Model Predictions Research Articles

Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis Shapley additive explanations algorithm

Ultra-high-performance concrete (UHPC) is a sustainable construction material; it can be applied as a substitute for cement concrete. Artificial intelligence methods have been used to evaluate concrete composites to reduce time and money in the construction industries. So, this study applied machine learning (ML) and hybrid ML approaches to predict the compressive and flexural strength of UHPC. A dataset of 626 compressive strength and 317 flexural strength data points was collected from the published research articles, where fourteen important variables were selected as input parameters for the analysis of hybrid ML and ML algorithms. This research used XGBoost, LightGBM, and hybrid XGBoost- LightGBM algorithms to predict UHPC materials. Grid search (GS) techniques were used to adjust model hyper-parameters in search of improved high accuracy and efficiency. ML and hybrid ML models were train, and the test stage utilized statistical assessments such as R-square, root mean square error (RMSE), mean absolute error (MAE), and coefficient of efficiency (CE). The results presented hybrid ML algorithm was superior to the XGBoost and LightGBM algorithms in terms of R-square and RMSE values for both compressive strength and flexural strength prediction. A hybrid ML model and two ML models showed CS considerable R-square values above 0.94 at the testing stages and just over 0.97 at the training phase. Hybrid ML model performance accuracy for CS prediction was R-square value found that almost 0.996 for training and 0.963 for testing phases. At the same time, the FS prediction result showed that the R-square value of the Hybrid ML model and two traditional ML models were founded at almost 0.95 for the training phase and around 0.81 for the testing phase. But among them, the hybrid XGB-LGB model prediction performance was high accuracy and lowest error for CS and FS of UHPC trained and its hyperparameters optimized. Additionally, the SHAP investigation reveals the impact and relationship of the input variables with the output variables. SHAP analysis outcome reveals that curing age and steel fiber content input parameter had the highest positive impact on compressive strength and flexural strength of UHPC.

Understanding the interface fracture mechanism by composite BFPMPC mortar and cement concrete bending beam

This study aims to utilize basalt fiber-reinforced polymer-modified magnesium phosphate cement (BFPMPC) mortar as an overlay for rapidly repairing surface defects such as raveling and delamination on Portland cement concrete (PCC) pavements, thereby swiftly restoring traffic flow and enhancing pavement service durability. Initially, double layered three-point bending fracture (DLTPB) tests were conducted on composite BFPMPC and PCC specimen with different predetermined crack lengths. The flexural strength was measured, fracture parameters were obtained based on the double-K fracture criterion, and crack propagation paths were determined using digital image correlation (DIC) methods. Subsequently, the fracture behavior of the composite beam was predicted by boundary effect model, and the interface microstructure between BFPMPC and PCC was characterized using nanoindentation, nano-scratch, and SEM/EDS techniques. Finally, by analyzing the fracture characteristics of the composite specimens, the crack propagation model was established and validated by finite element analysis (FEA). The results indicated that BFPMPC mortar overlay can enhance the flexural strength of composite structure. The unstable fracture toughness, cohesive fracture toughness, and fracture energy decreased with increase of crack length-to-height ratio (a0/D), while the initiation fracture toughness increased with increase of a0/D. DIC measurements provided insights into the morphology of the crack tip and the length of the fracture process zone (FPZ). The boundary effect model predictions aligned with the fracture characteristics of the specimens, which were controlled by nonlinear fracture criteria. Nanoindentation tests revealed that the mechanical properties of the interface are influenced by multiple sub-interfaces, with the elastic modulus relationship being ITZ-4 > ITZ-2 > ITZ-3 > ITZ-1. Further analysis of scratch hardness and scratch fracture toughness confirmed that the BFPMPC-PCC interface exhibited high fracture toughness, which was crucial for improving structural resistance to fracture. SEM/EDS observations revealed significant cracks at the interface between BFPMPC and ordinary Portland cement (OPC), while cracks between BFPMPC and coarse aggregates were smaller. Subsequently, the fracture analysis model for composite specimens was proposed to finely reveal interface fracture failure, and the FEA results exhibited a high degree of consistency with experimental results.

