A novel refined dynamic model of high-speed maglev train-bridge coupled system for random vibration and running safety assessment

Running safety assessment and tracking irregularity parametric sensitivity analysis of high-speed maglev train-bridge system are of great concern,especially need perfect refinement models in which all properties can be well characterized based on various stochastic excitations.A three-dimensional refined spatial random vibration analysis model of high-speed maglev train-bridge coupled system is established in this paper,in which multi-source uncertainty excitation can be considered simultaneously,and the probability density evolution method(PDEM)is adopted to reveal the system-specific uncertainty dynamic characteristic.The motion equation of the maglev vehicle model is composed of multi-rigid bodies with a total 210-degrees of freedom for each vehicle,and a refined electromagnetic force-air gap model is used to account for the interaction and coupling effect between the moving train and track beam bridges,which are directly established by using finite element method.The model is proven to be applicable by comparing with Monte Carlo simulation.By applying the proposed stochastic framework to the high maglev line,the random dynamic responses of maglev vehicles running on the bridges are studied for running safety and stability assessment.Moreover,the effects of track irregularity wavelength range under different amplitude and running speeds on the coupled system are investigated.The results show that the augmentation of train speed will move backward the sensitive wavelength interval,and track irregularity amplitude influences the response remarkably in the sensitive interval.

Dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates

Interactive holography offers unmatched levels of immersion and user engagement in the field of future display.Despite of the substantial progress has been made in dynamic meta-holography,the realization of real-time,highly smooth interactive holography remains a significant challenge due to the computational and display frame rate limitations.In this study,we introduced a dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates.To our knowledge,this is the first reported practical dynamic interactive metasurface holographic system.We spa-tially divided the metasurface device into multiple distinct channels,each projecting a reconstructed sub-pattern.The switching states of these channels were mapped to bitwise operations on a set of bit values,which avoids complex holo-gram computations,enabling an ultra-high computational frame rate.Our approach achieves a computational frame rate of 800 kHz and a display frame rate of 23 kHz on a low-power Raspberry Pi computational platform.According to this methodology,we demonstrated an interactive dynamic holographic Tetris game system that allows interactive gameplay,color display,and on-the-fly hologram creation.Our technology presents an inspiration for advanced dynamic meta-holography,which is promising for a broad range of applications including advanced human-computer interaction,real-time 3D visualization,and next-generation virtual and augmented reality systems.

ST-Map:an Interactive Map for Discovering Spatial and Temporal Patterns in Bibliographic Data

Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.

Simulation and Traffic Safety Assessment of Heavy-Haul Railway Train-Bridge Coupling System under Earthquake Action

Aiming at the problem that it is difficult to obtain the explicit expression of the structural matrix in the traditional train-bridge coupling vibration analysis,a combined simulation system of train-bridge coupling system(TBCS)under earthquake(MAETB)is developed based on the cooperative work of MATLAB and ANSYS.The simulation system is used to analyze the dynamic parameters of the TBCS of a prestressed concrete continuous rigid frame bridge benchmark model of a heavy-haul railway.The influence of different driving speeds,seismic wave intensities,and traveling wave effects on the dynamic response of the TBCS under the actions of the earthquakes is discussed.The results show that the bridge displacement increase in magnitude in the lateral direction is more significant than in the vertical direction under the action of an earthquake.The traveling wave effect can significantly reduce the lateral response of the bridge,but it will significantly increase the train derailment coefficient.When the earthquake intensity exceeds 0.2 g,the partial derailment coefficient of the train has exceeded the limit value of the specification.

Seismic safety assessment with non-Gaussian random processes for train-bridge coupled systems

Extensive high-speed railway(HSR)network resembled the intricate vascular system of the human body,crisscrossing mainlands.Seismic events,known for their unpredictability,pose a significant threat to both trains and bridges,given the HSR’s extended operational duration.Therefore,ensuring the running safety of train-bridge coupled(TBC)system,primarily composed of simply supported beam bridges,is paramount.Traditional methods like the Monte Carlo method fall short in analyzing this intricate system efficiently.Instead,efficient algorithm like the new point estimate method combined with moment expansion approximation(NPEM-MEA)is applied to study random responses of numerical simulation TBC systems.Validation of the NPEM-MEA’s feasibility is conducted using the Monte Carlo method.Comparative analysis confirms the accuracy and efficiency of the method,with a recommended truncation order of four to six for the NPEM-MEA.Additionally,the influences of seismic magnitude and epicentral distance are discussed based on the random dynamic responses in the TBC system.This methodology not only facilitates seismic safety assessments for TBC systems but also contributes to standard-setting for these systems under earthquake conditions.

MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.

Design Method for Optimizing the Interactive Interface of Live Broadcasting Platform for the Elderly Users

In the era of network live broadcasting for everyone,the development of live broadcasting platforms is also more intelligent and diversified.However,in the face of a large group of elderly users,the interface interaction design mode used is still mainly based on the interaction mode for young groups,and is not designed for elderly users.Therefore,a design method for optimizing the interaction interface of live broadcasting platform for elderly users was proposed in this study.Firstly,the case study method and Delphi expert survey method were used to determine the design needs of elderly users and the design mode was analysed.Secondly,the orthogonal design principle was used to design a test sample of the interactive interface of live broadcasting platform applicable for the elderly users,and then a user evaluation system was established to calculate the weights of the design elements using hierarchical analysis,and then the predictive relationship between the design mode of the interactive interface of live broadcasting platform and the elderly users was established by Quantitative Theory I.Finally,Genetic Algorithm was applied to generate the optimized design scheme.The results showed that the design method based on the Genetic Algorithm and the combination of Quantitative Theory can scientifically and effectively optimize the design of the interactive interface of the live broadcasting platform for the elderly users,and improve the experience of the elderly users.

Study on the interactive deformation of bone needles and circum-pelvic soft tissue

During the robot-assisted pelvic fracture reduction,the needle-tissue interactive deformation characteristic is not clear,which affects the accuracy of robotic surgery.In this paper,a layered rig-id-flexible coupling model is proposed,and the needle-tissue interactive deformation under the load-ing is analyzed by the Rayleigh-Ritz method,in accordance with the principle of minimum potential energy.The pelvic musculoskeletal tissue is reversely reconstructed,and the structure of the bone is segmented into cancellous bone and cortical bone.The Mooney-Rivlin five-parameter hyperelastic model is used to simulate muscle,and the Ogden hyperelastic model is used to simulate adipose tis-sue.Finite element simulation is performed by loading different magnitudes of forces.The accuracy of the rigid-flexible coupling model is 0.432 mm,which indicates the correctness of the needle-tis-sue interactive deformation theory analysis.

The Application of Formative Assessment in Interactive Comprehensive English Classroom Teaching

Teaching evaluation is an important part of the teaching process of college English courses,and effective formative assessment can reflect the entire teaching process from multiple perspectives and ensure the smooth achievement of teaching objectives.Formative assessment is an assessment that runs through the entire learning process,based on humanism and constructivism,with various forms of evaluation.

基于Interactive Physics的物理习题教学--以2023年高考全国乙卷25题和湖北卷15题为例

在传统高中物理教学中,多过程碰撞的物理过程很难在板书上展现出来,成为教学的一大难点。基于Interactive Physics仿真功能,以2023年高考全国乙卷25题和湖北卷15题为例,将多过程碰撞的物理过程可视化,直观展现物理模型,帮助学生理解题目的复杂情境,有效缩短学习时间,提高教学质量和教学效率,实现最优化的教学目标。

Parameter calibration of the tensile-shear interactive damage constitutive model for sandstone failure

The tensile-shear interactive damage(TSID)model is a novel and powerful constitutive model for rock-like materials.This study proposes a methodology to calibrate the TSID model parameters to simulate sandstone.The basic parameters of sandstone are determined through a series of static and dynamic tests,including uniaxial compression,Brazilian disc,triaxial compression under varying confining pressures,hydrostatic compression,and dynamic compression and tensile tests with a split Hopkinson pressure bar.Based on the sandstone test results from this study and previous research,a step-by-step procedure for parameter calibration is outlined,which accounts for the categories of the strength surface,equation of state(EOS),strain rate effect,and damage.The calibrated parameters are verified through numerical tests that correspond to the experimental loading conditions.Consistency between numerical results and experimental data indicates the precision and reliability of the calibrated parameters.The methodology presented in this study is scientifically sound,straightforward,and essential for improving the TSID model.Furthermore,it has the potential to contribute to other rock constitutive models,particularly new user-defined models.

Research on fault time prediction method for high speed rail BTM unit based on multi method interactive validation

The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.

Exploration and Practice of“Guiding Interactive”Teaching in Advanced Geochemistry Courses from the Perspective of Constructivist Theory

The construction of geochemical disciplines has brought new vitality to the development of traditional geology.In the new round of“Double First-Class”discipline construction at Central South University,the course of Advanced Geochemistry has effectively stimulated students’interest in learning and further improved their scientific thinking and research innovation skills through the implementation of“Guiding Interactive”teaching reform measures,which has important theoretical significance and practical value.

