bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Seismic Performance of Prefabricated Double-Column Piers by Finite Element Analysis
Authors: Yin Shujun , Zhang Qiang , Fei Wen , Zhou Mi
Published: December 2024

Prefabricated assembled bridge piers have certain differences in their mechanical behavior under seismic loads due to the existence of joints, which limits their development and application in high-intensity seismic areas compared to cast-in-place bridge piers. This paper focuses on the structural design of prefabricated double-column piers that are connected by grouting sleeves at both the top and bottom joints. Based on this, grouting sleeve joint specimen pullout tests were designed and implemented to investigate the effect of grouting defects on their mechanical properties. Furthermore, finite element analysis was conducted on the designed pier model using ABAQUS software. The results indicate that defects with a length of 2.5 times the diameter of the reinforcement are sufficient to cause a change in the damage pattern of the member. Under cyclic horizontal loads, the damage initially occurs at the top and bottom of the piers and eventually the plastic hinges forms at the joints. The contact surface between the bottom of the piers and the seat grout layer undergoes opening behavior. The failure modes of two columns exhibit a certain degree of symmetry. Compared to the prefabricated double-column pier with a stirrup ratio of 0.55%, the load-carrying capacity improves slightly for the piers with stirrup ratios of 0.86% and 1.24%. When the axial compression ratio is 0.05 and 0.1, the load-displacement curves are almost identical. When the axial compression ratio is 0.2, the descent phase of the load-displacement curve of the prefabricated double-column pier is slightly more apparent, but the overall difference is small. It can be concluded that the influence of the stirrup ratio on the seismic performance of this double-column pier type is greater than that of the axial compression ratio.
Driving Behavior Analysis Near the Entrance of Highway Tunnels Using Naturalistic Trajectory Data
Authors: Yue Weijie , Wang Guojun , Xu Guilong , Yang Zhen
Published: December 2024

The entrance of highway tunnels has been reported as cash black spots due to changing driving environment. This study aims to understand driving behavior characteristics near the entrance of highway tunnels and identify risk mechanism. High-field video with 4K definition near the entrance of a highway tunnel in Yunnan province was obtained by an aerial drone. Then we employed video identification software to extract detailed naturalistic trajectory of vehicles. The motion information (e.g. acceleration, velocity) of vehicles near the entrance of highway tunnel was analyzed. The results showed that the speeds present a significant fluctuation near the entrance. The speed of cars is higher than that of trucks. By contrary, the decoration rate shows inverse characteristic. The changing behaviors also involves complex driving task due to the preceding low-speed truck. Drivers with excessive psychological pressure are prone to involve lager speed fluctuation, leading high crash risk. The current paper provides a good insight to understand the relationship between driving behaviors and risk mechanism near entrance of the highway tunnels.
Enhance the Understanding for the Permanent Deformation of Subgrade Clay: Considering the Characteristics of Traffic Load
Authors: Fu Wei , Shen Luo , Zhang Zhe
Published: December 2024

This study delves into the permanent deformation of subgrade soil by conducting triaxial shear tests and cyclic loading dynamic triaxial tests on compacted clay. These tests account for varying moisture content, stress levels, and loading characteristics, thereby revealing the evolution of permanent deformation in subgrade soil and enabling the construction of a predictive model. In a subgrade condition, the stress-strain relationship within the experimental clay exhibits a hardening tendency, with no evidence of strain softening. Moreover, an increase in moisture content and a decrease in confining pressure lead to reduced shear failure strength. The amplification of permanent deformation in the soil specimen can be attributed to the increase in cyclic deviatoric stress amplitude and the decrease in confining pressure, with the hygroscopic damage effect further exacerbating this deformation. Furthermore, both prolonged load durations and reduced intermittent times play a role in exacerbating the permanent deformation observed in the soil specimen. Notably, the impact of the intermittent time exhibits a critical threshold, beyond which the permanent deformation stabilizes. Utilizing the test findings, a comprehensive mechanical-empirical model has been developed. This model succinctly captures the influence of various factors on the permanent deformation of subgrade soil, including cyclic loading times, shear strain, constraint effect, hygroscopic damage effect, and the impact of both load duration and intermittent times. Verification indicated that this model possesses high predictive accuracy and broad applicability.
Characterization of Carbon Fluxes and Drivers of Large-Scale Photovoltaic Development in Deserts
Authors: Liu Yingjie , Wu Wei , Chen Hang , Lu Gang , Ye Deli , Ma Chao , Ren Lei , Zhou Xiaode
Published: December 2024

