This study explored a novel systemic community-based model for detecting and manag-ing people living with HIV/AIDS (PLWHA). Both quantitative and qualitative research methods were used in this study. A quantitative qu...This study explored a novel systemic community-based model for detecting and manag-ing people living with HIV/AIDS (PLWHA). Both quantitative and qualitative research methods were used in this study. A quantitative questionnaire investigation was conducted in a sample of 1192 subjects which were randomly selected from two areas with high HIV prevalence, Xiangfan City and Shiyan City of Hubei Province, China. Twenty-two medical and health service staffs were inter-viewed by semi-structured questionnaire focusing on awareness, status, problems, and suggestions about community-based Voluntary Counseling and Testing and Provider Initiated Testing and Coun-seling (VCT/PITC). And they were organized to discuss about the aforementioned issues in Xiangfan City and Shiyan City, respectively. Our results showed that the accessibility and availability of the general VCT/PITC were bad. About 28.3% had known and only 4.9% had made use of VCT/PITC. Developing community-based VCT/PITC had some special advantages that can overcome some ex-isting problems to remedy the aforementioned defects. We are led to conclude that, to maximize the availability and uptake rate of the VCT/PITC, we plan to detect PLWHA by developing the commu-nity-based VCT/PITC through 4 paths. Then we establish the community HIV health care center con-stituted of 8 sectors to provide an overall management. Thus, we can effectively detect and manage the PLWHA with a new systemic community-based model.展开更多
Neurostimulation remarkably alleviates the symptoms in a variety of brain disorders by modulating the brain-wide network. However, how brain-wide effects on the direct and indirect pathways evoked by focal neurostimul...Neurostimulation remarkably alleviates the symptoms in a variety of brain disorders by modulating the brain-wide network. However, how brain-wide effects on the direct and indirect pathways evoked by focal neurostimulation elicit therapeutic effects in an individual patient is unknown. Understanding this remains crucial for advancing neural circuit-based guidance to optimize candidate patient screening, pre-surgical target selection, and post-surgical parameter tuning. To address this issue, we propose a functional brain connectome-based modeling approach that simulates the spreading effects of stimulating different brain regions and quantifies the rectification of abnormal network topology in silico. We validated these analyses by pinpointing nuclei in the basal ganglia circuits as top-ranked targets for 43 local patients with Parkinson’s disease and 90 patients from a public database. Individual connectome-based analysis demonstrated that the globus pallidus was the best choice for 21.1% and the subthalamic nucleus for 19.5% of patients. Down-regulation of functional connectivity(up to 12%) at these prioritized targets optimally maximized the therapeutic effects. Notably, the priority rank of the subthalamic nucleus significantly correlated with motor symptom severity(Unified Parkinson’s Disease Rating Scale III) in the local cohort. These findings underscore the potential of neural network modeling for advancing personalized brain stimulation therapy,and warrant future experimental investigation to validate its clinical utility.展开更多
Increasing time-spent online has amplified users＇ exposure to tile tilreat oI miormanon leakage. Although existing security systems （such as firewalls and intrusion detection systems） can satisfy most of the securi...Increasing time-spent online has amplified users＇ exposure to tile tilreat oI miormanon leakage. Although existing security systems （such as firewalls and intrusion detection systems） can satisfy most of the security requirements of network administrators, they are not suitable for detecting the activities of applying the HTTP-tunnel technique to steal users＇ private information. This paper focuses on a network behavior-based method to address the limitations of the existing protection systems. At first, it analyzes the normal network behavior pattern over HTI＇P traffic and select four features. Then, it pres- ents an anomaly-based detection model that applies a hierarchical clustering technique and a scoring mechanism. It also uses real-world data to validate that the selected features are useful. The experiments have demonstrated that the model could achieve over 93% hit-rate with only about 3% false- positive rate. It is regarded confidently that the approach is a complementary technique to the existing security systems.展开更多
Mg alloys are extremely valuable in the automotive and aerospace industries because of their lightweight properties and excellent machin-ability.The applications in these industries necessitate the accurate prediction...Mg alloys are extremely valuable in the automotive and aerospace industries because of their lightweight properties and excellent machin-ability.The applications in these industries necessitate the accurate prediction of fatigue life under cyclic loading.However,this is challenging for many wrought Mg alloys owing to their pronounced plastic anisotropy.Conventional predictive methods such as the Coffin-Manson equa-tion require manual parameter adjustment for different conditions,thus limiting their applicability.Accordingly,a novel predictive model for low-cycle fatigue(LCF)life that combines machine learning(ML)with an energy-based physical model,referred to as the hybrid ML/E model,is proposed herein.The hybrid ML/E model leverages a substantial hysteresis-loop dataset generated from LCF tests on a rolled AZ31 Mg alloy to effectively predict fatigue life.The proposed approach addresses the inherent challenges of small fatigue datasets,hysteresis-loop perception,and algorithm selection.The hybrid ML/E model demonstrates superior predictive accuracy and robustness in various loading directions,based on validation against conventional methods.The integration of ML and physical principles offers a unified framework for the LCF life prediction of anisotropic materials and represents a significant advancement for industrial applications.展开更多
BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale c...BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.展开更多
The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine b...The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
