Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Machine learning insights on intensive care unit-acquired weakness

Intensive care unit-acquired weakness(ICU-AW)significantly hampers patient recovery and increases morbidity.With the absence of established preventive strategies,this study utilizes advanced machine learning methodologies to unearth key predictors of ICU-AW.Employing a sophisticated multilayer perceptron neural network,the research methodically assesses the predictive power for ICU-AW,pinpointing the length of ICU stay and duration of mechanical ventilation as pivotal risk factors.The findings advocate for minimizing these elements as a preventive approach,offering a novel perspective on combating ICU-AW.This research illuminates critical risk factors and lays the groundwork for future explorations into effective prevention and intervention strategies.

A Multilevel Design Method of Large-scale Machine System Oriented Network Environment

The design of large-scale machine system is a very complex problem.These design problems usually have a lot of design variables and constraints so that they are difficult to be solved rapidly and efficiently by using conventional methods.In this paper,a new multilevel design method oriented network environment is proposed,which maps the design problem of large-scale machine system into a hypergraph with degree of linking strength (DLS) between vertices.By decomposition of hypergraph,this method can divide the complex design problem into some small and simple subproblems that can be solved concurrently in a network.

Series Design of Large-Scale NC Machine Tool

Product system design is a mature concept in western developed countries. It has been applied in war industry during the last century. However,up until now,functional combination is still the main method for product system de-sign in China. Therefore,in terms of a concept of product generation and product interaction we are in a weak position compared with the requirements of global markets. Today,the idea of serial product design has attracted much attention in the design field and the definition of product generation as well as its parameters has already become the standard in serial product designs. Although the design of a large-scale NC machine tool is complicated,it can be further optimized by the precise exercise of object design by placing the concept of platform establishment firmly into serial product de-sign. The essence of a serial product design has been demonstrated by the design process of a large-scale NC machine tool.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Description of a Propulsion Unit Used in Guiding a Walking Machine by Recognizing a Three-point Bordered Path

A reconfigurable propulsion unit based on the Peaucellier-Lipkin mechanism has the ability to describe exact straight or curved paths depending on the selected ratio between the lengths of two of its links. The Peaucellier-Lipkin mechanism with one degree of freedom is transformed into a more sophisticated parallel kinematic chain by including four more degrees of freedom. The resulting propulsion unit is able to adapt its kinematic structure and reach instant centers of rotation, in accordance with the presence of three points that border a geometric path. A laser sensor mounted on the body of the machine detects each point. Once the machine has detected the exact location of the border of the road, it walks along a curve parallel to that border. Although the proposed research describes only one propulsion unit or leg, the methodology can be applied to all the legs of the walking machine. The novel 5-DOF leg is able to reach different centers of rotation, providing either the concave or convex arcs that satisfy the basic principle of displacement of walking machines.

IMPROVE THE KINETIC PERFORMANCE OF THE PUMP CONTROLLED CLAMPING UNIT IN PLASTIC INJECTION MOLDING MACHINE WITH ADAPTIVE CONTROL STRATEGY

The kinetic characteristics of the clamping unit of plastic injection molding machine that is controlled by close loop with newly developed double speed variable pump unit are investigated. Considering the wide variation of the cylinder equivalent mass caused by the transmission ratio of clamping unit and the severe instantaneous impact force acted on the cylinder during the mold closing and opening process, an adaptive control principle of parameter and structure is proposed to improve its kinetic performance. The adaptive correlation between the acceleration feedback gain and the variable mass is derived. The pressure differential feedback is introduced to improve the dynamic performance in the case of small inertia and heavy impact load. The adaptation of sum pressure to load is used to reduce the energy loss of the system. The research results are verified by the simulation and experiment, The investigation method and the conclusions are also suitable for the differential cylinder system controlled by the traditional servo pump unit.

Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population:NHANES database

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)is the most common chronic liver disease,affecting over 30% of the United States population.Early patient identification using a simple method is highly desirable.AIM To create machine learning models for predicting NAFLD in the general United States population.METHODS Using the NHANES 1988-1994.Thirty NAFLD-related factors were included.The dataset was divided into the training(70%)and testing(30%)datasets.Twentyfour machine learning algorithms were applied to the training dataset.The bestperforming models and another interpretable model(i.e.,coarse trees)were tested using the testing dataset.RESULTS There were 3235 participants(n=3235)that met the inclusion criteria.In the training phase,the ensemble of random undersampling(RUS)boosted trees had the highest F1(0.53).In the testing phase,we compared selective machine learning models and NAFLD indices.Based on F1,the ensemble of RUS boosted trees remained the top performer(accuracy 71.1%and F10.56)followed by the fatty liver index(accuracy 68.8% and F10.52).A simple model(coarse trees)had an accuracy of 74.9% and an F1 of 0.33.CONCLUSION Not every machine learning model is complex.Using a simpler model such as coarse trees,we can create an interpretable model for predicting NAFLD with only two predictors:fasting C-peptide and waist circumference.Although the simpler model does not have the best performance,its simplicity is useful in clinical practice.

