Development Of Model Research Articles

Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients.

To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the "low-risk" classification for tailored treatment strategies. This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis. Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule-capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility. This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of "low-risk" categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.

Electro-thermal performance evaluation of a prismatic battery pack for an electric vehicle

Abstract In recent years, electric vehicles (EVs) have grown in popularity as a viable way to reduce greenhouse gas emissions by replacing conventional vehicles. The need for EV batteries is steadily increasing. An essential and expensive part of electric transportation is the battery. The operating temperature of the lithium-ion (Li-ion) battery significantly impacts the performance of the EV battery pack. Battery packs undergo temperature fluctuations during the charging and discharging procedures due to internal heat generation, necessitating an examination of the temperature distribution of the battery pack. The geometrical spacing between cells is considered larger and identical and is kept open on two sides for free air circulation. A novel battery thermal management system (BTMS) design is required to effectively dissipate heat from the prismatic battery pack module. The electro-thermal behaviour of the prismatic Li-ion battery pack module was investigated based on the high charge/discharge rate. This study presents the development of a three-dimensional free open-source OpenFOAM computational fluid dynamics model for prismatic cell battery packs that simulates heat generation, air flow field, and temperature distribution across the width and depth of the battery pack module. The prismatic battery pack simulation results are compared with the experimental and simulation results of the cylindrical battery pack. It was also revealed that prismatic cells generate more heat on the backside, requiring battery packs to have increased cooling and space between individual cells to ensure sufficient air circulation for cooling and gas removal. The BTMS is improved by designing with increased space among the prismatic battery cells as compared with the conventional prismatic cell battery pack design.

Multi-Task Learning for Design Under Uncertainty With Multi-Fidelity Partially Observed Information

Abstract The assessment of system performance and identification of failure mechanisms in complex engineering systems often requires the use of computation-intensive finite element software or physical experiments, which are both costly and time-consuming. Moreover, when accounting for uncertainties in the manufacturing process, material properties, and loading conditions, the process of reliability-based design optimization (RBDO) for complex engineering systems necessitates the repeated execution of expensive tasks throughout the optimization process. To address this problem, this paper proposes a novel methodology for RBDO. First, a multi-fidelity surrogate modeling strategy is presented, leveraging partially observed information (POI) from diverse sources with varying fidelity and dimensionality to reduce computational cost associated with evaluating expensive high-dimensional complex systems. Second, a multi-task surrogate modeling framework is proposed to address the concurrent evaluation of multiple constraints for each design point. The multi-task framework aids in the development of surrogate models and enhances the effectiveness of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly accurate and efficient framework for reliability-based design optimization through knowledge sharing. The proposed method was applied to two design case studies. By incorporating POI from various sources, the proposed approach improves the accuracy and efficiency of system performance prediction, while simultaneously addressing the cost and complexity associated with the design of complex systems.

Open Science Recommendation Systems for Academic Libraries

An interdisciplinary academic team offers a comprehensive case study describing the development of a predictive model as the cornerstone for an open science recommendation system tailored to the Carnegie Mellon University community. This initiative will empower users in choosing open science services that align with their academic requirements, introduce academics to resources they find valuable, and bridge gaps within academic library service offerings.As an institution with a longstanding commitment to a science-informed approach and a focus on computer science, engineering, and artificial intelligence, Carnegie Mellon University has enthusiastically embraced open science practices. The Carnegie Mellon University’s Libraries has been instrumental in bringing these practices into our academic landscape.The authors strive to develop a predictive model which will evolve into a recommendation system. The pursuit of this endeavor has led the authors through several ethical considerations, such as data privacy, the involvement of student contributors, and the design of a persuasive recommendation system. We are committed to exploring ethical approaches for delivering user-centered recommendations and to preserving individual autonomy.The authors have actively engaged with diverse academic departments, students, and faculty, embarking on data exploration, and applying open science principles throughout the process. The resulting system will raise awareness of library services and deliver tailored recommendations for the adoption of proven research tools and practices.This case study serves as an exemplar of how universities can enact open science principles and develop systems that prioritize the user's interests, navigate institutional complexities to forge interdisciplinary collaboration, and muster resources to support innovative, multi-disciplinary efforts. 