Seismic resilience(SR)has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure,recover from,and adapt to seismic events.This study presents an entropy-based multicriter...Seismic resilience(SR)has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure,recover from,and adapt to seismic events.This study presents an entropy-based multicriteria approach for selecting optimal intensity measures(IMs)to assess SR of structures.Eight representative IMs,derived from time histories and response spectrum are evaluated.Incremental dynamic analysis is con-ducted on a reinforced concrete structure,using engineering demand parameters such as the maximum interstory drift and floor acceleration to generate fragility curves via a probabilistic seismic demand model.The optimal IMs are identified through a multi-criteria decision-making process,with scores calculated using the entropy weight method to incorporate factors such as efficiency,proficiency,and uncertainty based on infor-mation entropy.An effective SR framework is derived from fragility results.The findings indicate that peak ground velocity and spectral IMs are the most effective,while energy-related IMs underestimate SR.The study highlights the importance of optimizing IMs for more accurate seismic resilience assessments.The proposed entropy-based multi-criteria approach is shown to be both reliable and effective for selecting optimal IMs in this context.展开更多
The high-temperature deformation and dynamic recrystallization(DRX)behaviors of GH4698 superalloy were investigated via hot compression tests,and an improved unified dislocation density-based constitutive model was es...The high-temperature deformation and dynamic recrystallization(DRX)behaviors of GH4698 superalloy were investigated via hot compression tests,and an improved unified dislocation density-based constitutive model was established.The results indicate that with the temperature decreasing or the strain rate increasing,the flow stress increases and the DRX fraction decreases.However,as the strain rate increases from 1 to 10 s^(-1),rapid dislocation multiplication and deformation heat accelerate the DRX nucleation,which further increases the DRX fraction.Discontinuous DRX nucleation is the dominant DRX nucleation mechanism,and continuous DRX nucleation mainly occurs under low strain rates.For the developed improved unified dislocation density-based constitutive model,the correlation coefficient,average absolute relative error,and root mean square error between the measured and predicted stresses are 0.994,7.32%and 10.8 MPa,respectively.Meanwhile,the correlation coefficient between the measured and predicted DRX fractions is 0.976.These indicate that the developed model exhibits high accuracy in predicting the high-temperature deformation and DRX behaviors of GH4698 superalloy.展开更多
Non-Schmid(NS)effects in body-centered cubic(BCC)single-phase metals have received special attention in recent years.However,a deep understanding of these effects in the BCC phase of dual-phase(DP)steels has not yet b...Non-Schmid(NS)effects in body-centered cubic(BCC)single-phase metals have received special attention in recent years.However,a deep understanding of these effects in the BCC phase of dual-phase(DP)steels has not yet been reached.This study explores the NS effects in ferrite-martensite DP steels,where the ferrite phase has a BCC crystallographic structure and exhibits NS effects.The influences of NS stress components on the mechanical response of DP steels are studied,including stress/strain partitioning,plastic flow,and yield surface.To this end,the mechanical behavior of the two phases is described by dislocation density-based crystal plasticity constitutive models,with the NS effect only incorporated into the ferrite phase modeling.The NS stress contribution is revealed for two types of microstructures commonly observed in DP steels:equiaxed phases with random grain orientations,and elongated phases with preferred grain orientations.Our results show that,in the case of a microstructure with equiaxed phases,the normal NS stress components play significant roles in tension-compression asymmetry.By contrast,in microstructures with elongated phases,a combined influence of crystallographic texture and NS effect is evident.These findings advance our knowledge of the intricate interplay between microstructural features and NS effects and help to elucidate the mechanisms underlying anisotropic-asymmetric plastic behavior of DP steels.展开更多
In recent years,there has been a concerted effort to improve anomaly detection tech-niques,particularly in the context of high-dimensional,distributed clinical data.Analysing patient data within clinical settings reve...In recent years,there has been a concerted effort to improve anomaly detection tech-niques,particularly in the context of high-dimensional,distributed clinical data.Analysing patient data within clinical settings reveals a pronounced focus on refining diagnostic accuracy,personalising treatment plans,and optimising resource allocation to enhance clinical outcomes.Nonetheless,this domain faces unique challenges,such as irregular data collection,inconsistent data quality,and patient-specific structural variations.This paper proposed a novel hybrid approach that integrates heuristic and stochastic methods for anomaly detection in patient clinical data to address these challenges.The strategy combines HPO-based optimal Density-Based Spatial Clustering of Applications with Noise for clustering patient exercise data,facilitating efficient anomaly identification.Subsequently,a stochastic method based on the Interquartile Range filters unreliable data points,ensuring that medical tools and professionals receive only the most pertinent and accurate information.The primary objective of this study is to equip healthcare pro-fessionals and researchers with a robust tool for managing extensive,high-dimensional clinical datasets,enabling effective isolation and removal of aberrant data points.Furthermore,a sophisticated regression model has been developed using Automated Machine Learning(AutoML)to assess the impact of the ensemble abnormal pattern detection approach.Various statistical error estimation techniques validate the efficacy of the hybrid approach alongside AutoML.Experimental results show that implementing this innovative hybrid model on patient rehabilitation data leads to a notable enhance-ment in AutoML performance,with an average improvement of 0.041 in the R2 score,surpassing the effectiveness of traditional regression models.展开更多
With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learnin...With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learning methods.Therefore,in the process of reforming and developing higher education,it is essential to take digital technology empowering the optimization of the education industry as a breakthrough,focusing on five key areas:the construction of smart classrooms,the digital integration of teaching resources,the development of personalized learning support systems,the reform of online-offline hybrid teaching,and the intelligentization of educational management.This paper also examines the experiences,challenges,and shortcomings of typical universities in using digital technology to improve teaching quality,optimize resource allocation,and innovate teaching management models.Finally,corresponding countermeasures and suggestions are proposed to facilitate the smooth implementation of digital transformation in higher education institutions.展开更多
