The use of a CO2 laser system for fabrication of microfluidic chip on polymethyl methacrylate （PMMA） is presented to reduce fabrication cost and time of chip. The grooving process of the laser system and a model for...The use of a CO2 laser system for fabrication of microfluidic chip on polymethyl methacrylate （PMMA） is presented to reduce fabrication cost and time of chip. The grooving process of the laser system and a model for the depth of microchannels are investigated. The relations between the depth of laser-cut channels and the laser beam power, velocity or the number of passes of the beam along the same channel are evaluated. In the experiments, the laser beam power varies from 0 to 50 W, the laser beam scanning velocity varies from 0 to 1 000 mm/s and the passes vary in the range of 1 to 10 times. Based on the principle of conservation of energy, the influence of the laser beam velocity, the laser power and the number of groove passes are examine. Considering the grooving interval energy loss, a modified mathematical model has been obtained and experimental data show good agreement with the theoretical model. This approach provides a simple way of predicting groove depths. The system provides a cost alternative of the other methods and it is especially useful on research work of rnicrofluidic prototyping due to the short cycle time of production.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram...BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram(ECG)during resting conditions and physical exertion in individuals diagnosed with IHD and those without the condition using vasodilator-induced stress computed tomography(CT)myocardial perfusion imaging as the diagnostic reference standard.METHODS This single center observational study included 80 participants.The participants were aged≥40 years and given an informed written consent to participate in the study.Both groups,G1(n=31)with and G2(n=49)without post stress induced myocardial perfusion defect,passed cardiologist consultation,anthropometric measurements,blood pressure and pulse rate measurement,echocardiography,cardio-ankle vascular index,bicycle ergometry,recording 3-min single-lead ECG(Cardio-Qvark)before and just after bicycle ergometry followed by performing CT myocardial perfusion.The LASSO regression with nested cross-validation was used to find the association between Cardio-Qvark parameters and the existence of the perfusion defect.Statistical processing was performed with the R programming language v4.2,Python v.3.10[^R],and Statistica 12 program.RESULTS Bicycle ergometry yielded an area under the receiver operating characteristic curve of 50.7%[95%confidence interval(CI):0.388-0.625],specificity of 53.1%(95%CI:0.392-0.673),and sensitivity of 48.4%(95%CI:0.306-0.657).In contrast,the Cardio-Qvark test performed notably better with an area under the receiver operating characteristic curve of 67%(95%CI:0.530-0.801),specificity of 75.5%(95%CI:0.628-0.88),and sensitivity of 51.6%(95%CI:0.333-0.695).CONCLUSION The single-lead ECG has a relatively higher diagnostic accuracy compared with bicycle ergometry by using machine learning models,but the difference was not statistically significant.However,further investigations are required to uncover the hidden capabilities of single-lead ECG in IHD diagnosis.展开更多
Zhou et al’s investigation on the creation of a non-invasive deep learning(DL)method for colorectal tumor immune microenvironment evaluation using preoperative computed tomography(CT)radiomics published in the World ...Zhou et al’s investigation on the creation of a non-invasive deep learning(DL)method for colorectal tumor immune microenvironment evaluation using preoperative computed tomography(CT)radiomics published in the World Journal of Gastrointestinal Oncology is thorough and scientific.The study analyzed preoperative CT images of 315 confirmed colorectal cancer patients,using manual regions of interest to extract DL features.The study developed a DL model using CT images and histopathological images to predict immune-related indicators in colorectal cancer patients.Pathological(tumor-stroma ratio,tumor-infiltrating lymphocytes infiltration,immunohistochemistry,tumor immune microenvir-onment and immune score)parameters and radiomics(CT imaging and model construction)data were combined to generate artificial intelligence-powered models.Clinical benefit and goodness of fit of the models were assessed using receiver operating characteristic,area under curve and decision curve analysis.The developed DL-based radiomics prediction model for non-invasive evaluation of tumor markers demonstrated potential for personalized treatment planning and immunotherapy strategies in colorectal cancer patients.The study,involving a small group from a single medical center,lacks inclusion/exclusion criteria and should include clinicopathological features for valuable therapeutic practice insights in colorectal cancer patients.展开更多
Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouri...Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources.Particularly considering the implications of the aggressive GHG emission targets,accurate GHI forecasting has become vital for developing,designing,and operational managing solar energy systems.This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA(Autoregressive Integrated Moving Average),Elaman NN(Elman Neural Network),RBFN(Radial Basis Function Neural Network),SVM(Support Vector Machine),LSTM(Long Short-Term Memory),Persistent,BPN(Back Propagation Neural Network),MLP(Multilayer Perceptron Neural Network),RF(Random Forest),and XGBoost(eXtreme Gradient Boosting)for assessing multi-seasonal forecasting of GHI.Used the India region data to evaluate the models’performance and forecasting ability.Research using forecasting models for seasonal Global Horizontal Irradiance(GHI)forecasting in winter,spring,summer,monsoon,and autumn.Substantiated performance effectiveness through evaluation metrics,such as Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),and R-squared(R^(2)),coded using Python programming.The performance experimentation analysis inferred that the most accurate forecasts in all the seasons compared to the other forecasting models the Random Forest and eXtreme Gradient Boosting,are the superior and competing models that yield Winter season-based forecasting XGBoost is the best forecasting model with MAE:1.6325,RMSE:4.8338,and R^(2):0.9998.Spring season-based forecasting XGBoost is the best forecasting model with MAE:2.599599,RMSE:5.58539,and R^(2):0.999784.Summer season-based forecasting RF is the best forecasting model with MAE:1.03843,RMSE:2.116325,and R^(2):0.999967.Monsoon season-based forecasting RF is the best forecasting model with MAE:0.892385,RMSE:2.417587,and R^(2):0.999942.Autumn season-based forecasting RF is the best forecasting model with MAE:0.810462,RMSE:1.928215,and R^(2):0.999958.Based on seasonal variations and computing constraints,the findings enable energy system operators to make helpful recommendations for choosing the most effective forecasting models.展开更多
The investigation by Zhu et al on the assessment of cellular proliferation markers to assist clinical decision-making in patients with hepatocellular carcinoma(HCC)using a machine learning model-based approach is a sc...The investigation by Zhu et al on the assessment of cellular proliferation markers to assist clinical decision-making in patients with hepatocellular carcinoma(HCC)using a machine learning model-based approach is a scientific approach.This study looked into the possibilities of using a Ki-67(a marker for cell proliferation)expression-based machine learning model to help doctors make decisions about treatment options for patients with HCC before surgery.The study used reconstructed tomography images of 164 patients with confirmed HCC from the intratumoral and peritumoral regions.The features were chosen using various statistical methods,including least absolute shrinkage and selection operator regression.Also,a nomogram was made using Radscore and clinical risk factors.It was tested for its ability to predict receiver operating characteristic curves and calibration curves,and its clinical benefits were found using decision curve analysis.The calibration curve demonstrated excellent consistency between predicted and actual probability,and the decision curve confirmed its clinical benefit.The proposed model is helpful for treating patients with HCC because the predicted and actual probabilities are very close to each other,as shown by the decision curve analysis.Further prospective studies are required,incorporating a multicenter and large sample size design,additional relevant exclusion criteria,information on tumors(size,number,and grade),and cancer stage to strengthen the clinical benefit in patients with HCC.展开更多
The backwater effect caused by tributary inflow can significantly elevate the water level profile upstream of a confluence point.However,the influence of mainstream and confluence discharges on the backwater effect in...The backwater effect caused by tributary inflow can significantly elevate the water level profile upstream of a confluence point.However,the influence of mainstream and confluence discharges on the backwater effect in a river reach remains unclear.In this study,various hydrological data collected from the Jingjiang Reach of the Yangtze River in China were statistically analyzed to determine the backwater degree and range with three representative mainstream discharges.The results indicated that the backwater degree increased with mainstream discharge,and a positive relationship was observed between the runoff ratio and backwater degree at specific representative mainstream discharges.Following the operation of the Three Gorges Project,the backwater effect in the Jingjiang Reach diminished.For instance,mean backwater degrees for low,moderate,and high mainstream discharges were recorded as 0.83 m,1.61 m,and 2.41 m during the period from 1990 to 2002,whereas these values decreased to 0.30 m,0.95 m,and 2.08 m from 2009 to 2020.The backwater range extended upstream as mainstream discharge increased from 7000 m3/s to 30000 m3/s.Moreover,a random forest-based machine learning model was used to quantify the backwater effect with varying mainstream and confluence discharges,accounting for the impacts of mainstream discharge,confluence discharge,and channel degradation in the Jingjiang Reach.At the Jianli Hydrological Station,a decrease in mainstream discharge during flood seasons resulted in a 7%–15%increase in monthly mean backwater degree,while an increase in mainstream discharge during dry seasons led to a 1%–15%decrease in monthly mean backwater degree.Furthermore,increasing confluence discharge from Dongting Lake during June to July and September to November resulted in an 11%–42%increase in monthly mean backwater degree.Continuous channel degradation in the Jingjiang Reach contributed to a 6%–19%decrease in monthly mean backwater degree.Under the influence of these factors,the monthly mean backwater degree in 2017 varied from a decrease of 53%to an increase of 37%compared to corresponding values in 1991.展开更多
Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in t...Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in three coastal cities in Jiangsu Province,China.Seasonal and Trend decomposition using Loess(STL)together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis(HCA).Machine learning models were developed to predict GWD,then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations(SHAP)method.Results showed that eXtreme Gradient Boosting(XGB)was superior to other models in terms of prediction performance and computational efficiency(R^(2)>0.95).GWD in Yancheng and southern Lianyungang were greater than those in Nantong,exhibiting larger fluctuations.Groundwater within 5 km of the coastline was affected by tides,with more pronounced effects in agricultural areas compared to urban areas.Shallow groundwater(3-7 m depth)responded immediately(0-1 day)to rainfall,primarily influenced by farmland and topography(slope and distance from rivers).Rainfall recharge to groundwater peaked at 50%farmland coverage,but this effect was suppressed by high temperatures(>30℃)which intensified as distance from rivers increased,especially in forest and grassland.Deep groundwater(>10 m)showed delayed responses to rainfall(1-4 days)and temperature(10-15 days),with GDP as the primary influence,followed by agricultural irrigation and population density.Farmland helped to maintain stable GWD in low population density regions,while excessive farmland coverage(>90%)led to overexploitation.In the early stages of GDP development,increased industrial and agricultural water demand led to GWD decline,but as GDP levels significantly improved,groundwater consumption pressure gradually eased.This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.展开更多
Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery conditi...Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.展开更多
To overcome the challenges of limited experimental data and improve the accuracy of empirical formulas,we propose a low-cycle fatigue(LCF)life prediction model for nickel-based superalloys using a data augmentation me...To overcome the challenges of limited experimental data and improve the accuracy of empirical formulas,we propose a low-cycle fatigue(LCF)life prediction model for nickel-based superalloys using a data augmentation method.This method utilizes a variational autoencoder(VAE)to generate low-cycle fatigue data and form an augmented dataset.The Pearson correlation coefficient(PCC)is employed to verify the similarity of feature distributions between the original and augmented datasets.Six machine learning models,namely random forest(RF),artificial neural network(ANN),support vector machine(SVM),gradient-boosted decision tree(GBDT),eXtreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost),are utilized to predict the LCF life of nickel-based superalloys.Results indicate that the proposed data augmentation method based on VAE can effectively expand the dataset,and the mean absolute error(MAE),root mean square error(RMSE),and R-squared(R^(2))values achieved using the CatBoost model,with respective values of 0.0242,0.0391,and 0.9538,are superior to those of the other models.The proposed method reduces the cost and time associated with LCF experiments and accurately establishes the relationship between fatigue characteristics and LCF life of nickel-based superalloys.展开更多
Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall ...Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
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 propose...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.展开更多
The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning mode...The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.展开更多
This paper presents a state-of-the-art review in modeling approach of hardware in the loop simulation(HILS)realization of electric machine drives using commercial real time machines.HILS implementation using digital s...This paper presents a state-of-the-art review in modeling approach of hardware in the loop simulation(HILS)realization of electric machine drives using commercial real time machines.HILS implementation using digital signal processors(DSPs)and field programmable gate array(FPGA)for electric machine drives has been investigated but those methods have drawbacks such as complexity in development and verification.Among various HILS implementation approaches,more efficient development and verification for electric machine drives can be achieved through use of commercial real time machines.As well as implementation of the HILS,accurate modeling of a control target system plays an important role.Therefore,modeling trend in electric machine drives for HILS implementation is needed to be reviewed.This paper provides a background of HILS and commercially available real time machines and characteristics of each real time machine are introduced.Also,recent trends and progress of permanent magnet synchronous machines(PMSMs)modeling are presented for providing more accurate HILS implementation approaches in this paper.展开更多
Pain is a subjective and complex phenomenon. Its complexity is related to its heterogeneity： multiple component processes, including sensation, affect, and cognition, contribute to pain experience and reporting. Thes...Pain is a subjective and complex phenomenon. Its complexity is related to its heterogeneity： multiple component processes, including sensation, affect, and cognition, contribute to pain experience and reporting. These components are likely to be encoded in distributed brain networks that interact to create pain experience and pain-related decision-making. Therefore, to understand pain, we must identify these networks and build models of these interactions that yield testable predictions about pain-related outcomes. We have developed several such models or ＇signatures＇ of pain, by （1） integrating activity across multiple systems, and （2） using pattern-recognition to identify processes related to pain experience. One model, the Neurologic Pain Signature, is sensitive and specific to pain in individuals, involves brain regions that receive nociceptive afferents, and shows little effect of expectation or self-regulation in tests to date. Another, the ＇Stimulus Intensity-Independent Pain Signature＇, explains substantial additional variation in trial-to-trial pain reports. It involves many brain regions that do not show increased activity in proportion to noxious stimulus intensity, includ- ing medial and lateral prefrontal cortex, nucleus accum- bens, and hippocampus. Responses in this system mediate expectancy and perceived control effects in several studies. Overall, this approach provides a pathway to understanding pain by identifying multiple systems that track different aspects of pain. Such componential models can be combined in unique ways on a subject-by-subject basis to explain an individual＇s pain experience.