RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performa...RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performances of existingtop RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum freeenergy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods.Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensivelyexamined the performances of the RNA secondary structure prediction methods through classifying the RNAs into differentlength ranges and different types. Our examination shows that the DL-based methods generally perform better thanthe MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achievegood performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy forpseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.展开更多
With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation wind...With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation window period is crucial for navigating in the Arctic route,which is of great significance to the selection of the route and the optimization of navigation.This paper introduces the establishment of a risk index system,determination of risk index weight,establishment of a risk evaluation model,and prediction algorithm for the window period.In addition,data sources of both environmental factors and ship factors are introducted,and their shortcomings are analyzed,followed by introduction of various methods involved in window prediction and analysis of their advantages and disadvantages.The quantitative risk evaluation and window period algorithm can provide a reference for the research of polar navigation window period prediction.展开更多
BACKGROUND Ki-67 is a routine test item in clinical pathology departments.However,its prognostic value requires further investigation,especially in the context of research using machine learning(ML),which remains rela...BACKGROUND Ki-67 is a routine test item in clinical pathology departments.However,its prognostic value requires further investigation,especially in the context of research using machine learning(ML),which remains relatively underdeveloped.AIM To investigate the prognostic value of Ki-67 in cases of colorectal carcinoma(CRC)and explore the potential application of ML algorithms to predict the Ki-67 index.METHODS Case data and pathological sections from two centers were systematically collected.To analyze the prognostic value of the Ki-67 index in CRC,multiple cutoff values were established.Meanwhile,by virtue of the histological features presented in the hematoxylin and eosin-stained CRC images,three mainstream ML algorithms,support vector machine(SVM),random forest(RF),and eXtreme gradient boosting(XGBoost)were employed to construct prediction models.Subsequently,the potential of these algorithms to classify and predict the Ki-67 index was explored.RESULTS Non-parametric tests revealed that Ki-67≥40%correlated with a high histological grade(P=0.017),deficient mismatch repair protein status associated with≥50%-90%cutoffs(all P≤0.028),and≥80%linked to lymph node metastasis(P=0.006).Kaplan-Meier analysis showed that Ki-67≥50%predicted higher survival(log-rank P=0.0299,hazard ratio=2.142),with no differences for other cutoffs.COX regression identified the Ki-67 positive rate as a significant predictor(P=0.027,hazard ratio=2.583),while other variables had no association.In algorithmic model predictions,the SVM,RF,and XGBoost models achieved training area under the curve(AUC)values of 0.851,0.948,and 0.872,respectively,with corresponding test set AUC values of 0.795,0.755,and 0.750,respectively.During external validation,their AUC values for predicting Ki-67 status reached 0.757,0.749,and 0.783,respectively.CONCLUSION In algorithmic model predictions,the SVM,RF,and XGBoost models achieved training AUC values of 0.851,0.948,and 0.872,respectively,with corresponding test set AUC values of 0.795,0.755,and 0.750,respectively.During external validation,their AUC values for predicting Ki-67 status reached 0.757,0.749,and 0.783,respectively.展开更多
With the large-scale promotion of distributed photovoltaics,new challenges have emerged in the photovoltaic consumptionwithin distribution networks.Traditional photovoltaic consumption schemes have primarily focused o...With the large-scale promotion of distributed photovoltaics,new challenges have emerged in the photovoltaic consumptionwithin distribution networks.Traditional photovoltaic consumption schemes have primarily focused on static analysis.However,as the scale of photovoltaic power generation devices grows and the methods of integration diversify,a single consumption scheme is no longer sufficient to meet the actual needs of current distribution networks.Therefore,this paper proposes an optimal evaluation method for photovoltaic consumption schemes based on BASS model predictions of installed capacity,aiming to provide an effective tool for generating and evaluating photovoltaic consumption schemes in distribution networks.First,the BASS diffusion model,combined with existing photovoltaic capacity data and roof area information,is used to predict the trends in photovoltaic installed capacity for each substation area,providing a scientific basis for consumption evaluation.Secondly,an improved random scenario simulation method is proposed for assessing the photovoltaic consumption capacity in distribution networks.This method generates photovoltaic integration schemes based on the diffusion probabilities of different regions and evaluates the consumption capacity of each scheme.Finally,the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)is used to comprehensively evaluate the generated schemes,ensuring that the selected scheme not only meets the consumption requirements but also offers high economic benefits and reliability.The effectiveness and feasibility of the proposedmethod are validated through simulations of the IEEE 33-node system,providing strong support for optimizing photovoltaic consumption schemes in distribution networks.展开更多
Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocar...Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocardiographic data,traditional Chinese medicine(TCM)tongue manifestations,and facial features were collected from patients who underwent coro-nary computed tomography angiography(CTA)in the Cardiac Care Unit(CCU)of Shanghai Tenth People's Hospital between May 1,2023 and May 1,2024.An adaptive weighted multi-modal data fusion(AWMDF)model based on deep learning was constructed to predict the severity of coronary artery stenosis.The model was evaluated using metrics including accura-cy,precision,recall,F1 score,and the area under the receiver operating characteristic(ROC)curve(AUC).Further performance assessment was conducted through comparisons with six ensemble machine learning methods,data ablation,model component ablation,and various decision-level fusion strategies.Results A total of 158 patients were included in the study.The AWMDF model achieved ex-cellent predictive performance(AUC=0.973,accuracy=0.937,precision=0.937,recall=0.929,and F1 score=0.933).Compared with model ablation,data ablation experiments,and various traditional machine learning models,the AWMDF model demonstrated superior per-formance.Moreover,the adaptive weighting strategy outperformed alternative approaches,including simple weighting,averaging,voting,and fixed-weight schemes.Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.展开更多
Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both g...Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
The popular constitutive models used in the field of hot forming of magnesium alloys can be divided into phenomenological models,machine learning models,and internal state variables(ISV)models based on physical mechan...The popular constitutive models used in the field of hot forming of magnesium alloys can be divided into phenomenological models,machine learning models,and internal state variables(ISV)models based on physical mechanisms.Currently,there is a lack of comparison and evaluation regarding the suitability of different types of models.In this study,Mg-Gd-Y-Zr alloy is taken as the research object.The hot deformation behavior of the alloy was studied systematically.Subsequently,Arrhenius model with strain compensation,artificial neural network(ANN)model,and ISV model involving dynamic recrystallization(DRX),dislocation density and grain size evolution were established.ANN model demonstrates a higher level of accuracy in fitting the original stress-strain curves compared to both ISV model and modified Arrhenius model,but ANN model is not suitable for predicting the experimental results outside of the initial database.ISV model considers the impact of microstructure evolution history on stress,making it highly effective in reflecting the mechanical responses under complex loading condition.The established ISV model is embedded in the ABAQUS software,which shows good ability in calculating the mechanical response,dimension,and microstructure evolution information of the component during hot forming.展开更多
Metal-ion batteries(MIBs),including alkali metal-ion(Li^(+),Na^(+),and K^(3)),multi-valent metal-ion(Zn^(2+),Mg^(2+),and Al^(3+)),metal-air,and metal-sulfur batteries,play an indispensable role in electrochemical ener...Metal-ion batteries(MIBs),including alkali metal-ion(Li^(+),Na^(+),and K^(3)),multi-valent metal-ion(Zn^(2+),Mg^(2+),and Al^(3+)),metal-air,and metal-sulfur batteries,play an indispensable role in electrochemical energy storage.However,the performance of MIBs is significantly influenced by numerous variables,resulting in multi-dimensional and long-term challenges in the field of battery research and performance enhancement.Machine learning(ML),with its capability to solve intricate tasks and perform robust data processing,is now catalyzing a revolutionary transformation in the development of MIB materials and devices.In this review,we summarize the utilization of ML algorithms that have expedited research on MIBs over the past five years.We present an extensive overview of existing algorithms,elucidating their details,advantages,and limitations in various applications,which encompass electrode screening,material property prediction,electrolyte formulation design,electrode material characterization,manufacturing parameter optimization,and real-time battery status monitoring.Finally,we propose potential solutions and future directions for the application of ML in advancing MIB development.展开更多
BACKGROUND Liver cancer is one of the most prevalent malignant tumors worldwide,and its early detection and treatment are crucial for enhancing patient survival rates and quality of life.However,the early symptoms of ...BACKGROUND Liver cancer is one of the most prevalent malignant tumors worldwide,and its early detection and treatment are crucial for enhancing patient survival rates and quality of life.However,the early symptoms of liver cancer are often not obvious,resulting in a late-stage diagnosis in many patients,which significantly reduces the effectiveness of treatment.Developing a highly targeted,widely applicable,and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals.AIM To develop a liver cancer risk prediction model by employing machine learning techniques,and subsequently assess its performance.METHODS In this study,a total of 550 patients were enrolled,with 190 hepatocellular carcinoma(HCC)and 195 cirrhosis patients serving as the training cohort,and 83 HCC and 82 cirrhosis patients forming the validation cohort.Logistic regression(LR),support vector machine(SVM),random