BACKGROUND The computed tomography(CT)-based preoperative risk score was developed to predict recurrence after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma(PDAC)in South Korea.However,w...BACKGROUND The computed tomography(CT)-based preoperative risk score was developed to predict recurrence after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma(PDAC)in South Korea.However,whether it performs well in other countries remains unknown.AIM To externally validate the CT-based preoperative risk score for PDAC in a country outside South Korea.METHODS Consecutive patients with PDAC who underwent upfront surgery from January 2016 to December 2019 at our institute in a country outside South Korea were retrospectively included.The study utilized the CT-based risk scoring system,which incorporates tumor size,portal venous phase density,tumor necrosis,peripancreatic infiltration,and suspicious metastatic lymph nodes.Patients were categorized into prognosis groups based on their risk score,as good(risk score<2),moderate(risk score 2-4),and poor(risk score≥5).RESULTS A total of 283 patients were evaluated,comprising 170 males and 113 females,with an average age of 63.52±8.71 years.Follow-up was conducted until May 2023,and 76%of patients experienced tumor recurrence with median recurrence-free survival(RFS)of 29.1±1.9 months.According to the evaluation results of Reader 1,the recurrence rates were 39.0%in the good prognosis group,82.1%in the moderate group,and 84.5%in the poor group.In comparison,Reader 2 reported recurrence rates of 50.0%,79.5%,and 88.9%,respectively,across the same prognostic categories.The study validated the effectiveness of the risk scoring system,demonstrating better RFS in the good prognosis group.CONCLUSION This research validated that the CT-based preoperative risk scoring system can effectively predict RFS in patients with PDAC,suggesting that it may be valuable in diverse populations.展开更多
Various deep learning based methods have significantlyimpacted the realm of drug discovery.The development of deep learning methods for identifying novel structural types of active compounds has become an urgent chall...Various deep learning based methods have significantlyimpacted the realm of drug discovery.The development of deep learning methods for identifying novel structural types of active compounds has become an urgent challenge.In this paper,we introduce a self-supervised representation learning framework,i.e.,Geometry-based Bidirectional Encoder Representations from Transformers(GEO-BERT).GEO-BERT considers the information of atoms and chemical bonds in chemical structures as the input,and integrates the positional information of the three-dimensional conformation of the molecule for training.Specifically,GEO-BERT enhances its ability to characterize molecular structures by introducing three different positional relationships:atom-atom,bond-bond,and atom-bond.By benchmarking study,GEO-BERT has demonstrated optimal performance on multiple benchmarks.We also performed prospective study to validate the GEO-BERT model,with screening for DYRK1A inhibitors as a case.Two potent and novel DYRK1A inhibitors(IC_(50):<1μM)were ultimately discovered.Taken together,we have developed an open-source GEO-BERT model for molecular property prediction(https://github.com/drug-designer/GEO-BERT)and proved its practical utility in early-stage drug discovery.展开更多
Objective:To evaluate the use of infrared thermography technology for objective and quantitative syndrome differentiation and treatment in traditional Chinese medicine(TCM),specifically in patients with Chaihu Guizhi ...Objective:To evaluate the use of infrared thermography technology for objective and quantitative syndrome differentiation and treatment in traditional Chinese medicine(TCM),specifically in patients with Chaihu Guizhi Ganjiang Decoction syndrome.Methods:Data were collected from over 100 patients diagnosed with Chaihu Guizhi Ganjiang Decoction syndrome at Professor Li Leyu’s endocrinology clinic,Zhongshan Hospital of Traditional Chinese Medicine,Guangdong Province,between April 2021 and April 2022.Body surface temperature data were obtained using the MTI-EXPRO-2013-B infrared thermography system.Principal component analysis(PCA)was applied to differentiate temperature distribution characteristics between genders,and a neural network prediction model was constructed for syndrome diagnosis.Results:Infrared thermography effectively captured surface temperature characteristics of patients with Chaihu Guizhi Ganjiang Decoction syndrome.PCA identified one principal component with a variance explanation rate of 73.953%for females and two principal components with a cumulative variance explanation rate of 77.627%for males.The neural network model demonstrated high predictive performance,with an area under the ROC curve of 0.9743 for the training set and 0.9889 for the validation set.Sensitivity was 1,specificity 0.8636,precision 0.8846,accuracy 0.9333,and the F1 score 0.9388.Conclusion:Infrared thermography provides an innovative,objective,and quantitative method for syndrome differentiation and treatment in TCM.It represents a significant advancement in transitioning from traditional empirical approaches to modern,visualized,and precise diagnosis and treatment.This study underscores the potential of integrating advanced technologies in TCM for enhanced clinical application and modernization.展开更多
This article investigates virtual reality (VR)-based teleoperation with robustness against modeling errors. VR technology is an effective way to overcome the large time delay during space robot teleoperation. However,...This article investigates virtual reality (VR)-based teleoperation with robustness against modeling errors. VR technology is an effective way to overcome the large time delay during space robot teleoperation. However, it depends highly on the accuracy of model. Model errors between the virtual and real environment exist inevitably. The existing way to deal with the problem is by means of either model matching or robot compliance control. As distinct from the existing methods, this article tries to combine m...展开更多