In recent years, due to the excessive use of fossil fuels has led to a gradual increase in greenhouse gas concentrations, in order to avoid the continued deterioration of the global climate, countries have gradually begun to rise the trend of the use of green and clean energy, photovoltaic power generation by virtue of its non-polluting, sustainable and other advantages in China is in the high-speed stage of development, which is in the province of Qinghai, by virtue of its vast geographical area, sparsely populated, and other advantages in the national photovoltaic installed capacity continues to be in the leading position in consecutive years. The province of Qinghai has been in the leading position in terms of installed PV capacity for years. This study takes the photovoltaic park in Gonghe County, Qinghai Province, as the research object, and continuously monitors the carbon flux in the study area based on the vorticity correlation observation system, analyzes the characteristics of the changes in carbon flux and its influencing factors under different time scales in the study area, and clarifies the influencing mechanism of the environmental factors on the carbon flux. The following results were obtained: the annual totals of NEE (net ecosystem carbon exchange), Re (ecosystem respiration), and GPP (gross primary productivity of ecosystems) in the Qinghai Republic PV Park were –60.56, 171.15, and 231.72 gC-m–2, respectively, during the period of June 2021 to May 2022, and the study area in general showed a “carbon sink”.
Analysis of Tidal Energy Resources in Zhoushan Sea
Authors: Jin Weikang , He Qianqian
Published: December 2024

Zhoushan sea has rich tidal energy resources, but has not been widely developed, this paper is based on the high resolution ocean numerical model SCHISM (Semi-implicit Cross-scale Hydroscience Integrated System Model) to simulate the tidal currents in Zhoushan sea for a period of one year, and through the data of four tidal stations and one tide station to validate the model simulation results, the validation results are good, and can provide good feedback on the characteristics of the simulated area tidal currents. The data from four tidal stations and one tide station are used to validate the model simulation results, and the validation results are good, which can provide good feedback on the characteristics of tidal currents in the simulated area. Through the simulation results, the tidal energy resources in Zhoushan Sea are systematically analyzed. The results show that the areas with the fastest average and maximum tidal currents in Zhoushan waters are Gushan Navigational Gate, Xihoumen Waterway, Luotou Waterway and Guan Waterway, and the areas with the highest energy density are also located in these areas, which are considered to have high tidal current energy power generation potentials.
Investigation on Indoor Thermal Comfort of University Classrooms in Transitional Season in Severe Cold Region
Authors: Ma Fusheng , Chen Xuanqin , Tang Yu
Published: December 2024

Good indoor thermal comfort is beneficial to improve teaching. In order to study the thermal comfort characteristics of naturally ventilated classrooms in universities in severe cold regions during autumn, thermal parameters of 9 classrooms in a university building in Shenyang were measured in October 2023. The indoor temperature range of 9 sample classrooms was 17.03°C∼26.84°C, and the indoor relative humidity range was 29.45%∼68.68%. During the measurement period, a questionnaire survey was conducted on the thermal sensation and satisfaction of 252 students. The proportion of students who feel the temperature is comfortable is highest when the average indoor temperature is between 24°C∼26°C. The measured and predicted thermal neutral temperatures for students are 24.12°C and 22.28°C, respectively. The acceptable temperature range for 80% and 90% of students was 19.12°C∼26.71°C and 21.66°C∼24.17 °C, respectively. This study provides references for the evaluation and design of thermal comfort of university classrooms in severe cold region.
Influence of Seaweed Polysaccharide-Based Materials on Soil Infiltration and Erosion Processes
Authors: Li Hao , Li Jianming , Sun Baoyang , Yan Zhiwei
Published: December 2024

The loss of soil and water in sloping farmlands has become a significant focus in soil and water conservation. This study examines the impact of varying concentrations of seaweed polysaccharide-based materials (SA-01) on runoff characteristics and sediment production in sloping farmland, using simulated rainfall experiments. Furthermore, the intrinsic mechanism of SA-01 in regulating soil erosion on slopes is elucidated through an analysis of soil disintegration rates and permeability coefficients. Results showed: (1) Soil disintegration rate decreased by 66.1% to 92.8% with the application of different concentrations of SA-01 compared to the control group. (2) The effect of slope on runoff generation did not exhibit significant differences. However, the runoff rate on the slope with a 1.00% concentration of SA-01 was twice that of the control group, and the runoff rate on the slope with concentrations ranging from 0.50% to 1.00% exceeded that of the control group. (3) The stable sediment yield rate after the application of SA-01 ranged from 0.38 to 0.67 times that of the control group. As the application concentration increased, there was a notable decrease in cumulative sediment production, leading to sediment reduction benefits amounting to 72.21% to 94.84%. The findings suggest that seaweed polysaccharide-based materials have substantial impacts on the regulation of farmland erosion. These results can establish a theoretical basis for future research on novel materials for preventing and controlling soil erosion in sloping farmland.
Numerical Simulation on the Mechanical Properties of Negative Poisson’s Ratio Anchor Bolts Under Impact Load
Authors: Wu Xunyu , Cheng Peng , Yu Yalin , Chen Jiahao , Li Yishuang , Wang Yining
Published: December 2024

