Propagation Prediction Model Research Articles

DMHANT: DropMessage Hypergraph Attention Network for Information Propagation Prediction.

Predicting propagation cascades is crucial for understanding information propagation in social networks. Existing methods always focus on structure or order of infected users in a single cascade sequence, ignoring the global dependencies of cascades and users, which is insufficient to characterize their dynamic interaction preferences. Moreover, existing methods are poor at addressing the problem of model robustness. To address these issues, we propose a predication model named DropMessage Hypergraph Attention Networks, which constructs a hypergraph based on the cascade sequence. Specifically, to dynamically obtain user preferences, we divide the diffusion hypergraph into multiple subgraphs according to the time stamps, develop hypergraph attention networks to explicitly learn complete interactions, and adopt a gated fusion strategy to connect them for user cascade prediction. In addition, a new drop immediately method DropMessage is added to increase the robustness of the model. Experimental results on three real-world datasets indicate that proposed model significantly outperforms the most advanced information propagation prediction model in both MAP@k and Hits@K metrics, and the experiment also proves that the model achieves more significant prediction performance than the existing model under data perturbation.

An effective drift-diffusion model for pandemic propagation and uncertainty prediction

Predicting pandemic evolution involves complex modeling challenges, typically involving detailed discrete mathematics executed on large volumes of epidemiological data. Making them physics based provides added intuition as well as predictive value. Differential equations have the advantage of offering smooth, well-behaved solutions that try to capture overall predictive trends and averages. In this paper, the canonical susceptible-infected-recovered model is simplified, in the process generating quasi-analytical solutions and fitting functions that agree well with the numerics, as well as infection data across multiple countries. The equations provide an elegant way to visualize the evolution of the pandemic spread, by drawing equivalents with the similar dynamics of a particle, whose location over time represents the growing fraction of the population that is infected. This particle slides down a potential whose shape is set by model epidemiological parameters such as reproduction rate. Potential sources of errors and their growth over time are identified, and the uncertainties are mapped into a diffusive jitter that tends to push the particle away from its minimum. The combined physical understanding and analytical expressions offered by such an intuitive drift-diffusion model sets the foundation for their eventual extension to a multi-patch model while offering practical error bounds and could thus be useful in making policy decisions going forward.

Parameter analysis and prediction of crack propagation for thermoplastic composite pipes under lateral pressure

ABSTRACT The research investigates the process of crack propagation in thermoplastic composite pipes (TCPs) with initial racks subjected to lateral pressure, through a combination of numerical simulations and experimental studies. In the numerical analysis, a TCP is subjected to lateral pressure, considering parameters such as initial crack length, initial crack angle, and lateral displacement. This analysis employs the extended finite element method (XFEM). The results reveal that crack propagation predominantly occurs perpendicular to the fibre winding angle. Moreover, it is observed that longer initial cracks result in lower crack initiation loads, while the final extent of crack propagation increases with the initial crack length. The experimental study considers three distinct initial crack angles and verifies the conclusions drawn from the numerical simulations. Furthermore, a crack propagation prediction model is developed that combines particle swarm optimization (PSO) with backpropagation (BP) neural networks. The predictive results demonstrate a high level of accuracy.

A Comprehensive Review of Radio Signal Propagation Prediction for Terrestrial Wireless Communication Systems

The subject of study known as radio propagation prediction refers to predicting the behaviour and characteristics of radio waves as they propagate through the atmosphere. It is a basic element of all wireless communication systems, including satellite communications, broadcasting and cellular networks. While there is no study covers all the techniques used to predict the radio signal prediction, this study presents a review of the propagation prediction models between 2018-2023 used for terrestrial wireless communication systems; the classic empirical models were briefly explained, followed by the deterministic propagation models that have been developed using ray-tracing with deep and machine learning techniques. Recent studies on an improvement of the computational efficiency and accuracy of propagation prediction models were also reviewed, in addition to an overview of some of the traditional statistical models. Furthermore, some of the new techniques in propagation prediction were described. The results of these studies explain that techniques using ray tracing produce better results than other techniques.

