Complex Nonlinear Relationship Research Articles

Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP

The blasting block size of open-pit mines is influenced by many factors, and the influencing factors have a very complex nonlinear relationship. Traditional empirical formulas and a single neural network model cannot meet the requirements of modern blasting safety. To improve the prediction accuracy of blasting block size, the measured data of Beskuduk open-pit coal mine is used as training and testing samples. Seven factors including rock tensile strength, rock compressive strength, and blast hole spacing are selected as input variables of the prediction model. The average size of blasting fragmentation X50 is used as the output variable of the prediction model. The kernel principal component analysis (KPCA) is adopted to reduce the dimensionality of the input variables. The beetle antennae search algorithm (BAS) is selected to optimize the parameters of the initial weights and thresholds of the back propagation (BP) neural network. Finally, prediction model of blasting fragmentation in open-pit coal mine based on KPCA-BAS-BP is established. The results show that the average relative error of the model is 1.77%, and the root mean square error is 1.52%. Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value.

COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS

Gold price fluctuations have a significant impact because gold is a haven asset. When financial markets are volatile, investors tend to turn to safer instruments such as gold, so gold price forecasting becomes important in economic uncertainty. The novelty of this research is the comparative analysis of time series forecasting models using ARIMA and the NNAR methods to predict gold price movements specifically applied to gold price data with non-stationary and non-linear characteristics. The aim is to identify the strengths and limitations of ARIMA and NNAR on such data. ARIMA can only be applied to time series data that are already stationary or have been converted to stationary form through differentiation. However, ARIMA may struggle to capture complex non-linear patterns in non-stationary data. Instead, NNAR can handle non-stationary data more effectively by modeling the complex non-linear relationships between input and output variables. In the NNAR model, the lag values of the time series are used as input variables for the neural network. The dataset used is the closing price of gold with 1449 periods from January 2, 2018, to October 5, 2023. The augmented Dickey-Fuller test dataset obtained a p-value = 0.6746, meaning the data is not stationary. The ARIMA(1, 1, 1) model was selected as the gold price forecasting model and outperformed other candidate ARIMA models based on parameter identification and model diagnosis tests. Model performance is evaluated based on the RMSE and MAE values. In this study, the ARIMA(1, 1, 1) model obtained RMSE = 16.20431 and MAE = 11.13958. The NNAR(1, 10) model produces RMSE = 16.10002 and MAE = 11.09360. Based on the RMSE and MAE values, the NNAR(1, 10) model produces better accuracy than the ARIMA(1, 1, 1) model.

More transparent and explainable machine learning algorithms are required to provide enhanced and sustainable dataset understanding

For detailed dataset interrogation and auditing purposes the lack of dataset explainability/transparency of the majority of available machine-learning (ML) models poses limitations. There is a tendency for ML models to focus on prediction speed and accuracy at the expense of transparently revealing dataset relationships. A case is made here to broaden that focus and for ML models to offer alternative configurations tailored to provide more explanations about how individual predictions are derived. Indeed, those striving to achieve sustainable objectives should not rely on opaque ML models and seek transparency as a fundamental objective of good modelling practice (GMP). Doing so tends to boost trust and confidence in the outputs of models relating to complex socio-environmental systems (SES), particularly those being used to potentially justify controversial social, political and ethical decisions. Currently, the transparent open box algorithms (TOB) are the only ML algorithms available that are configured specifically to routinely provide detailed data record relationships for each of their predictions. This study describes the data mining benefits of the Python-coded optimized data-matching TOB algorithms generally, and when applied to environmental datasets characterized by complex non-linear relationships involving many variables.

Traffic noise prediction model using GIS and ensemble machine learning: a case study at Universiti Teknologi Malaysia (UTM) Campus.