To elucidate the mechanical transmission along Negative Poisson’s Ratio (NPR) anchor bolts under the influence of impact loads and to analyze their impact resistance characteristics, this study employs numerical modelling method to investigate the effects of impact loads on NPR bolts. Initially, a finite element model for the NPR bolt is constructed, followed by transient analysis under impact loads of 1 kJ, 5 kJ, and 10 kJ to explore the stress distribution pattern of the NPR bolt. The findings indicate that NPR bolts effectively absorb impact energy and maintain structural integrity even under high impact levels without breakage or dislodgement. The stress transmission mechanism under impact loads on NPR bolts is characterized by a reciprocating decay transmission pattern between the anchorage end and the free end, which is crucial for understanding the resistance capabilities of anchor bolts to impact loads.
Bibliometric Analysis of Driver’s Behavior on Signalized Intersections
Authors: Rifai Andri Irfan , Isradi Muhammad , Prasetijo Joewono , Immanuel Gabriel
Published: December 2024

A bibliometric analysis of 1000 publications were obtained from the Publish and Perish application from 2000-2023, which is then processed in VOSviewer to display the connection and relevance between publications. This research produced 826 journal articles, 128 proceedings articles, 29 book chapters, 6 posted content, 3 datasets, 3 others, 2 components, 2 reports, and 1 book. In addition, the most frequently used keyword is driver, and 2019 was the year with the most publications, 109. In conclusion, research on driver’s behavior on signalized intersections is still relevant and can be used for research discussion.
Analysis of Chaotic Dynamics and Improved Prediction Accuracy for Passenger Flow in Chengdu Metro During COVID Pandemic
Authors: Liu Jie , Lai Shiyang
Published: December 2024

This study aims to investigate the chaotic characteristics of subway passenger flow during the COVID pandemic in order to improve the prediction accuracy of passenger flow. The research focuses on the daily traffic and passenger flow growth at Chengdu subway stations, using the maximum Lyapunov index to determine the temporal characteristics of chaos. Additionally, the study compares the prediction accuracy of passenger flow growth using an ARIMA model and a Volterra model. Furthermore, a Granger causality test is conducted to examine the relationship between chaos and the prediction results. The findings reveal that chaos accounts for 96.85% of the time series of daily passenger flow growth during COVID, and the overall prediction error of the Volterra model is 23.37% lower than that of the ARIMA model. Causal analysis demonstrates that chaos is an important factor impacting prediction accuracy.
Enhancing Urban Traffic Management Through Machine Learning Prediction Models for Sensor-Less Roads
Authors: De Souza Oliveira Thamires , Di Grande Sarah , Berlotti Mariaelena , Cavalieri Salvatore , Torrisi Vincenza , Calabrò Giovanni , Inturri Giuseppe
Published: December 2024

The process of urbanization on a global scale has generated a significant increase in metropolitan populations, which in turn brings with it a series of challenges for the management of transport infrastructure. In this context, the importance of accurate traffic flow prediction in urban traffic management systems stands out, with the city of Catania being used as the focus of this study. However, predicting traffic flow faces challenges, such as the high costs associated with acquiring extensive real traffic data, particularly when installing sensors on every road is not feasible. To address the issue of missing traffic flow data on roads without sensors, this study proposes an innovative approach based on machine learning, using the Long Short-Term Memory architecture and incorporating both sensor and floating car data. The results obtained are thoroughly analyzed and discussed, highlighting the ability of the LSTM model to provide accurate predictions of urban traffic, even with a limited amount of data available. These findings highlight the relevance of employing advanced machine learning techniques in the efficient management of urban mobility, aiming to improve the quality of life in cities and deal more effectively with the challenges of contemporary urban traffic.
Reinforcement Learning-Based Traffic Light Control Using a Clipped Double Deep Q-Learning Algorithm
Authors: Jia Yanzhao , Lu Jianlin , Görges Daniel
Published: December 2024

This paper presents the results of intelligent traffic light management (TLM) which improves the traffic efficiency via optimally controlling the green lights’ time interval and selecting the traffic phases. As the control problem is stochastic and difficult to be modeled accurately, model-free reinforcement learning (RL) is applied in this work. To stabilize the training process and mitigate the overestimation issue of conventional deep Q-learning based RL methods, we developed an RL algorithm with double deep Q-network (DQN) and a clipping function for the TLM problem with a discrete action space. The advantage of this clipped version of double DQN over other Q-learning-based algorithms is demonstrated in this work. Furthermore, the performance of RL-based TLM is compared with both fixed-time and adaptive rule-based TLM by using PTV Vissim which is a multi-modal traffic simulation software as the testing platform in this work.
Multi-Agent Simulation Scheme for Cooperative Connected Automated Mobility Based on ROS and Carla
Authors: Yagüe-Cuevas David , Paz-Sesmero María , Marín-Plaza Pablo , Smith Stephen , Sanchis Araceli , María-Armingol José
Published: December 2024

