicrobial peptides, but there are few studies on antimicrobial peptides based on sea cucumber at present With the deciphering of genomic proteomic information of sea cucumber and the publication of antimicrobial peptides of sea cucumber, it has attracted wide attention, which provides a new way for rapid acquisition of antimicrobial peptides of sea cucumber In this paper, the characteristics and mechanism of antimicrobial peptides from sea cucumber were summarized, and the mining and preparation strategies of antimicrobial peptides from sea cucumber were summarized.
Data-Driven Urban Traffic Flow Prediction and Optimization of Intelligent Transportation Systems in Hong Kong
Authors: Lin Yutao
Published: December 2024

This article delves into the optimization of intelligent transportation systems, specifically focusing on the practical impact of a deep learning-based traffic flow forecasting model. The introduction highlights the severity of urban congestion, emphasizes the significance of intelligent systems, and outlines the study's objectives. To this end, the LSTM model is employed for traffic flow prediction, accompanied by a comprehensive experimental design aimed at validating both model accuracy and strategy effectiveness. Throughout the experimentation, extensive traffic data is gathered, the model is refined, and its predictions are integrated into various intelligent transportation aspects, including signal control and route planning. Findings reveal the LSTM model's strong performance in traffic flow forecasting, characterized by high accuracy. At the same time, the optimization strategy based on the prediction results has also achieved remarkable results, effectively reducing traffic congestion. This result proves the great potential of deep learning in intelligent transportation system optimization. This study not only has theoretical value, but also provides useful reference for practical application.
Research of Containment Leakage for Marine Nuclear Power Platform
Authors: Ji Gang , Wang Xiru , Tan Mei
Published: December 2024

The purpose of this paper is to clarify the overall and local leakage rates of the Marine reactor containment, establish the containment seal test acceptance criteria, and support the analysis of airborne radioactive material concentration in the compartments around the containment. Based on the containment seal test standard of onshore nuclear power plants, the leakage rate index allocation, leakage rate calculation and feasibility analysis of Marine Nuclear Power Platform (MNPP) were carried out through the general idea of "standard analysis-proposed index-verification index". The study showed that the overall leakage rate of the containment of the MNPP was proposed about 3‰/24h, and the leakage rate distribution of the penetration parts of Class B and C accounted for 10% and 50% of the overall leakage rate respectively under the condition of the design accident. During the sealing test phase, the overall leakage rate of the containment should consider 25% of the design margin. The results of numerical analysis and calculation of containment leakage of the MNPP meet the requirements of index and have a good design margin, which is of great reference value for the establishment of containment sealing test acceptance criteria.
Layout and Target Estimation Method of Sheltered Area for Multipath Signal
Authors: Dong Can , Song Wenyin , Li Haibin
Published: December 2024

Building environment sensing and shelter target detection technology based on low frequency electromagnetic wave has attracted the attention of researchers around the world. Low frequency electromagnetic wave has good penetration and low loss characteristics, which can realize building structure layout imaging and shelter target detection and tracking, and has important application prospects in the fields of anti-terrorism and stability maintenance, disaster relief and urban mapping. Due to the occlusion of urban buildings, electromagnetic wave propagation will produce complex electromagnetic phenomena such as reflection, transmission and diffraction, resulting in imaging defocusing and unstable target detection and tracking. This paper proposes a reconstruction method of building layout based on the combination of transmitted and reflected signals. Through the result level fusion of reflected projection images and transmission tomography images, the problems of wall position deviation and serious artifact interference existing in the reconstruction process are solved, and efficient and high-precision imaging of building layout under limited scanning paths is realized. The research on building layout and target imaging methods for multipath signals conducted in this paper can not only improve the detection performance of radar equipment in urban buildings, but also be applied to reconnaissance and surveillance in non-line-of-sight areas such as street corners, providing reliable information for anti-terrorism, stability maintenance and automatic driving, and playing an important role in national defense and social applications.
Research on effect of the water-based permeable crystalline material on concrete durability
Authors: Zhang Jiakun , Li Xiji , Yang Kandi
Published: December 2024

