Thermodynamic properties of L-menthol in crystalline and gaseous states by A. V. Blokhin, Ya. N. Yurkshtovich

Get full text

«Out of sight, out of mind?», or Transferring adolescents with type 1 diabetes to an adult network by V. V. Platonov, Yu. L. Skorodok, E. V. Plotnikova, E. M. Patrakeeva, T. A. Dubinina

“…It is known that incorrect transition can lead to patient's «falling out» of medical supervision system and errors in transition have a negative effect on the overall morbidity and mortality, frequency of severe hypoglycemia and diabetic ketoacidosis in the first year after the transition to the adult's service in young adults with T1D. …”
Get full text

Study on Prediction of Domestic Water Consumption Based on Residual Grey Markov Chain Model by LIU Xian, YUAN Dan, ZHANG Xiaoli, MU Duo

“…Aiming at the problems of traditional GM (1,1) model in forecasting non-growth sequences in terms of water consumption,such as poor precision and over-fitting,a residual grey prediction model corrected with Markov chains is used to predict domestic water consumption.Based on the traditional grey prediction model,this paper firstly establishes an improved residual grey prediction model as follows:establish grey model on the absolute residual,and judge the sign of the predictive residual when t＞n based on Markov state transition matrix to correct the predictive value from grey model,and then apply the model to the prediction of domestic water consumption in Henan Province from 2007 to 2018.The results show that the average relative error of the traditional grey prediction model is 4.14%,while that of the improved residual grey prediction model is only 2.04%.The precision class of improved residual grey prediction model is “good”,meanwhile,the posterior variance of improved model is also smaller than that of the traditional model,which indicating that the improved model has higher precision and better reliability than the traditional grey prediction model,therefore,it is a new method for water consumption prediction.…”
Get full text

A Real-time Train Perception Method for Obstacle Intrusion Based on Front View Projection by HE Qian, JIANG Guotao, DONG Wenbo, PI Zhichao, YANG Hailang, CHEN Meilin

“…By incorporating correction of sensor synchronization errors based on point cloud prediction, a judgment regarding track intrusion can be made, dependent on the projected obstacle point cloud and calculated clearance. …”
Get full text

Noise Currents Measurement Method for Rail Vehicles Based on Broad Learning System by HAN Ran, LI Jie, LIAN Cheng, ZHANG Kun

Get full text

TBM Net Advance Rate Prediction Model Based on Ridge Regression Analysis by SHI Jian, ZHANG Shilin, FAN Zuosong, KONG Desen

Get full text

Research on Speed and Position Detection Technology for Train Operation Control System Based on Multi-Source Information Fusion by YANG Chongfu

Get full text

Tools to improve discharge equity: Protocol for the pilot TIDE trial by Kirsten Austad, Cecilia Thai, Alegna Zavatti, Nhi Nguyen, Diana Bautista-Hurtado, Patrick Kenney, Noelia Lugo, Joo H. Lee, Howard Lanney, Ziming Xuan, Erika G. Cordova-Ramos, Mari-Lynn Drainoni, Brian Jack

Get full text

Perturbative gravitational wave predictions for the real-scalar extended Standard Model by Oliver Gould, Paul M. Saffin

“…While leading and next-to-leading order predictions of the gravitational wave amplitude typically suffer from relative errors between 10 and 104, at next-to-next-to-leading order the typical relative errors are reduced to between 0.5 and 50. …”
Get full text

A prediction model of massive 5G network users’ revisit behavior based on telecom big data by Yudi SUN

“…Users in 5G networks will generate a large amount of access data, which makes it difficult to accurately predict users’ revisit behavior.Therefore, a prediction model of massive 5G network users’ revisit behavior based on telecom big data was proposed.The user’s historical online behavior characteristic data was extracted from the telecom big data to build a data set.Multi order weighted Markov chain model was introduced.The model weight value was obtained by calculating the autocorrelation coefficient of each order, and the statistics of the model were calculated.After analysis, the one-step transition probability matrix of Markov chain with each step size was obtained, so as to accurately predict the revisit behavior of massive users in 5G network.The experimental results show that the proposed model has the lowest mean error and standard deviation, as well as the highest accuracy, recall, precision and F1 indicators, which can prove that the proposed method has a very obvious advantage in predicting users’ revisit behavior.…”
Get full text

A prediction model of massive 5G network users’ revisit behavior based on telecom big data by Yudi SUN