The current scarcity of viable simulation solutions for cooperative and connected driving research is one of the main reasons to start dedicating efforts to generate robust validation environments. Most current simulators focus on the egocentric perspective of the automated vehicle, with cooperative information being either non-existent or difficult to access. If soon the industry migrates towards connectivity and cooperation paradigms, it will then be necessary to build simulation environments to validate the cooperative behaviors that arise from the interactions between traffic agents. This work focuses on the possibilities offered by Carla simulator integrated with ROS framework for multi-agent simulation and arbitration capabilities between intelligent infrastructure and automated connected vehicles. By combining a wide variety of methodologies, the solution proposed in this work allows generating an effective validation framework for coordinated and autonomous cooperative driving.
Pipeline for Modeling Urban Traffic Density as the Data Basis for Micrologistics and Autonomous Delivery Robots: Project MoVMi
Authors: Münst Philipp , Damer Lara , Meier Selina , Graef Marcel , Vosswinkel Rick
Published: December 2024

This paper presents a framework for modeling urban traffic density to support micrologistics using automated guided vehicles (AGV), more specifically autonomous delivery robots (ADR). The project MoVMi integrates survey data, pedestrian simulations using SUMO, and AGV interaction modeling in CARLA to optimize last-mile delivery in urban environments. Our approach addresses challenges such as congestion and access restrictions, and balances routing efficiency with delivery time windows. The framework’s adaptability allows application in various cities globally, enhancing urban freight transportation and sustainability. Future work will refine models with real-time data and extend applicability to diverse urban settings.
Dynamic Emergency Vehicle Prioritization in a Connected Urban Environment: A Rule-Based Approach
Authors: Adnane Ayoub , Harkat Yacine
Published: December 2024

Efficient and rapid management of Emergency Vehicles (EVs) such as ambulances, fire trucks, and police vehicles is crucial for saving lives and protecting property. Road intersections often become bottlenecks, hindering the swift movement of EVs despite their priority. This paper proposes an Emergency Vehicle Priority Management (EVPM) system leveraging IoV, 5G connectivity, GPS, and V2X communication. The system employs a rule-based algorithm and real-time data to dynamically adjust traffic signals and vehicle lanes, minimizing disruptions to general traffic while prioritizing EVs. Simulations conducted using the SUMO traffic simulator and real-world traffic data from TAPASCologne demonstrate significant improvements in travel times, waiting times, and overall traffic flow efficiency. Specifically, the EVPM system achieved reductions in average travel times by 367s, waiting times by 31s/km, waiting counts by 9, and time loss by 40s/km, which correspond to an improvement of 27.18%, 60.91%, 59.42%, and 53.54% respectively. These results highlight the potential of the proposed system to enhance urban traffic management and emergency response efficiency.
Traffic Speed Forecasting in Congested Conditions: Evaluation and Improvements
Authors: Jagadeesh George Rosario , Thia Kai Xin
Published: December 2024

Traffic forecasts are especially valuable in congested conditions for travelers and traffic managers. While deep learning-based forecasting models perform impressively on data from mostly normal traffic conditions, their performance in congested conditions has not been adequately examined and improved upon. Towards addressing this gap, we first present and apply a method for determining the spatial and temporal extents of congestion episodes in a traffic speed dataset. We evaluate two deep learning models and a baseline model on data within the congestion extents and analyze the results. Subsequently, we propose and evaluate two simple modifications related to the input data features, which improve the prediction accuracy of a state-of-the-art model in congested conditions. Overall, this study indicates the need for more targeted research efforts towards developing robust prediction models that perform well in congested conditions.
Large Language Models and Foundational Ontologies
Authors: Garbacz Paweł
Published: December 2024

Large language models are capable of translating natural language texts into context-free grammar languages. The paper presents an initial assessment of whether such models can be used to produce ontological theories that formalise natural language descriptions of certain situations. More specifically speaking, I will focus on translating a small set of natural language descriptions of some situations of ontological interests into a fixed formal ontological framework. The model I use will not be trained or fine-tuned for this purpose but prompted. In order to build the appropriate prompts I will take advantage of the formalisations from the 17th volume of the Applied Ontology journal, where six examples of such situations were formalised within the context of seven upper-level formal ontologies.
Interpreting Texts and Their Characters
Authors: Sanfilippo Emilio M. , Masolo Claudio , Bottazzi Emanuele , Ferrario Roberta
Published: December 2024

Research in Digital Humanities calls for computational systems to document, compare, and analyze interpretations of cultural artifacts such as literary texts. These systems are intended to support scholars, critics, and students by facilitating access to existing analyses of texts, identifying similarities and divergences between interpretations, and more. We propose an approach for documenting interpretations of literary characters, grounded in the empirical practices of literary interpretation to align closely with experts’ methods. To achieve this, we remain neutral regarding the ontological status of characters, instead relying on formal approaches based on linguistics. We demonstrate how our approach can analyze relations between names of fictional characters across texts and authors, bridging discussions in analytic philosophy about identity with the interests of literary scholars.
Ontological Analysis of Money
Authors: Mizoguchi Riichiro , Borgo Stefano , Kitamura Yoshinobu
Published: December 2024