This study represents a pioneering effort to integrate geographic information systems (GIS) and ensemble machine learning methods to predict noise levels on a university campus. Three ensemble models including random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) were developed to predict traffic noise based on data collected over a 4-week period at the Universiti Teknologi Malaysia (UTM) campus. Noise measurements were obtained during peak morning hours (7:30 to 9:30 a.m.) on weekdays within the UTM campus in Johor. Additional predictor variables, including data from the digital elevation model (DEM) and land use, were incorporated to capture the complex nonlinear relationships influencing noise levels. The models were optimized through hyperparameter tuning, resulting in high precision, as evidenced by performance metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). The XGB model emerged as the most accurate, with R2 = 0.96, MAE = 0.9, and MSE = 0.3. Noise maps generated using the inverse distance weighting (IDW) interpolation technique highlighted the spatial distribution of noise levels, classified into five classes considering WHO standards. The findings identified distance from roads, the number of light vehicles, and proximity to green areas as the most significant predictors. However, challenges remain in accurately predicting noise levels associated with other predictors. The outcomes of the study indicate the superior performance of the XGB model compared to the GB and RF models. The study recommends several measures to manage and control noise pollution on the UTM campus, including raising awareness, regulating and enforcing vehicle speed limits, reevaluating land use, installing sound insulation systems, and planting trees and vegetation buffer zones around and within educational buildings.

Combining life cycle assessment and dynamic qualitative comparative analysis to analyze the environmental impacts of urban development across different provinces in China

Examining the environmental pollution landscape is a vital link for urban development. Across production, distribution, and usage stages of 24 provinces in China between 2010 and 2019, this study conducts Life Cycle Assessment and Dynamic Qualitative Comparative Analysis to explore the effects of conditional combination on environmental impact assessment indicators including Photochemical Oxidation, Eutrophication, Global Warming Potential, Human Toxicity, and Acidification. The results reveal that: (1) Shandong contributes most significantly to environmental impact assessment indicators in the production and distribution stages, while Inner Mongolia dominates the usage stage. In contrast, Beijing, Hainan, and Shanghai show lower contributions, though Hainan exhibits a rising trend in the Global Warming Potential index across all stages; (2) The production stage consistently accounts for the largest environmental impact, with notable differences in contributions from Beijing, Ningxia, Inner Mongolia, and Shanghai across the three stages; (3) Environmental impact is not driven by individual factors alone; rather, it is the combination of factors that proves influential. Complex nonlinear relationships are revealed between energy consumption patterns and environmental impacts in the production stage. The combination of transport operating distance and traffic volume has an interaction during the distribution stage. The usage stage leads only to the environmental impact of Eutrophication, which is caused by the resident population and ecological water consumption excluding residential water use. These findings present a new strategy for optimizing energy structure and water resource utilization, and emphasize the synergies to reduce the environmental impacts, providing instrumental implications for policymakers and government managers in environmental management and urbanization development.

Integrating Canonical Correlation Analysis with Random Forest for Heart Disease Prediction

Heart disease, a leading global cause of death over the past several decades, encompasses a range of disorders affecting the heart. Researchers use various data mining and machine learning techniques to analyze complex medical data, aiding healthcare professionals in predicting cardiac conditions. Despite these advances, existing models often struggle with effectively modelling non-linear relationships, maximizing feature correlation, and addressing challenges related to dimensionality and overfitting. This research paper introduces the Hybrid CCRF model for heart disease prediction, which integrates Canonical Correlation Analysis (CCA) with Random Forest. The proposed model generates polynomial features to capture non-linear relationships and applies Canonical Correlation Analysis to identify canonical variables that maximize correlations between heart disease features and chronic condition features. By combining these canonical variables into a single feature set, the model enhances prediction accuracy. The objectives of the Hybrid CCRF model are threefold: 1) To capture complex non-linear relationships between heart disease and chronic condition features by integrating polynomial feature generation with Canonical Correlation Analysis, thereby improving the model’s ability to represent intricate data patterns; 2) To use CCA to identify and integrate canonical variables that enhance feature correlation, creating a more informative feature set; and 3) To address high-dimensional data and overfitting issues by combining canonical variables with polynomial features in a Random Forest model, balancing complexity and performance for improved generalization and robustness across various datasets. The proposed model achieved an accuracy of 99.45%, with a sensitivity of 98.53%, specificity of 99.54%, precision of 95.73%, and an F1 Score of 0.9711, outperforming all existing models.