This study investigates the effect of the water-based permeable crystalline material on the compressive strength, permeability resistance, freeze-thaw resistance, abrasion resistance, and chloride ion permeability resistance of concrete through multiple sets of experiments. The results illustrate that the water-based permeable crystalline material enhances the compressive strength of concrete by approximately 9% and enhances the permeability resistance by 42.9%. Due to the notably higher permeability resistance, the chloride ion permeability resistance of concrete correspondingly increases by 17.2%. Moreover, this material significantly mitigates the damage caused by the freeze-thaw cycles and abrasion, making concrete more suitable for various working environments.
Judge The Function of Rules In The Power Grid Emergency Command System
Authors: Ni Ming , Liu Huan , Zhang Wei , Wang Buchao , Kang Rui , Wang Jun , Liu Yitian
Published: December 2024

The research and judgment rules are the core of the research and judgment function. The research and judgment function will strengthen the construction of the emergency technical support capabilities of the power grid company, break the data barriers, strengthen the interconnection and resource sharing of integration, and realize information. Intelligent, digitalization, effectively improve the level of response to emergencies, and promote the modernization of the company's emergency management system and emergency capacity, some have a significant role.
Research on intelligent interpretation of covering measures of dense mesh net based on UAV remote sensing images
Authors: Zhang Ying , Li Zhibin , Tan Rongrong , Li Yang
Published: December 2024

In order to explore the method of intelligent interpretation of typical measures of environmental protection and water conservation in pumped storage power station by UAV remote sensing technology, Wei Fang of pumped storage power station in North China was selected as the research area and the measure of dense mesh covering was selected for intelligent interpretation study. Firstly, a sample database of typical measures of dense mesh cover was built based on the orthophoto image of the UAV. Then the dense mesh cover recognition model is constructed using ENVI deep learning model framework. At last, the loss, accuracy and recall rate indicators provided by the ENVI deep learning module TensorBoard is usd to evaluate the accuracy of the recognition of model, which the accuracy reaches to 93%.
Screening of Antagonistic Strain against Phytophthora capsici and Study on Its Physiological-Biochemical Characteristics
Authors: Xie Yongdong , Gu Fengling , Zhu Jiawen , Lu Qiyu , Liu Ji
Published: December 2024

Pepper (Capsicum annuum L.) is rich in flavors and nutrients, it can be eaten fresh and processed, making it an important economic crop. However, pepper phytophthora blight, caused by Phytophthora capsica, is a devastating soil borne disease of peppers, causing a decrease in yield and quality. In this study, biocontrol agent with high antagonistic effect against P. capsici in vivo were screened, and the physiological and biochemical characteristics and bacteriostatic spectrum of this biocontrol strain were studied. The results showed that among the 30 strains of biocontrol bacteria tested, JK2 strain had an in vivo inhibition rate of 90.48% against P. capsici. JK2 grew rapidly within 2-10 hours after inoculation with spore suspension, and can grow at a temperature of 4-50℃, with an optimal growth temperature of 37℃; It exhibits good growth ability at pH 5-10, with an optimal pH of 7, and can grow in environments with strong acidity (pH=3). In addition, the JK2 strain also showed a high inhibition rate against P. sojae (83.88%). Therefore, JK2 is a highly effective biocontrol agent against P. capsici, and the strain has a broad antibacterial spectrum with inhibiting a variety of pathogens, so it has high application value.
The Historical Evolution of Green Mining Policies in China
Authors: Xiang Li , Lv Peng , Chai Xinxia
Published: December 2024

While driving economic development, geological prospecting activities inevitably exert an impact on the ecological environment. This paper examines the policy-making process of China's green mining development and emphasizes the significant achievements in green mining after 75 years of exploration. Lastly, the paper offers a forward-looking perspective on China's mines' green development.
The Life Cycle Cost Evaluation Model of Typical Power Grid Equipment Asset
Authors: Cui Lingxiao , Wang Dongdong , Yao Shengping , Wei Shimin , Gao Hongda , Zhang Haitao , Xu Ning , Wang Fei
Published: December 2024

The life cycle cost evaluation of the power grid equipment asset is a systematic tool for assessing all the costs associated with the equipment throughout its life cycle, including all stages of planning, design, procurement, installation, operation, maintenance, and decommissioning. By comprehensively calculating these costs, it provides in-depth financial analysis support for the enterprise. The modeling process involves defining objectives and scope, identifying and refining cost elements, collecting data, constructing mathematical models. Through the systematic costing, companies can optimize asset management, improve the accuracy of investment decisions, and effectively control costs throughout the life cycle.
The impact of El Nino Southern Oscillation on stratospheric Quasi-Biennial Oscillation structure revealed by ECMWF reanalysis data
Authors: Li Haiyan , Sun Fangling
Published: December 2024