Environmental and social disclosure, managerial entrenchment, and investment efficiency

This study investigates the effect of environmental and social (E&S) disclosure and managerial entrenchment on investment efficiency. E&S disclosure increases not only capital accessibility but also external monitoring of entrenched managers’ actions. We develop a theoretical model that demonstrates how these benefits and costs of firms’ voluntary E&S disclosure affect investment efficiency. Using a large sample of U.S.-listed firms over the period 2016–2022, we test the model predictions and provide empirical evidence suggesting that E&S disclosure is positively associated with investment efficiency and that this effect is stronger for firms with lower managerial entrenchment or disclosing more financially material E&S information. Our study contributes to the investment efficiency literature by demonstrating the relevance of an incentive-compatible mechanism reflecting managers’ choice of E&S voluntary disclosure to the efficient capital allocation.

Apple Firmness Detection Method Based on Hyperspectral Technology

Firmness is a key indicator of apple quality. Building a predictive model for apple firmness based on hyperspectral technology and regression algorithms can achieve rapid, non-destructive, and high-throughput detection of apple firmness. This paper adopts an Adaptive Window Length Savitzky-Golay Smoothing (AWL-SG smoothing) algorithm based on the Savitzky-Golay Smoothing (SG smoothing) algorithm, which can adaptively adjust the window length according to the change rate of spectral data at different wavelengths. SG smoothing, AWL-SG smoothing, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC) algorithms were used to preprocess the original spectral data, and Partial Least Squares (PLS), Ridge Regression (Ridge), and Kernel Ridge Regression (Kernel Ridge) predictive models were constructed to analyze the impact of different preprocessing methods on model prediction accuracy. The prediction models established with spectral data preprocessed by SG smoothing and AWL-SG smoothing algorithms showed significant improvement in predictive performance on the basis of the original spectral data, among which the AWL-SG smoothing algorithm performed the best. The Ridge model established with spectra data preprocessed by AWL-SG smoothing achieved an of 0.8914 in the test set. Successive Projection Algorithm (SPA), Principal Component Analysis (PCA), and Independent Component Analysis (ICA) dimensionality reduction algorithms were used to reduce the dimensions of the full-band spectral data preprocessed by SG smoothing and AWL-SG smoothing algorithms, and Ridge and Kernel Ridge prediction models were constructed. The results showed that both SPA and PCA algorithms could improve the predictive performance of the models, with the PCA performing the best. The combination of AWL-SG+PCA+Ridge achieved the best predictive effect, with an of 0.9146 in the test set.

Evaluation and Modelling of Polycyclic Aromatic Hydrocarbon (PAH) Bioavailability in Soils Affected by Coal Tar Asphalt

There are large masses of coal tar asphalt present in old roads, containing high concentrations of polycyclic aromatic hydrocarbons (PAHs). Uncertainty surrounding the risk they pose causes problems during road reconstruction and for the reuse of the asphalt present. To help elucidate potential risks, a parsimonious linear equilibrium partitioning model for the bioavailability of PAHs in soils contaminated by tar asphalt particles was developed. Furthermore, a set of partitioning coefficients for PAHs between sampled coal tar binders and water were determined experimentally, as well as measurements of freely dissolved concentrations using polyoxymethylene samplers in batch tests and column recirculation experiments with various mixtures of different soils (peat and sandy loam) and tar asphalts. The model predictions of freely dissolved concentrations were conservative and within an order of magnitude of measurements in both batch and column tests. The model presented here only relies on soil organic carbon content and the fraction coal tar binder in the soil to model PAH partitioning. This model could be used for more realistic. low tier risk assessments towards rational prioritization of sensitive areas for risk reduction efforts.