展开更多
The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper ...The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.展开更多
BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in ...BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS Data of patients treated for colorectal cancer(n=2044)at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected.Patients were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation occurrence.Patients were also divided into a training group and a validation group(7:3 ratio).We used three different machine learning methods to screen characteristic variables.A nomogram was created based on multifactor logistic regression,and the model performance was assessed using receiver operating characteristic curve,calibration curve,Hosmer-Lemeshow test,and decision curve analysis.The risk scores of the two groups were calculated and compared to validate the model.RESULTS More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control group.Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(P<0.05):Prognostic Nutritional Index value,history of laparotomy,hypertension,or stroke,hypoproteinemia,age,tumor-node-metastasis staging,surgical time,gender,and American Society of Anesthesiologists classification.Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer,which can improve treatment decisions and prognosis.展开更多
In recent years evidence has emerged suggesting that Mini-basketball training program(MBTP)can be an effec-tive intervention method to improve social communication(SC)impairments and restricted and repetitive beha-vio...In recent years evidence has emerged suggesting that Mini-basketball training program(MBTP)can be an effec-tive intervention method to improve social communication(SC)impairments and restricted and repetitive beha-viors(RRBs)in preschool children suffering from autism spectrum disorder(ASD).However,there is a considerable degree if interindividual variability concerning these social outcomes and thus not all preschool chil-dren with ASD profit from a MBTP intervention to the same extent.In order to make more accurate predictions which preschool children with ASD can benefit from an MBTP intervention or which preschool children with ASD need additional interventions to achieve behavioral improvements,further research is required.This study aimed to investigate which individual factors of preschool children with ASD can predict MBTP intervention out-comes concerning SC impairments and RRBs.Then,test the performance of machine learning models in predict-ing intervention outcomes based on these factors.Participants were 26 preschool children with ASD who enrolled in a quasi-experiment and received MBTP intervention.Baseline demographic variables(e.g.,age,body,mass index[BMI]),indicators of physicalfitness(e.g.,handgrip strength,balance performance),performance in execu-tive function,severity of ASD symptoms,level of SC impairments,and severity of RRBs were obtained to predict treatment outcomes after MBTP intervention.Machine learning models were established based on support vector machine algorithm were implemented.For comparison,we also employed multiple linear regression models in statistics.Ourfindings suggest that in preschool children with ASD symptomatic severity(r=0.712,p<0.001)and baseline SC impairments(r=0.713,p<0.001)are predictors for intervention outcomes of SC impair-ments.Furthermore,BMI(r=-0.430,p=0.028),symptomatic severity(r=0.656,p<0.001),baseline SC impair-ments(r=0.504,p=0.009)and baseline RRBs(r=0.647,p<0.001)can predict intervention outcomes of RRBs.Statistical models predicted 59.6%of variance in post-treatment SC impairments(MSE=0.455,RMSE=0.675,R2=0.596)and 58.9%of variance in post-treatment RRBs(MSE=0.464,RMSE=0.681,R2=0.589).Machine learning models predicted 83%of variance in post-treatment SC impairments(MSE=0.188,RMSE=0.434,R2=0.83)and 85.9%of variance in post-treatment RRBs(MSE=0.051,RMSE=0.226,R2=0.859),which were better than statistical models.Ourfindings suggest that baseline characteristics such as symptomatic severity of 144 IJMHP,2022,vol.24,no.2 ASD symptoms and SC impairments are important predictors determining MBTP intervention-induced improvements concerning SC impairments and RBBs.Furthermore,the current study revealed that machine learning models can successfully be applied to predict the MBTP intervention-related outcomes in preschool chil-dren with ASD,and performed better than statistical models.Ourfindings can help to inform which preschool children with ASD are most likely to benefit from an MBTP intervention,and they might provide a reference for the development of personalized intervention programs for preschool children with ASD.展开更多
基金This project is supported by National Hi-tech Research and Development Program of China (863 Program, No.2002AA421150)Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20030335091).