We explored the influence of El Nino-Southern Oscillation (ENSO) on the stratospheric Quasi-Biennial Oscillation (QBO) by analyzing the European Central for Medium-range Weather Forecasts (ECMWF) reanalysis data from 1958 to 2017. Results show that the influence of the warm ENSO phase on the stratospheric QBO is more significant than the cold ENSO phase event. The amplitude of QBO westerly phase is decreased in El Nino condition and is enhanced in La Nina condition around 50 hPa. The influence of ENSO on QBOE phase amplitude is not significant around 50 hPa. ENSO phases have obvious effects on tropospheric zonal wind structure. The role of Kelvin wave may be important for the influence of ENSO phase on the zonal wind structure from troposphere to stratosphere. The tropospheric convection activity is more stronger during El Nino condition compared to La Nina condition which leads to more Kelvin waves excited during El Nino condition around the tropopause regions.
Quality Assessment of CMPAS Precipitation Product Data over the Dadu River Basin
Authors: Zhang Zihang , Li Jia , Mou Shiyu , Qing Dingchao
Published: December 2024

The Dadu River is the largest tributary of the Minjiang system in the Yangtze River basin. In recent years, frequent storm events in the upper reaches of the Yangtze River have made accurate evaluation of precipitation data in the basin particularly important. The CMPAS (CMA Multi-source Precipitation Analysis System) precipitation product, generated by the National Meteorological Information Center through the fusion of multiple data sources, boasts high spatial and temporal resolution. This study evaluates the accuracy of CMPAS precipitation data at an hourly scale for the Dadu River basin and applies it to the distributed Xinanjiang model to assess the results of hourly runoff simulation. The results show that the spatial distribution of hourly CMPAS precipitation data aligns with isohyets, and the overall accuracy indicators of the data are relatively good. However, the reliability in terms of precipitation identification and hydrological simulation is poor, and the error distribution is uneven. Consequently, while CMPAS precipitation data can serve as a reference for the spatial distribution of precipitation in the Dadu River basin, further calibration is required for its use in hydrological forecasting and other applications.
Research on Key Technologies and Value Creation for Large-Scale Development of New Energy
Authors: Gong Yichun , Jia Yufang , Yuan Bo
Published: December 2024

In the context of promoting high-quality development of large new energy bases in the Gobi Desert and other areas, it is urgent to conduct researches on key technologies for large-scale development and utilization of new energy, as well as on value analysis and value creation of the new energy industry, so as to derive relevant recommendations for industrial and policy development. This project starts from the key technologies for “wind-solar-thermal-storage” integrated and efficient development and utilization, and focuses on the scale ratio study of wind power and solar power generation to achieve power source complementation and stable operation. At the same time, based on the scale advantages and agglomeration effects brought by large-scale development of new energy, it analyzes the value orientation and value creation model of the industrial chain based on the current status and trends of new energy development, and puts forward opinions and suggestions from both industrial and corporate perspectives.
Auto Bayesian Multi-Scale RetineX with Color Restoration of Low-Light image enhancement
Authors: Jiang Yuhao , Chen Yu , Li Chenhao , Wen Zixiu , Wang Jiahui
Published: December 2024

This paper presents AutoBMSRCR (Auto Bayesian Multi-Scale Retinex with Color Restoration), a novel method for low-light image enhancement. By combining Retinex theory with Bayesian optimization, AutoBMSRCR adaptively fine-tunes parameters for superior image quality with low computational cost. Using GPU-accelerated bilateral filtering via PyTorch and custom color restoration, the method significantly improves key metrics like UIQM and NIQE, with 9.6% and 64.5% gains, respectively, over competing methods. AutoBMSRCR is ideal for real-time applications on mobile and embedded systems, offering a promising solution for low-light enhancement.
Load Frequency Control for Electric Vehicle Battery System under Mixed Attack Based on Stochastic Event-triggered Mechanism
Authors: Deng Dedong
Published: December 2024

In this paper, a mode of load frequency control system with electric vehicles is firstly constructed. Then, for simulating the real information transmission environment, random Deception attacks and aperiodical Denial of Service(DoS) attacks are considered. A resilient stochastic event-triggering mechanism(RSETM) is proposed to save network resources. Finally, the closed-loop switching system model is constructed via the previous analytical work. On this basis, the power system’s exponential stability and  performance are investigated by the Lyapunov functional. The numerical examples verify the accuracy of the proposed mechanism.
Machine Translation Use in English Academic Reading and Writing: From the Perspective of Technology Acceptance Model
Authors: Yang Han , Liu Ming
Published: December 2024