A more general narrowband regular-shaped geometry-based statistical model(RS-GBSM) combined with the line of sight(LoS) and single bounce(SB) rays for unmanned aerial vehicle(UAV) multiple-input multiple-output(MIMO) ...A more general narrowband regular-shaped geometry-based statistical model(RS-GBSM) combined with the line of sight(LoS) and single bounce(SB) rays for unmanned aerial vehicle(UAV) multiple-input multiple-output(MIMO) channel is proposed in this paper. The channel characteristics, including space-time correlation function(STCF), Doppler power spectral density(DPSD), level crossing rate(LCR) and average fade duration(AFD), are derived based on the single sphere reference model for a non-isotropic environment. The corresponding sum-of-sinusoids(SoS) simulation models including both the deterministic model and statistical model with finite scatterers are also proposed for practicable implementation. The simulation results illustrate that the simulation models well reproduce the channel characteristics of the single sphere reference model with sufficient simulation scatterers. And the statistical model has a better approximation of the reference model in comparison with the deterministic one when the simulation trials of the stochastic model are sufficient. The effects of the parameters such as flight height, moving direction and Rice factor on the characteristics are also studied.展开更多
Scenario modelling and the risk assessment of natural disasters is one of the hotspots in disaster research. However, up until now, urban natural disaster risk assessments lack common procedures and programmes. This p...Scenario modelling and the risk assessment of natural disasters is one of the hotspots in disaster research. However, up until now, urban natural disaster risk assessments lack common procedures and programmes. This paper selects rainstorm waterlogging as a disaster to research, which is one of the most frequently occurring hazards for most cities in China. As an example, we used a small-scale integrated methodology to assess risks relating to rainstorm waterlogging hazards in the Jing＇an District of Shanghai. Based on the basic concept of disaster risk, this paper applies scenario modelling to express the risk of small-scale urban rainstorm waterlogging disasters in different return periods. Through this analysis of vulnerability and exposure, we simulate different disaster scenarios and propose a comprehensive analysis method and procedure for small-scale urban storm waterlogging disaster risk assessments. A grid-based Geographical Information System （GIS） approach, including an urban terrain model, an urban rainfall model and an urban drainage model, was applied to simulate inundation area and depth. Stage-damage curves for residential buildings and contents were then generated by the loss data of waterlogging from field surveys, which were further applied to analyse vulnerability, exposure and loss assessment. Finally, the exceedance probability curve for disaster damage was constructed using the damage of each simulated event and the respective exceedance probabilities. A framework was also developed for coupling the waterlogging risk with the risk planning and management through the exceedance probability curve and annual average waterlogging loss. This is a new exploration for small-scale urban natural disaster scenario simulation and risk assessment.展开更多
The Statistical Priority-based Multiple Access Protocol(SPMA)is the de facto standard for Tactical Target Network Technology(TTNT)and has also been implemented in ad hoc networks.In this paper,we present a non-preempt...The Statistical Priority-based Multiple Access Protocol(SPMA)is the de facto standard for Tactical Target Network Technology(TTNT)and has also been implemented in ad hoc networks.In this paper,we present a non-preemptive M/M/1/K queuing model to analyze the performance of different priorities in SPMA in terms of average packet loss rate and delay.And based on this queuing model,we designed a percentile scoring system combined with Q-learning algorithm to optimize the protocol parameters.The simulation results show that our theoretical model is closely matched with the reality,and the proposed algorithm improves the efficiency and accuracy in finding the optimal parameter set of SPMA protocol.展开更多
A simplified physically-based model was developed to simulate the breaching process of the Gouhou concrete-faced rockfill dam （CFRD）, which is the only breach case of a high CFRD in the world. Considering the dam he...A simplified physically-based model was developed to simulate the breaching process of the Gouhou concrete-faced rockfill dam （CFRD）, which is the only breach case of a high CFRD in the world. Considering the dam height, a hydraulic method was chosen to simulate the initial scour position on the downstream slope, with the steepening of the downstream slope taken into account; a headcut erosion formula was adopted to simulate the backward erosion as well. The moment equilibrium method was utilized to calculate the ultimate length of a concrete slab under its self-weight and water loads. The calculated results of the Gouhou CFRD breach case show that the proposed model provides reasonable peak breach flow, final breach width, and failure time, with relative errors less than 15% as compared with the measured data. Sensitivity studies show that the outputs of the proposed model are more or less sensitive to different parameters. Three typical parametric models were compared with the proposed model, and the comparison demonstrates that the proposed physically-based breach model performs better and provides more detailed results than the parametric models.展开更多
基金supported by a grant from the Global Fund(No. 2008-NGS-26)
文摘This study explored a novel systemic community-based model for detecting and manag-ing people living with HIV/AIDS (PLWHA). Both quantitative and qualitative research methods were used in this study. A quantitative questionnaire investigation was conducted in a sample of 1192 subjects which were randomly selected from two areas with high HIV prevalence, Xiangfan City and Shiyan City of Hubei Province, China. Twenty-two medical and health service staffs were inter-viewed by semi-structured questionnaire focusing on awareness, status, problems, and suggestions about community-based Voluntary Counseling and Testing and Provider Initiated Testing and Coun-seling (VCT/PITC). And they were organized to discuss about the aforementioned issues in Xiangfan City and Shiyan City, respectively. Our results showed that the accessibility and availability of the general VCT/PITC were bad. About 28.3% had known and only 4.9% had made use of VCT/PITC. Developing community-based VCT/PITC had some special advantages that can overcome some ex-isting problems to remedy the aforementioned defects. We are led to conclude that, to maximize the availability and uptake rate of the VCT/PITC, we plan to detect PLWHA by developing the commu-nity-based VCT/PITC through 4 paths. Then we establish the community HIV health care center con-stituted of 8 sectors to provide an overall management. Thus, we can effectively detect and manage the PLWHA with a new systemic community-based model.
