Theoretical Modeling and Surface Roughness Prediction of Microtextured Surfaces in Ultrasonic Vibration-Assisted Milling

Textured surfaces with certain micro/nano structures have been proven to possess some advanced functions,such as reducing friction,improving wear and increasing wettability.Accurate prediction of micro/nano surface textures is of great significance for the design,fabrication and application of functional textured surfaces.In this paper,based on the kinematic analysis of cutter teeth,the discretization of ultrasonic machining process,transformation method of coordinate systems and the cubic spline data interpolation,an integrated theoretical model was established to characterize the distribution and geometric features of micro textures on the surfaces machined by different types of ultrasonic vibration-assisted milling(UVAM).Based on the theoretical model,the effect of key process parameters(vibration directions,vibration dimensions,cutting parameters and vibration parameters)on tool trajectories and microtextured surface morphology in UVAM is investigated.Besides,the effect of phase difference on the elliptical shape in 2D/3D ultrasonic elliptical vibration-assisted milling(UEVAM)was analyzed.Compared to conventional numerical models,the method of the cubic spline data interpolation is applied to the simulation of microtextured surface morphology in UVAM,which is more suitable for characterizing the morphological features of microtextured surfaces than traditional methods due to the presence of numerous micro textures.The prediction of surface roughness indicates that the magnitude of ultrasonic amplitude in z-direction should be strictly limited in 1D rotary UVAM,2D and 3D UEVAM due to the unfavorable effect of axial ultrasonic vibration on the surface quality.This study can provide theoretical guidance for the design and fabrication of microtextured surfaces in UVAM.

Prediction and optimization of flue pressure in sintering process based on SHAP

Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

Early prediction cardiac arrest in intensive care units:the value of laboratory indicator trends

The incidence of in-hospital cardiac arrest (IHCA) has increased over the past decade,with more than half occurring in intensive care units (ICUs).^([1])ICU cardiac arrest (ICU-CA)presents unique challenges,with worse outcomes than those in monitored wards,highlighting the need for early detection and intervention.^([2])Up to 80%of patients exhibit signs of deterioration hours before IHCA.^([3])Although early warning scores based on vital signs are useful,their eff ectiveness in ICUs is limited due to abnormal physiological parameters.^([4])Laboratory markers,such as sodium,potassium,and lactate,are predictive of poor outcomes,^([5])but static measurements may not capture the patient’s trajectory.Trends in laboratory indicators,such as variability and extremes,may offer better predictive value.^([6])This study aimed to evaluate ICU-CA predictive factors,with a focus on vital signs and trends of laboratory indicators.

Short-Term Rolling Prediction of Tropical Cyclone Intensity Based on Multi-Task Learning with Fusion of Deviation-Angle Variance and Satellite Imagery

Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging behind.At present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning.However,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting applications.Therefore,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity.For TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial features.For real-time intensity estimation,multi-task learning acts as an implicit time-series enhancement.The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions.Since multiple tasks are correlated,the loss function of 12 h and 24 h are corrected.After testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.

Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM

Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.

A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.

Short-Term Photovoltaic Power Prediction Based onMulti-Stage Temporal Feature Learning

Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challenges for its extensive incorporation into power grids.Thus,enhancing the precision of PV power prediction is particularly important.Although existing studies have made progress in short-term prediction,issues persist,particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data.These factors hinder improvements in PV power prediction performance.To overcome these challenges,this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning.First,the improved LSTMand SA-ConvLSTMare employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images,respectively.Subsequently,a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities,enhancing the capacity to focus on the most relevant features.Finally,theTransformermodel is applied to further capture the short-termtemporal patterns and long-term dependencies within multi-modal feature information.The paper also compares the proposed method with various competitive methods.The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.

Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces

The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.