Money is enigmatic. Despite extensive investigation to date, conflicting theories persist regarding its nature. Examples include views that “Money is a physical object,” “Money is an abstract concept,” and “Money is institutional status,” among others. This paper aims to unravel this mystery by engaging in an ontological discourse on the essence of money and constructing a three-layer model that comprises (i) the representation (legitimacy) layer, (ii) the role layer, and (iii) the property layer. In the representation layer, official state-issued objects such as banknotes are examined through the lens of the representation theory. In the role layer, these objects act as monetary role holders, referred to as monetary objects, by playing the monetary role which is inherently social in nature. The three fundamental properties/functions of money—serving as a unit of economic value, being exchangeable with commodities, and capable of being stored—are inherent in monetary role, and hence in monetary objects. However, these properties/functions remain latent until they are possessed by agents. The property layer elucidates that money is a contingent property of the owner of monetary objects, who can engage in economic activities by harnessing/actualizing these three properties/functions. In summary, our ontological theory of money posits Money as a property, Monetary objects holding the Monetary role, whose player is the Legitimate representing thing issued by the authority. For instance, a freshly minted 20 Euro banknote is a legitimate representing thing, transitioning into a monetary object upon holding the monetary role within the economic context. Its institutional/causal power becomes operative upon ownership by an agent. Our theory adopts a monistic perspective rather than a dualistic one, facilitated by the above nuanced distinctions made among entities pertaining to “money.” Discussion about how our theory works for resolving some of the current issues is presented together with a justification for the observation that money virtually has use value in addition to exchange value.
A Textual Syntax and Toolset for Well-Founded Ontologies
Authors: Coutinho Matheus L. , Almeida João Paulo A. , Sales Tiago Prince , Guizzardi Giancarlo
Published: December 2024

Diagrammatic and textual languages differ significantly with respect to the experience they offer to language users. While diagrammatic languages leverage visual variables to improve communication and problem solving, textual languages facilitate significantly a number of tasks including version control, model editing, model merging, parsing, etc. In this paper, we explore the design of a textual language for UFO-based ontologies, whose constructs mirror those of the OntoUML language. The language is supported by a rich VS Code-based editor, supporting (semantically-motivated) syntax verification, syntax highlight, autocomplete, and full integration into the OntoUML server ecosystem. A package manager is also offered to support ontology modularization and reuse, drawing inspiration from software package managers. Such functionality is currently not available to languages such as (Onto)UML and Semantic Web languages such as OWL.
Know Your Exceptions
Authors: Sacco Gabriele , Bozzato Loris , Kutz Oliver
Published: December 2024

Defeasible reasoning is a kind of reasoning where some generalisations may not be valid in all circumstances, that is general conclusions may fail in some cases. Various formalisms have been developed to model this kind of reasoning, which is characteristic of common-sense contexts. However, it is not easy for a modeller to choose among these systems the one that better fits its domain from an ontological point of view. In this paper we first propose a framework based on the notions of exceptionality and defeasibility in order to be able to compare formalisms and reveal their ontological commitments. Then, we apply this framework to compare four systems, showing the differences that may occur from an ontological perspective.
Fiat Surfaces in the Basic Formal Ontology
Authors: Rabenberg Michael , Ceusters Werner
Published: December 2024

The Basic Formal Ontology (BFO) class continuant fiat boundary and its subclasses, including fiat surface, are not heavily axiomatized; they have elucidations, not definitions; and the meanings of these elucidations are poorly captured by the relevant BFO axioms. This paper is an effort to make progress in these respects for fiat surface. We identify a range of desiderata for a BFO-conformant view of fiat surface, argue that the GitHub does not satisfy them, argue that the view of fiat surfaces in Arp et al. (2015) does a better job, and supplement that view in ways that do a still better job. Our discussion allows us to, inter alia, present a number of axioms relevant to our topic worthy of consideration for inclusion in BFO or dependent ontologies in specific domains and for specific purposes.
A Low-Cost Microwave Sensor Based on Complementary Circular Spiral Resonator
Authors: Zhang Xingyun , Ren Qunting , Wang Tiexing , Bai Yang
Published: December 2024

We propose a microwave sensor on the basis of complementary circular spiral resonator (CCSR) for substrate determination and thickness identification in this paper. The developed sensor consists of a microstrip transmission line and CCSR. By taking advantage of High Frequency Structure Simulator (HFSS), the designed device operates at 2.9 GHz with high Q factor. Through simulation, a strong electric field is generated besides CCSR, making this region very susceptible to the transformation in nearby dielectric substrate. When different substrates to be tested are put on sensing area, resonance frequencies change greatly. The designed device can also be used for thickness determination of a particular substrate. Two parabolic functions are calculated and fitted to evaluate relative permittivity of unknown material to be tested and the thickness of FR4 substrate. The sensor has many advantages, such as low cost, easy operation, high sensitivity and robustness, making it popular in substrates dielectric characterization.
Measurement of Refractive Index of Seawater by Double Triangular Prism Method and Its Error Analysis
Authors: Luan Zhongqi , Wang Lina , Qu Bing
Published: December 2024