B-122 Improving the Diagnostic Utility of Multiplexed Assays in Laboratory Medicine with Machine Learning

Abstract Background The application of machine learning (ML) and artificial intelligence (AI) in precision medicine is of great interest and continues to grow. ML approaches offer a flexible and data-centric methodology for modeling diagnostic and prognostic trends, capable of identifying complex non-linear patterns and relationships between predictor variables and outcomes within large data sets. Although ML holds great promise, there are several common pitfalls that must be avoided for ML models to provide clinical utility. In the current study, we provide a rigorous and systematic evaluation of the utility of ML algorithms to predict disease in several representative data sets generated via multiplexed biomarker assays. We compare five major classes of ML algorithms compared to more traditional combinatorial analysis (logistic regression) to offer perspective against the current gold standard. Methods We obtained five data sets from published literature that include measurements of multiplexed biomarkers in patients with obstructive sleep apnea, sepsis, and breast and colon cancers in comparison to healthy controls. These datasets were generated by bead-based and planar multiplexed immunoassays. We chose representative ML algorithms from the classes of support vector machines (SVM), random forests (RF), neural networks (NNet), and extreme gradient boosting (xgBoost) in comparison to logistic regression (LR). Datasets were randomly split into internal validation and external validation sets via 10 iterations of permutation analysis. We tuned and validated the models using 5-fold cross validation, then tested those on external subsets. We report the average of several model classification metrics and associated variances across all resampled data sets. Results For the classification of sepsis using inflammatory cytokines measured on the Luminex bead-based immunoassay, RF outperformed LR in external validation sets by a 9.8% increase in AUROC. For classification of breast cancer patients, xgBoost and RF performed best with 20.0% and 16.9% increases in AUROC, respectively, whereas for classification of colon cancer patients, NNet outperformed LR by 11.5%. Across all datasets, on average, all ML algorithms outperformed LR in the internal validation sets by a 7.9% (6.3% - 9.9%) increase in AUROC. However, in external validation sets, only xgBoost and SVM outperformed LR, where RF and NNet showed signs of overfitting. Conclusions Overall, certain ML algorithms show improvement over LR for diagnostic applications using multiplexed assays, by up to 20% increase in AUROC. Interpretation of ML results must be performed rigorously to prevent overly optimistic and unrealistic conclusions. In the context of laboratory medicine, our results showcase the utility of ML in clinical applications, while highlighting potential disadvantages of ML. This analysis underscores the need for establishing rigorous guidelines to support data analytics during the development of novel multiplexed assays, which is needed to advance precision medicine.

Intelligence analysis of membrane distillation via machine learning models for pharmaceutical separation

This study investigates simulation of pharmaceutical separation via membrane distillation process by computational simulation and machine learning modeling strategy. The efficacy of three regression models, i.e., Multi-layer Perceptron (MLP), Gamma Regression, and Support Vector Regression (SVR) in predicting the solute concentration, C(mol/m³), was evaluated. The hyper-parameters were optimized by fine-tuning the models using the Red Deer Algorithm (RDA). Computational analyses were carried out for removal of pharmaceuticals from solution by membrane distillation in continuous mode. Mass transfer and machine learning models were implemented focusing on concentration of solute in the feed section of membrane. Results indicate that the Multi-layer Perceptron model achieved great accuracy with an R2 of 0.9955, an MAE of 0.0084, and an RMSE of 0.0148, effectively capturing complex nonlinear relationships in the data. Gamma Regression also performed acceptably, with fitting R2 of 0.9214, showing its suitability for positively skewed data. The Support Vector Regression model, while capturing the general trend, showed the lowest performance with an R2 of 0.8710. These findings suggest that the Multi-layer Perceptron is the most accurate model for this dataset, followed by Gamma Regression and Support Vector Regression. This underscores the importance of careful model selection and optimization in regression analysis in combination with computational simulation of membrane processes.