基金supported by the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02050006)the National Natural Science Foundation of China (81571300, 81527901, 31771174, 81271518 and 81471387)+4 种基金the National Key R&D Program of China (2017YFC1310400)the Natural Science Foundation and Major Basic Research Program of Shanghai (16JC1420100)the support from Shanghai JiaoTong University School of Medicine Institute of Neuroscience Research Center for Brain Disordersthe Shanghai JiaoTong University K.C. Wong Medical Fellowship Fundfunded by the Michael J. Fox Foundation for Parkinson’s Research
文摘Neurostimulation remarkably alleviates the symptoms in a variety of brain disorders by modulating the brain-wide network. However, how brain-wide effects on the direct and indirect pathways evoked by focal neurostimulation elicit therapeutic effects in an individual patient is unknown. Understanding this remains crucial for advancing neural circuit-based guidance to optimize candidate patient screening, pre-surgical target selection, and post-surgical parameter tuning. To address this issue, we propose a functional brain connectome-based modeling approach that simulates the spreading effects of stimulating different brain regions and quantifies the rectification of abnormal network topology in silico. We validated these analyses by pinpointing nuclei in the basal ganglia circuits as top-ranked targets for 43 local patients with Parkinson’s disease and 90 patients from a public database. Individual connectome-based analysis demonstrated that the globus pallidus was the best choice for 21.1% and the subthalamic nucleus for 19.5% of patients. Down-regulation of functional connectivity(up to 12%) at these prioritized targets optimally maximized the therapeutic effects. Notably, the priority rank of the subthalamic nucleus significantly correlated with motor symptom severity(Unified Parkinson’s Disease Rating Scale III) in the local cohort. These findings underscore the potential of neural network modeling for advancing personalized brain stimulation therapy,and warrant future experimental investigation to validate its clinical utility.
基金Supported by the National Natural Science Foundation of China(No.61070185,61003261)the Knowledge Innovation Program of the Chinese Academy of Sciences(No.XDA06030200)
文摘Increasing time-spent online has amplified users＇ exposure to tile tilreat oI miormanon leakage. Although existing security systems （such as firewalls and intrusion detection systems） can satisfy most of the security requirements of network administrators, they are not suitable for detecting the activities of applying the HTTP-tunnel technique to steal users＇ private information. This paper focuses on a network behavior-based method to address the limitations of the existing protection systems. At first, it analyzes the normal network behavior pattern over HTI＇P traffic and select four features. Then, it pres- ents an anomaly-based detection model that applies a hierarchical clustering technique and a scoring mechanism. It also uses real-world data to validate that the selected features are useful. The experiments have demonstrated that the model could achieve over 93% hit-rate with only about 3% false- positive rate. It is regarded confidently that the approach is a complementary technique to the existing security systems.
基金supported by the Korea Research Institute for Defense Technology Planning and Advancement(KRIT)grant funded by the Defense Acquisition Program Administration(DAPA)(Grant No.KRIT-CT-23-059)supported by the Korea Basic Science Institute(National Research Facilities and Equipment Center)grant funded by the Ministry of Education(Grant No.2021R1A6C101A449)of the Republic of Korea.
文摘Mg alloys are extremely valuable in the automotive and aerospace industries because of their lightweight properties and excellent machin-ability.The applications in these industries necessitate the accurate prediction of fatigue life under cyclic loading.However,this is challenging for many wrought Mg alloys owing to their pronounced plastic anisotropy.Conventional predictive methods such as the Coffin-Manson equa-tion require manual parameter adjustment for different conditions,thus limiting their applicability.Accordingly,a novel predictive model for low-cycle fatigue(LCF)life that combines machine learning(ML)with an energy-based physical model,referred to as the hybrid ML/E model,is proposed herein.The hybrid ML/E model leverages a substantial hysteresis-loop dataset generated from LCF tests on a rolled AZ31 Mg alloy to effectively predict fatigue life.The proposed approach addresses the inherent challenges of small fatigue datasets,hysteresis-loop perception,and algorithm selection.The hybrid ML/E model demonstrates superior predictive accuracy and robustness in various loading directions,based on validation against conventional methods.The integration of ML and physical principles offers a unified framework for the LCF life prediction of anisotropic materials and represents a significant advancement for industrial applications.
文摘BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.