Due to the rapid advancement of AI-driven translation technology, the application of machine translation (MT) in foreign language education has become the focus of current research. Based on the Technology Acceptance Model (TAM), this study investigates science and engineering students’ use of machine translation in English academic reading and writing. A questionnaire survey was conducted to uncover their perception of MT use in the academic setting, how, when and why they use MT, and their evaluations of the tool. The findings indicate that students, having acquired certain knowledge about MT, are keen to utilize it. They have adopted a scientifically sound use pattern and hold a positive evaluation of MT. Additionally, students believe that the application of MT positively impacts their academic English proficiency, expressing optimism towards MT-use teaching and MT literacy development. In the future, foreign language education should contribute to high-quality academic talents cultivation, enhance students' academic English proficiency and guide them in the correct and scientific use of MT.
Industry 4.0 Transition in China’s Manufacturing: A Case Study of Liaoning Province Using System Dynamics
Authors: Li Qingwen , Wai Fan Tang , Li Zhaobin
Published: December 2024

The transition to Industry 4.0 is a critical phase for China's manufacturing sector. This study applies a System Dynamics (SD) model to evaluate and simulate the industrial capabilities of 10 listed equipment manufacturing companies in Liaoning Province. The research identifies key factors, such as technological investment, talent development, and enterprise performance. Our findings demonstrate that an increase in technological investment significantly enhances enterprise competitiveness, particularly in companies like Siasun Robot & Automation and Anzhong Corporation. We propose strategic recommendations for policymakers and businesses, including boosting research and development (R&D) investments and fostering collaboration between academia and industry to support the transition to Industry 4.0 in Liaoning.
An alternated inertial Popov’s method for solving Pseudomonotone variational inequalities
Authors: Zhao Yali , Yu Jiaxin , Li Xinhao
Published: December 2024

In this work, we propose a new modified Popov’s method by using inertial effect for solving pseudomonotone variational inequalities in real Hilbert spcaes, which does not require prior information about the Lipschitz constant of the mapping, and only needs to compute the value of the mapping and once a projection onto the feasible set at each iterative step. We present weak a convergence theorem of the proposed algorithm. Our results generalize some related results in the literature and primary numerical experiments demonstrate the applicability of the scheme.
fNIRS-based hyperscanning reveals increased inter-brain synchronization in dorsolateral prefrontal cortex during interdisciplinary cooperation
Authors: Li Yan , Chen Junxian , Song Wu
Published: December 2024

The digital age has accelerated collaborative innovation among interdisciplinary groups, and the collaborative behavior between individuals from different disciplinary backgrounds has gradually become normalized.Previous studies have suggested that interdisciplinary collaboration has certain advantages , there are currently few studies that empirically demonstrate this point through cognitive observations during collaborative processes.we used functional near-infrared spectroscopy (fNIRS) to measure the brain Blood oxygen data(HbO2&Hb) of two individuals from both design and non-design majors during real-world presented problems (RPP) and RPP adapted (DES). The inter-brain synchronization (IBS) between the two participants was calculated by Matlab wavelet coherence package. It was found that the signal consistency generated on the dorsolateral prefrontal cortex (DLPFC) in participants showed significant fluctuations in the RPP, but not in the DES. The results showed that the IBS of DLPFC increased under the RPP, which further supported previous research and also expanded the possibility of team collaboration task paradigms. The increase in IBS also considered of good cooperation, and this study can provide empirical evidence based on neuroimaging for interdisciplinary team collaboration.
Multi-Attention Feature Fusion for Nuclei Segmentation and Classification
Authors: Zhang Xingpeng , Guo Peng
Published: December 2024

Nucleus instance segmentation is crucial in the digital pathology, serving as a foundational step for subsequent tasks like precision medicine and cancer prognosis. In this paper, we introduce MAP-SegNet, a new network architecture designed for the segmentation and classification of nuclei in H&E stained images from multiple tissues. The key innovation of MAP-SegNet lies in its Multi-Attention Feature Fusion Module, which enhances the network's ability to efficiently combine multi-scale feature aggregation mechanisms with local and global features. The core component is the dual attention fusion module, which possesses the capability to adjust spatial attention weights dynamically by the input contextual information. This functionality enables the model to concentrate on various image regions, effectively capture long-range dependencies, and mitigate the risk of information loss. MAP-SegNet's uplifting accuracy in nuclei segmentation and classification is validated by rigorous quantitative experiments on the standard PanNuKe dataset.
ECC-based certificate-less multi-factor authentication scheme
Authors: Jiang Jing , Wang Xiaofeng , Xing Qianqian , Tang Jin
Published: December 2024