forest(RF),and least absolute shrinkage and selection operator(LASSO)regression models were developed in the training cohort.Model performance was assessed in the validation cohort.Additionally,this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve,calibration curve,and decision curve analysis(DCA)to determine the optimal predictive model for assessing liver cancer risk.RESULTS Six variables including age,white blood cell,red blood cell,platelet counts,alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR,SVM,RF,and LASSO regression models.The RF model exhibited superior discrimination,and the area under curve of the training and validation sets was 0.969 and 0.858,respectively.These values significantly surpassed those of the LR(0.850 and 0.827),SVM(0.860 and 0.803),LASSO regression(0.845 and 0.831),and ASAP(0.866 and 0.813)models.Furthermore,calibration and DCA indicated that the RF model exhibited robust calibration and clinical validity.CONCLUSION The RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.展开更多
BACKGROUND Choledocholithiasis is a common clinical bile duct disease,laparoscopic choledocholithotomy is the main clinical treatment method for choledocho-lithiasis.However,the recurrence of postoperative stones is a...BACKGROUND Choledocholithiasis is a common clinical bile duct disease,laparoscopic choledocholithotomy is the main clinical treatment method for choledocho-lithiasis.However,the recurrence of postoperative stones is a big challenge for patients and doctors.AIM To explore the related risk factors of gallstone recurrence after laparoscopic choledocholithotomy,establish and evaluate a clinical prediction model.METHODS A total of 254 patients who underwent laparoscopic choledocholithotomy in the First Affiliated Hospital of Ningbo University from December 2017 to December 2020 were selected as the research subjects.Clinical data of the patients were collected,and the recurrence of gallstones was recorded based on the postope-rative follow-up.The results were analyzed and a clinical prediction model was established.RESULTS Postoperative stone recurrence rate was 10.23%(26 patients).Multivariate Logistic regression analysis showed that cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube were risk factors associated with postoperative recurrence(P<0.05).The clinical prediction model was ln(p/1-p)=-6.853+1.347×cholangitis+1.535×choledochal diameter+2.176×stone diameter+1.784×stone number+2.242×lithotripsy+0.021×preoperative total bilirubin+2.185×T tube.CONCLUSION Cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube are the associated risk factors for postoperative recurrence of gallstone.The prediction model in this study has a good prediction effect,which has a certain reference value for recurrence of gallstone after laparoscopic choledocholi-thotomy.展开更多
Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of po...Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.展开更多
By conducting organic geochemical analysis of the samples taken from the drilled wells in Baiyun Sag of Pearl River Mouth Basin,China,the development characteristics of hydrocarbon source rocks in the sag are clarifie...By conducting organic geochemical analysis of the samples taken from the drilled wells in Baiyun Sag of Pearl River Mouth Basin,China,the development characteristics of hydrocarbon source rocks in the sag are clarified.Reconstruct the current geothermal field of the sag and restore the tectonic-thermal evolution process to predict the type,scale,and distribution of resources in Baiyun Sag through thermal pressure simulation experiments and numerical simulation.The Baiyun Sag is characterized by the development of Paleogene shallow lacustrine source rocks,which are deposited in a slightly oxidizing environment.The source rocks are mainly composed of terrestrial higher plants,with algae making a certain contribution,and are oil and gas source rocks.Current geothermal field of the sag was reconstructed,in which the range of geothermal gradients is(3.5–5.2)℃/100 m,showing an overall increasing trend from northwest to southeast,with significant differences in geothermal gradients across different sub-sags.Baiyun Sag has undergone two distinct periods of extensional process,the Eocene and Miocene,since the Cenozoic era.These two periods of heating and warming events have been identified,accelerating the maturation and evolution of source rocks.The main body of ancient basal heat flow value reached its highest at 13.82 Ma.The basin modelling results show that the maturity of source rocks is significantly higher in Baiyun main sub-sag than that in other sub-sags.The Eocene Wenchang Formation is currently in the stage of high maturity to over maturity,while the Eocene Enping Formation has reached the stage of maturity to high maturity.The rock thermal simulation experiment shows that the shallow lacustrine mudstone of the Wenchang Formation has a good potential of generating gas from kerogen cracking with high gas yield and long period of gas window.Shallow lacustrine mudstone of the Enping Formation has a good ability to generate light oil,and has ability to generate kerogen cracking gas in the late stage.The gas yield of shallow lacustrine mudstone of the Enping Formation is less than that of shallow lacustrine mudstone of the Wenchang Formation and the delta coal-bearing mudstone of the Enping Formation.The numerical simulation results indicate that the source rocks of Baiyun main sub-sag generate hydrocarbons earlier and have significantly higher hydrocarbon generation intensity than other sub-sags,with an average of about 1200×10^(4)t/km^(2).Oil and gas resources were mainly distributed in Baiyun main sub-sag and the main source rocks are distributed in the 3^(rd)and 4^(th)members of Wenchang Formation.Four favorable zones are selected for the division and evaluation of migration and aggregation units:No.(1)Panyu 30 nose-shaped structural belt,No.(3)Liuhua 29 nose-shaped uplift belt and Liwan 3 nose-shaped uplift belt,No.(2)gentle slope belt of Baiyun east sag,and No.(8)Baiyun 1 low-uplift.展开更多