基金supported by the National Natural Science Foundation of China(No.12301672)the Shanghai Science and Technology Innovation Action Plan(Yangfan Special Project),China(No.23YF1401300)。
文摘The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
基金partly supported by Engineering Partners Inter-national,LLC,Richfield,MN 55423(PC13803,482842-58309).
文摘Seismic resilience(SR)has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure,recover from,and adapt to seismic events.This study presents an entropy-based multicriteria approach for selecting optimal intensity measures(IMs)to assess SR of structures.Eight representative IMs,derived from time histories and response spectrum are evaluated.Incremental dynamic analysis is con-ducted on a reinforced concrete structure,using engineering demand parameters such as the maximum interstory drift and floor acceleration to generate fragility curves via a probabilistic seismic demand model.The optimal IMs are identified through a multi-criteria decision-making process,with scores calculated using the entropy weight method to incorporate factors such as efficiency,proficiency,and uncertainty based on infor-mation entropy.An effective SR framework is derived from fragility results.The findings indicate that peak ground velocity and spectral IMs are the most effective,while energy-related IMs underestimate SR.The study highlights the importance of optimizing IMs for more accurate seismic resilience assessments.The proposed entropy-based multi-criteria approach is shown to be both reliable and effective for selecting optimal IMs in this context.
基金supported by the National Natural Science Foundation of China(No.52375337)the Key Research and Development Program of Hubei Province,China(No.2022BAA024)the Fundamental Research Funds for the Central Universities,China(No.2019kfyXJJS001).
文摘The high-temperature deformation and dynamic recrystallization(DRX)behaviors of GH4698 superalloy were investigated via hot compression tests,and an improved unified dislocation density-based constitutive model was established.The results indicate that with the temperature decreasing or the strain rate increasing,the flow stress increases and the DRX fraction decreases.However,as the strain rate increases from 1 to 10 s^(-1),rapid dislocation multiplication and deformation heat accelerate the DRX nucleation,which further increases the DRX fraction.Discontinuous DRX nucleation is the dominant DRX nucleation mechanism,and continuous DRX nucleation mainly occurs under low strain rates.For the developed improved unified dislocation density-based constitutive model,the correlation coefficient,average absolute relative error,and root mean square error between the measured and predicted stresses are 0.994,7.32%and 10.8 MPa,respectively.Meanwhile,the correlation coefficient between the measured and predicted DRX fractions is 0.976.These indicate that the developed model exhibits high accuracy in predicting the high-temperature deformation and DRX behaviors of GH4698 superalloy.
基金supported by the National Natural Science Foundation of China(Grant Nos.12202153 and 12072123).
文摘Non-Schmid(NS)effects in body-centered cubic(BCC)single-phase metals have received special attention in recent years.However,a deep understanding of these effects in the BCC phase of dual-phase(DP)steels has not yet been reached.This study explores the NS effects in ferrite-martensite DP steels,where the ferrite phase has a BCC crystallographic structure and exhibits NS effects.The influences of NS stress components on the mechanical response of DP steels are studied,including stress/strain partitioning,plastic flow,and yield surface.To this end,the mechanical behavior of the two phases is described by dislocation density-based crystal plasticity constitutive models,with the NS effect only incorporated into the ferrite phase modeling.The NS stress contribution is revealed for two types of microstructures commonly observed in DP steels:equiaxed phases with random grain orientations,and elongated phases with preferred grain orientations.Our results show that,in the case of a microstructure with equiaxed phases,the normal NS stress components play significant roles in tension-compression asymmetry.By contrast,in microstructures with elongated phases,a combined influence of crystallographic texture and NS effect is evident.These findings advance our knowledge of the intricate interplay between microstructural features and NS effects and help to elucidate the mechanisms underlying anisotropic-asymmetric plastic behavior of DP steels.
文摘In recent years,there has been a concerted effort to improve anomaly detection tech-niques,particularly in the context of high-dimensional,distributed clinical data.Analysing patient data within clinical settings reveals a pronounced focus on refining diagnostic accuracy,personalising treatment plans,and optimising resource allocation to enhance clinical outcomes.Nonetheless,this domain faces unique challenges,such as irregular data collection,inconsistent data quality,and patient-specific structural variations.This paper proposed a novel hybrid approach that integrates heuristic and stochastic methods for anomaly detection in patient clinical data to address these challenges.The strategy combines HPO-based optimal Density-Based Spatial Clustering of Applications with Noise for clustering patient exercise data,facilitating efficient anomaly identification.Subsequently,a stochastic method based on the Interquartile Range filters unreliable data points,ensuring that medical tools and professionals receive only the most pertinent and accurate information.The primary objective of this study is to equip healthcare pro-fessionals and researchers with a robust tool for managing extensive,high-dimensional clinical datasets,enabling effective isolation and removal of aberrant data points.Furthermore,a sophisticated regression model has been developed using Automated Machine Learning(AutoML)to assess the impact of the ensemble abnormal pattern detection approach.Various statistical error estimation techniques validate the efficacy of the hybrid approach alongside AutoML.Experimental results show that implementing this innovative hybrid model on patient rehabilitation data leads to a notable enhance-ment in AutoML performance,with an average improvement of 0.041 in the R2 score,surpassing the effectiveness of traditional regression models.