Currently, personal information leakage and identity theft have become risks that cannot be ignored. As a robust authentication mechanism, multi-factor authentication (MFA) has gained prominence. However, many existing MFA schemes based on certificate-less public key cryptography amalgamate multiple authentication factors into a single factor for local verification, which fails to achieve independent authentication of each factor and is difficult to resist internal privilege attacks. To address these issues, this paper proposes a certificate-less multi-factor privacy authentication scheme based on elliptic curve cryptography. The proposed scheme divides the full private key into multiple authentication factors and employs the Schnorr zero-knowledge proof technique to independently generate challenge values for each factor, facilitating separate authentication of multiple factors. Notably, this approach eliminates the need to reconstruct the full private key during the authentication process, thereby effectively mitigating the risk of private key leakage.
Attention Mechanism-ResNet-based Feature Recognition of Depressed Patients
Authors: Yang Yixuan , Song Ziyi , Qin Banghua , Zhang Lei
Published: December 2024

In this paper, a multimodal feature fusion model for the face and eyes based on convolutional neural networks is proposed, which excels in diagnostic accuracy. The key features of the face and eyes are extracted by watching videos with emotional features and playing an eye movement mini-game. The most important features were effectively extracted by increasing the number of features using convolutional layers. The model has a high accuracy of 87.6% in recognizing depressed patients. In this paper, we hope to extend the generalization of the model and improve the recognition of patients with mild depression, in order to help more patients to be diagnosed and treated at an early stage. This paper realizes two innovations in modeling method and diagnostic purpose. In the modeling method, this paper chooses ResNet convolutional neural network,adds channel and spatial attention mechanism, uses fuzzy synthesis algorithm to couple features of face and eyes, and finally classifies and recognizes by A-SoftMax. The facial and eye images of the examined patients are used as input features, feature extraction and classification of depression diagnosis type based on convolutional neural network. For diagnostic purposes, this more focuses on the recognition rate of patients with mild depression in the hope that depressed patients can get timely and effective treatment at the initial stage.
Fog Machine Remote Control System Design and Implementation
Authors: Zhang Hengwen , Dai Keke , Wang Chunqiu , Ma Bokai , Li WeiBo
Published: December 2024

This system is designed to address the odors produced by wet waste and its negative impact on the environment during urban waste disposal in China. The waste disposal site in Shunyi District, Beijing uses spray cannons to clean the air and reduce the concentration of pollutants. In order to improve efficiency and reduce manual operation, the system was designed to remotely control and automate the spray cannons through a PLC (Programmable Logic Controller). The architecture of the system includes actuation module, control module, transmission module and status monitoring module. The system is capable of real-time data transmission, remote monitoring and automated control via 4G network. The system significantly improves operational efficiency and environmental management, but still suffers from high cost and stability problems.
A computationally efficient model predictive control for Vienna rectifier with current distortion elimination
Authors: Zhang Guangbiao , Li Ning
Published: December 2024

Due to the advantages such less switching diodes and lower system size, the Vienna rectifier has been adopted widely in electric vehicle charging system. However, the current zero-crossing distortions and the neutral-point (NP) balancing are a coupled issue. In order to overcome the intrinsic problem of the Vienna rectifier, a computationally efficient model predictive control (CE-MPC) is proposed. Firstly, the discrete mathematics model is built. Then in order to eliminate the current zero-crossing distortions, the space voltage vector diagram is divided into six sectors and some of the vectors are abandoned. Thirdly, the proper N- or P-type small vectors are selected to balance the NP voltage. Finally, the effectiveness of proposed method has been verified by the simulation.
Research on Multi-scenario Prediction and Assessment Methods for Regional Carbon Storage Based on Large Language Models
Authors: Feng Xiaoqi , Tao Peiyuan , Yao Peng
Published: December 2024