Prediction of mechanical properties of manufactured sand polymer-modified mortar based on swarm intelligence algorithm

Because polymer-modified mortar (PMM) exhibits a complex and diverse composition, there is a complex nonlinear relationship between its mechanical properties and mix proportions that is challenging for conventional mechanical performance prediction methods to accurately predict. Consequently, in this study, a predictive model utilizing a backpropagation neural network (BPNN) was formulated, featuring a structure of 6–14-2. Simultaneously, three swarm intelligence algorithms were integrated—the ant colony optimization algorithm, grey wolf optimization algorithm, and bat optimization algorithm (BAT)—to collectively refine the optimization process of the BPNN prediction model. The model's input layer included cement (OPC), cellulose ether (CE), dispersible polymer powder (DPP), antifoam (AF), fly ash (FA), and tuff stone powder (SP), and the output layer consisted of the compressive and bond strengths. The model dataset comprised 520 samples (260 × 2), with 60 % (312) used for model establishment and 40 % (208) used for validation. Correlation matrix and principal component analyses were conducted on the dataset, along with a comparative analysis of the factors influencing the mechanical performance evaluation indicators. The results indicate that at 7 and 28 d, there was a positive correlation between the DPP and AF with the development of PMM mechanical properties. At 7 d, the SP and FA were negatively correlated with the compressive and flexural strength, whereas the CE was positively correlated with the bond strength. At 28 d, the OPC was negatively correlated with the compressive, bond, and flexural strengths, and positively correlated with the SP and FA. C3 represents the optimal mix proportion for the PMM, and considering the influence of all raw materials, F3 was identified as the comprehensive optimal mix proportion. The predictive performance evaluation indicators of the BAT–BPNN for the compressive and bond strengths were R2 = 0.980 and 0.942, MAE = 5.967 and 1.958, MAPE = 0.071 and 0.041, and RMSE = 4.686 and 1.594, respectively. BAT significantly improves the predictive accuracy of the BPNN model, enabling the prediction of PMM mechanical properties and providing a scientific basis for its mix proportion design.

Construction and verification of pancreatic fistula risk prediction model after pancreaticoduodenectomy based on ensemble machine learning

Objective: To construct an ensemble machine learning model for predicting the occurrence of clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy and evaluate its application value. Methods: This is a research on predictive model. Clinical data of 421 patients undergoing pancreaticoduodenectomy in the Department of Pancreatic Surgery,Union Hospital, Tongji Medical College,Huazhong University of Science and Technology from June 2020 to May 2023 were retrospectively collected. There were 241 males (57.2%) and 180 females (42.8%) with an age of (59.7±11.0)years (range: 12 to 85 years).The research objects were divided into training set (315 cases) and test set (106 cases) by stratified random sampling in the ratio of 3∶1. Recursive feature elimination is used to screen features,nine machine learning algorithms are used to model,three groups of models with better fitting ability are selected,and the ensemble model was constructed by Stacking algorithm for model fusion. The model performance was evaluated by various indexes,and the interpretability of the optimal model was analyzed by Shapley Additive Explanations(SHAP) method. The patients in the test set were divided into different risk groups according to the prediction probability (P) of the alternative pancreatic fistula risk score system (a-FRS). The a-FRS score was validated and the predictive efficacy of the model was compared. Results: Among 421 patients,CR-POPF occurred in 84 cases (20.0%). In the test set,the Stacking ensemble model performs best,with the area under the curve (AUC) of the subject's work characteristic curve being 0.823,the accuracy being 0.83,the F1 score being 0.63,and the Brier score being 0.097. SHAP summary map showed that the top 9 factors affecting CR-POPF after pancreaticoduodenectomy were pancreatic duct diameter,CT value ratio,postoperative serum amylase,IL-6,body mass index,operative time,albumin difference before and after surgery,procalcitonin and IL-10. The effects of each feature on the occurrence of CR-POPF after pancreaticoduodenectomy showed a complex nonlinear relationship. The risk of CR-POPF increased when pancreatic duct diameter<3.5 mm,CT value ratio<0.95,postoperative serum amylase concentration>150 U/L,IL-6 level>280 ng/L,operative time>350 minutes,and albumin decreased by more than 10 g/L. The AUC of a-FRS in the test set was 0.668,and the prediction performance of a-FRS was lower than that of the Stacking ensemble machine learning model. Conclusion: The ensemble machine learning model constructed in this study can predict the occurrence of CR-POPF after pancreaticoduodenectomy,and has the potential to be a tool for personalized diagnosis and treatment after pancreaticoduodenectomy.