A Comprehensive Review: Radio Propagation Prediction Models for 5G and Beyond

A Comprehensive Review: Radio Propagation Prediction Models for 5G and Beyond

High-temperature thermal fatigue of Ni3Al-based single crystal alloy affected by wall thickness and peak temperature

Complex thin-walled structures are commonly employed in turbine components to improve cooling efficiency. However, thin-walled blades are prone to thermal fatigue failure due to the high thermal stress induced by frequent and abrupt temperature fluctuations during duty cycles. The thermal fatigue behavior of Ni3Al-based single crystal alloy with thin-walled structure during 25 °C ↔ 1000 °C/1100 °C was investigated by combining thermal fatigue tests and finite element simulation. The thermal fatigue demonstrated a significant thickness debit effect, with thermal fatigue property dropping as peak temperature and wall thickness increased. Wall thickness had a more pronounced effect than peak temperature. Visible slip traces were found on the wall thickness surface, and their density was positively correlated with wall thickness. Further investigation revealed that thermal fatigue is primarily driven by the octahedral slip system ({111}<110>), with significant contributions from (111)[101‾] and (1‾1‾1)[101]. Simulations of thermal stress distribution and J-integral at the crack tip during crack initiation and propagation stages indicated that wall thickness influences thermal fatigue properties by affecting thermal stress concentration at the notch and crack tip. Higher peak temperatures increased thermal stress and reduced yield strength, thus diminishing thermal fatigue performance. A thermal fatigue crack propagation prediction model was constructed incorporating Paris' law and the J-integral. The model revealed an unfavorable correlation between material Cj/ nj and both peak temperature/wall thickness, indicating that both factors adversely affect thermal fatigue resistance.

A dynamic prediction model of landslide displacement based on VMD–SSO–LSTM approach

Addressing the limitations of existing landslide displacement prediction models in capturing the dynamic characteristics of data changes, this study introduces a novel dynamic displacement prediction model for landslides. The proposed method combines Variational Mode Decomposition (VMD) with Sparrow Search Optimization (SSO) and Long Short-Term Memory (LSTM) techniques to formulate a comprehensive VMD–SSO–LSTM model. Through the application of VMD, the method dissects cumulative displacement and rainfall data, thereby extracting distinct components such as trend, periodicity, and fluctuation components for displacement, as well as low-frequency and high-frequency components for rainfall. Furthermore, leveraging Gray Correlational Analysis, the interrelationships between the periodic component of displacement and the low-frequency component of rainfall, as well as the fluctuation component of displacement and the high-frequency component of rainfall, are established. Building upon this foundation, the SSO–LSTM model dynamically predicts the interrelated displacement components, synthesizing the predicted values of each component to generate real-time dynamic forecasts. Simulation results underscore the effectiveness of the proposed VMD–SSO–LSTM model, indicating root-mean-square error (RMSE) and mean absolute percentage error (MAPE) values of 1.2329 mm and 0.1624%, respectively, along with a goodness of fit (R2) of 0.9969. In comparison to both back propagation (BP) prediction model and LSTM prediction model, the VMD–SSO–LSTM model exhibits heightened predictive accuracy.

A prediction model for rumor user propagation behavior based on sparse representation and transfer learning

This paper introduces a prediction model rooted in sparse representation and transfer learning, with the primary objective of predicting user behavior during rumor propagation. Users' behavior is dynamic, and rumor, rumor-refuting, and rumor-promoting messages interact dynamically, according to the model. Firstly, this paper proposes to compensate for the low performance of propagation prediction models due to data sparsity at the beginning of rumor topics' lifes. Transfer learning is used to compensate for the data sparsity problem at the beginning of the topic. To map the rumor topic space effectively, a low-rank dense vectorization algorithm based on sparse representation is proposed. Finally, to mine the potential impact of multiple types of information on users, a model based on three-party game theory is constructed. It considers the complex interaction between rumor, rumor-refuting, and rumor-promoting information in the propagation of rumor. Additionally, this paper develops a model of dynamic user behavior prediction using Rumor-Attention-Mechanism-Graph-Attention (RAM-GAT) to predict rumor propagation. Experiments demonstrate that our model can effectively mine the interaction influence between multiple types of information. It can accurately predict user behavior when initial data is insufficient. In addition, rumor propagation patterns and trends are revealed.

Three-Dimensional Ray-Tracing-Based Propagation Prediction Model for Macrocellular Environment at Sub-6 GHz Frequencies

This paper presents a 3D ray-tracing model using geometrical optics and the uniform theory of diffraction in radio channel characterizations of macrocellular environments. On the basis of the environmental information obtained from a digitized map, the model is effectively applied. A technique considering multiple reflections and diffractions through the ray path classification is utilized in this model. Ray paths belonging to each ray category are determined using different methods. The proposed model is justified (the prediction accuracy of the model is better than 6.5 dB) with measurement data for the two scenarios and can provide reliable theory as a basis for radio wave propagation prediction and network planning in urban macrocellular environments.