“…Users in 5G networks will generate a large amount of access data, which makes it difficult to accurately predict users’ revisit behavior.Therefore, a prediction model of massive 5G network users’ revisit behavior based on telecom big data was proposed.The user’s historical online behavior characteristic data was extracted from the telecom big data to build a data set.Multi order weighted Markov chain model was introduced.The model weight value was obtained by calculating the autocorrelation coefficient of each order, and the statistics of the model were calculated.After analysis, the one-step transition probability matrix of Markov chain with each step size was obtained, so as to accurately predict the revisit behavior of massive users in 5G network.The experimental results show that the proposed model has the lowest mean error and standard deviation, as well as the highest accuracy, recall, precision and F1 indicators, which can prove that the proposed method has a very obvious advantage in predicting users’ revisit behavior.…”
Get full text

Advancing amorphous solid dispersions through empirical and hybrid modeling of drug–polymer solubility and miscibility: A case study using Ibuprofen by Matheus de Castro, Ana Sara Cordeiro, Mingzhong Li, Christian Lübbert, Catherine McColl, Jatin Khurana, Mark Evans, Walkiria S. Schlindwein

“…HPMCAS also exhibited consistently prediction errors across all Tg models, (AARD ∼4.5 %), indicating poorer agreement with experimental data. …”
Get full text

Detection Technology for Catenary Stagger Value Based on Acceleration Signals by CHEN Hongming, ZHOU Ning, LU Wenwei, YANG Zixian, CHENG Yao, WANG Dong, ZHANG Weihua

Get full text

Design of Network Control System for Automatic Train Operation with High-Precision Synchronization and High-Reliability Redundancy by WANG Xianbing, LI Cheng, WU Wenhui, TANG Zhilong, MO Yun, WEN Fa

“…The advent of automatic train operation (ATO) technologies for rail transit vehicles has increasingly elevated the corresponding safety and reliability standards of network control systems in this sector. …”
Get full text

Machine learning based method for forecasting short-term passenger flow in urban rail stations by HU Mingwei, SHI Xiaolong, WU Wenlin, HE Guoqing

“…Then, five types of subway stations are selected (residential type, occupation type, residential-occupation type, business type, and transportation hubs type) and three accuracy evaluation indicators are chosen (mean absolute error, root mean square error and mean absolute percentage error) to evaluate the prediction accuracy of the five prediction models. …”
Get full text

Thrust Resistance Calculation Method in Diaphragm Wall Grab Trenching Considering Soil Accumulation and Slurry Pressure by CAO Yu, MENG Hongfeng, WANG Qian, WANG Xiaohui, LI Jie, CHEN Fudan

“…[Result & Conclusion] The error between the calculated results of thrust resistance during grab excavation by the constructed model and the experimental results is -0.55%. …”
Get full text

Blood Pressure and Heart Rate Measurements Using Fiber Bragg Grating Sensor with Optical Power Detection Scheme by Yu-Jie Wang, Likarn Wang

“…The results demonstrate that the errors between the calculated values and reference values of SBP and DBP for all of the 29 subjects both range from −4 to 5 mmHg with mean errors of 0.72 mmHg and 0.83 mmHg, respectively. …”
Get full text

A Hybrid Strategy-Improved SSA-CNN-LSTM Model for Metro Passenger Flow Forecasting by Jing Liu, Qingling He, Zhikun Yue, Yulong Pei

Get full text

An improved multi-step prediction control algorithm for urban rail hybrid energy storage system by YANG Fengping, ZHOU Mingzhi, CHENG Quan, ZHANG Yin

Get full text

Impact of frailty in older people on health care demand: simulation modelling of population dynamics to inform service planning by Bronagh Walsh, Carole Fogg, Tracey England, Sally Brailsford, Paul Roderick, Scott Harris, Simon Fraser, Andrew Clegg, Simon de Lusignan, Shihua Zhu, Francesca Lambert, Abigail Barkham, Harnish Patel, Vivienne Windle

“…The system dynamics (SD) model has been extensively validated against summary descriptive data from the RCGP RSC cohort (with a 6.9% error) and externally against a similar data set from SAIL (9.3% error) before being scaled up (using ONS estimates for the number of people entering the 50 + population and those turning 65, 75 or 85 in a given year) to consider how frailty incidence and prevalence at a national population level could be represented over the period of the cohort study (2006–17) and 10 years into the future. …”
Get full text