The refractive index of seawater is one of the important optical properties of the air-sea interface, which is of great significance to the study of ocean optics, ocean dynamics, and air-sea interaction. The refractive index of seawater refers to the ratio of the propagation speed of light waves in a vacuum to that in seawater. In this paper, the refractive index of seawater is measured quickly and accurately by a spectrophotometer combined with the double triangular prism method. By comparing with the measured data of the Abbe refractometer, the measured data of this experimental method are accurate and reliable, which can be extended to the rapid measurement of the refractive index of other liquids by simple instruments. The double-prism method for rapid measurement of seawater refractive index is to use the existing simple equipment to measure the liquid refractive index. It provides accurate optical parameters for investigating the optical properties of the ocean or the propagation law of light in the sea and provides accurate optical parameters for studying marine optics.
A Review of Research Progress of Low-Field Nuclear Magnetic Resonance Technology in the Detection of Aquatic Products
Authors: Jiang Yuxi , Zhao Bangzhe , Feng Bochun , Yang Zunfu , Wang Xiangyang , Liu Ying , Zhang Xin , Luan Zhongqi
Published: December 2024

During the processing of aquatic products, the detection of water content is a very important indicator. In this paper, by studying and analyzing the changes of water content in different aquatic products processing by low field nuclear magnetic resonance technology, it is found that it can provide information on the kinetic behavior of water molecules, the content and distribution of components, the storage process and the preservation method. Therefore, low-field nuclear magnetic resonance technology has broad application prospects in the field of aquatic product processing and research, and can provide important scientific basis for the quality control and improvement of aquatic products.
Analysis of Force on Stable Floating Water Surface of a Disc with a Central Hole
Authors: Liu Ying , He Qihuai , Gao Ran , Feng Yunwei , Li Xuanrong , Luan Zhongqi
Published: December 2024

In order to study the phenomenon of a circular disc with a hole at its center being able to float on the water surface under vertical inflow, this article conducted experimental measurements and analysis. We tested discs with diameters of 15 cm and 20 cm and center hole diameters of 0.5 cm, and compared the flow velocity and flow rate ranges of discs with and without a concave center hole. The results showed that discs with a concave center hole had a larger range of flow velocity and flow rate than those without, and as the diameter of the disc increased, so did the flow velocity and flow rate. We also measured the pressure difference between the upper and lower surfaces of the disc through experiments and found that the larger the diameter of the disc, the greater the pressure difference between the upper and lower surfaces. In addition, we found that the additional pressure force generated by the water leap was very small and could be ignored. Studying these thin plate phenomena is of great significance for fields such as aviation, navigation, and civil engineering.
Campus Environment Monitoring and Management System Based on SDN Architecture
Authors: Bao Yiqin , Xu Wenbin , Bao Yulu
Published: December 2024

Campus has always been the main battlefield for students’ learning, and environmental monitoring on campus, including indoor and outdoor environmental monitoring, has always been a focus of attention. Therefore, in the context of promoting sustainable development in the country, campus environment monitoring and management are imperative. Design a campus environment monitoring and management system based on SDN (Software Defined Network) architecture to address issues such as large campus area and multiple monitoring points. Environmental sensing data is transmitted to the gateway through ZigBee wireless network, and the gateway data is uploaded to the cloud server through SDN network. Finally, the mobile APP connects to the server through 5G network to achieve real-time monitoring and display of environmental data. The innovation lies in the fact that software defined network technology not only makes the network more intelligent as a pipeline, but also allows software and hardware to be placed according to current needs, achieving customization of software and hardware networks.
Research on Prediction of ChiNext and Blue-Chip Stocks in the Chinese Stock Market Based on Random Forest Algorithm
Authors: Xie Guoli , Yang Lei , Tian Liwei , Li Sijin , Lai Jiacheng
Published: December 2024

This research focuses on the predictive analysis of ChiNext and blue-chip stocks in the Chinese stock market, employing the random forest model to conduct in-depth research on the stock trends of these two sectors. In the experimental process, we randomly selected six stocks from each sector as research samples, aiming to explore whether there are significant differences in prediction results between the two. Through rigorous experimental verification and data analysis, we found no significant difference in the predictive effect of random forest models between ChiNext and blue-chip stocks.
Design and Implementation of Public Security Semi-Structured Data Reasoning Based on Knowledge Graph Technology
Authors: Liang Yi , Yao Yunxin , Che Lixing
Published: December 2024

In the era of big data, public security departments are faced with the challenge of efficiently leveraging vast amounts of semi-structured data to support law enforcement and investigative activities. This paper focuses on the integration of knowledge graph technology and large language models to enhance the processing and reasoning capabilities in the public security domain. By constructing a public security knowledge graph, the paper outlines key processes, including data preprocessing, entity extraction using a BiLSTM-CRF-based Named Entity Recognition (NER) approach, and knowledge storage. Furthermore, a reasoning question-and-answer system is developed, combining the rich relational data of the knowledge graph with the powerful reasoning abilities of large language models, fine-tuned for public security tasks. This integrated system enables complex logical reasoning, supporting public security personnel in tasks such as crime prediction, case analysis, and suspect relationship mapping, ultimately improving decision-making quality and operational efficiency.
Improved Particle Swarm Optimization for Rayleigh Wave Frequency-Velocity Spectrum Inversion
Authors: Yan Haitao , Le Zhao , Shi Xueming , Ai Hanbing , Zhang Wei , Yang Yonglong , Ye Hui , Jin Wentao
Published: December 2024