This study explored the application of machine learning techniques for flood prediction and analysis in southern Nigeria. Machine learning is an artificial intelligence technique that uses computer-based instructions ...This study explored the application of machine learning techniques for flood prediction and analysis in southern Nigeria. Machine learning is an artificial intelligence technique that uses computer-based instructions to analyze and transform data into useful information to enable systems to make predictions. Traditional methods of flood prediction and analysis often fall short of providing accurate and timely information for effective disaster management. More so, numerical forecasting of flood disasters in the 19th century is not very accurate due to its inability to simplify complex atmospheric dynamics into simple equations. Here, we used Machine learning (ML) techniques including Random Forest (RF), Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), and Neural Networks (NN) to model the complex physical processes that cause floods. The dataset contains 59 cases with the goal feature “Event-Type”, including 39 cases of floods and 20 cases of flood/rainstorms. Based on comparison of assessment metrics from models created using historical records, the result shows that NB performed better than all other techniques, followed by RF. The developed model can be used to predict the frequency of flood incidents. The majority of flood scenarios demonstrate that the event poses a significant risk to people’s lives. Therefore, each of the emergency response elements requires adequate knowledge of the flood incidences, continuous early warning service and accurate prediction model. This study can expand knowledge and research on flood predictive modeling in vulnerable areas to inform effective and sustainable contingency planning, policy, and management actions on flood disaster incidents, especially in other technologically underdeveloped settings.展开更多
In order to evaluate the prediction performances of the climate prediction system developed by the China Meteorological Administration(CMA-CPSv3)over the Tibetan Plateau region,the precipitation and temperature predic...In order to evaluate the prediction performances of the climate prediction system developed by the China Meteorological Administration(CMA-CPSv3)over the Tibetan Plateau region,the precipitation and temperature predicted by CMA-CPSv3 at different lead time was evaluated for the period of 2001-2023,by comparing with observations—the monthly precipitation of the CMAP and 2 m temperature data of the NCEP/DOE reanalysis data.The main conclusions were as follows:1)the forecast skill of the model is very sensitive to the initial conditions and value,and the model forecast capability decreases rapidly as the lead time is extended.2)CPSv3 performed well in capturing the spatial distribution of precipitation over Xizang in January,August,October,November and December at 0-month lead,and the forecast skill is relatively poor in March and April.CPSv3 has better performance in the east-central part of the land in January,in the central and western part of the region in March,in the whole region in April,in the northern part of Nagchu,northern part of Chamdo and central part of Shigatse in July,in the eastern part of Chamdo,northern part of Nagchu and northern part of Ali in August,in the whole region in September,and in the cities of Shigatse and Shannan in November.3)At each lead time,the forecast skill for 2-m temperatures was higher than that for precipitation.For 2-m temperature,CPSv3 performed best in January,May,July,August,September,October,and December,while performing relatively poor in March,April,and November.Specifically,the prediction skill is higher in January and December for most of the regions,for Shigatse and Ali in May,for southern Shannan in July,and for northern Nagchu and southern Shannan in August,and there is some prediction skill in March,April and October,while CPSv3 performed poorly in November.展开更多
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 a...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.展开更多
Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the p...Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.展开更多
Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditiona...Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.展开更多
Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various as...Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various aspects.By integrating multi-view information into phenotypic prediction,a multi-view best linear unbiased prediction(MVBLUP)method is proposed in this paper.To measure the importance of multiple data views,the differential evolution algorithm with an early stopping mechanism is used,by which we obtain a multi-view kinship matrix and then incorporate it into the BLUP model for phenotypic prediction.To further illustrate the characteristics of MVBLUP,we perform the empirical experiments on four multi-view datasets in different crops.Compared to the single-view method,the prediction accuracy of the MVBLUP method has improved by 0.038–0.201 on average.The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data.展开更多
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 Reg...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.展开更多
基金supported by grants from the National Science Foundation of China(Grant Nos.12375038 and 12075171 to ZJT,and 12205223 to YLT).