文摘The use of a CO2 laser system for fabrication of microfluidic chip on polymethyl methacrylate （PMMA） is presented to reduce fabrication cost and time of chip. The grooving process of the laser system and a model for the depth of microchannels are investigated. The relations between the depth of laser-cut channels and the laser beam power, velocity or the number of passes of the beam along the same channel are evaluated. In the experiments, the laser beam power varies from 0 to 50 W, the laser beam scanning velocity varies from 0 to 1 000 mm/s and the passes vary in the range of 1 to 10 times. Based on the principle of conservation of energy, the influence of the laser beam velocity, the laser power and the number of groove passes are examine. Considering the grooving interval energy loss, a modified mathematical model has been obtained and experimental data show good agreement with the theoretical model. This approach provides a simple way of predicting groove depths. The system provides a cost alternative of the other methods and it is especially useful on research work of rnicrofluidic prototyping due to the short cycle time of production.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
基金Supported by Government Assignment,No.1023022600020-6RSF Grant,No.24-15-00549Ministry of Science and Higher Education of the Russian Federation within the Framework of State Support for the Creation and Development of World-Class Research Center,No.075-15-2022-304.
文摘BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram(ECG)during resting conditions and physical exertion in individuals diagnosed with IHD and those without the condition using vasodilator-induced stress computed tomography(CT)myocardial perfusion imaging as the diagnostic reference standard.METHODS This single center observational study included 80 participants.The participants were aged≥40 years and given an informed written consent to participate in the study.Both groups,G1(n=31)with and G2(n=49)without post stress induced myocardial perfusion defect,passed cardiologist consultation,anthropometric measurements,blood pressure and pulse rate measurement,echocardiography,cardio-ankle vascular index,bicycle ergometry,recording 3-min single-lead ECG(Cardio-Qvark)before and just after bicycle ergometry followed by performing CT myocardial perfusion.The LASSO regression with nested cross-validation was used to find the association between Cardio-Qvark parameters and the existence of the perfusion defect.Statistical processing was performed with the R programming language v4.2,Python v.3.10[^R],and Statistica 12 program.RESULTS Bicycle ergometry yielded an area under the receiver operating characteristic curve of 50.7%[95%confidence interval(CI):0.388-0.625],specificity of 53.1%(95%CI:0.392-0.673),and sensitivity of 48.4%(95%CI:0.306-0.657).In contrast,the Cardio-Qvark test performed notably better with an area under the receiver operating characteristic curve of 67%(95%CI:0.530-0.801),specificity of 75.5%(95%CI:0.628-0.88),and sensitivity of 51.6%(95%CI:0.333-0.695).CONCLUSION The single-lead ECG has a relatively higher diagnostic accuracy compared with bicycle ergometry by using machine learning models,but the difference was not statistically significant.However,further investigations are required to uncover the hidden capabilities of single-lead ECG in IHD diagnosis.
文摘Zhou et al’s investigation on the creation of a non-invasive deep learning(DL)method for colorectal tumor immune microenvironment evaluation using preoperative computed tomography(CT)radiomics published in the World Journal of Gastrointestinal Oncology is thorough and scientific.The study analyzed preoperative CT images of 315 confirmed colorectal cancer patients,using manual regions of interest to extract DL features.The study developed a DL model using CT images and histopathological images to predict immune-related indicators in colorectal cancer patients.Pathological(tumor-stroma ratio,tumor-infiltrating lymphocytes infiltration,immunohistochemistry,tumor immune microenvir-onment and immune score)parameters and radiomics(CT imaging and model construction)data were combined to generate artificial intelligence-powered models.Clinical benefit and goodness of fit of the models were assessed using receiver operating characteristic,area under curve and decision curve analysis.The developed DL-based radiomics prediction model for non-invasive evaluation of tumor markers demonstrated potential for personalized treatment planning and immunotherapy strategies in colorectal cancer patients.The study,involving a small group from a single medical center,lacks inclusion/exclusion criteria and should include clinicopathological features for valuable therapeutic practice insights in colorectal cancer patients.