Machine Learning for Treatment Management Prediction in Laryngeal Fractures

ObjectivesLaryngeal fractures are rare but potentially life-threatening traumas. Complications, such as airway obstruction and disrupted laryngeal anatomy, associate with significant morbidity. Early identification of at-risk patients and optimal management remain crucial for improved outcomes. Recently, machine learning (ML) has attained great attention as a unique and novel technique for evaluating complex non-linear relationships between multiple observations to create a predictive model with greater accuracy. This study aimed to demonstrate the potential of ML in predicting airway and surgical management of laryngeal fracture patients and identify key contributing parameters for the predictive performance of the ML models. MethodsThe ML models were developed using a patient series managed at the Helsinki University Hospital during 2005–2019. The developed models were further evaluated independently using a different cohort collected from the same institution between 1995–2004. ResultsThe ML showed a weighted area under curve (AUC) of 0.93 and accuracy of 0.86 following training for airway management. Likewise, for treatment approach, weighted AUC was 0.85 and accuracy 0.78. Injury type, Schaefer-Fuhrman grade (SF gr), age at incident, cause of injury, and fracture of the cricoid, in decreasing order of significance, were the most prominent features for the model's predictive performance for airway management. Similarly, our model identified SF gr, fracture of the cricoid, injury type, age at incident, and cause of injury as the most significant predictors for surgical treatment approach. ConclusionsThe proposed prediction of management approach by an ML technique can provide accurate predictions and thus aid clinicians in administering early and personalized interventions. The model may serve as a supporting tool in recognizing at-risk patients and in timely decision making. Further independent external validation is warranted for model generalizability.

Spatiotemporal evolution of ecological environmental quality and its dynamic relationships with landscape pattern in the Zhengzhou Metropolitan Area: A perspective based on nonlinear effects and spatiotemporal heterogeneity

Understanding the spatiotemporal dynamics between landscape pattern changes and ecological environmental quality is essential for optimizing regional landscapes and improving the ecological environment. However, previous studies have certain shortcomings in understanding both aspects: On one hand, ecological environment assessments that overlook air quality may be misleading; On the other hand, the spatiotemporal driving mechanisms between landscape patterns and ecological environmental quality are still unclear. This study considers air quality information, refines ecological remote sensing indices, and employs the Random Forest-Multiscale Geographically and Temporally Weighted Regression (RF-MGTWR) analytical framework to reveal the nonlinear effects and spatiotemporal heterogeneity between landscape patterns and ecological environmental quality across the dimensions of aggregation, connectivity, and diversity. The results indicate:(1) ARSEI (Advanced Remote Sensing Ecological Index) is more suitable for assessing ecological environment quality in areas with air pollution compared to RSEI (Remote Sensing Ecological Index). (2) Ecological environmental quality is most strongly influenced by landscape connectivity, followed by diversity and aggregation. The driving effects of landscape aggregation, diversity, and connectivity will change around the values of 91, 0.64, and 80, respectively, all exhibiting complex nonlinear relationships.(3) Landscape patterns exhibit opposing trends in ecological benefits between urban centers and rural areas: In urban centers, landscape aggregation is negatively correlated with ecological environmental quality, while increased connectivity and diversity have positive effects; this is reversed in areas with good ecological conditions. As urbanization progresses, the positive impacts of landscape connectivity and diversity are enhanced, although the positive effects of diversity gradually diminish. (4) The combination of random forests and MGTWR offers a comprehensive and innovative perspective for analyzing driving mechanisms. Based on the findings, landscape planners should pay attention to the impact thresholds, scales of action, and spatiotemporal heterogeneity between landscape indicators and ecological environment quality.