Micro-seismic Event Detection of Hot Dry Rock based on the Gated Recurrent Unit Model and a Support Vector Machine

Micro-seismic monitoring is one of the most critical technologies that guide hydraulic fracturing in hot dry rock resource development. Micro-seismic monitoring requires high precision detection of micro-seismic events with a low signal-to-noise ratio. Because of this requirement, we propose a recurrent neural network model named gated recurrent unit and support vector machine(GRU;VM). The proposed model ensures high accuracy while reducing the parameter number and hardware requirement in the training process. Since micro-seismic events in hot dry rock produce large wave amplitudes and strong vibrations, it is difficult to reverse the onset of each individual event. In this study, we utilize a support vector machine(SVM) as a classifier to improve the micro-seismic event detection accuracy. To validate the methodology, we compare the simulation results of the short-term-average to the long-term-average(STA/LTA) method with GRU;VM method by using hot dry rock micro-seismic event data in Qinghai Province, China. Our proposed method has an accuracy of about 95% for identifying micro-seismic events with low signal-to-noise ratios. By ignoring smaller micro-seismic events, the detection procedure can be processed more efficiently, which is able to provide a real-time observation on the types of hydraulic fracturing in the reservoirs.

Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach

BACKGROUND Intensive care unit(ICU)patients demand continuous monitoring of several clinical and laboratory parameters that directly influence their medical progress and the staff’s decision-making.Those data are vital in the assistance of these patients,being already used by several scoring systems.In this context,machine learning approaches have been used for medical predictions based on clinical data,which includes patient outcomes.AIM To develop a binary classifier for the outcome of death in ICU patients based on clinical and laboratory parameters,a set formed by 1087 instances and 50 variables from ICU patients admitted to the emergency department was obtained in the“WiDS(Women in Data Science)Datathon 2020:ICU Mortality Prediction”dataset.METHODS For categorical variables,frequencies and risk ratios were calculated.Numerical variables were computed as means and standard deviations and Mann-Whitney U tests were performed.We then divided the data into a training(80%)and test(20%)set.The training set was used to train a predictive model based on the Random Forest algorithm and the test set was used to evaluate the predictive effectiveness of the model.RESULTS A statistically significant association was identified between need for intubation,as well predominant systemic cardiovascular involvement,and hospital death.A number of the numerical variables analyzed(for instance Glasgow Coma Score punctuations,mean arterial pressure,temperature,pH,and lactate,creatinine,albumin and bilirubin values)were also significantly associated with death outcome.The proposed binary Random Forest classifier obtained on the test set(n=218)had an accuracy of 80.28%,sensitivity of 81.82%,specificity of 79.43%,positive predictive value of 73.26%,negative predictive value of 84.85%,F1 score of 0.74,and area under the curve score of 0.85.The predictive variables of the greatest importance were the maximum and minimum lactate values,adding up to a predictive importance of 15.54%.CONCLUSION We demonstrated the efficacy of a Random Forest machine learning algorithm for handling clinical and laboratory data from patients under intensive monitoring.Therefore,we endorse the emerging notion that machine learning has great potential to provide us support to critically question existing methodologies,allowing improvements that reduce mortality.

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus

BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive impairment(MCI),and these patterns predicted their cognitive performance.It has been reported that patients with type 2 diabetes mellitus(T2DM)may develop MCI that could progress to dementia.We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM,using connectome-based predictive modeling(CPM)and a support vector machine.AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls.Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA).Patients with T2DM were divided into two groups,according to the presence(T2DM-C;n=16)or absence(T2DM-NC;n=26)of MCI.Brain regions were marked using Harvard Oxford(HOA-112),automated anatomical labeling(AAL-116),and 264-region functional(Power-264)atlases.LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique.Subsequently,we used a support vector machine based on LSFC patterns for among-group differentiation.The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTS CPM could predict the MoCA scores in patients with T2DM(Pearson’s correlation coefficient between predicted and actual MoCA scores,r=0.32,P=0.0066[HOA-112 atlas];r=0.32,P=0.0078[AAL-116 atlas];r=0.42,P=0.0038[Power-264 atlas]),indicating that LSFC patterns represent cognition-level measures in these patients.Positive(anti-correlated)LSFC networks based on the Power-264 atlas showed the best predictive performance;moreover,we observed new brain regions of interest associated with T2DM-related cognition.The area under the receiver operating characteristic curve values(T2DM-NC group vs.T2DM-C group)were 0.65-0.70,with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value(0.70).Most discriminative and attractive LSFCs were related to the default mode network,limbic system,and basal ganglia.CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

Design and Research of Similarity and its Serialization of the Flotation Machine's Key Structure