Bankruptcy Analysis of Polish Waste Management Companies

The waste management system should be constantly monitored to ensure rational waste management, limit the possibility of contamination of surface and groundwater, reduce the risk of waste fires and illegal storage. Unfortunately, in many cases this system remains without constant supervision, and subsequent waste management enterprises remain bankrupt, are closed or are subject to bankruptcy proceedings. Main aim of this paper is the construction of the author's model of financial bankruptcy prediction dedicated to the specificity of waste management companies activity. That discriminatory model will allow entities dealing with waste management to be given early warning of the risk of bankruptcy and for them to take corrective actions that will allow them to continue their very important activities aimed at protecting the natural environment. Based on the backward stepwise regression procedure on a group of 136 bankrupt and 136 still operating enterprises from the analyzed sector of economy, a discriminatory model was proposed. It was able to accurately predict the bankruptcy/continuation of business activity of over 76% of the analyzed waste management enterprises. The effectiveness of the predictions of the original model is much higher than the predictions calculated using other, popular discriminatory models. Obtained research results indicate that the proposed models should be constantly developed for waste management companies, not only for economic reasons, but above all for environmental reasons.

Research on User Default Prediction Algorithm Based on Adjusted Homogenous and Heterogeneous Ensemble Learning

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the “dual-carbon” goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system.

Multimodal Machine Learning for Prognosis and Survival Prediction in Renal Cell Carcinoma Patients: A Two-Stage Framework with Model Fusion and Interpretability Analysis

Current medical limitations in predicting cancer survival status and time necessitate advancements beyond traditional methods and physical indicators. This research introduces a novel two-stage prognostic framework for renal cell carcinoma, addressing the inadequacies of existing diagnostic approaches. In the first stage, the framework accurately predicts the survival status (alive or deceased) with metrics Accuracy, Precision, Recall, and F1 score to evaluate the effects of the classification results, while the second stage focuses on forecasting the future survival time of deceased patients with Root Mean Square Error and Mean Absolute Error to evaluate the regression results. Leveraging popular machine learning models, such as Adaptive Boosting, Extra Trees, Gradient Boosting, Random Forest, and Extreme Gradient Boosting, along with fusion models like Voting, Stacking, and Blending, our approach significantly improves prognostic accuracy as shown in our experiments. The novelty of our research lies in the integration of a logistic regression meta-model for interpreting the blending model’s predictions, enhancing transparency. By the SHapley Additive exPlanations’ interpretability, we provide insights into variable contributions, aiding understanding at both global and local levels. Through modal segmentation and multimodal fusion applied to raw data from the Surveillance, Epidemiology, and End Results program, we enhance the precision of renal cell carcinoma prognosis. Our proposed model provides an interpretable analysis of model predictions, highlighting key variables influencing classification and regression decisions in the two-stage renal cell carcinoma prognosis framework. By addressing the black-box problem inherent in machine learning, our proposed model helps healthcare practitioners with a more reliable and transparent basis for applying machine learning in cancer prognostication.

Flow stability analysis on low-speed compressor with swept rotor blades under radial inlet distortions

Flow stability enhancement for axial flow compressors under radially distorted inlet conditions through rotor sweep design is an important issue for designers. To investigate the couped effect of rotor swept and radial inlet distortion on flow stability, steady numerical simulations, theoretical model predictions, and experimental validations were conducted for a low-speed, single-stage axial compressor (TA36) under various intensities of tip total pressure distorted inlet conditions with different swept rotor configurations. The steady pressure rise significantly decreases as the intensity of the distortion increases, whereas the rotor sweep has little effect on the pressure rise characteristic. The maximum error via model prediction is 1.25 %, while for steady numerical calculation, it is 3.89 %. Higher intensities of distortion lead to worse flow stability. Under radially distorted inlet conditions, forward sweep enhances flow stability whereas backward sweep further worsens it. What's more, quantitative analysis shows that the stability enhancement of forward sweep cannot fully counteract the adverse impact of radial distortion on flow stability. Elevated rotor tip loading was identified as the primary cause of instability under tip total pressure distorted inlet conditions. Forward sweep can slightly reduce this loading, whereas backward sweep has the opposite effect. Furthermore, the impact of rotor sweep on rotor tip loading intensifies with increasing distortion intensity. Unsteady flow field analysis was conducted. Which shows that radial distortion and blade sweep both affect flow stability by altering the intensity of tip leakage flow.

Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

IntroductionRescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models.MethodsIn this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2) as performance metrics.ResultsIn this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA’s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions.DiscussionWhile the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

Data-driven prediction of continuous renal replacement therapy survival

Continuous renal replacement therapy (CRRT) is a form of dialysis prescribed to severely ill patients who cannot tolerate regular hemodialysis. However, as the patients are typically very ill to begin with, there is always uncertainty whether they will survive during or after CRRT treatment. Because of outcome uncertainty, a large percentage of patients treated with CRRT do not survive, utilizing scarce resources and raising false hope in patients and their families. To address these issues, we present a machine learning-based algorithm to predict short-term survival in patients being initiated on CRRT. We use information extracted from electronic health records from patients who were placed on CRRT at multiple institutions to train a model that predicts CRRT survival outcome; on a held-out test set, the model achieves an area under the receiver operating curve of 0.848 (CI = 0.822–0.870). Feature importance, error, and subgroup analyses provide insight into bias and relevant features for model prediction. Overall, we demonstrate the potential for predictive machine learning models to assist clinicians in alleviating the uncertainty of CRRT patient survival outcomes, with opportunities for future improvement through further data collection and advanced modeling.

Computational model predicts patient outcomes in Luminal B breast cancer treated with endocrine therapy and CDK4/6 inhibition.

Development of a computational biomarker to predict, prior to treatment, the response to CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy in patients with breast cancer. A mechanistic mathematical model that accounts for protein signaling and drug mechanisms of action was developed and trained on extensive, publicly available data from breast cancer cell lines. The model was built to provide a patient-specific response score based on the expression of six genes (CCND1, CCNE1, ESR1, RB1, MYC and CDKN1A). The model was validated in five independent cohorts of 148 patients in total with early-stage or advanced breast cancer treated with endocrine therapy and CDK4/6i. Response was measured either by evaluating Ki67 levels and PAM50 risk of relapse (ROR) after neoadjuvant treatment or by evaluating progression-free survival (PFS). The model showed significant association with patient´s outcomes in all five cohorts. The model predicted high Ki67 (area under the curve; AUC (95% confidence interval) of 0.80 (0.64 - 0.92), 0.81 (0.60 - 1.00) and 0.80 (0.65 - 0.93)) and high PAM50 ROR (AUC of 0.78 (0.64 - 0.89)). This observation was not obtained in patients treated with chemotherapy. In the other cohorts, patient stratification based on the model prediction was significantly associated with PFS (hazard ratio=2.92 (95% CI 1.08 - 7.86), p=0.034 and HR=2.16 (1.02 4.55), p=0.043). A mathematical modeling approach accurately predicts patient outcome following CDK4/6i plus endocrine therapy, which marks a step towards more personalized treatments in patients with Luminal B breast cancer.

Comparing the three-dimensional morphological asymmetries in the ejecta of Kepler and Tycho in X-rays

Aims. Recent simulations have shown that asymmetries in the ejecta distribution of supernova remnants (SNRs) may be a reflection of asymmetries left over from the initial supernova explosion. Thus, SNR studies provide a vital means for testing and constraining model predictions in relation to the distribution of heavy elements, which are key to improving our understanding of the explosion mechanisms in Type Ia supernovae. Methods. The use of a novel blind source separation method applied to the megasecond X-ray observations of the historic Kepler and Tycho supernova remnants has revealed maps of the ejecta distribution. These maps are endowed with an unprecedented level of detail and clear separations from the continuum emission. Our method also provides a three-dimensional (3D) view of the ejecta by individually disentangling red- and blueshifted spectral components associated with images of the Si, S, Ar, Ca, and Fe emission. This approach provides insights into the morphology of the ejecta distribution in those two remnants. Results. Those mappings have allowed us to thoroughly investigate the asymmetries in the intermediate-mass elements and Fe distribution in two Type Ia supernova remnants. We also compared the results with the core-collapse Cassiopeia A remnant, which we had studied previously. The images obtained confirm, as expected for Type Ia SNRs, that the Fe distribution is mostly closer to the core than that of intermediate-mass elements. They also highlight peculiar features in the ejecta distribution, such as the Fe-rich southeastern knot in Tycho.