Improving lithofacies prediction in lacustrine shale by combining deep learning and well log curve morphology in Sanzhao Sag, Songliao Basin, China

The accurate identification of shale lithofacies is crucial for characterizing the hydrocarbon potential of lacustrine shale oil reservoirs. Petrophysical logging, serving as an effective tool for acquiring subsurface lithofacies information, provides a convenient and reliable lithofacies identification solution. Deep learning technology, capable of adapting to the nonlinearity and non-stationarity inherent in geological statistics, exhibits unique advantages in conventional reservoir lithofacies prediction. However, the lithofacies of lacustrine shale formations undergo rapid spatial and temporal changes, rendering lithofacies prediction more complex compared to conventional reservoirs. In this study, the lower Qingshankou member in the Sanzhao Sag was selected as the research target, and the Deep Residual Shrinkage Network (DRSN), known for its ability to handle complex nonlinear relationships and mitigate the effects of noisy data through residual connections and shrinkage mechanisms, was employed as a deep learning framework for predicting lithofacies in lacustrine shale formations for the first time. Well logging data, including natural gamma ray (GR), acoustic (AC), deep investigate double lateral resistivity log (RD), shallow investigate double lateral resistivity log (RS), and corrected compensated neutron log (CNC), were used as input features for the model. The results indicate that the DRSN model achieves an accuracy of 76.3% in predicting lithofacies in lacustrine shale formations. However, the DRSN model still exhibits shortcomings in capturing lithofacies change information. To enhance the model's ability to identify lithofacies change interfaces, this study further explicitly introduces Well Logging Curve Morphological Features (WLCM) as additional features and establishes a recognition method combining DRSN with WLCM. The combined DRSN-WLCM model was validated using a separate test dataset, demonstrating an improved accuracy of 85.5%, using the five well logging attributes and the derivative of the AC as inputs. Furthermore, the study reveals the lithofacies spatial distribution characteristics of the lower Qingshankou member in the Sanzhao Sag. This method can be widely applied to lithofacies delineation in lacustrine shale formations and similar stratigraphic units.

Time Series Forecasting of Emission Trends Using Recurrent Neural Networks

This paper explores the application of Recurrent Neural Networks (RNNs) for forecasting emission trends, a critical aspect of addressing climate change and formulating effective environmental policies. Traditional forecasting methods, such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing, often fail to capture the complex nonlinear relationships and temporal dependencies inherent in emission data. In contrast, RNNs, particularly Long Short-Term Memory (LSTM) networks, are designed to recognize patterns in sequential data, making them well-suited for time series forecasting tasks. This study employs a comprehensive methodology that includes data collection from reputable sources, feature selection, RNN model design, and rigorous evaluation using various performance metrics. The results indicate that RNNs significantly outperform traditional forecasting techniques in terms of accuracy, providing valuable insights into future emission trajectories. The findings underscore the potential of RNNs as powerful tools for policymakers and researchers, facilitating more informed decision-making in the fight against climate change.

The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

Machine Learning Prediction of Fuel Cell Remaining Life Enhanced by Variational Mode Decomposition and Improved Whale Optimization Algorithm

In predicting the remaining lifespan of Proton Exchange Membrane Fuel Cells (PEMFC), it is crucial to accurately capture the multi-scale variations in cell performance. This study employs Variational Mode Decomposition (VMD) to decompose performance data into intrinsic modes, elucidating critical multi-scale dynamics vital for understanding the complex degradation processes in fuel cells. In addition to VMD, this research utilizes an Improved Whale Optimization Algorithm (IWOA) to optimize a Back Propagation (BP) Neural Network. The IWOA focuses on precise adjustments of weights and biases, enabling the BP network to effectively interpret complex nonlinear relationships within the dataset. This optimization enhances the predictive model’s reliability and stability. Extensive experimental evaluations demonstrate that the integration of VMD, and the learning capabilities of the IWOA-optimized BP network significantly improves the model’s accuracy and stability across multiple predictions, thereby increasing the reliability of lifespan predictions for PEMFCs. This methodology offers a robust framework for extending the operational life and efficiency of fuel cells.