Rayleigh wave inversion plays a role in near-surface exploration at high resolution. Conventional dispersion curve inversion methods require to extract the dispersion curves manually, and their success depends heavily on personal experience. Mode misidentification is easily occurred in frequency-velocity spectrum with complex geological conditions, which increases the difficulties to extract the accurate dispersion curve. Full waveform inversion (FWI) is a prospective approach to resolve complex stratigraphic inversions avoiding manual dispersion curve extraction. However, the current application of FWI is limited by several factors, such as cycle-skipping issues, seismic source estimation, dependence on the initial model. Frequency-velocity spectrum inversion (FVSI) is another potential approach for solving complex near-surface problems. The objective function of FVSI is the residual of frequency-velocity spectrum, which makes it unnecessary to extract the dispersion curve manually. In this paper, we present an improved particle swarm optimization (IPSO) for Rayleigh wave FVSI. Compared with traditional particle swarm optimization (PSO), we reduce the swarm size in the later iterations, which enhances the inversion efficiency; we replace the invalid particles with valid ones, which improves the global search capability. The application of both synthetic and field data has achieved good results, demonstrating the applicability and practicability of the IPSO method.
Logistic Map Learning for Gross Domestic Product Prediction
Authors: Lim Feng Yann , Kek Sie Long
Published: December 2024

This paper discusses using a logistic map to learn and predict gross domestic product (GDP). First, we introduce a least squares optimization problem. The aim is to minimize the differences between the model and the GDP. After that, we derive the necessary conditions for optimality. Second, we update the model parameters in the logistic map through the recursion equations. We denote the updating parameters as the learning process to tackle the GDP changes for learning and prediction. We summarize the calculation procedure as an iterative algorithm. Third, we study the GDPs of the United States and Ukraine for illustration. The simulation result shows that the prediction solution fits the GDP appropriately, and the small mean square error value validates the efficiency of the proposed algorithm. Therefore, the computational algorithm based on a logistic map for predicting the GDP is satisfactory.
Generative AI Privacy Issues
Authors: Zlateva Plamena , Velev Dimiter
Published: December 2024

Generative AI (GenAI) is a powerful tool capable of creating text, images, music and other forms of content, but its use raises serious questions about privacy and data protection. Models trained on large amounts of data often include private or sensitive information, leading to risks of data leakage and inadvertent reproduction of confidential data. The main challenges include the collection of data from different sources, the storage of personal information in the models and the possibility of generating content that reveals sensitive information. Regulatory and other data protection laws play an important role in limiting these risks, imposing requirements for transparency, consent and the right to erasure of personal data. The aim of the paper is to additionally explore the key privacy risks in GenAI, analyze the applicable regulatory requirements, and propose strategies for managing personal data that comply with modern laws and ethical standards.
Study of Series Synchronization Scenario from Quasi-Periodic to Hyperchaotic State in an Optic Fiber Laser Link Network
Authors: Yan Senlin , Lu Yuzheng , Ma Ligang
Published: December 2024

This paper reports series synchronization scenario from quasi-periodic to hyperchaotic state in an optic laser link network. An erbium-doped fiber three-ring laser is used to as a driver of the link network while its dynamics is controlled to guide dynamic variety of laser link by adjusting the laser parameters. An erbium-doped fiber dual-ring laser is used as a repeater node of the link, and an erbium-doped fiber single-ring laser is used as an end node of the link. An optic “cross-drive-feedback” technology is applied to obtain series synchronization. Our work illustrates how the driver laser to guide the repeater node laser and the end node laser to form the series synchronization laser link network and how to obtain a series synchronization in a lots of dynamics states. The realizations of series synchronization link in different physical dynamic states, such as perord-4, period-7, period-9, hyperchaos, are discussed by controlling the erbium-doped fiber three-ring laser. The dynamic path of the series synchronization link to hyperchaos from quasi-period is numerically illustrated. The synchronization differences of node lasers are numerically analyzed. Our study has an important reference value for fiber laser transmission technology, fiber laser synchronization network and optic hyperchaos synchronization application.
The Course Reform Practice of “Electronic Circuit Design” in Higher Vocational Colleges Based on the Training of Engineering Practice Innovation Ability
Authors: Luo Fang , Wang Xiaohong , Liu Ying , Zhang Weixiong , Sun Zijiao , Zhang Zhiliang
Published: December 2024