Quantum model prediction for frequency regulation of novel power systems which includes a high proportion of energy storage

As the proportion of renewable energy generation continues to increase, the participation of new energy stations with high-proportion energy storage in power system frequency regulation is of significant importance for stable and secure operation of the new power system. To address this issue, an energy storage control method based on quantum walks and model predictive control (MPC) has been proposed. First, historical frequency deviation signals and energy storage charge–discharge state signals are collected. Simulation data are generated through amplitude encoding and quantum walks, followed by quantum decoding. Subsequently, the decoded data are inputted into the MPC framework for real-time control, with parameters of the predictive model continuously adjusted through a feedback loop. Finally, a novel power system frequency regulation model with high-proportion new energy storage stations is constructed on the MATLAB/Simulink platform. Simulation verification is conducted with the proportional–integral–derivative (PID) and MPC methods as comparative approaches. Simulation results under step disturbances and random disturbances demonstrate that the proposed method exhibits stronger robustness and better control accuracy.

Challenges in Multi-Task Learning for fMRI-Based Diagnosis: Benefits for Psychiatric Conditions and CNVs Would Likely Require Thousands of Patients

Abstract There is a growing interest in using machine learning (ML) models to perform automatic diagnosis of psychiatric conditions; however, generalising the prediction of ML models to completely independent data can lead to sharp decrease in performance. Patients with different psychiatric diagnoses have traditionally been studied independently, yet there is a growing recognition of neuroimaging signatures shared across them as well as rare genetic copy number variants (CNVs). In this work, we assess the potential of multi-task learning (MTL) to improve accuracy by characterising multiple related conditions with a single model, making use of information shared across diagnostic categories and exposing the model to a larger and more diverse dataset. As a proof of concept, we first established the efficacy of MTL in a context where there is clearly information shared across tasks: the same target (age or sex) is predicted at different sites of data collection in a large fMRI dataset compiled from multiple studies. MTL generally led to substantial gains relative to independent prediction at each site. Performing scaling experiments on the UK Biobank, we observed that performance was highly dependent on sample size: for large sample sizes (N>6000) sex prediction was better using MTL across three sites (N=K per site) than prediction at a single site (N=3K), but for small samples (N<500) MTL was actually detrimental for age prediction. We then used established machine learning methods to benchmark the diagnostic accuracy of each of the 7 CNVs (N=19-103) and 4 psychiatric conditions (N=44-472) independently, replicating the accuracy previously reported in the literature on psychiatric conditions. We observed that MTL hurt performance when applied across the full set of diagnoses, and complementary analyses failed to identify pairs of conditions which would benefit from MTL. Taken together, our results show that if a successful multi-task diagnostic model of psychiatric conditions were to be developed with resting-state fMRI, it would likely require datasets with thousands of patients across different diagnoses.

Influence of heat input and preheating on residual stresses in pipe weld

AbstractUsing finite element methods, residual stresses were estimated in pipe welds. Experiments were also conducted to verify the numerical results. An alternative to a three-dimensional model was used to simplify the numerical calculation for residual stresses investigation. Model predictions were validated by measuring residual stresses using X-ray diffraction. As compared to measured residual stress distributions, the computational approaches developed in this study can accurately predict welding residual stress distributions. The focused welding parameters have a significant impact on residual stresses even when all the other parameters are the same.

Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design.

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

Dynamic ESG Equilibrium

This paper proposes a conditional asset pricing model that integrates environmental, social, and governance (ESG) demand and supply dynamics. Shocks in the demand for sustainable investing represent a novel risk source, characterized by diminishing marginal utility and positive premium. Green assets exhibit positive exposure to ESG demand shocks, hence commanding higher premia. Conversely, time-varying convenience yield leads to lower expected returns for green assets. Moreover, ESG demand shocks have positive contemporaneous effects on unexpected returns, contributing to large positive payoffs in the green-minus-brown portfolio over extended horizons. The model predictions align closely with evidence on return spreads between green and brown assets, further reinforcing the apparent gap between realized and expected spreads. This paper was accepted by Lin William Cong, finance. Funding: Y. Liu acknowledges funding by the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant T35/710/20R]. A. Tarelli acknowledges funding by the European Union - Next Generation EU [Project Code 2022FWZ2CR]. The views and opinions expressed are only those of the authors. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.03491 .

Effects of upstream and downstream boundary conditions on lacustrine shallow-water delta morphologies: A numerical modeling approach

Lacustrine shallow-water deltas exhibit highly variable morphologies, owing to their high sensitivity to changes in the upstream and downstream boundary conditions, where a small change significantly alters their morphology. In this study, we present the results from a numerical model (DELFT3D) used to explore the mechanism of distributary network amalgamation and delta evolution under various boundary conditions. We established a reference case to model the delta building-out process considering the basin water depth difference (shallow and deep). Geological and hydrodynamic models were established by varying the water level under a steady river discharge and sediment supply. Despite similar initial conditions, each modeled delta exhibits a unique morphological evolution, progressing through distinct stages: (i) formation of unstable, narrow channels with quasi-radial shapes; (ii) channel development, shaping the delta based on controlling factors; (iii) reduction of deltaic activity and increased channel avulsion due to delta submergence, and; (iv) new channels emerging, paving the way for reshaping and migration towards new paths, resulting in fewer bifurcations and straighter channels. Quantitative relationships characterizing delta morphology and distributary channels were established, and the following results were noted: (i) a positive correlation between active and abandoned channels, distributary channel network opening angle, and delta area with discharge and sediment grain size, and negative correlation with basin depth and falling water level; (ii) a positive correlation of lobe elongation with basin depth and discharge, and a negative correlation with sediment grain size; and (iii) roughness and length-to-width ratio emerging as key predictors of delta shape. By applying the model predictions to real-world scenarios, including modern deltas (Sile and Chiemsee) and ancient systems (Bohai Bay Basin), this study can provide valuable insights into the dynamics of lacustrine deltaic systems and their responses to changing boundary conditions. This highlights the importance of understanding deltaic systems for both theoretical research and practical applications.

Research on Control Strategy of PMSG-PWM Power Generation System with Tidal Energy

Aiming at the characteristics of machine-side output instability and the wide-range variation in the tidal energy PMSG-PWM power generation system, we propose a control scheme with a high anti-interference capability applied to the PWM rectifier. The outer voltage loop adopts sliding mode control (SMC) with the variable exponential convergence law based on expanded state observer (ESO). By compensating the perturbation observation value into the sliding mode controller in advance, it improves the robustness and response speed of the system and suppresses the direct current (DC)-side voltage jitter phenomenon. The current inner loop combines model prediction (MP) with direct power control (DPC). By introducing time-delay compensation, it improves the control accuracy, reduces the machine-side harmonic content, and enables the system to maintain unit power factor operation despite external disturbances. Simulation and experiment results show that, under the wide range of PMSG output, the PMSG-PWM power generation system adopting the “ESO_SMC+MDPDC” scheme has a fast dynamic regulation of DC output voltage, obvious vibration suppression effect, low harmonic content of the current on the PMSG side, and fast current tracking speed, which verify the feasibility of applying this system to the field of tidal energy generation.