With the background of global climate change and carbon neutrality, the urgent requirement for more accurate estimates of carbon storage at the regional scale has been increasingly required for environmental issues. Due to subjective influences, traditional carbon storage prediction methods have poor prediction performance in a multi-scenario setting, with a lack of effective technical support. Considering the above-mentioned limitation, the novelty of the present study lies in its efforts to improve the accuracy and objectivity of carbon storage predictions on regional and urban scales using large language models. To this end, this work developed a carbon storage prediction simulation system with multi-scenario settings based on LLMs. It can be matched and retrieved by inputting regional information from national land cover and carbon stock databases in the system. It processes user-scenario settings, matches them with a simulation database, then selects proper driving factors and simulation parameters to generate multi-group scenario prediction results. These are estimated for carbon storage by means of InVEST software in comparison and optimization with historical data. According to the experimental results, the multi-scenario simulation based on LLM has a significant improvement in the accuracy of carbon storage prediction and a substantial reduction of subjective influences when setting the scenarios. This approach simulates more effectively the land use changes in future carbon storage, using better accuracy and stability than the traditional methods. The approach proposed in this study greatly extends the technical boundary for regional carbon storage assessment and introduces large language models that might provide an effective strategy for multi-scenario simulations. In such a way, that would be very useful to make future carbon storage predictions and provide optimization strategies for low-carbon urban planning in support of achieving carbon neutrality.
Stability analysis of freeway ramp traffic flow based on floating vehicle data
Authors: Xiao Feng , Tang Bangxiao , Pan Xiazhuo
Published: December 2024

Type A trumpet interchanges are a common form of interchange, facilitating the transfer of traffic between different routes. Frequent behaviors such as acceleration, deceleration, overtaking, and lane changing occur here, often leading to greater instability in traffic flow, making these areas prone to accidents. The article collects high-frequency trajectory point data of logistics trucks to analyze ramp operation characteristics, accurately reflecting the driving behavior of drivers. Implement the ramp and trajectory data matching using the Haversine model with consideration of buffer zones, selecting parameters such as speed, acceleration, and coefficient of variation to analyze the stability of ramp traffic flow. The research results indicate that the speed variation on Ramp A is relatively stable, while Ramp B experiences significant speed fluctuations in the first half of the minimum radius circular curve, particularly before and after the connection between the exit ramp and the roundabout ramp. Ramp C shows notable speed decreases and acceleration fluctuations.
The Application of Medical Information in Clinical Decision Support
Authors: Wang YiLang
Published: December 2024

With the deepening of the reform of the medical and health system, patients have put forward higher requirements for medical technology level and health service quality. Clinical Decision Support System (CDSS) utilizes computerized medical knowledge, combined with patient condition information, and utilizes inference engines to make inferential decisions, thereby helping healthcare professionals make correct decisions and improve their medical skills. Technological progress has driven the growth of CDSS, and its application in hospitals has shown significant results. It is not only a professional learning tool, but also capable of analyzing difficult problems, improving the quality of work for medical staff, and ultimately reducing medical errors. This paper proposes a medical information based CDSS to address issues such as rapid updates of medical knowledge, flexible clinical needs, and heterogeneous information systems among medical institutions. The aim is to improve the accuracy and efficiency of doctor diagnosis and treatment. This system provides doctors with fast and accurate diagnosis and treatment plans through a Clinical Data Center (CDR). The experimental results show that the designed system can achieve decision support for clinical diagnosis, treatment, rational drug use, hospital management, and other aspects, achieving the application effect of improving medical quality.
Ecological Dynamics of Lamprey Parasitism
Authors: Deng Jiawei , Zhang Rui , Cui Jiaming
Published: December 2024

Over the long course of evolution, the lamprey has developed a parasitic predatory strategy, rendering it atypical in most environments. This report investigates the impact of lampreys on ecosystems and their interactions with other organisms, with the primary objective of developing a dynamic model of the population and sex ratio of lampreys. At first, a population dynamics model simulates interactions between male and female lamprey groups, using Lotka-Volterra equations to depict the relationship between lampreys and their primary hosts, Lake Trout and Atlantic Salmon. Changes in the sex ratio reveal the effects of lampreys on the food chain and enable conclusions about ecosystem health based on Atlantic Salmon populations. Secondly, the lifespan of lampreys is studied to evaluate their advantages and disadvantages. A periodic function simulates their reproductive process, combined with a logistic function to analyze population fluctuations under different sex ratios. Eight years of data elucidate why lamprey populations rapidly increase and then stabilize. Thirdly, in the Great Lakes, lampreys are detrimental to the local ecosystem. The Gompertz model simulates the host-parasite relationship to assess benefits to smaller parasites, with Atlantic Salmon used to quantify water quality. The death rate, integrated into the logistic function of the salmon, provides a direct indicator. Additionally, sensitivity analysis examines differential changes in lamprey birth and death rates across various environments, adjusting the model for human impact on the ecosystem. Potential biases in birth and death rates are considered, testing the model's adaptability. Finally, the pros and cons of the models are critically analyzed.
Study on Anti-hepatocellular Carcinoma Active Ingredients and Mechanism of Prunella Spica-Figwort Root Based on Network Pharmacology
Authors: Niu Xinyao
Published: December 2024