To enhance students’ proficiency in solving engineering problems, foster their awareness of innovation and entrepreneurship, the main focus is on developing skills in understanding, drawing, and innovating drawings. This approach closely aligns with the advancements in science and technology as well as industrial changes by incorporating real-world PCB design engineering cases, PCB process specifications requirements, and electronic product design. Through a blended learning approach combining online and offline teaching methods, we aim to integrate standards and expand engineering practice cases to establish a ‘three-level, four-ability’ model for practical training and innovation. Furthermore, we strive to instill an entrepreneurial mindset along with high standards of craftsmanship throughout the teaching implementation. Ultimately, this comprehensive approach effectively enhances students’ ability to apply theoretical knowledge into practice while solving practical engineering problems and fostering innovative thinking. It lays a solid foundation for nurturing talents specialized in circuit board design.
Performance Analysis of the Sidelink Broadcast Channel for 5G NR Systems in the Unlicensed Band
Authors: Maltsev Alexander , Serunin Igor , Pudeev Andrey , Hwang Jin-Yup , Huh Joong Kwan , Lee Sang-Wook
Published: December 2024

One of the new research challenges of recent times has been the extension of 5G New Radio-Unlicensed (NR-U) to Sidelink. The essence of the challenge was to develop a fair coexistence of Sidelink devices with other unlicensed band users. One such challenge was to modify the physical channels and signals of Sidelink to meet channelization requirements in the unlicensed band. The interlaced structure for allocating physical channel resources was a solution to this challenge. In this paper, we provide a detailed performance analysis of Physical Sidelink Broadcast Channel (PSBCH) for an unlicensed band with interlaced structure. The performance is investigated in the frequency-selective channel with different parameters. It is shown that the use of interlaced structure can provide a performance gain for some channel parameters. The present analysis in the time-varying channel has shown performance gains due to the Doppler diversity of the channel provided by the structure of the broadcast channel. The results of the correlation between the channel estimation accuracy and the observed performance improvement due to the Doppler diversity of the channel are demonstrated.
Towards Quantum Telecommunications: Point of View of the Implementation Task of the Experimental Quantum Key Distribution Backbone Deployment Project
Authors: Opmane Inara , Balodis Rihards , Bulavs Arvils , Celms Edgars , Kalnina Elina , Kucinska Rita , Medenis Arturs , Kozlovics Sergejs , Petrucena Krisjanis , Rencis Edgars , Viksna Juris
Published: December 2024

The team of authors implements an experimental Quantum Key Distribution backbone within the framework of the European Quantum Communication Infrastructure initiative. Two network segments are being established: a government-physically protected infrastructure segment for restricted information communications and a public segment in finance and health. The authors share their experience of developing such a network. This experience can be useful to the reader of the article if a similar task is carried out. Also, the provided information can be used to estimate the amount of work, and the finances and time required for it.
A Fully Homomorphic Encryption Scheme Based on Fibonacci Recursive Matrix
Authors: Bao Yiqin , Xu Wenbin , Bao Yulu
Published: December 2024

Homomorphic encryption is widely used in fields such as data aggregation, secure multi-party computation, and federated learning, and is one of the main technologies for addressing data privacy protection. However, existing homomorphic encryption schemes require noise reduction operations to be performed before the noise level of the ciphertext increases to a certain scale, as the noise level of the ciphertext increases with computation. To address these issues, this paper uses the characteristics of third-order Fibonacci to design a homomorphic encryption scheme based on Fibonacci matrix (FM-HE), which solves the noise problem of ciphertext. The experimental results show that under the same environment, the proposed homomorphic encryption scheme (FM-HE) significantly reduces the decryption time compared to typical homomorphic encryption algorithms such as Paillier and CKKS, and it also has fault tolerance performance.
A Knowledge Graph-Based Approach to Anti-Smuggling Intelligence Analysis
Authors: Ding Zhibang , Zhao Pengbo , Liang Shuangjie , Wang Xinmeng
Published: December 2024

Customs anti-smuggling intelligence as an important link in the anti-smuggling chain. From the massive case information in time to find effective intelligence clues, all kinds of anti-smuggling case data for automated intelligence extraction, high efficiency according to the case of time, location and other elements of the case thread; anti-smuggling case intelligence under the environment of big data for intelligent correlation, anti-smuggling cases in the intelligence of intelligent research and analysis. This is especially important for the construction of China’s public security informatization in recent years, through big data technology, entity and attribute as nodes, relationship as the edge, the establishment of a semantic network based on the structure of the knowledge graph, through the integration of a large number of anti-smuggling intelligence knowledge graph structure, and further to realize the semantic relationship network of potential criminal individuals or groups predicted to stimulate the value of the existing public security big data to create a combination of the experience of public security business A knowledge graph-based anti-smuggling intelligence analysis method.This paper introduces the following aspects: firstly, we construct the spatio-temporal based expression of anti-smuggling intelligence elements to realize the logical relationship representation of anti-smuggling cases. At the same time, we construct an information extraction model combining deep learning and conditional random field driven by elements of anti-smuggling intelligence under big data environment. In addition, we further design and propose the association method of anti-smuggling intelligence based on graph convolutional neural network, so as to construct the three-dimensional anti-smuggling intelligence research and judgment model based on space and time.