Development and Application of a New Exponential Model for Hydraulic Conductivity with Depth of Rock Mass

Hydraulic conductivity generally decreases with depth in the Earth’s crust. The hydraulic conductivity–depth relationship has been assessed through mathematical models, enabling predictions of hydraulic conductivity in depths beyond the reach of direct measurements. However, it is observed that beyond a certain depth, hydraulic conductivity tends to stabilize; this phenomenon cannot be effectively characterized by the previous models. Thus, these models may make inaccurate predictions at deeper depths. In this work, we introduce an innovative exponential model to effectively assess the conductivity–depth relationship, particularly addressing the stabilization at greater depths. This model, in comparison with an earlier power-like model, has been applied to a globally sourced dataset encompassing a range of lithologies and geological structures. Results reveal that the proposed exponential model outperforms the power-like model in correctly representing the stabilized conductivity, and it well captures the fast stabilization effect of multiple datasets. Further, the proposed model has been utilized to analyze three distinct groups of datasets, revealing how lithology, geological stabilization, and faults impact the conductivity–depth relationship. The hydraulic conductivity decays to the residual hydraulic conductivity in the order (fast to slow): metamorphic rocks, sandstones, igneous rock, mudstones. The mean hydraulic conductivity in stable regions is roughly an order of magnitude lower than unstable regions. The faults showcase a dual role in both promoting and inhibiting hydraulic conductivity. The new exponential model has been successfully applied to a dataset from a specific engineering site to make predictions, demonstrating its practical usage. In the future, this model may serve as a potential tool for groundwater management, geothermal energy collection, pollutant transport, and other engineering projects.

Process Optimization of Spark Plasma Sintered Parameters for Ti-Al-Cr-Nb-Ni-Cu-Co High Entropy Alloy by Response Surface Methodology

In this study, the influence of operating parameters on the relative density and microhardness property of a septenary equiatomic Ti-Al-Cr-Nb-Ni-Cu-Co high entropy alloy developed via spark plasma sintering (SPS) process was investigated at constant heating rate (100 °C/min), dwell time (5 min), pressure (50 MPa). Using response surface methodology (RSM) on the sintering temperature (ST) and milling time (MT) as the process variable parameters, a predictive model was established. The design of experiment approach was employed to minimize numbers of runs of experiment, which invariably eliminates trial by error associated with traditional experimental methods. MT and ST were taken as the variables towards the development of the design model. The optimum operating parameters were predicted using the user-defined design (UDD) under RSM and the result was validated through experiments. Observation from the results shows that MT and ST play a significant role in achieving high densification, which translates to high hardness. At 900 °C ST and MT of 10 hours, the highest hardness value of 580.1 HV, densification of 99.98%, and percentage porosity of 0.02% were recorded.

Hybrid RANS/LES modeling of hypersonic turbulent boundary layers with cold walls

Various strategies for hybrid RANS/LES modeling of hypersonic turbulent boundary layers subjected to strong wall-cooling are developed and analyzed. By defining a turbulent thermal length scale, the eddy viscosity and diffusivity of the hybrid model are calculated by the same formula, ℓ2ω. This allows a similar blending of the length scales and the development of analogous dynamic models for turbulent stress and heat flux. The dynamic procedure is applied to heat transfer by equating norms, rather than invoking least-squares with a lower bound, which improves robustness. The shielding function, as used in detached eddy simulations models, is revised to prevent a spurious early switch from the RANS to the LES mode. This revision significantly improves the accuracy of hybrid models on coarse grids. Finally, the proposed models are tested and validated for Mach numbers between 2.5 and 14, and wall-to-recovery temperature ratios between 0.18 and 1.

