Using Coupled Rheometer-FBRM to Study Rheological Properties and Microstructure of Cemented Paste Backfill by Hongjiang Wang, Liuhua Yang, Hong Li, Xu Zhou, Xiaotian Wang

“…Shear thinning can be found in CPB suspensions with a microstructure that is either loose interconnection or random. With an increase in the shear rate, random collisions among particles become organized in the flow, lowering the yield stress and viscosity. …”
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RESEARCH ON GEAR BOX FAULT DIAGNOSIS BASED ON DCNN AND XGBOOST ALGORITHM by ZHANG RongTao, CHEN ZhiGao, LI BinBin, JIAO Bin

“…In order to verify the validity of the model and the superiority of XGBoost algorithm,the model was compared with three models: DNN-BP（ Back Propagation neural network） model,DCNN-RF（ Random Forest） model and DCNN-SVM（ Support Vector Machine） model. …”
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Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms by Shamsa Khalid, Muhammad Anees Khan, M.S. Mazliham, Muhammad Mansoor Alam, Nida Aman, Muhammad Tanvir Taj, Rija Zaka, Muhammad Jehangir

“…Our results prove that AI techniques can accurately predict risk with minimum error values, and among all the techniques used, the random forest technique outperforms as compared to the rest of the techniques.…”
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Compactness of quantics tensor train representations of local imaginary-time propagators by Haruto Takahashi, Rihito Sakurai, Hiroshi Shinaoka

“…To study worst-case scenarios, we employ random pole models, where the number of poles grows logarithmically with the inverse temperature and coefficients are random. …”
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Power consumption modeling and optimization for NB-IoT eDRX by Xin JIAN, Yixiao WEI, Yuqin LIU, Jian SONG, Xiaoping ZENG, Xiaoheng TAN

“…To extend the battery life of massive machine type devices (MTD),narrow-band Internet of things (NB-IoT) system extended discontinuous reception (eDRX) mechanism of LTE to the maximum as well as introduced a lower power state named as power saving mode (PSM).Three Markov models were established for three typical NB-IoT traffic scenarios,which called mobile autonomous reporting periodic report (MAR-P),mobile autonomous reporting exception report (MAR-E),software update/reconfiguration (SUR).The states of each Markov model were the working status of MTDs,including connected,idle and PSM state,in which the connected state was divided into random access state and data translating/receiving state to further evaluate the additional power consumption caused by collisions from massive MTDs concurrent access.Thereby,the power consumption and delay models with respect to each traffic scenarios were derived.Since the frequency of MAR-P traffic was far greater than the other two,the battery life of this traffic case with its optimal design choice was comprehensively analyzed.Numerical results show that,the battery life is mostly influenced by transmission period,maximum number of random access attempts,maximum number of data transmissions and traffic load,which can be maximized by appropriate parameters setting up.These works provide good references for NB-IoT device behavior modeling and optimization design.…”
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Machine learning-based prediction of soil organic matter via smartphone by Qingying Gao, Yi Chen, Hui Zhang, Jingjing Chen, Liang Wang

“…The aim of this study is to construct models to predict SOM for a range of colors using a smartphone as an image-capturing device. Random forest of classification (RFC), random forest of logical regression (RFLR), convolutional neural network (CNN) and MobileNet models are compared, which is better for SOM prediction. …”
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Process Optimization of Biodiesel Production Using the Laplacian Harris Hawk Optimization (LHHO) Algorithm by Ashutosh Sharma, Akash Saxena, Shail Kumar Dinkar, Rajesh Kumar, Ameena Saad Al-Sumaiti

“…The developed variant is based on the replacement of random numbers of normal distribution at the initialization phase by the random numbers generated from the Laplacian distribution. …”
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Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network by Xiang Yin, Junyang Yu, Xiaoyu Duan, Lei Chen, Xiaoli Liang