文摘RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performances of existingtop RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum freeenergy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods.Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensivelyexamined the performances of the RNA secondary structure prediction methods through classifying the RNAs into differentlength ranges and different types. Our examination shows that the DL-based methods generally perform better thanthe MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achievegood performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy forpseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.
文摘With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation window period is crucial for navigating in the Arctic route,which is of great significance to the selection of the route and the optimization of navigation.This paper introduces the establishment of a risk index system,determination of risk index weight,establishment of a risk evaluation model,and prediction algorithm for the window period.In addition,data sources of both environmental factors and ship factors are introducted,and their shortcomings are analyzed,followed by introduction of various methods involved in window prediction and analysis of their advantages and disadvantages.The quantitative risk evaluation and window period algorithm can provide a reference for the research of polar navigation window period prediction.
基金Supported by the Guangxi Zhuang Autonomous Region Health Commission Scientific Research Project,No.Z20210442。
文摘BACKGROUND Ki-67 is a routine test item in clinical pathology departments.However,its prognostic value requires further investigation,especially in the context of research using machine learning(ML),which remains relatively underdeveloped.AIM To investigate the prognostic value of Ki-67 in cases of colorectal carcinoma(CRC)and explore the potential application of ML algorithms to predict the Ki-67 index.METHODS Case data and pathological sections from two centers were systematically collected.To analyze the prognostic value of the Ki-67 index in CRC,multiple cutoff values were established.Meanwhile,by virtue of the histological features presented in the hematoxylin and eosin-stained CRC images,three mainstream ML algorithms,support vector machine(SVM),random forest(RF),and eXtreme gradient boosting(XGBoost)were employed to construct prediction models.Subsequently,the potential of these algorithms to classify and predict the Ki-67 index was explored.RESULTS Non-parametric tests revealed that Ki-67≥40%correlated with a high histological grade(P=0.017),deficient mismatch repair protein status associated with≥50%-90%cutoffs(all P≤0.028),and≥80%linked to lymph node metastasis(P=0.006).Kaplan-Meier analysis showed that Ki-67≥50%predicted higher survival(log-rank P=0.0299,hazard ratio=2.142),with no differences for other cutoffs.COX regression identified the Ki-67 positive rate as a significant predictor(P=0.027,hazard ratio=2.583),while other variables had no association.In algorithmic model predictions,the SVM,RF,and XGBoost models achieved training area under the curve(AUC)values of 0.851,0.948,and 0.872,respectively,with corresponding test set AUC values of 0.795,0.755,and 0.750,respectively.During external validation,their AUC values for predicting Ki-67 status reached 0.757,0.749,and 0.783,respectively.CONCLUSION In algorithmic model predictions,the SVM,RF,and XGBoost models achieved training AUC values of 0.851,0.948,and 0.872,respectively,with corresponding test set AUC values of 0.795,0.755,and 0.750,respectively.During external validation,their AUC values for predicting Ki-67 status reached 0.757,0.749,and 0.783,respectively.
基金supported in part by theThe Planning Subject Project of Guangdong Power Grid Co.,Ltd.(62273104).
文摘With the large-scale promotion of distributed photovoltaics,new challenges have emerged in the photovoltaic consumptionwithin distribution networks.Traditional photovoltaic consumption schemes have primarily focused on static analysis.However,as the scale of photovoltaic power generation devices grows and the methods of integration diversify,a single consumption scheme is no longer sufficient to meet the actual needs of current distribution networks.Therefore,this paper proposes an optimal evaluation method for photovoltaic consumption schemes based on BASS model predictions of installed capacity,aiming to provide an effective tool for generating and evaluating photovoltaic consumption schemes in distribution networks.First,the BASS diffusion model,combined with existing photovoltaic capacity data and roof area information,is used to predict the trends in photovoltaic installed capacity for each substation area,providing a scientific basis for consumption evaluation.Secondly,an improved random scenario simulation method is proposed for assessing the photovoltaic consumption capacity in distribution networks.This method generates photovoltaic integration schemes based on the diffusion probabilities of different regions and evaluates the consumption capacity of each scheme.Finally,the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)is used to comprehensively evaluate the generated schemes,ensuring that the selected scheme not only meets the consumption requirements but also offers high economic benefits and reliability.The effectiveness and feasibility of the proposedmethod are validated through simulations of the IEEE 33-node system,providing strong support for optimizing photovoltaic consumption schemes in distribution networks.