The effect of fibre orientation on fatigue crack propagation in CFRP: Finite fracture mechanics modelling for open-hole configuration

The demand to capture translaminar crack growth under fatigue loading scenarios led this work contribution to carry out the Finite Fracture Mechanics (FFM) method in fatigue damage growth and the application of the Paris model to generate the translaminar damage propagation prediction. The purpose of this study is to analyse the effect of fibre orientation on translaminar crack propagation rate using the FFM model, which includes cycle damage increment estimation and fractographic analysis. The results confirm the feasibility of FFM in predicting crack growth and estimating life under cyclic loading. However, C-scan analysis and the revised crack propagation direction are critical in determining the realistic crack length, considering adhesive failure along the fibre direction. Additionally, this work contribution is also related to the application of the Paris model (based on dL/dN vs ΔK) to generate the translaminar damage propagation prediction model. The most dominant damage mechanism was the splitting pattern, which changed the aspect of failure for each laminate architecture as a function of fibre orientation. The laminate with multidirectional fibre orientation exhibited higher resistance to translaminar crack propagation due to the growth of splitting and delamination in multiple directions. The fibre orientation changed the propagation path, which influenced the fracture toughness and crack propagation rate behaviour.

An SVR-Based Radio Propagation Prediction Model for Terrestrial FM Broadcasting Services in Beijing and Its Surrounding Area

An SVR-Based Radio Propagation Prediction Model for Terrestrial FM Broadcasting Services in Beijing and Its Surrounding Area

Blockage Effects of Road Bridge on mmWave Channels for Intelligent Autonomous Vehicles

Vehicular communication and sensing technologies are key to enabling 6G Intelligent Autonomous Transportation Systems (IATS). With the introduction of massive sensors and artificial intelligence (AI) fusion applications, IATS is needed to support data transmission rates up to 10 Gb/s. Millimeter-wave (mmWave) technology has attracted extensive attention owing to abundant spectrum resources, which can support the timely transmission of massive data. However, performance degradation of mmWave due to signal blockage has become one of the critical technical challenges. Road bridges as one of the common obstacles in urban scenarios, which has severe blockage effects on communication links. Therefore, this paper comprehensively studies the impact of road bridge blockage effects on mmWave vehicle-to-infrastructure (V2I) links and proposes an empirical model that can accurately characterize the bridge blockage effect. First, we use a self-developed mmWave channel sounder to carry out channel measurements on typical urban roads. Measurement results indicate that a maximum extra propagation loss of up to 23 dB is caused by road bridges. In addition, to address the deficiencies of existing propagation prediction models, the Single Road Bridge (SRB) model is proposed in this work. This model reveals for the first time the extra propagation loss caused by the road bridge to the channel. Compared with existing models, the SRB model can make the mean absolute error (MAE) and root mean square error (RMSE) within 5 dB. The proposed SRB model is of great value for accurately simulating real-world road bridge blockage events when designing future IATS.

The evaluation of EMI characteristics of 4G-IMT system on FAST around its RQZ

Mobile communication base stations have become ubiquitous with the popularization and development of mobile communication services. Although the Chinese government has designated a radio quiet zone (RQZ) specifically for Five-hundred-meter Aperture Spherical radio Telescope (FAST), the 4G International Mobile Telecommunications (4G-IMT) system is still heavily used to meet the communication needs around the RQZ. To assess the Electromagnetic Interference (EMI) caused by 4G-IMT base stations on the performance of FAST, this paper employs the International Telecommunication Union (ITU) deterministic radio wave propagation prediction model. The interference of 4G-IMT base stations on FAST is evaluated based on relevant parameters, such as the frequency used by both FAST and the base station, in conjunction with the FAST threshold protection requirements. The results indicate that 47.73 % of the data exceed the radio astronomy protection threshold of FAST. In addition, a field experiment was conducted to verify the accuracy of the radio wave propagation prediction method used. The root-mean-square error between the predicted value and the measured value was found to be 6.94 dB, supporting the accuracy of the predicted results. The findings of this study can help suppress interference from mobile communication services around the RQZ of FAST and optimize the electromagnetic environment around FAST.

Risk Propagation Mechanism and Prediction Model for the Highway Merging Area

The merging area is one of the most accident-prone areas on highways. After an accident occurs, the risk will propagate along the main road over a certain range and time. Therefore, the study of the propagation mechanism of accident risk will help to quantify the driving risk in this region. An effective risk prediction model is important for improving traffic control measures in this specific area. In this study, simulation experiments were conducted in SUMO (Simulation of Urban Mobility) to obtain the accident and risk propagation data in merging areas. Firstly, the Gaussian plume model was optimized for the merging area situation to determine and divide the impact range of the accidents. Then, different accident scenarios in the merging area and downstream were simulated with different input flow rates to study the time and speed of risk propagation in the three-level affected areas. Finally, LSTM (long short-term memory) and RNN (recurrent neural network) models were built to predict the accident risk in the merging area. The results showed that the LSTM model had higher accuracy. This study provides an innovative insight into the propagation process of merging area accidents. It is of benefit to the development of post-accident control measures.