Abstract. Objective: This paper aims to screen the target and mechanism of action of the "Prunella Spica-Figwort Root" combination against hepatocellular carcinoma based on network pharmacology. Methods: The active ingredients and related targets of Prunella Spica and Figwort Root are searched through the TCMSP database (http://tcmspw. com/tcmsp.php), and the target of hepatocellular carcinoma is obtained through the Genecards database (https://www.genecards.org/). Meanwhile, the target of Prunella Spica-Figwort Root acting on hepatocellular carcinoma is generated through their combination. GO function analysis and KEGG enrichment analysis are conducted via the Metascape database (https://www.metascape.org/gp/index.html #/main/step1). The String database (https://string-db.org) is used to construct protein-protein interaction (PPI) and screen the core target genes. Finally, the "drug-active ingredients-target" network is built through Cytoscape 3.7. 2. Results: In this study, 10 active ingredients and 70 hepatocellular carcinoma targets of "Prunella Spica-Figwort Root" are screened out.And got five core target genes: SRC, ESR1, KDR, MAPK14 and MAPK3 through the algorithm. According to GO enrichment results, "Prunella Spica-Figwort Root" may play a role against hepatocellular carcinoma through nuclear receptor activity, hormone response, regulation of defense response, and steroid metabolism. In addition, KEGG enrichment results proved that "Prunella Spica-Figwort Root" may regulate hepatocellular carcinoma through signaling pathways such as AMPK, Apelin, JAK-STAT, and CAMP. Conclusion: "Prunella Spica-Figwort Root" can regulate multiple target genes of hepatocellular carcinoma through various active ingredients. In this study, the mechanism of "Prunella Spica-Figwort Root" against hepatocellular carcinoma is preliminarily discussed, which provides a basis for future research.
Collective Behavior of COMSOL-Based Microrobot Swarms in Various Fluids
Authors: Liu Hanwen
Published: December 2024

Microrobot swarms hold great promise for targeted therapy at cellular and molecular levels, particularly in hard-to-reach areas of the human body. While progress has been made, most research remains confined to homogeneous fluid environments, overlooking the complex rheological properties of biological fluids. To address this, a fluid-structure coupling model using CFD technology was developed to study the collective behavior of spherical microrobot swarms in Newtonian, shear-thickening, and shear-thinning fluids. By examining lateral and longitudinal swarm motion under varying wall conditions, the research uncovers the hydrodynamic interaction mechanisms, providing a foundation for optimizing microrobot swarm strategies in biomedical applications.
The Roles of CYFRA 21-1 in Cancer
Authors: Chen Chia-Jui
Published: December 2024

CYFRA21-1, The full name is cytokeratin 19 fragment, which is an acidic cell protein. In malignant epithelial tumors, activated proteases cause cellular degradation, and CYFRA21-1 is released in large quantities into body fluids. Clinical diagnosis is made by diagnosing abnormal levels of CYFRA21-1. This article reviews the clinical diagnostic function of CYFRA21-1 in lung cancer, colorectal cancer, bladder cancer and other cancers, and briefly introduces the biological mechanism of CYFRA21-1 as a tumor marker, which provides a basis for clinical diagnosis. Research has shown that CYFRA21-1, as a tumor biomarker, has no tissue or organ specificity, and it is particularly important to combine other indicators for clinical diagnosis. In addition, CYFRA21-1 can be used as a detection indicator for other non tumor diseases, but the specific mechanism lacks relevant research.
The effect of seed morphology on the biosynthesis of transglutaminase
Authors: Xi Xiangheng , Sun Xinjie , Ma Yunzhe , Jiang Xinyue , Guo Yangzi , Liu Xinli
Published: December 2024