文摘Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources.Particularly considering the implications of the aggressive GHG emission targets,accurate GHI forecasting has become vital for developing,designing,and operational managing solar energy systems.This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA(Autoregressive Integrated Moving Average),Elaman NN(Elman Neural Network),RBFN(Radial Basis Function Neural Network),SVM(Support Vector Machine),LSTM(Long Short-Term Memory),Persistent,BPN(Back Propagation Neural Network),MLP(Multilayer Perceptron Neural Network),RF(Random Forest),and XGBoost(eXtreme Gradient Boosting)for assessing multi-seasonal forecasting of GHI.Used the India region data to evaluate the models’performance and forecasting ability.Research using forecasting models for seasonal Global Horizontal Irradiance(GHI)forecasting in winter,spring,summer,monsoon,and autumn.Substantiated performance effectiveness through evaluation metrics,such as Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),and R-squared(R^(2)),coded using Python programming.The performance experimentation analysis inferred that the most accurate forecasts in all the seasons compared to the other forecasting models the Random Forest and eXtreme Gradient Boosting,are the superior and competing models that yield Winter season-based forecasting XGBoost is the best forecasting model with MAE:1.6325,RMSE:4.8338,and R^(2):0.9998.Spring season-based forecasting XGBoost is the best forecasting model with MAE:2.599599,RMSE:5.58539,and R^(2):0.999784.Summer season-based forecasting RF is the best forecasting model with MAE:1.03843,RMSE:2.116325,and R^(2):0.999967.Monsoon season-based forecasting RF is the best forecasting model with MAE:0.892385,RMSE:2.417587,and R^(2):0.999942.Autumn season-based forecasting RF is the best forecasting model with MAE:0.810462,RMSE:1.928215,and R^(2):0.999958.Based on seasonal variations and computing constraints,the findings enable energy system operators to make helpful recommendations for choosing the most effective forecasting models.
文摘The investigation by Zhu et al on the assessment of cellular proliferation markers to assist clinical decision-making in patients with hepatocellular carcinoma(HCC)using a machine learning model-based approach is a scientific approach.This study looked into the possibilities of using a Ki-67(a marker for cell proliferation)expression-based machine learning model to help doctors make decisions about treatment options for patients with HCC before surgery.The study used reconstructed tomography images of 164 patients with confirmed HCC from the intratumoral and peritumoral regions.The features were chosen using various statistical methods,including least absolute shrinkage and selection operator regression.Also,a nomogram was made using Radscore and clinical risk factors.It was tested for its ability to predict receiver operating characteristic curves and calibration curves,and its clinical benefits were found using decision curve analysis.The calibration curve demonstrated excellent consistency between predicted and actual probability,and the decision curve confirmed its clinical benefit.The proposed model is helpful for treating patients with HCC because the predicted and actual probabilities are very close to each other,as shown by the decision curve analysis.Further prospective studies are required,incorporating a multicenter and large sample size design,additional relevant exclusion criteria,information on tumors(size,number,and grade),and cancer stage to strengthen the clinical benefit in patients with HCC.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC3209504)the National Natural Science Foundation of China(Grants No.U2040215 and 52479075)the Natural Science Foundation of Hubei Province(Grant No.2021CFA029).
文摘The backwater effect caused by tributary inflow can significantly elevate the water level profile upstream of a confluence point.However,the influence of mainstream and confluence discharges on the backwater effect in a river reach remains unclear.In this study,various hydrological data collected from the Jingjiang Reach of the Yangtze River in China were statistically analyzed to determine the backwater degree and range with three representative mainstream discharges.The results indicated that the backwater degree increased with mainstream discharge,and a positive relationship was observed between the runoff ratio and backwater degree at specific representative mainstream discharges.Following the operation of the Three Gorges Project,the backwater effect in the Jingjiang Reach diminished.For instance,mean backwater degrees for low,moderate,and high mainstream discharges were recorded as 0.83 m,1.61 m,and 2.41 m during the period from 1990 to 2002,whereas these values decreased to 0.30 m,0.95 m,and 2.08 m from 2009 to 2020.The backwater range extended upstream as mainstream discharge increased from 7000 m3/s to 30000 m3/s.Moreover,a random forest-based machine learning model was used to quantify the backwater effect with varying mainstream and confluence discharges,accounting for the impacts of mainstream discharge,confluence discharge,and channel degradation in the Jingjiang Reach.At the Jianli Hydrological Station,a decrease in mainstream discharge during flood seasons resulted in a 7%–15%increase in monthly mean backwater degree,while an increase in mainstream discharge during dry seasons led to a 1%–15%decrease in monthly mean backwater degree.Furthermore,increasing confluence discharge from Dongting Lake during June to July and September to November resulted in an 11%–42%increase in monthly mean backwater degree.Continuous channel degradation in the Jingjiang Reach contributed to a 6%–19%decrease in monthly mean backwater degree.Under the influence of these factors,the monthly mean backwater degree in 2017 varied from a decrease of 53%to an increase of 37%compared to corresponding values in 1991.
基金supported by the Natural Science Foundation of Jiangsu province,China(BK20240937)the Belt and Road Special Foundation of the National Key Laboratory of Water Disaster Prevention(2022491411,2021491811)the Basal Research Fund of Central Public Welfare Scientific Institution of Nanjing Hydraulic Research Institute(Y223006).