Risk factors for biometry prediction error by Barrett Universal II intraocular lens formula in Chinese patients

AIM:To investigate the influence of postoperative intraocular lens(IOL)positions on the accuracy of cataract surgery and examine the predictive factors of postoperative biometry prediction errors using the Barrett Universal II(BUII)IOL formula for calculation.METHODS:The prospective study included patients who had undergone cataract surgery performed by a single surgeon from June 2020 to April 2022.The collected data included the best-corrected visual acuity(BCVA),corneal curvature,preoperative and postoperative central anterior chamber depths(ACD),axial length(AXL),IOL power,and refractive error.BUII formula was used to calculate the IOL power.The mean absolute error(MAE)was calculated,and all the participants were divided into two groups accordingly.Independent t-tests were applied to compare the variables between groups.Logistic regression analysis was used to analyze the influence of age,AXL,corneal curvature,and preoperative and postoperative ACD on MAE.RESULTS:A total of 261 patients were enrolled.The 243(93.1%)and 18(6.9%)had postoperative MAE<1 and>1 D,respectively.The number of females was higher in patients with MAE>1 D(χ^(2)=3.833,P=0.039).The postoperative BCVA(logMAR)of patients with MAE>1 D was significantly worse(t=-2.448;P=0.025).After adjusting for gender in the logistic model,the risk of postoperative refractive errors was higher in patients with a shallow postoperative anterior chamber[odds ratio=0.346;95% confidence interval(CI):0.164,0.730,P=0.005].CONCLUSION:Risk factors for biometry prediction error after cataract surgery include the patient’s sex and postoperative ACD.Patients with a shallow postoperative anterior chamber are prone to have refractive errors.

A robust statistical prediction model for late-summer heavy precipitation days in North China

Recently,heavy precipitation(HP)events have occurred frequently in North China(NC),causing devastating economic losses and human fatalities.However,the short-term climate prediction of HP is quite limited.Combining year-to-year increment(DY)method and sliding correlations,we developed a robust seasonal prediction model for late-summer HP days(HPDs)in NC during 1982–2022,utilizing three independent predictors—February sea surface temperature(SST)in the Indian Ocean(SST_IO),February snow depth over North Asia(SDE_NA),and May melted snow depth in NC(MSDE_NC).The SST_IO anomalies affect NC's precipitation through the Pacific-Japan pattern.The SDE_NA anomalies are associated with East Asian anomalous anticyclone by southeastern propagation of Rossby wave at Eurasia.The MSDE_NC anomalies are followed by vertical motion and moisture anomalies in situ and thereby cause precipitation anomalies.This prediction model can well simulate the variations of the HPDs,with a correlation coefficient(CC)of 0.81(0.65)between the observed and predicted HPDs_DY(HPDs_anomaly).The percentage with the same sign for 15 extreme HPDs_anomaly years(PSSE)is 100%.Moreover,in the cross-validation test during 1982–2022,the PSSE for HPDs_anomaly is as high as 100%,along with a low rootmean-square error of 1.14.For independent hindcasts during 2013–2022,the CC between the observed and predicted HPDs_DY(HPDs_anomaly)is 0.93(0.83),together with high Nash-Sutcliffe efficiency(0.82)and agreement index(0.89).Specifically,the predictions are broadly consistent with the observations for 2015,2016,2017,2021,and 2022,reflecting excellent performance of this prediction model of HPDs in NC.

Data driven prediction of fragment velocity distribution under explosive loading conditions

This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.

Theoretical prediction of forming limit diagram of AZ31 magnesium alloy sheet at warm temperatures

A theoretical prediction on forming limit diagram（FLD） of AZ31 magnesium alloy sheet was developed at warm temperatures based on the M-K theory. Two different yield criteria of von Mises and Hill＇48 were applied in this model. Mechanical properties of AZ31 magnesium alloy used in the prediction were obtained by uniaxial tensile tests and the Fields-Backofen equation was incorporated in the analysis. In addition, experimental FLDs of AZ31 were acquired by conducting rigid die swell test at different temperatures to verify the prediction. It is demonstrated from a comparison between the predicted and the experimental FLDs at 473 K and 523 K that the predicted results are influenced by the type of yield criterion used in the calculation, especially at lower temperatures. Furthermore, a better agreement between the predicted results and experimental data for AZ31 magnesium alloy sheet at warm temperatures was obtained when Hill＇48 yield criterion was applied.