Prediction of mechanical properties of rolled steel based on dual-attention multiscale convolutional neural network

The mechanical properties of carbon steel sheet directly depend on the chemical composition and process parameters of the steel. There is a complex nonlinear relationship between chemical composition, process parameters, and mechanical properties, and the establishment of a model with the ability to automatically distinguish the importance of different parameters, and to have good prediction accuracy and generalisation in different data sets is one of the important issues in the field of carbon steel mechanical properties prediction. In this paper, we proposed a multi-scale convolutional network model based on dual-attention mechanism (DAM-MSDSC), where the dual-attention module mines from features and channels at the same time, making the model focus on the important features. The multiscale feature fusion module effectively improves the model prediction accuracy by mining features from the output of the middle layer of the network to obtain features containing multiscale fusion features. In hot-rolled steel, for the two parameters of carbon content and temperature, it is confirmed that the change of carbon content has a greater impact on TS, while FTT and CT have a greater impact on YS. Through comparative experiments on different datasets, the proposed model had the best prediction performance, in which the correct rates of YS, TS, and EL are 98 %, 98.7 %, and 99.4 %, respectively, in cold rolled steel, and 97.8 %, 98.6 %, and 99.2 %, respectively, in hot rolled steel.

A multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models

Agricultural water demand, groundwater extraction, surface water delivery and climate exhibit complex nonlinear relationships with groundwater storage in agricultural regions. As alternatives to computationally intensive physical models, data-driven machine learning methods are frequently employed as surrogates to capture these complex relationships, owing to their high computational efficiency. Inevitably, reliance on a single machine learning model may lead to underestimation of prediction uncertainty and potentially result in reduced accuracy. This paper presents a multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models. The key contributions of the research are threefold: 1) the development of a comprehensive input variable selection process considering the lag effects and driving mechanisms of groundwater level variations; 2) the implementation of an optimization-based hyperparameter tuning method to enhance the performance of individual machine learning models; and 3) the establishment of a multi-model integration framework based on a multi-layer nesting technique. This approach combines the outputs of multiple machine learning models to consolidate predictions and expand the hypothesis space. The effectiveness of the proposed framework is demonstrated through case studies in the Huaihe River Basin, a major agricultural region in China. The results show that the multi-layer nesting and integration approach outperforms the use of individual machine learning models, providing more accurate and reliable groundwater level predictions. This framework offers valuable insights for decision-makers and water resource managers, supporting sustainable groundwater management and addressing the challenges faced in agriculturally intensive areas.

Investigating the ability of deep learning to predict welding depth and pore volume in hairpin welding

To advance quality assurance in the welding process, this study presents a deep learning (DL) model that enables the prediction of two critical welds’ key performance characteristics (KPCs): welding depth and average pore volume. In the proposed approach, a wide range of laser welding key input characteristics (KICs) is utilized, including welding beam geometries, welding feed rates, path repetitions for weld beam geometries, and bright light weld ratios for all paths, all of which were obtained from hairpin welding experiments. Two DL networks are employed with multiple hidden dense layers and linear activation functions to investigate the capabilities of deep neural networks in capturing the complex nonlinear relationships between the welding input and output variables (KPCs and KICs). Applying DL networks to the small numerical experimental hairpin welding dataset has shown promising results, achieving mean absolute error values of 0.1079 for predicting welding depth and 0.0641 for average pore volume. This, in turn, promises significant advantages in controlling welding outcomes, moving beyond the current trend of relying only on defect classification in weld monitoring to capture the correlation between the weld parameters and weld geometries.

Research on the influencing factors of housing price based on multiple linear regression and random forest

This paper aims to use multiple linear regression model and random forest models to analyze and study the factors affecting the housing price in Boston. The multiple linear regression model describes the relationship between multiple independent variables and one dependent variable through linear equations, and the random forest improves the accuracy and robustness by constructing multiple decision trees and combining their prediction results. To deal with complex nonlinear relationships and high dimensional data. Housing price is an important index to reflect the level and condition of economic and social development of a region, so it is of theoretical value and practical significance to explore its influencing factors and ways and degrees. Multiple factors are selected to analyze the weight and importance of each influencing factor, so as to help the government and decision makers to formulate more accurate policies, promote the stable development of the market, and provide scientific decision-making support for real estate developers, investors and ordinary buyers. In this study, the random forest model based on decision tree was used to clean, select and reduce the acquired housing price data, and to find out the main factors affecting housing price from the perspective of information gain, so as to obtain a relatively complete mathematical model and provide a reference scheme for future research by scholars.