文摘Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in three coastal cities in Jiangsu Province,China.Seasonal and Trend decomposition using Loess(STL)together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis(HCA).Machine learning models were developed to predict GWD,then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations(SHAP)method.Results showed that eXtreme Gradient Boosting(XGB)was superior to other models in terms of prediction performance and computational efficiency(R^(2)>0.95).GWD in Yancheng and southern Lianyungang were greater than those in Nantong,exhibiting larger fluctuations.Groundwater within 5 km of the coastline was affected by tides,with more pronounced effects in agricultural areas compared to urban areas.Shallow groundwater(3-7 m depth)responded immediately(0-1 day)to rainfall,primarily influenced by farmland and topography(slope and distance from rivers).Rainfall recharge to groundwater peaked at 50%farmland coverage,but this effect was suppressed by high temperatures(>30℃)which intensified as distance from rivers increased,especially in forest and grassland.Deep groundwater(>10 m)showed delayed responses to rainfall(1-4 days)and temperature(10-15 days),with GDP as the primary influence,followed by agricultural irrigation and population density.Farmland helped to maintain stable GWD in low population density regions,while excessive farmland coverage(>90%)led to overexploitation.In the early stages of GDP development,increased industrial and agricultural water demand led to GWD decline,but as GDP levels significantly improved,groundwater consumption pressure gradually eased.This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.
基金supported by the National Natural Science Foundation of China under Grant Nos.523B2043 and 52475112.
文摘Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.
基金Financial support from the Fundamental Research Funds for the Central Universities(ZJ2022-003,JG2022-27,J2020-060,and J2021-060)Sichuan Province Engineering Technology Research Center of General Aircraft Maintenance(GAMRC2021YB08)the Young Scientists Fund of the National Natural Science Foundation of China(No.52105417)is acknowledged.
文摘To overcome the challenges of limited experimental data and improve the accuracy of empirical formulas,we propose a low-cycle fatigue(LCF)life prediction model for nickel-based superalloys using a data augmentation method.This method utilizes a variational autoencoder(VAE)to generate low-cycle fatigue data and form an augmented dataset.The Pearson correlation coefficient(PCC)is employed to verify the similarity of feature distributions between the original and augmented datasets.Six machine learning models,namely random forest(RF),artificial neural network(ANN),support vector machine(SVM),gradient-boosted decision tree(GBDT),eXtreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost),are utilized to predict the LCF life of nickel-based superalloys.Results indicate that the proposed data augmentation method based on VAE can effectively expand the dataset,and the mean absolute error(MAE),root mean square error(RMSE),and R-squared(R^(2))values achieved using the CatBoost model,with respective values of 0.0242,0.0391,and 0.9538,are superior to those of the other models.The proposed method reduces the cost and time associated with LCF experiments and accurately establishes the relationship between fatigue characteristics and LCF life of nickel-based superalloys.
文摘Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
基金funded by the Natural Science Foundation of China(Grant Nos.41807285,41972280 and 52179103).
文摘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.
基金funded by the National Natural Science Foundation of China (41807285)。
文摘The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.
基金supported in part by the National Research Foundation of Korea(NRF)grant funded by Korea government(No.2020R1C1C1013260)in part by INHA UNIVERSITY Research Grant.
文摘This paper presents a state-of-the-art review in modeling approach of hardware in the loop simulation(HILS)realization of electric machine drives using commercial real time machines.HILS implementation using digital signal processors(DSPs)and field programmable gate array(FPGA)for electric machine drives has been investigated but those methods have drawbacks such as complexity in development and verification.Among various HILS implementation approaches,more efficient development and verification for electric machine drives can be achieved through use of commercial real time machines.As well as implementation of the HILS,accurate modeling of a control target system plays an important role.Therefore,modeling trend in electric machine drives for HILS implementation is needed to be reviewed.This paper provides a background of HILS and commercially available real time machines and characteristics of each real time machine are introduced.Also,recent trends and progress of permanent magnet synchronous machines(PMSMs)modeling are presented for providing more accurate HILS implementation approaches in this paper.