Transglutaminase (TGase) is a crucial enzyme, widely utilized in the food, pharmaceutical, and textile industries due to its ability to catalyze protein cross-linking reactions. Despite its significant industrial relevance, the effects of spore age and concentration on seed mycelium morphology and subsequent TGase synthesis have not been thoroughly investigated. Moreover, studies on the application of particle-enhanced cultivation techniques are also limited, such as using talc powder to regulate seed morphology and enhance enzymatic activity. This study aims to explore the effects of spore age, concentration, and the addition of talc powder as particles on TGase synthesis. By optimizing spore age and concentration, and precisely controlling seed morphology through particle-enhanced cultivation techniques, we obtained the optimal seed morphology for TGase production, namely free mycelial clumps. The optimal conditions for enzyme synthesis were found to be a spore age of 8 days, a spore concentration of 4.24×107 spores/mL, and a talc powder treatment concentration of 10 g/L. Under these conditions, the enzyme activity reached its peak value of 16.28±0.57 U/mL. These results indicate that precise control of morphology is key to maximizing TGase yield.
Review of EEG Signal Recognition and Classification
Authors: Liu Yilong
Published: December 2024

Electroencephalogram (EEG) signals are widely used in neuroscience and clinical medicine, which are non-invasive, real-time and low-cost. However, their complexity and noise interference limit their application in brain stripe recognition, emotion recognition, motor imagination, and epilepsy detection. This paper aims to explore the application of EEG signals in different fields, analyzing the role of deep learning in EEG signal classification and recognition tasks, so as to propose the challenges faced by current technologies and future development. Through the literature review, the definition and analysis of EEG signals are summarized, and its applications in brain striate recognition, emotion recognition, motor imagination and epilepsy detection are discussed in detail. Meanwhile, this paper analyzes the contributions to improving classification accuracy, feature extraction and model generalization ability. Finally, the research status of four sub-fields is discussed, and their research prospects are prospected. With the technology advancement and in-depth interdisciplinary cooperation, EEG signal processing technology will make greater contributions to human health and scientific and technological development.
Thermodynamic analysis and optimization of carbon capture system
Authors: Zhang Chao , Qin Feng , Li Dashu , Ming Hongfang , Gao Yutong , Cai Zixuan , Yang Wenbao
Published: December 2024

With the acceleration of the global industrialization process, the problem of greenhouse gas emissions is becoming more and more serious. Carbon dioxide is one of the most important greenhouse gases, and carbon capture technology can make an important contribution to the mitigation of climate change. In this paper, Aspen Plus is used to establish a numerical model for thermodynamic analysis of carbon dioxide capture system, and the thermal efficiency and exergic efficiency of system equipment under different conditions are studied. The results show that: When the inlet flue gas temperature of the absorber tower was 35℃, exergic efficiency and exergic efficiency reached the best, while the efficiency of the supplementary liquid cooler decreased with the increase of the outlet temperature. The efficiency of the liquid-rich heater and the liquid-poor cooler decreases with the increase of the inlet and outlet temperature, and the system runs in a better state when the flue gas temperature and the absorber temperature are between 30℃ and 40℃. Exergic efficiency of equipment decreased with the increase of temperature, so research results are of great significance for improving the efficiency of carbon capture system.
Advancements in Rehabilitation Therapy and Imaging for Functional Movement Disorders
Authors: Chen Anyou
Published: December 2024

This article reviews the latest advancements in rehabilitation therapy and imaging research for functional movement disorders. As an important subtype of functional neurological disorders, functional movement disorders are recommended to be treated through a multidisciplinary approach, in which rehabilitation therapy plays a significant role in improving patients' motor control abilities and enhancing their ability to perform daily living activities. Rehabilitation therapies include physical therapy, non-invasive neural stimulation, and occupational therapy, among others. These comprehensive methods aim to precisely enhance patients' functional recovery. Additionally, with the continuous development of imaging techniques such as fMRI, diffusion tensor imaging, and PET, as well as advancements in analytical methods like morphometric analysis and fiber tractography, the neurobiological mechanisms of functional movement disorders have been thoroughly explored, providing crucial technical support for differential diagnosis, therapeutic evaluation, and prognosis prediction. The review aims to provide theoretical foundations for precision rehabilitation therapy and in-depth research on the pathophysiological mechanisms of functional movement disorders.
Intelligent Diagnostic System for Starter Motor Testers Based on Fuzzy Neural Networks
Authors: Zhang Dunli
Published: December 2024

In response to the multiple and ambiguous faults characteristic of starter motor testers, a fault diagnosis expert system based on fuzzy neural networks has been proposed. This design leverages the strengths of neural networks, fuzzy control, and expert systems. Neural networks diagnose faults in testing equipment, while fuzzy membership functions represent the degree of these faults. The system offers fast inference speed and strong fault tolerance. A control program was developed using Matlab, featuring a new user-friendly human-machine interface to enhance usability and operational ease of the system.