基金Construction Program of the Key Discipline of State Administration of Traditional Chinese Medicine of China(ZYYZDXK-2023069)Research Project of Shanghai Municipal Health Commission (2024QN018)Shanghai University of Traditional Chinese Medicine Science and Technology Development Program (23KFL005)。
文摘Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocardiographic data,traditional Chinese medicine(TCM)tongue manifestations,and facial features were collected from patients who underwent coro-nary computed tomography angiography(CTA)in the Cardiac Care Unit(CCU)of Shanghai Tenth People's Hospital between May 1,2023 and May 1,2024.An adaptive weighted multi-modal data fusion(AWMDF)model based on deep learning was constructed to predict the severity of coronary artery stenosis.The model was evaluated using metrics including accura-cy,precision,recall,F1 score,and the area under the receiver operating characteristic(ROC)curve(AUC).Further performance assessment was conducted through comparisons with six ensemble machine learning methods,data ablation,model component ablation,and various decision-level fusion strategies.Results A total of 158 patients were included in the study.The AWMDF model achieved ex-cellent predictive performance(AUC=0.973,accuracy=0.937,precision=0.937,recall=0.929,and F1 score=0.933).Compared with model ablation,data ablation experiments,and various traditional machine learning models,the AWMDF model demonstrated superior per-formance.Moreover,the adaptive weighting strategy outperformed alternative approaches,including simple weighting,averaging,voting,and fixed-weight schemes.Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2342210 and 42275043)the National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant Nos.J2223806,ZDJ2024-25 and ZDJ2025-34)。
文摘Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
基金supported by the fund of the National Natural Science Foundation of China(52275322,51875127)。
文摘The popular constitutive models used in the field of hot forming of magnesium alloys can be divided into phenomenological models,machine learning models,and internal state variables(ISV)models based on physical mechanisms.Currently,there is a lack of comparison and evaluation regarding the suitability of different types of models.In this study,Mg-Gd-Y-Zr alloy is taken as the research object.The hot deformation behavior of the alloy was studied systematically.Subsequently,Arrhenius model with strain compensation,artificial neural network(ANN)model,and ISV model involving dynamic recrystallization(DRX),dislocation density and grain size evolution were established.ANN model demonstrates a higher level of accuracy in fitting the original stress-strain curves compared to both ISV model and modified Arrhenius model,but ANN model is not suitable for predicting the experimental results outside of the initial database.ISV model considers the impact of microstructure evolution history on stress,making it highly effective in reflecting the mechanical responses under complex loading condition.The established ISV model is embedded in the ABAQUS software,which shows good ability in calculating the mechanical response,dimension,and microstructure evolution information of the component during hot forming.
基金supported by the National Natural Science Foundation of China(52203364,52188101,52020105010)the National Key R&D Program of China(2021YFB3800300,2022YFB3803400)+2 种基金the Strategic Priority Research Program of Chinese Academy of Science(XDA22010602)the China Postdoctoral Science Foundation(2022M713214)the China National Postdoctoral Program for Innovative Talents(BX2021321)。
文摘Metal-ion batteries(MIBs),including alkali metal-ion(Li^(+),Na^(+),and K^(3)),multi-valent metal-ion(Zn^(2+),Mg^(2+),and Al^(3+)),metal-air,and metal-sulfur batteries,play an indispensable role in electrochemical energy storage.However,the performance of MIBs is significantly influenced by numerous variables,resulting in multi-dimensional and long-term challenges in the field of battery research and performance enhancement.Machine learning(ML),with its capability to solve intricate tasks and perform robust data processing,is now catalyzing a revolutionary transformation in the development of MIB materials and devices.In this review,we summarize the utilization of ML algorithms that have expedited research on MIBs over the past five years.We present an extensive overview of existing algorithms,elucidating their details,advantages,and limitations in various applications,which encompass electrode screening,material property prediction,electrolyte formulation design,electrode material characterization,manufacturing parameter optimization,and real-time battery status monitoring.Finally,we propose potential solutions and future directions for the application of ML in advancing MIB development.
基金Cuiying Scientific and Technological Innovation Program of the Second Hospital,No.CY2021-BJ-A16 and No.CY2022-QN-A18Clinical Medical School of Lanzhou University and Lanzhou Science and Technology Development Guidance Plan Project,No.2023-ZD-85.