Measurement, data analysis and modeling of electromagnetic wave propagation gain in a typical vegetation environment.

This paper takes the specific environment covered by vegetation as the research object, carries out modeling and analysis, takes the large-scale fading model of wireless channel as the basis of data processing, researches the transmission law of electromagnetic wave in a typical vegetation environment, which can be divided into four situations. The signal attenuation in each case is theoretically derived and numerically simulated. From the view point of supporting vegetation environment channel, the large-scale channel measurement system is built to meet the actual needs, such as bandwidth, frequency, vegetation coverage, etc. the final vegetation environment channel model under the large-scale fading model is obtained. The results show that the path gain of four scenarios respectively are 81.3 dB, 36.5 dB, 1.6 dB, 1.5 dB, the value of path gain index is within the range of 2~3.5, four scenarios shadow fading standard deviation values are 7.1, 4.8, 10.1, 9.2, reflects the change of received power at the point caused by random factors such as reflection, absorption and scattering. In addition, the proposed channel model improves the gain about 15% compared with the tradition SUI model within vegetation coverage scene. The design process of the proposed model is carried out in the order of "studied the existing foundation → analyzed the existing problems → proposed the optimization scheme → simulation and verification results → actual measurement system". The advantage of paper's method is that, when the signal frequency, transceiver distance, antenna height and vegetation environment characteristic parameters are given, the statistical analysis results of wireless channel data are obtained. The purpose of the proposed work establishes a signal propagation prediction model under the vegetation environment, realizes a theoretical basis for channel simulation, and provides the basis of anti-fading technologies.

Coupled coordination of water resources–economy–ecosystem complex in the Henan section of the Yellow River basin

Abstract This study investigates the coordination of factors in the three coupled systems of water resources, economy and ecosystem. The study data were economic, social, environmental and water resource indicators of the Henan section of the Yellow River basin from 2000 to 2019. Data were analyzed using a model of coupling coordination, grey relational analysis and a combined sparrow search algorithm–back propagation (SSA-BP) prediction model. Evaluation, analysis and prediction of the water resources–economy–ecosystem complex were undertaken. The results show that: (1) the degree of coupling coordination of the water resources–economy–ecosystem complex in the Henan section of the Yellow River basin showed an increasing trend that reached 0.8105 in 2019, which indicates good coordination; (2) the water resources subsystem had the greatest influence on the overall degree of coupling coordination, followed by the economic and natural environment subsystems; and (3) the SSA-BP model predicted that the degree of coupling coordination would remain good for the next six years and reach 0.8333 in 2025. To ensure the sustainability of expected rapid economic development, Henan Province must increase the utilization efficiency of water resources, strengthen environmental protection, and coordinate development in the region.

Experimental study and simulation for unfrozen water and compressive strength of frozen soil based on artificial freezing technology

Artificial ground freezing is widely used in various projects, such as mine, bridge, and subway tunnel construction and emergency repair, and has achieved good engineering results. The freezing strength and freezing effect based on artificial freezing technology have become a hot research topic. However, the relationship between the freezing strength and unfrozen water content under extremely low-temperature conditions has not been sufficiently studied. In view of this, a series of laboratory experiments were carried out. The unfrozen water characteristics and compressive strength of soil at ultralow temperatures from 0 °C to −80 °C were studied by nuclear magnetic resonance (NMR). The applicability of a genetic algorithm-back propagation (GA-BP) prediction model to unfrozen water content was discussed. The results show that there is a very small amount of unfrozen water present at −80 °C, with a content of approximately 0.1%. The GA-BP network prediction model can be used to predict the unfrozen water content of soil at very low temperatures. The compressive strength of frozen soils is significantly affected by temperature and unfrozen water content. The compressive strength of frozen soil is directly proportional to the absolute value of the soil temperature. The unfrozen water content and compressive strength follow a power function. As the temperature decreases, the stress–strain relationship of frozen soil is of the elasto-plastic type. Interestingly, soil at temperatures down to −80 °C show significant brittle failure. This paper supplements the basic theory of extremely-low-temperature frozen soil and quantitatively determines that the unfrozen water content affects the frozen soil strength. These conclusions are highly significant and can provide a theoretical reference for practical engineering applications, especially artificial freezing engineering.