Aiming at the problems such as more repeatedly design and longer design cycle, in this paper, the similarity theory was introduced to the design process of the key structures of flotation machine. The impeller and U-shaped tank of flotation machine system were analyzed as similarity unit. Meanwhile, the level of similarity of the units and the similarity of the system were calculated. Based on the analysis of the impeller and the size of U-shaped tank, the similarity criteria were derived. The derived conclusions are： （1） The relationship between the diameter of the impeller and the volume of the tank was power function and calculated as the similarity criteria of the impeller; （2） The relationship between the ratio between the U-shaped tank＇s cross-sectional area and impeller＇s diameter and the volume of the tank was power function and calculated as the similarity criterions of the U-shaped tank. Using the similarity criterion combined with computer technology and database technology to realize part and system serialization design. The results show that the research can efficiency. avoid repeatedly design, shorten design cycle, and raise the design

Piezoresistive Prediction of CNTs-Embedded Cement Composites via Machine Learning Approaches

Conductive cementitious composites are innovated materials that have improved electrical conductivity compared to general types of cement,and are expected to be used in a variety of future infrastructures with unique functionalities such as self-heating,electromagnetic shielding,and piezoelectricity.In the present study,machine learning methods that have been recently applied in various fields were proposed for the prediction of piezoelectric characteristics of carbon nanotubes(CNTs)-incorporated cement composites.Data on the resistivity change of CNTs/cement composites according to various water/binder ratios,loading types,and CNT content were considered as training values.These data were applied to numerous machine learning techniques including linear regression,decision tree,support vector machine,deep belief network,Gaussian process regression,genetic algorithm,bagging ensemble,random forest ensemble,boosting ensemble,long short-term memory,and gated recurrent units to estimate the time-independent and-dependent electrical properties of conductive cementitious composites.By comparing and analyzing the computed results of the proposed methods,an optimal algorithm suitable for application to CNTs-embedded cementitious composites was derived.

Calibration of parallel kinematics machine using generalized distance error model

This paper focus on the accuracy enhancement of parallel kinematics machine through kinematics calibration. In the calibration processing, well-structured identification Jacobian matrix construction and end-effector position and orientation measurement are two main difficulties. In this paper, the identification Jacobian matrix is constructed easily by numerical calculation utilizing the unit virtual velocity method. The generalized distance errors model is presented for avoiding measuring the position and orientation directly which is difficult to be measured. At last, a measurement tool is given for acquiring the data points in the calibration processing. Experimental studies confirmed the effectiveness of method. It is also shown in the paper that the proposed approach can be applied to other typed parallel manipulators.

An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Predicting intensive care unit-acquired weakness:A multilayer perceptron neural network approach

In this editorial,we comment on the article by Wang and Long,published in a recent issue of the World Journal of Clinical Cases.The article addresses the challenge of predicting intensive care unit-acquired weakness(ICUAW),a neuromuscular disorder affecting critically ill patients,by employing a novel processing strategy based on repeated machine learning.The editorial presents a dataset comprising clinical,demographic,and laboratory variables from intensive care unit(ICU)patients and employs a multilayer perceptron neural network model to predict ICUAW.The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW.This editorial contributes to the growing body of literature on predictive modeling in critical care,offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.

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.

Multi-area Frequency-constrained Unit Commitment for Power Systems with High Penetration of Renewable Energy Sources and Induction Machine Load

The increasing penetration of renewable energy sources(RESs)brings great challenges to the frequency security of power systems.The traditional frequency-constrained unit commitment(FCUC)analyzes frequency by simplifying the average system frequency and ignoring numerous induction machines(IMs)in load,which may underestimate the risk and increase the operational cost.In this paper,we consider a multiarea frequency response(MAFR)model to capture the frequency dynamics in the unit scheduling problem,in which regional frequency security and the inertia of IM load are modeled with high-dimension differential algebraic equations.A multi-area FCUC(MFCUC)is formulated as mixed-integer nonlinear programming(MINLP)on the basis of the MAFR model.Then,we develop a multi-direction decomposition algorithm to solve the MFCUC efficiently.The original MINLP is decomposed into a master problem and subproblems.The subproblems check the nonlinear frequency dynamics and generate linear optimization cuts for the master problem to improve the frequency security in its optimal solution.Case studies on the modified IEEE 39-bus system and IEEE 118-bus system show a great reduction in operational costs.Moreover,simulation results verify the ability of the proposed MAFR model to reflect regional frequency security and the available inertia of IMs in unit scheduling.

基于机器学习结合非结构化HER数据建立ICU患者死亡风险预测模型

目的构建机器学习模型预测重症监护病房(ICU)患者全因死亡的风险。方法基于重症监护医疗信息市场Ⅲ(MIMIC-Ⅲ)数据库,使用机器学习法将电子病历(EHR)中的结构化和非结构化数据相整合,创建ICU患者的死亡风险预测模型。结果结合结构化和非结构化数据的机器学习模型提高了ICU患者临床结局预测的准确性,优选出的梯度增强模型的受试者操作特征曲线下面积(AUROC)值为0.88,表明可以准确预测患者生命状态。结论采用机器学习模型,基于少量易于收集的结构化变量结合非结构化数据,可以显著提高ICU患者死亡风险预测模型的预测性能。