文摘BACKGROUND Liver cancer is one of the most prevalent malignant tumors worldwide,and its early detection and treatment are crucial for enhancing patient survival rates and quality of life.However,the early symptoms of liver cancer are often not obvious,resulting in a late-stage diagnosis in many patients,which significantly reduces the effectiveness of treatment.Developing a highly targeted,widely applicable,and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals.AIM To develop a liver cancer risk prediction model by employing machine learning techniques,and subsequently assess its performance.METHODS In this study,a total of 550 patients were enrolled,with 190 hepatocellular carcinoma(HCC)and 195 cirrhosis patients serving as the training cohort,and 83 HCC and 82 cirrhosis patients forming the validation cohort.Logistic regression(LR),support vector machine(SVM),random forest(RF),and least absolute shrinkage and selection operator(LASSO)regression models were developed in the training cohort.Model performance was assessed in the validation cohort.Additionally,this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve,calibration curve,and decision curve analysis(DCA)to determine the optimal predictive model for assessing liver cancer risk.RESULTS Six variables including age,white blood cell,red blood cell,platelet counts,alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR,SVM,RF,and LASSO regression models.The RF model exhibited superior discrimination,and the area under curve of the training and validation sets was 0.969 and 0.858,respectively.These values significantly surpassed those of the LR(0.850 and 0.827),SVM(0.860 and 0.803),LASSO regression(0.845 and 0.831),and ASAP(0.866 and 0.813)models.Furthermore,calibration and DCA indicated that the RF model exhibited robust calibration and clinical validity.CONCLUSION The RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.
文摘BACKGROUND Choledocholithiasis is a common clinical bile duct disease,laparoscopic choledocholithotomy is the main clinical treatment method for choledocho-lithiasis.However,the recurrence of postoperative stones is a big challenge for patients and doctors.AIM To explore the related risk factors of gallstone recurrence after laparoscopic choledocholithotomy,establish and evaluate a clinical prediction model.METHODS A total of 254 patients who underwent laparoscopic choledocholithotomy in the First Affiliated Hospital of Ningbo University from December 2017 to December 2020 were selected as the research subjects.Clinical data of the patients were collected,and the recurrence of gallstones was recorded based on the postope-rative follow-up.The results were analyzed and a clinical prediction model was established.RESULTS Postoperative stone recurrence rate was 10.23%(26 patients).Multivariate Logistic regression analysis showed that cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube were risk factors associated with postoperative recurrence(P<0.05).The clinical prediction model was ln(p/1-p)=-6.853+1.347×cholangitis+1.535×choledochal diameter+2.176×stone diameter+1.784×stone number+2.242×lithotripsy+0.021×preoperative total bilirubin+2.185×T tube.CONCLUSION Cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube are the associated risk factors for postoperative recurrence of gallstone.The prediction model in this study has a good prediction effect,which has a certain reference value for recurrence of gallstone after laparoscopic choledocholi-thotomy.
基金European Commission,Joint Research Center,Grant/Award Number:HUMAINTMinisterio de Ciencia e Innovación,Grant/Award Number:PID2020‐114924RB‐I00Comunidad de Madrid,Grant/Award Number:S2018/EMT‐4362 SEGVAUTO 4.0‐CM。
文摘Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.
基金Supported by the National Oil and Gas Resource Evaluation Project for the 14th Five Year Plan of the Ministry of Natural Resources(QGYQZYPJ2022-3)China National Offshore Oil Corporation"14th Five Year Plan"Major Science and Technology Project(KJGG2022-0103-03)。
文摘By conducting organic geochemical analysis of the samples taken from the drilled wells in Baiyun Sag of Pearl River Mouth Basin,China,the development characteristics of hydrocarbon source rocks in the sag are clarified.Reconstruct the current geothermal field of the sag and restore the tectonic-thermal evolution process to predict the type,scale,and distribution of resources in Baiyun Sag through thermal pressure simulation experiments and numerical simulation.The Baiyun Sag is characterized by the development of Paleogene shallow lacustrine source rocks,which are deposited in a slightly oxidizing environment.The source rocks are mainly composed of terrestrial higher plants,with algae making a certain contribution,and are oil and gas source rocks.Current geothermal field of the sag was reconstructed,in which the range of geothermal gradients is(3.5–5.2)℃/100 m,showing an overall increasing trend from northwest to southeast,with significant differences in geothermal gradients across different sub-sags.Baiyun Sag has undergone two distinct periods of extensional process,the Eocene and Miocene,since the Cenozoic era.These two periods of heating and warming events have been identified,accelerating the maturation and evolution of source rocks.The main body of ancient basal heat flow value reached its highest at 13.82 Ma.The basin modelling results show that the maturity of source rocks is significantly higher in Baiyun main sub-sag than that in other sub-sags.The Eocene Wenchang Formation is currently in the stage of high maturity to over maturity,while the Eocene Enping Formation has reached the stage of maturity to high maturity.The rock thermal simulation experiment shows that the shallow lacustrine mudstone of the Wenchang Formation has a good potential of generating gas from kerogen cracking with high gas yield and long period of gas window.Shallow lacustrine mudstone of the Enping Formation has a good ability to generate light oil,and has ability to generate kerogen cracking gas in the late stage.The gas yield of shallow lacustrine mudstone of the Enping Formation is less than that of shallow lacustrine mudstone of the Wenchang Formation and the delta coal-bearing mudstone of the Enping Formation.The numerical simulation results indicate that the source rocks of Baiyun main sub-sag generate hydrocarbons earlier and have significantly higher hydrocarbon generation intensity than other sub-sags,with an average of about 1200×10^(4)t/km^(2).Oil and gas resources were mainly distributed in Baiyun main sub-sag and the main source rocks are distributed in the 3^(rd)and 4^(th)members of Wenchang Formation.Four favorable zones are selected for the division and evaluation of migration and aggregation units:No.(1)Panyu 30 nose-shaped structural belt,No.(3)Liuhua 29 nose-shaped uplift belt and Liwan 3 nose-shaped uplift belt,No.(2)gentle slope belt of Baiyun east sag,and No.(8)Baiyun 1 low-uplift.