Research on Visualization and Interactive Design of Plant Configuration Virtual Reality in the Context of Digitalization

This study aims to explore the application of digital technology in landscape design,focusing on the research of virtual reality visualization and interactive design in the process of plant configuration.Through an in-depth analysis of digital technology,the study outlines its important role in landscape design,especially in the application of plant configuration.The current application status of virtual reality technology in landscape design is discussed,as well as how interactive design can enhance user experience and participation.Furthermore,the achievements and challenges of digital technology in landscape design are summarized.Finally,it proposes future research directions and suggestions,aiming to provide new ideas and methods for practice and research in the field of landscape design and promote the further application and development of digital technology in landscape design.

Interactive Media Visual Interpretation of Traditional Chinese Graphics in Urban Public Spaces

This paper focuses on the application of interactive media technology in the visual interpretation of traditional graphic urban public spaces in China.Case studies and practical exploration show that interactive media technologies such as projection mapping,interactive devices,virtual reality technology,etc.,have realized the diversity of traditional graphics display forms in urban public space.The rich interactive experience design enhances the sense of participation and experience of urban citizens and tourists and promotes the visual culture transmission of traditional Chinese graphics.The future urban public space exhibition is destined to continue to deepen the integration of technology and graphics,promote the visual communication of traditional Chinese graphics visual interpretation in urban public space,and promote sustainable innovation in cultural output in urban public space exhibitions around the world.

Research and Application of AI-Based Interactive Exhibits in Wuhan Museum of Science and Technology

This article aims to explore the development and application of AI-based interactive exhibits in Wuhan Museum of Science and Technology.By utilizing computer vision,natural language processing,and machine learning technologies,an innovative exhibit development and application system is proposed.This system employs deep learning algorithms and data analysis methods to achieve real-time perception of visitor behavior and adaptive interaction.The development process involves designing user interfaces and interaction methods to effectively enhance visitor engagement and learning outcomes.Through evaluation and comparison in practical applications,the potential of this system in enhancing exhibit interaction,increasing visitor engagement,improving educational effectiveness,and expanding avenues for scientific knowledge dissemination are validated.

A Train-Bridge Dynamic Interaction Analysis Method and Its Experimental Validation

The train-bridge dynamic interaction problem began with the development of railway technology, and requires an evaluation method for bridge design in order to ensure the safety and stability of the bridge and the running train. This problem is studied using theoretical analysis, numerical simulation, and experimental study. In the train-bridge dynamic interaction system proposed in this paper, the train vehicle model is established by the rigid-body dynamics method, the bridge model is established by the finite element method, and the wheel/rail vertical and lateral interaction are simulated by the corresponding assumption and the Kalker linear creep theory, respectively. Track irregularity, structure deformation, wind load, collision load, structural damage, foundation scouring, and earthquake action are regarded as the excitation for the system. The train-bridge dynamic interaction system is solved by inter-history iteration. A case study of the dynamic response of a CRH380BL high-speed train running through a standard-design bridge in China is discussed. The dynamic responses of the vehicle and of the bridge subsystems are obtained for speeds ranging from 200 km-b-1 to 400 km.h-1, and the vibration mechanism are analyzed.

Risk factors and their interactive effects on severe acute pancreatitis complicated with acute gastrointestinal injury

BACKGROUND There are many risk factors for severe acute pancreatitis(SAP)complicated with acute gastrointestinal injury(AGI),but few reports on the interaction between these risk factors.AIM To analyze the risk factors for SAP complicated with AGI and their interactive effects.METHODS We selected 168 SAP patients admitted to our hospital between December 2019 and June 2022.They were divided into AGI group and non-AGI group according to whether AGI was present.Demographic data and laboratory test data were compared between the two groups.The risk factors for SAP with concomitant AGI were analyzed using multifactorial logistic regression,and an analysis of the interaction of the risk factors was performed.RESULTS The percentage of patients with multiple organ dysfunction syndrome,acute physiological and chronic health scoring system II(APACHE II)score,white blood cell count and creatinine(CRE)level was higher in the AGI group than in the non-AGI group.There was a statistically significant difference between the two groups(P<0.05).Logistic regression analysis indicated that an APACHE II score>15 and CRE>100μmol/L were risk factors for SAP complicating AGI.The interaction index of APACHE II score and CRE level was 3.123.CONCLUSION An APACHE II score>15 and CRE level>100μmol/L are independent risk factors for SAP complicated with AGI,and there is a positive interaction between them.

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations.In this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors.Under the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'screening.The results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully,which provided efficient guidance for the development of pharmaceutical formulations.