文摘With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learning methods.Therefore,in the process of reforming and developing higher education,it is essential to take digital technology empowering the optimization of the education industry as a breakthrough,focusing on five key areas:the construction of smart classrooms,the digital integration of teaching resources,the development of personalized learning support systems,the reform of online-offline hybrid teaching,and the intelligentization of educational management.This paper also examines the experiences,challenges,and shortcomings of typical universities in using digital technology to improve teaching quality,optimize resource allocation,and innovate teaching management models.Finally,corresponding countermeasures and suggestions are proposed to facilitate the smooth implementation of digital transformation in higher education institutions.
文摘BACKGROUND The computed tomography(CT)-based preoperative risk score was developed to predict recurrence after upfront surgery in patients with resectable pancreatic ductal adenocarcinoma(PDAC)in South Korea.However,whether it performs well in other countries remains unknown.AIM To externally validate the CT-based preoperative risk score for PDAC in a country outside South Korea.METHODS Consecutive patients with PDAC who underwent upfront surgery from January 2016 to December 2019 at our institute in a country outside South Korea were retrospectively included.The study utilized the CT-based risk scoring system,which incorporates tumor size,portal venous phase density,tumor necrosis,peripancreatic infiltration,and suspicious metastatic lymph nodes.Patients were categorized into prognosis groups based on their risk score,as good(risk score<2),moderate(risk score 2-4),and poor(risk score≥5).RESULTS A total of 283 patients were evaluated,comprising 170 males and 113 females,with an average age of 63.52±8.71 years.Follow-up was conducted until May 2023,and 76%of patients experienced tumor recurrence with median recurrence-free survival(RFS)of 29.1±1.9 months.According to the evaluation results of Reader 1,the recurrence rates were 39.0%in the good prognosis group,82.1%in the moderate group,and 84.5%in the poor group.In comparison,Reader 2 reported recurrence rates of 50.0%,79.5%,and 88.9%,respectively,across the same prognostic categories.The study validated the effectiveness of the risk scoring system,demonstrating better RFS in the good prognosis group.CONCLUSION This research validated that the CT-based preoperative risk scoring system can effectively predict RFS in patients with PDAC,suggesting that it may be valuable in diverse populations.
基金supported by the National Natural Science Foundation of China(Grant Nos.:62173282,62472363,and 62573367)CAMS Innovation Fund for Medical Sciences(Grant No.:2021-I2M-1–069)+1 种基金the 2024 China Industrial Technology Infrastructure Public Service Platform Project(Grant No.:GN2024-31-4700)the Foreign Expert Program of State Administration of Foreign Experts Affairs(Grant No.:H20240802)。
文摘Various deep learning based methods have significantlyimpacted the realm of drug discovery.The development of deep learning methods for identifying novel structural types of active compounds has become an urgent challenge.In this paper,we introduce a self-supervised representation learning framework,i.e.,Geometry-based Bidirectional Encoder Representations from Transformers(GEO-BERT).GEO-BERT considers the information of atoms and chemical bonds in chemical structures as the input,and integrates the positional information of the three-dimensional conformation of the molecule for training.Specifically,GEO-BERT enhances its ability to characterize molecular structures by introducing three different positional relationships:atom-atom,bond-bond,and atom-bond.By benchmarking study,GEO-BERT has demonstrated optimal performance on multiple benchmarks.We also performed prospective study to validate the GEO-BERT model,with screening for DYRK1A inhibitors as a case.Two potent and novel DYRK1A inhibitors(IC_(50):<1μM)were ultimately discovered.Taken together,we have developed an open-source GEO-BERT model for molecular property prediction(https://github.com/drug-designer/GEO-BERT)and proved its practical utility in early-stage drug discovery.
基金Zhongshan Science and Technology Bureau Project“The Application of Infrared Thermography in the Syndrome Differentiation of Chaihu Guizhi Ganjiang Decoction”(Project No.2021B1066)Zhongshan Science and Technology Bureau Project“Exploring the Diagnostic Approach of the TCM Syndrome Type‘Chaihu Guizhi Ganjiang Decoction’Based on Infrared Thermal Imaging Systems and Digital Modeling Methods of Ancient and Modern Literature”(Project No.2022B1131)。
文摘Objective:To evaluate the use of infrared thermography technology for objective and quantitative syndrome differentiation and treatment in traditional Chinese medicine(TCM),specifically in patients with Chaihu Guizhi Ganjiang Decoction syndrome.Methods:Data were collected from over 100 patients diagnosed with Chaihu Guizhi Ganjiang Decoction syndrome at Professor Li Leyu’s endocrinology clinic,Zhongshan Hospital of Traditional Chinese Medicine,Guangdong Province,between April 2021 and April 2022.Body surface temperature data were obtained using the MTI-EXPRO-2013-B infrared thermography system.Principal component analysis(PCA)was applied to differentiate temperature distribution characteristics between genders,and a neural network prediction model was constructed for syndrome diagnosis.Results:Infrared thermography effectively captured surface temperature characteristics of patients with Chaihu Guizhi Ganjiang Decoction syndrome.PCA identified one principal component with a variance explanation rate of 73.953%for females and two principal components with a cumulative variance explanation rate of 77.627%for males.The neural network model demonstrated high predictive performance,with an area under the ROC curve of 0.9743 for the training set and 0.9889 for the validation set.Sensitivity was 1,specificity 0.8636,precision 0.8846,accuracy 0.9333,and the F1 score 0.9388.Conclusion:Infrared thermography provides an innovative,objective,and quantitative method for syndrome differentiation and treatment in TCM.It represents a significant advancement in transitioning from traditional empirical approaches to modern,visualized,and precise diagnosis and treatment.This study underscores the potential of integrating advanced technologies in TCM for enhanced clinical application and modernization.