“…The difficulty of short-term urban traffic forecasting is that the traffic flow is random and will be dynamically changed by the traffic conditions of nearby nodes. …”
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Microbes, macrophages, and melanin: a unifying theory of disease as exemplified by cancer by Stacie Z. Berg, Jonathan Berg

“…It is widely accepted that cancer mostly arises from random spontaneous mutations triggered by environmental factors. …”
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Differential Measurement for Cavity Ring-Down Spectroscopy with Dynamic Allan Variance by Zeqiang Mo, Jin Yu, Jinduo Wang, Jianguo He, Shoujun Dai, Yang Liu

“…The method of dynamic Allan variance (DAVAR) is used to analyze the time-varying characteristics of a nonstationary signal and is thus incorporated to evaluate the random error in the cavity ring-down spectroscopy (CRDS) experiments. …”
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Integrated Navigation Method of Aerospace Vehicle Based on Rank Statistics by Jun Kang, Zhi Xiong, Rong Wang, Xinrui Zhang

“…The large dynamic and high-speed flight of aerospace vehicle will bring unpredictable conditions to its navigation system, resulting in that its system random noise probability distribution will no longer meet the preconditions of Gaussian distribution preset by the existing filter algorithm, thus reducing the accuracy of the navigation system. …”
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Research on Identification Method of Scour Depth for Bridge Based on ERA and SVM by Xiaozhong Zhang, Wenjuan Yao, Yimin Liu, Bo Chen

“…Secondly, free response signal of bridge were extracted from random vibration signal of bridge upper structure using random decrement technique. …”
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A Probabilistic Assessment Model for Train-Bridge Systems: Special Attention on Track Irregularities by Dejun Liu, Lifeng Xin, Xiaozhen Li, Jiaxin Zhang

“…In this paper, a probabilistic model devoted to investigating the dynamic behaviors of train-bridge systems subjected to random track irregularities is presented, in which a train-ballasted track-bridge coupled model with nonlinear wheel-rail contacts is introduced, and then a new approach for simulating a random field of track irregularities is developed; moreover, the probability density evolution method is used to describe the probability transmission from excitation inputs to response outputs; finally, extended analysis from three aspects, that is, stochastic analysis, reliability analysis, and correlation analysis, are conducted on the evaluation and application of the proposed model. …”
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A hybrid particle swarm optimization algorithm for single machine scheduling with sequence-dependent setup times and learning effects by Payam Chiniforooshan, Dragan Marinkovic

“…In order to utilize Particle Swarm Optimization (PSO) to solve the scheduling problems, the proposed HPSO approach uses a random key representation to encode solutions, which can convert the job sequences to continuous position values. …”
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Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition by Chunyan Shuai, Zhengyang Pan, Lun Gao, HongWu Zuo

“…This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.…”
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Chebyshev inequalities for unimodal distributions by Tomas Juškevičius

“… We provide precise upper bounds for the survival function of bounded unimodal random variables. …”
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Move theorem of the sample range and centre by Darius Petronaitis

“… The limit distribution functions are obtained for the sample range and sample centre with random indices under nonrandom normalization. …”
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On complete convergence for Lp-mixingales by Yijun Hu

“…We provide in this paper sufficient conditions for the complete convergence for the partial sums and the random selected partial sums of B-valued Lp-mixingales.…”
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Impact of hot alkali modification conditions on secondary structure of peanut protein and embedding rate of curcumin by Wei Li, Shugang Li, Yong Hu, Mengzhou Zhou, Chao Wang, Dongsheng Li, Deyuan Li

“…The percentage of α-helices and β-sheets gradually decreased with increasing pH, while that of random coils gradually increased with increasing pH, reaching a maximum 11.24 ± 0.87% when the pH was 11(p<0.05). …”
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Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Sch&ouml;nlein Purpura by Guo Q, Xia S, Ding Y, Liu F

“…The use of machine learning models can enhance the early identification and management of high-risk patients, potentially improving clinical outcomes.Keywords: Henoch-Schönlein Purpura, gastrointestinal complications, laboratory markers, machine learning, random forest classifier…”
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