Phase-Controlled Closing Strategy for UHV Circuit Breakers with Arc-Chamber Insulation Deterioration Consideration by Hao Li, Qi Long, Xu Yang, Xiang Ju, Haitao Li, Zhongming Liu, Dehua Xiong, Xiongying Duan, Minfu Liao

“…Compared with the least squares fitting, this algorithm achieves a reasonable balance between goodness of fit and complexity, with prediction deviations tending to be randomly distributed, no obvious systematic offset, and low dispersion degree. …”
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Digital processing of images of nano-dimensional elements on nanostructured material surface using the ImageJ software by H. T. Dinh, N. V. Lushpa, K. V. Chernyakova, I. A. Vrublevsky

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Innovation of Urban Circular Economy Growth Path Based on Neural Network by Weifeng Qiu, Yi Yang

“…Moreover, it has obvious advantages over the traditional algorithm in terms of error and recall rate. Compared with the actual economic data, the economic data predicted by the model is quite consistent, and the prediction of future data by the model basically accords with the development goal of the regional master plan. …”
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Informing antimicrobial stewardship with explainable AI. by Massimo Cavallaro, Ed Moran, Benjamin Collyer, Noel D McCarthy, Christopher Green, Matt J Keeling

“…An appropriately trained gradient boosted decision tree algorithm, supplemented by a Shapley explanation model, predicts the likely antimicrobial drug resistance, with the odds of resistance informed by characteristics of the patient, admission data, and historical drug treatments and culture test results. …”
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Research on test strategy for randomness based on deep learning by Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG

“…In order to achieve better test performance, researches on the randomness test strategies based on deep learning were conducted, including the batch average strategy proposed by EUROCRYPT 2021 and the selection strategy for data unit size.By introducing the randomness statistical test model based on deep learning methods, the statistical distribution and test power expression of two test strategies were theoretically derived, and it was pointed out that: (i) the batch average strategy could amplify the prediction accuracy of the model, but it was prone to an increase in the probability of the second type of error in statistics, instead reducing the statistical test power; (ii) the smaller data units of the deep model generally obtained the more powerful statistical tests.Based on the above understanding, a new bit-level deep learning model was proposed for randomness statistical tests, which gained the advantage of prediction with 80 times fewer parameters and 50% samples, compared with the previous work on linear congruent generator (LCG) algorithm, and achieved significant prediction advantages with 10～20 times fewer parameters by extending the model to apply to 5～7 rounds of Speck, compared with the model proposed by Gohr.…”
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A Method of Communication Delay Compensation for Urban Transit SystemBased on Long-term and Short-term Memory by HUANG Zihao, LI Hongbo, ZHANG Chao, XU Dongsheng

“…After measuring the communication parameters in a 4G communication test, the communication delay induced error is calculated and compared with the prediction method. The result shows that the prediction algorithm can reduce communication delay induced error by 21.8% and packet loss induced error by 25.8% ~ 26.9%, which can provide more accurate real-time train power information and make real-time improvement for energy flow more feasible.…”
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Research on test strategy for randomness based on deep learning by Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG

“…In order to achieve better test performance, researches on the randomness test strategies based on deep learning were conducted, including the batch average strategy proposed by EUROCRYPT 2021 and the selection strategy for data unit size.By introducing the randomness statistical test model based on deep learning methods, the statistical distribution and test power expression of two test strategies were theoretically derived, and it was pointed out that: (i) the batch average strategy could amplify the prediction accuracy of the model, but it was prone to an increase in the probability of the second type of error in statistics, instead reducing the statistical test power; (ii) the smaller data units of the deep model generally obtained the more powerful statistical tests.Based on the above understanding, a new bit-level deep learning model was proposed for randomness statistical tests, which gained the advantage of prediction with 80 times fewer parameters and 50% samples, compared with the previous work on linear congruent generator (LCG) algorithm, and achieved significant prediction advantages with 10～20 times fewer parameters by extending the model to apply to 5～7 rounds of Speck, compared with the model proposed by Gohr.…”
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EXTRAPOLATION BY ATTRACTION METHOD FOR IDENTIFICATION OF TRACKS AND PLOTS IN SYSTEM OF SECONDARY RADAR INFORMATION PROCESSING by U. A. Aparovich, R. V. Bondarenko

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A comparison of tools used for tuberculosis diagnosis in resource-limited settings: a case study at Mubende referral hospital, Uganda. by Adrian Muwonge, Sydney Malama, Barend M de C Bronsvoort, Demelash Biffa, Willy Ssengooba, Eystein Skjerve

“…Clinical variables from a questionnaire and DZM were used to predict TB status in multivariable logistic and Cox proportional hazard models, while optimization and visualization was done with receiver operating characteristics curve and algorithm-charts in Stata, R and Lucid-Charts respectively.…”
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Validation of eight endotypes of lupus based on whole-blood RNA profiles by Peter E Lipsky, Prathyusha Bachali, Amrie C Grammer, Erika Hubbard