Zero-valent iron enhanced methane production of anaerobic digestion by reinforcing microbial electron bifurcation coupled with direct inter-species electron transfer

Zero-valent iron (ZVI) can facilitate methanogens of anaerobic digestion (AD). However, the impact of ZVI on the micro-energetic strategies of AD microorganisms remains uncertain. This study aimed to elucidate the development of an energy conservation model involving direct interspecies electron transfer (DIET) and electron bifurcate (EB) by using four types of ZVI. Overall, the ZVI addition resulted in a substantial increase in methane production (1.26 to 2.18 times higher), and the effect of boron (B) doped ZVI was particularly pronounced. The underlying mechanism may be the formation of energy harvest pathway related to DIET. In detail, B-doped ZVI could enhance its interfacial binding to cytochrome c. Decreased polar solvation energy from 20.473 to 1.509 kJ/mol is beneficial for electron transfer, thereby augmenting the flavin-bounded Cytc activity and DIET process. Besides, ZVI-enhanced EB enzyme activity like HdrA2B2C2-MvhAGD could improve the EB process, which can couple with DIET for electron transfer and energy conservation. Energy analysis based on EB-coupled DIET metabolism pathways demonstrated that the ATP saved in this coupled model theoretically line in 0.25 to 0.5 mol ATP/mol substrate. Overall, this study offers valuable insights into microbial energetic strategies pertaining to the utilization of conductive materials, with the target of enhancing methane recovery efficiency from organic waste.

Automated Detection of Cat Facial Landmarks

AbstractThe field of animal affective computing is rapidly emerging, and analysis of facial expressions is a crucial aspect. One of the most significant challenges that researchers in the field currently face is the scarcity of high-quality, comprehensive datasets that allow the development of models for facial expressions analysis. One of the possible approaches is the utilisation of facial landmarks, which has been shown for humans and animals. In this paper we present a novel dataset of cat facial images annotated with bounding boxes and 48 facial landmarks grounded in cat facial anatomy. We also introduce a landmark detection convolution neural network-based model which uses a magnifying ensemble method. Our model shows excellent performance on cat faces and is generalizable to human and other animals facial landmark detection.

Tool-pin profile effects on thermal and material flow in friction stir butt welding of AA2219-T87 plates: computational fluid dynamics model development and study

Tool-pin profile effects on thermal and material flow in friction stir butt welding of AA2219-T87 plates: computational fluid dynamics model development and study

Predictive performance of population pharmacokinetic models of imatinib in chronic myeloid leukemia patients.

Chronic myeloid leukemia is a myeloproliferative neoplasm associated with the specific chromosomal translocation known as the Philadelphia chromosome. Imatinib is a potent BCR-ABL tyrosine kinase inhibitor, which is approved as the first line therapy for CML patients. There are various population pharmacokinetic studies available in the literature for this population. However, their use in other populations outside of their cohort for the model development has not been evaluated. This study was aimed to perform the predictive performance of the published population pharmacokinetic models for imatinib in CML population and propose a dosing nomogram. A systematic review was conducted through PubMed, and WoS databases to identify PopPK models. Clinical data collected in adult CML patients treated with imatinib was used for evaluation of these models. Various prediction-based metrics were used for assessing the bias and precision of PopPK models using individual predictions. Eight imatinib PopPK model were selected for evaluating the model performance. A total of 145 plasma imatinib samples were collected from 43 adult patients diagnosed with CML and treated with imatinib. The PopPK model reported by Menon et al. had better performance than all other PopPK models. Menon et al. model was able to predict well for our clinical data where it had the relative mean prediction error percentage ≤ 20%, relative median absolute prediction error ≤ 30% and relative root mean square error close to zero. Based on this final model, we proposed a dosing nomogram for various weight groups, which could potentially help to maintain the trough concentrations in the therapeutic range.