文摘This study explored the application of machine learning techniques for flood prediction and analysis in southern Nigeria. Machine learning is an artificial intelligence technique that uses computer-based instructions to analyze and transform data into useful information to enable systems to make predictions. Traditional methods of flood prediction and analysis often fall short of providing accurate and timely information for effective disaster management. More so, numerical forecasting of flood disasters in the 19th century is not very accurate due to its inability to simplify complex atmospheric dynamics into simple equations. Here, we used Machine learning (ML) techniques including Random Forest (RF), Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), and Neural Networks (NN) to model the complex physical processes that cause floods. The dataset contains 59 cases with the goal feature “Event-Type”, including 39 cases of floods and 20 cases of flood/rainstorms. Based on comparison of assessment metrics from models created using historical records, the result shows that NB performed better than all other techniques, followed by RF. The developed model can be used to predict the frequency of flood incidents. The majority of flood scenarios demonstrate that the event poses a significant risk to people’s lives. Therefore, each of the emergency response elements requires adequate knowledge of the flood incidences, continuous early warning service and accurate prediction model. This study can expand knowledge and research on flood predictive modeling in vulnerable areas to inform effective and sustainable contingency planning, policy, and management actions on flood disaster incidents, especially in other technologically underdeveloped settings.
文摘In order to evaluate the prediction performances of the climate prediction system developed by the China Meteorological Administration(CMA-CPSv3)over the Tibetan Plateau region,the precipitation and temperature predicted by CMA-CPSv3 at different lead time was evaluated for the period of 2001-2023,by comparing with observations—the monthly precipitation of the CMAP and 2 m temperature data of the NCEP/DOE reanalysis data.The main conclusions were as follows:1)the forecast skill of the model is very sensitive to the initial conditions and value,and the model forecast capability decreases rapidly as the lead time is extended.2)CPSv3 performed well in capturing the spatial distribution of precipitation over Xizang in January,August,October,November and December at 0-month lead,and the forecast skill is relatively poor in March and April.CPSv3 has better performance in the east-central part of the land in January,in the central and western part of the region in March,in the whole region in April,in the northern part of Nagchu,northern part of Chamdo and central part of Shigatse in July,in the eastern part of Chamdo,northern part of Nagchu and northern part of Ali in August,in the whole region in September,and in the cities of Shigatse and Shannan in November.3)At each lead time,the forecast skill for 2-m temperatures was higher than that for precipitation.For 2-m temperature,CPSv3 performed best in January,May,July,August,September,October,and December,while performing relatively poor in March,April,and November.Specifically,the prediction skill is higher in January and December for most of the regions,for Shigatse and Ali in May,for southern Shannan in July,and for northern Nagchu and southern Shannan in August,and there is some prediction skill in March,April and October,while CPSv3 performed poorly in November.
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘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.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.
基金supported in part by the Natural Science Foundation of China under Grant Nos.U2468201 and 62221001ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240420002。
文摘Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.
基金supported by National Natural Science Foundation of China(32122066,32201855)STI2030—Major Projects(2023ZD04076).
文摘Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various aspects.By integrating multi-view information into phenotypic prediction,a multi-view best linear unbiased prediction(MVBLUP)method is proposed in this paper.To measure the importance of multiple data views,the differential evolution algorithm with an early stopping mechanism is used,by which we obtain a multi-view kinship matrix and then incorporate it into the BLUP model for phenotypic prediction.To further illustrate the characteristics of MVBLUP,we perform the empirical experiments on four multi-view datasets in different crops.Compared to the single-view method,the prediction accuracy of the MVBLUP method has improved by 0.038–0.201 on average.The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd..(Grant No.H20230317)。
文摘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.