“…Objective We previously described a classification system of persons with SLE based on whole blood RNA profiles and a random forest (RF) algorithm to predict individual patient endotypes. …”
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Enhancing 3D A* path planning of intelligent bridge crane based on energy efficiency criteria by Heng YANG, Yue LI, Min LIU, Qing DONG

“…Hence, it aims to determine the inaccuracy of the prediction function of the traditional 3D A* algorithm. …”
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FORECASTING COHERENT VOLATILITY BREAKOUTS by A. S. Didenko, M. M. Dubovikov, B. A. Poutko

“…The paper develops an algorithm for making long-term (up to three months ahead) predictions of volatility reversals based on long memory properties of financial time series. …”
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Exploration of the clinicopathological and prognostic significance of BRCA1 in gastric cancer by Hongrong Zhang, Qi Xu, Hongxing Kan, Yinfeng Yang, Yunquan Cai

“…To explore potential biomarkers for GC, GC patient transcriptome data were subjected to a comprehensive approach involving machine learning, binary nomogram prediction model construction, the topological algorithm of CytoHubba, and Kaplan–Meier and Mendelian randomization (MR) analyses. …”
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Quantitative Detection of Quartz Sandstone SiO2 Grade Using Polarized Infrared Absorption Spectroscopy with Convolutional Neural Network Model by Banglong Pan, Hongwei Cheng, Shuhua Du, Hanming Yu, Shaoru Feng, Yi Tang, Juan Du, Huaming Xie

“…Then, generalized regression neural network (GRNN), partial least squares regression (PLSR), and convolutional neural network (CNN) were employed to establish a hyperspectral prediction model of SiO2 grade. The results show that the quantitative model by the PCA-CNN algorithm has the better prediction precision for the reciprocal logarithm data, with a coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to interquartile range (RPIQ) of 0.907, 0.023, and 5.11, respectively. …”
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CONSUMER PREFERENCE RESEARCH IN FORMING RATIONAL COMPONENT OF REGIONAL BRANDS IN THE MEAT PRODUCT MARKET by Mel'nikova E.I., Ponomareva N.V., Bogdanova E.V.

“…The research results confirm that the obtained neural network predicts the main characteristics of normalized mixes with α-lactoglobulin hydrolysate almost accurately; the relative error does not exceed 2.6% when predicting α-lactoglobulin content, 3.9% when predicting residual antigenicity and 3.1% when predicting titratable acidity and organoleptic characteristics. …”
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Research on Mechanical Properties of Steel Tube Concrete Columns Reinforced with Steel–Basalt Hybrid Fibers Based on Experiment and Machine Learning by Bohao Zhang, Xiao Xu, Wenxiu Hao

“…On the basis of the experiments, a parametric expansion analysis of several structural parameters of the specimen was carried out by using ABAQUS finite element software, and a combined model NRBO-XGBoost, based on the Newton-Raphson optimization algorithm (NRBO), and the advanced machine learning model XGBoost was proposed for the prediction of the BSFCFST’s ultimate carrying capacity. …”
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Ginkgetin Alleviates Inflammation and Senescence by Targeting STING by Yadan Liu, Jialin Ye, Zisheng Fan, Xiaolong Wu, Yinghui Zhang, Ruirui Yang, Bing Jiang, Yajie Wang, Min Wu, Jingyi Zhou, Jingyi Meng, Zhiming Ge, Guizhen Zhou, Yuan Zhu, Yichuan Xiao, Mingyue Zheng, Sulin Zhang

“…To reveal the molecular mechanism of Ginkgetin's anti‐aging effect, a graph convolutional network‐based drug “on‐target” pathway prediction algorithm for prediction is employed. The results indicate that the cGAS‐STING pathway may be a potential target for Ginkgetin. …”
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Research on Image Feature Extraction and Environment Inference Based on Invariant Learning by Yujian Ding, Xiaoxu Ma, Bingxue Yang

“…On the basis of this model, GRAD-CAM algorithm is used to extract environmental features of images. …”
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Snow Leopard Infrared Camera Image Detection Method Based on Improved EfficientDet Model by DAI Tianhong, LIU Chao

“… In view of the difficulty of snow leopard detection and recognition in infrared camera images, a snow leopard detection algorithm is proposed based on EfficientDet, which combines domain migration and new attention mechanism.Firstly, the algorithm adopts image enhancement to expand the training sample and adds non-snow leopard images to optimize the dataset structure to improve the robustness of the model. …”
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IASB Framework: Construction of Data Asset Accounting System Based on PO-BP Model by Haixia Guo

“…The PO algorithm optimizes the weights and biases of the BP neural network, improving its global search and local development capabilities. …”
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