Fatigue Crack Propagation Prediction of Corroded Steel Plate Strengthened with Carbon Fiber Reinforced Polymer (CFRP) Plates.

The purpose of this study is to investigate the mechanism of improving fatigue performance and the estimation model of fatigue life for corroded steel plate strengthened with CFRP plates. A new two-stage fatigue crack propagation prediction model for the corroded steel plate strengthened with CFRP plates was proposed; moreover, the identification of critical rust pits and the equivalent method of initial cracks, and the calculation method of stress intensity factor (SIF) values at the crack tip were established. The accuracy of the proposed model was verified by comparing the predicted and tested fatigue life of the corroded steel plate strengthened with CFRP plates. Finally, the proposed two-stage crack propagation model was applied to carry out a parameter analysis to investigate the effect of weight loss rate, equivalent initial crack size, adhesive thickness, CFRP stiffness and CFRP prestress level on the fatigue crack propagation of the corroded steel plate strengthened with CFRP plates. Results showed that the maximum depth and the average width of the rust pits were suggested to be taken as the equivalent dimensions of the initial semi-elliptical surface crack for the fatigue crack propagation prediction of corroded steel plate strengthened with CFRP plates. Increasing the weight loss rate of the corroded steel plate, the initial crack size or the adhesive thickness would accelerate the crack growth and reduce the fatigue life, whereas increasing the stiffness or prestress level of the CFRP plate would significantly reduce the crack growth rate and increase the fatigue life. The smaller the initial crack size, the more sensitive the crack propagation life was to the variation of equivalent initial crack size. The influence of adhesive thickness on the fatigue life was limited and convergent, and the application of prestressing could significantly improve the utilization rate of CFRP materials and the fatigue strengthening effect of the corroded steel plate.

A Fractional-Order SIR-C Cyber Rumor Propagation Prediction Model with a Clarification Mechanism

As communication continues to develop, the high freedom and low cost of the communication network environment also make rumors spread more rapidly. If rumors are not clarified and controlled in time, it is very easy to trigger mass panic and undermine social stability. Therefore, it is important to establish an efficient model for rumor propagation. In this paper, the impact of rumor clarifiers on the spread of rumors is considered and fractional order differentiation is introduced to solve the problem that traditional models do not take into account the “anomalous propagation” characteristics of information. A fractional-order Susceptible-Infected-Removal-Clarify (SIR-C) rumor propagation prediction model featuring the clarification mechanism is proposed. The existence and asymptotic stability conditions of the rumor-free equilibrium point (RFEP) E0; the boundary equilibrium points (BEPs) E1 and E2 are also given. Finally, the stability conditions and practical cases are verified by numerical simulations. The experimental results confirm the analysis of the theoretical study and the model fits well with the real-world case data with just minor deviations. As a result, the model can play a positive and effective role in rumor propagation prediction.

An Accurate Maritime Radio Propagation Loss Prediction Approach Employing Neural Networks

The radio propagation loss prediction model is essential for maritime communication. The oceanic tropospheric duct is much more complicated than the atmospheric structure on land due to the rough sea surface influence, and it leads to difficulties in loss prediction. Classical radio wave propagation loss prediction models are either based on complicated electromagnetic wave theories or rely on empirical data. Consequently, they suffer from low accuracy and a limited range of application. To address this issue, a novel maritime propagation loss prediction approach is proposed, which fully exploits the data for training. In this new approach, 3D sea surface contour profiles are generated based on the Pierson–Moskowitz spectrum theory and the direction expansion formula Stereo Wave Observation Program (SWOP), by sweeping the parameters. The full-wave propagation procedure of radio signals over the sea surface is simulated by commercial EM analysis software CST Studio Suite. Based on the large quantity of simulated data, the BP neural network is employed to fit the radio propagation loss and obtain the sea surface radio wave propagation prediction model. Other classical machine learning methods are also compared to validate the proposed approach. Traditional empirical model construction relies on observation data. This approach, for the first time, proposed an automatic scheme which covers the whole procedure from the data generation to prediction model training. It avoids the requirements for on-site observation, as well as significantly decreases the cost of experiments. The application scope of the prediction model such as propagation distance range and working frequency range could be adaptive through adjustments for simulated sea size and simulated working frequency. This approach is validated to save 99% of prediction time in comparison with the full-wave simulations. The prediction model trained via our proposed method could obtain the coefficient of determination R2 which is over 0.92, demonstrating the superiority of this method.