Enhancing rectal cancer liver metastasis prediction:Magnetic resonance imaging-based radiomics,bias mitigation,and regulatory considerations

In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.

Prediction of the amount of urban waste solids by applying a gray theoretical model

Urban waste solids are now becoming one of the most crucial environmental problems. There are several different kinds of technologies normally used for waste solids disposal, among which landfill is more favorable in China than others, especially for urban waste solids. Most of the design works up to now are based on a roughly estimation of the amount of urban waste solids without any theoretical support, which lead to a series problems. To meet the basic information requirements for the design work, the amount of the urban waste solids was predicted in this research by applying the gray theoretical model GM (1,1) through non linear differential equation simulation. The model parameters were estimated with the least square method (LSM) by running a certain MATALAB program, and the hypothesis test results show that the residual between the prediction value and the actual value approximately comply with the normal distribution N (0,0 21 2), and the probability of the residual within the range (-0 17, 0 19) is more than 95%, which indicate obviously that the model can be well used for the prediction of the amount of waste solids and those had been already testified by the latest two years data about the urban waste solids from Loudi City of China. With this model, the predicted amount of the waste solids produced in Loudi City in the next 30 years is 8049000 ton in total.

Theoretical Prediction of Gibbs Free Energies of Formation for Crystallineα-MOOH andα-M_2O_3 Based on a Linear Free-Energy Relationship

In the present study,the modified Sverjensky-Molling equation,derived from a linear-free energy relationship,is used to predict the Gibbs free energies of formation of crystalline phases ofα-MOOH （with a goethite structure）andα-M_2O_3（with a hematite structure）from the known thermodynamic properties of the corresponding aqueous trivalent cations（M^（3＋））.The modified equation is expressed asΔG_（f,M_VX）~0=a_（M_VX）ΔG_（0,M^（3＋））^（0）＋b_（M_VX）＋β_（M_VXγM^（3＋））,where the coefficients a_（M_VX）,b_（M_VX）,andβ_（M_VX） characterize a particular structural family of M_VX（M is a trivalent cation[M^（3＋）]and X represents the remainder of the composition of solid）;γ^（3＋）is the ionic radius of trivalent cations（M^（3＋））;ΔG_（f,M_VX）~0 is the standard Gibbs free energy of formation of M_vX;andΔG_（n,M^（3＋））~0 is the non-solvation energy of trivalent cations（M^（3＋））.By fitting the equation to the known experimental thermodynamic data,the coefficients for the goethite family（α-MOOH）are a_（M_VX）=0.8838,b_（M_VX）=-424.4431（kcal/mol）,andβ_（M_VX）=115（kcal/ mol.（？））,while the coefficients for the hematite family（α-M_2O_3）are a_（M_VX）=1.7468,b_（M_VX）=-814.9573（kcal/ mol）,andβ_（M_VX）=278（kcal/mol.（？））.The constrained relationship can be used to predict the standard Gibbs free energies of formation of crystalline phases and fictive phases（i.e.phases that are thermodynamically unstable and do not occur at standard conditions）within the isostructural families of goethite（α-MOOH）and hematite（α-M_2O_3）if the standard Gibbs free energies of formation of the trivalent cations are known.

Theoretical prediction on corrugated sandwich panels under bending loads

In this paper,an aluminum corrugated sandwich panel with triangular core under bending loads was investigated.Firstly,the equivalent material parameters of the triangular corrugated core layer,which could be considered as an orthotropic panel,were obtained by using Castigliano's theorem and equivalent homogeneous model.Secondly,contributions of the corrugated core layer and two face panels were both considered to compute the equivalent material parameters of the whole structure through the classical lamination theory,and these equivalent material parameters were compared with finite element analysis solutions.Then,based on the Mindlin orthotropic plate theory,this study obtain the closed-form solutions of the displacement for a corrugated sandwich panel under bending loads in specified boundary conditions,and parameters study and comparison by the finite element method were executed simultaneously.