文摘Pain is a subjective and complex phenomenon. Its complexity is related to its heterogeneity： multiple component processes, including sensation, affect, and cognition, contribute to pain experience and reporting. These components are likely to be encoded in distributed brain networks that interact to create pain experience and pain-related decision-making. Therefore, to understand pain, we must identify these networks and build models of these interactions that yield testable predictions about pain-related outcomes. We have developed several such models or ＇signatures＇ of pain, by （1） integrating activity across multiple systems, and （2） using pattern-recognition to identify processes related to pain experience. One model, the Neurologic Pain Signature, is sensitive and specific to pain in individuals, involves brain regions that receive nociceptive afferents, and shows little effect of expectation or self-regulation in tests to date. Another, the ＇Stimulus Intensity-Independent Pain Signature＇, explains substantial additional variation in trial-to-trial pain reports. It involves many brain regions that do not show increased activity in proportion to noxious stimulus intensity, includ- ing medial and lateral prefrontal cortex, nucleus accum- bens, and hippocampus. Responses in this system mediate expectancy and perceived control effects in several studies. Overall, this approach provides a pathway to understanding pain by identifying multiple systems that track different aspects of pain. Such componential models can be combined in unique ways on a subject-by-subject basis to explain an individual＇s pain experience.
文摘The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.
基金This study has been reviewed and approved by the Clinical Research Ethics Committee of Wenzhou Central Hospital and the First Hospital Affiliated to Wenzhou Medical University,No.KY2024-R016.
文摘BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS Data of patients treated for colorectal cancer(n=2044)at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected.Patients were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation occurrence.Patients were also divided into a training group and a validation group(7:3 ratio).We used three different machine learning methods to screen characteristic variables.A nomogram was created based on multifactor logistic regression,and the model performance was assessed using receiver operating characteristic curve,calibration curve,Hosmer-Lemeshow test,and decision curve analysis.The risk scores of the two groups were calculated and compared to validate the model.RESULTS More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control group.Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(P<0.05):Prognostic Nutritional Index value,history of laparotomy,hypertension,or stroke,hypoproteinemia,age,tumor-node-metastasis staging,surgical time,gender,and American Society of Anesthesiologists classification.Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer,which can improve treatment decisions and prognosis.
基金supported by grants from the National Natural Science Foundation of China(31771243)the Fok Ying Tong Education Foundation(141113)to Aiguo Chen.
文摘In recent years evidence has emerged suggesting that Mini-basketball training program(MBTP)can be an effec-tive intervention method to improve social communication(SC)impairments and restricted and repetitive beha-viors(RRBs)in preschool children suffering from autism spectrum disorder(ASD).However,there is a considerable degree if interindividual variability concerning these social outcomes and thus not all preschool chil-dren with ASD profit from a MBTP intervention to the same extent.In order to make more accurate predictions which preschool children with ASD can benefit from an MBTP intervention or which preschool children with ASD need additional interventions to achieve behavioral improvements,further research is required.This study aimed to investigate which individual factors of preschool children with ASD can predict MBTP intervention out-comes concerning SC impairments and RRBs.Then,test the performance of machine learning models in predict-ing intervention outcomes based on these factors.Participants were 26 preschool children with ASD who enrolled in a quasi-experiment and received MBTP intervention.Baseline demographic variables(e.g.,age,body,mass index[BMI]),indicators of physicalfitness(e.g.,handgrip strength,balance performance),performance in execu-tive function,severity of ASD symptoms,level of SC impairments,and severity of RRBs were obtained to predict treatment outcomes after MBTP intervention.Machine learning models were established based on support vector machine algorithm were implemented.For comparison,we also employed multiple linear regression models in statistics.Ourfindings suggest that in preschool children with ASD symptomatic severity(r=0.712,p<0.001)and baseline SC impairments(r=0.713,p<0.001)are predictors for intervention outcomes of SC impair-ments.Furthermore,BMI(r=-0.430,p=0.028),symptomatic severity(r=0.656,p<0.001),baseline SC impair-ments(r=0.504,p=0.009)and baseline RRBs(r=0.647,p<0.001)can predict intervention outcomes of RRBs.Statistical models predicted 59.6%of variance in post-treatment SC impairments(MSE=0.455,RMSE=0.675,R2=0.596)and 58.9%of variance in post-treatment RRBs(MSE=0.464,RMSE=0.681,R2=0.589).Machine learning models predicted 83%of variance in post-treatment SC impairments(MSE=0.188,RMSE=0.434,R2=0.83)and 85.9%of variance in post-treatment RRBs(MSE=0.051,RMSE=0.226,R2=0.859),which were better than statistical models.Ourfindings suggest that baseline characteristics such as symptomatic severity of 144 IJMHP,2022,vol.24,no.2 ASD symptoms and SC impairments are important predictors determining MBTP intervention-induced improvements concerning SC impairments and RBBs.Furthermore,the current study revealed that machine learning models can successfully be applied to predict the MBTP intervention-related outcomes in preschool chil-dren with ASD,and performed better than statistical models.Ourfindings can help to inform which preschool children with ASD are most likely to benefit from an MBTP intervention,and they might provide a reference for the development of personalized intervention programs for preschool children with ASD.