基金National Natural Science Foundation of China (60675054)National High-Tech Research and Development Program (2006AA04Z228)"111" Project (B07018)
文摘This article investigates virtual reality (VR)-based teleoperation with robustness against modeling errors. VR technology is an effective way to overcome the large time delay during space robot teleoperation. However, it depends highly on the accuracy of model. Model errors between the virtual and real environment exist inevitably. The existing way to deal with the problem is by means of either model matching or robot compliance control. As distinct from the existing methods, this article tries to combine m...
基金supported in part by the National Natural Science Foundation of China under Grant 61622101 and Grant 61571020National Science and Technology Major Project under Grant 2018ZX03001031
文摘A more general narrowband regular-shaped geometry-based statistical model(RS-GBSM) combined with the line of sight(LoS) and single bounce(SB) rays for unmanned aerial vehicle(UAV) multiple-input multiple-output(MIMO) channel is proposed in this paper. The channel characteristics, including space-time correlation function(STCF), Doppler power spectral density(DPSD), level crossing rate(LCR) and average fade duration(AFD), are derived based on the single sphere reference model for a non-isotropic environment. The corresponding sum-of-sinusoids(SoS) simulation models including both the deterministic model and statistical model with finite scatterers are also proposed for practicable implementation. The simulation results illustrate that the simulation models well reproduce the channel characteristics of the single sphere reference model with sufficient simulation scatterers. And the statistical model has a better approximation of the reference model in comparison with the deterministic one when the simulation trials of the stochastic model are sufficient. The effects of the parameters such as flight height, moving direction and Rice factor on the characteristics are also studied.
基金National Nature Science Foundation of China, No.41071324 No.40730526+2 种基金 Key Subject Developing Project by Shanghai Municipal Education Commission, No.J50402 Science and Technology Commission of Shanghai Municipality, No.08240514000 Leading Academic Discipline Project of Shanghai Normal University, No.DZL809
文摘Scenario modelling and the risk assessment of natural disasters is one of the hotspots in disaster research. However, up until now, urban natural disaster risk assessments lack common procedures and programmes. This paper selects rainstorm waterlogging as a disaster to research, which is one of the most frequently occurring hazards for most cities in China. As an example, we used a small-scale integrated methodology to assess risks relating to rainstorm waterlogging hazards in the Jing＇an District of Shanghai. Based on the basic concept of disaster risk, this paper applies scenario modelling to express the risk of small-scale urban rainstorm waterlogging disasters in different return periods. Through this analysis of vulnerability and exposure, we simulate different disaster scenarios and propose a comprehensive analysis method and procedure for small-scale urban storm waterlogging disaster risk assessments. A grid-based Geographical Information System （GIS） approach, including an urban terrain model, an urban rainfall model and an urban drainage model, was applied to simulate inundation area and depth. Stage-damage curves for residential buildings and contents were then generated by the loss data of waterlogging from field surveys, which were further applied to analyse vulnerability, exposure and loss assessment. Finally, the exceedance probability curve for disaster damage was constructed using the damage of each simulated event and the respective exceedance probabilities. A framework was also developed for coupling the waterlogging risk with the risk planning and management through the exceedance probability curve and annual average waterlogging loss. This is a new exploration for small-scale urban natural disaster scenario simulation and risk assessment.
基金supported by national fundamental research key project (No. JCKY2017203B082)
文摘The Statistical Priority-based Multiple Access Protocol(SPMA)is the de facto standard for Tactical Target Network Technology(TTNT)and has also been implemented in ad hoc networks.In this paper,we present a non-preemptive M/M/1/K queuing model to analyze the performance of different priorities in SPMA in terms of average packet loss rate and delay.And based on this queuing model,we designed a percentile scoring system combined with Q-learning algorithm to optimize the protocol parameters.The simulation results show that our theoretical model is closely matched with the reality,and the proposed algorithm improves the efficiency and accuracy in finding the optimal parameter set of SPMA protocol.
基金supported by the National Natural Science Foundation of China(Grants No.51779153,51539006,and 51509156)the Natural Science Foundation of Jiangsu Province(Grant No.BK20161121)
文摘A simplified physically-based model was developed to simulate the breaching process of the Gouhou concrete-faced rockfill dam （CFRD）, which is the only breach case of a high CFRD in the world. Considering the dam height, a hydraulic method was chosen to simulate the initial scour position on the downstream slope, with the steepening of the downstream slope taken into account; a headcut erosion formula was adopted to simulate the backward erosion as well. The moment equilibrium method was utilized to calculate the ultimate length of a concrete slab under its self-weight and water loads. The calculated results of the Gouhou CFRD breach case show that the proposed model provides reasonable peak breach flow, final breach width, and failure time, with relative errors less than 15% as compared with the measured data. Sensitivity studies show that the outputs of the proposed model are more or less sensitive to different parameters. Three typical parametric models were compared with the proposed model, and the comparison demonstrates that the proposed physically-based breach model performs better and provides more detailed results than the parametric models.