Theoretical prediction of wear of disc cutters in tunnel boring machine and its application

Predicting the cutter consumption and the exact time to replace the worn-out cutters in tunneling projects constructed with tunnel boring machine(TBM) is always a challenging issue. In this paper, we focus on the analyses of cutter motion in the rock breaking process and trajectory of rock breaking point on the cutter edge in rocks. The analytical expressions of the length of face along which the breaking point moves and the length of spiral trajectory of the maximum penetration point are derived. Through observation of rock breaking process of disc cutters as well as analysis of disc rock interaction, the following concepts are proposed: the arc length theory of predicting wear extent of inner and center cutters, and the spiral theory of predicting wear extent of gage and transition cutters. Data obtained from5621 m-long Qinling tunnel reveal that among 39 disc cutters, the relative errors between cumulatively predicted and measured wear values for nine cutters are larger than 20%, while approximately 76.9% of total cutters have the relative errors less than 20%. The proposed method could offer a new attempt to predict the disc cutter's wear extent and changing time.

Theoretical Predictions of Migration Probabilities of Liquid- Liquid Hydrocyclones Separating Light Dispersions

Based on Bloor & Ingham's approach for determining the fluid fieldand on the analyses of loci of fluid particles inside hydrocyclones,analytical models are developed for calculating the migrationprobability of single-cone and two-cone hydrocyclones separatinglight dispersions. The calculated results are in good agreement withThew's correlation at different flow rate, split ratio or fluidproperties if the structural parameters keep the same as those ofThew's 35 mm hydrocyclone. The difference between predictionsaccording to two-cone model and single-cone model is nearlynegligible, which is very close to thew's original idea that majorseparation happens in the small cone-angle zone.

Theoretical Prediction of the Photovoltaic Properties of BFBPD-PC61 BM System as a Promising Organic Solar Cell

In this work,the photovoltaic properties of BFBPD-PC61 BM system as a promising high-performance organic solar cell（OSC） were theoretically investigated by means of quantum chemistry and molecular dynamics calculations coupled with the incoherent charge-hopping model.Moreover,the hole carrier mobility of BFBPD thin-film was also estimated with the aid of an amorphous cell including 100 BFBPD molecules.Results revealed that the BFBPD-PC61 BM system possesses a middle-sized open-circuit voltage of 0.70 V,large short-circuit current density of 17.26 mA ·cm^-2,high fill factor of 0.846,and power conversion efficiency of 10%.With the Marcus model,in the BFBPD-PC61 BM interface,the exciton-dissociation rate,kdis,was predicted to be 2.684×10^13 s^-1,which is as 3-5 orders of magnitude large as the decay（radiative and non-radiative） one（10-8-10^10s^-1）,indicating a high exciton-dissociation efficiency of 100% in the BFBPD-PC61 BM interface.Furthermore,by the molecular dynamics simulation,the hole mobility of BFBPD thin-film was predicted to be as high as 1.265 × 10^-2 cm-2·V^-1·s^-1,which can be attributed to its dense packing in solid state.

A New Target for Synthesis: Theoretical Prediction for the Reaction between Boron Nitride Nanotube and Dichlorocarbene

Understanding the chemistry of BNNT is a crucial step toward their ultimate practical use. A comparative study of Reactions A （ASWCNT （5,5） and CCl2） and B （ASWBNNT （5,5） and CCl2） have been performed by using ONIOM （B3LYP/6-31G＊： AM1） method in Gaussian03 program package. The results show that （1） the two reactions are both exothermic; （2） the mechanism of Reaction B is a two-step mechanism; （3） the difference in energy barriers suggests that the reaction of CCl2 with BNNT is easier than with CNT; （4） in reaction B, CCl2 prefers to attack the boron atom of BNNT first.