Abstract B092: Development of predictive models for expression of a tumor specific biomarker and CD3 on H&E digital slides

Abstract Characterization of a patient’s tumor microenvironment is fundamental to advancing translational strategies in immuno-oncology. Histopathological evaluation of tissue slides from patients can provide this invaluable information, informing disease heterogeneity, tumor contexture, and target expression - all important to identifying patients more likely to benefit from therapy. However, availability of sufficient tissue is often a challenge to produce such a comprehensive tumor characterization. Measurement of target expression using an H&E slide could greatly reduce the usage of tissue for IHC making sections available for other investigative purposes. Here, an AI-driven predictive model was developed on H&E digital slides to simulate the expression of a tumor specific biomarker and CD3 across 4 different tumor types. Four epithelial tumor types (TNBC, NSCLC, ovarian, and cervical cancer) consisted of 400 H&E slides, 400 CD3 slides and 400 tumor specific biomarker IHC slides (100 cases per tumor) were used for this study. The Bio-AI Predict-X platform was utilized for the optimization of separate tumor models using pathology annotations as basis for development. The network output included tumor tiles as well as the tiles from adjacent stromal/normal tissue. Models were optimized until an AUC > 0.8 was achieved and prediction results were approved by the combined pathology teams. Predict-X was used for IHC marker quantification on a tile basis for both CD3 and the tumor specific biomarker slides with significant correlation to manual counts (p<0.001, r>0.8). Images from each case were co-registered so that ground truth for each tile with the IHC count was used in the predictive model. Tiles from tumor and corresponding adjacent stroma/normal tissue from all cases were subdivided into 3 groups for predictive model development. A training, validation, and test set were used, the latter of which was not used for development, only as a final assessment of model performance. A pan-tumor predictive model was developed for CD3 IHC on TNBC, cervical and ovarian cancers. The test set AUC values on cases from all 4 tumors were >0.7. Since the tumor specific biomarker stain was specific to tumor morphology, 4 separate predictive models were developed - one for each of the indications used with a test AUC >0.86. These findings suggest that deep learning can be used as a complementary method to prescreen H&E-stained images to enhance the detection rate of the tumor specific biomarker and CD3 positivity in patient tumors. Additional work will involve the optimization of these models for implementation in a clinical setting. Citation Format: Alan Jerusalmi, Krishna Bairavi, Ayushi Shah, Tom Chittenden, Mike Bonham, Chung-Wein Lee, Brandon Higgs, Anantharaman Muthuswamy. Development of predictive models for expression of a tumor specific biomarker and CD3 on H&E digital slides [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B092.

Abstract B052: Ovarian cancer risk prediction: a systematic assessment of the state of the art and development of a new model

Abstract B052: Ovarian cancer risk prediction: a systematic assessment of the state of the art and development of a new model

Survey on AI Applications for Product Quality Control and Predictive Maintenance in Industry 4.0

Survey on AI Applications for Product Quality Control and Predictive Maintenance in Industry 4.0

Personalized decision support system for tailoring IgA nephropathy treatment strategies

BackgroundThe ongoing debate surrounding the use of immunosuppressive treatments for IgA nephropathy (IgAN) underscores the demand for personalized and effective strategies. MethodsAnalyzed data from 807 IgAN patients over 5+ years using three methods: Random Forest with molecular biomarkers, network biomarkers with graph engineering, and an auto-encoder model. All models were trained using identical demographic, clinical, and pathological data, employing an 80–20 split for training and testing purposes. ResultsIn the comprehensive assessment of IgAN prognosis, the Random Forest model, employing molecular biomarkers, demonstrated strong performance metrics (AUC = 0.83, sensitivity = 0.51, specificity = 0.96). However, traditional graph feature engineering on patient-specific networks outperformed these results with an AUC of 0.90, sensitivity of 0.64, and specificity of 0.94. The Auto-encoder model showed the best accuracy (AUC = 0.91, sensitivity = 0.46, specificity = 0.96). The findings highlighted the superior predictive capabilities of network biomarkers over molecular biomarkers for adverse renal outcome prediction in IgAN. Consequently, we integrated Auto-encoder-derived Network Biomarkers with Random Forest Models to enhance prognostic precision in diverse IgAN treatment scenarios. The prediction for the prognosis of patients receiving supportive care, glucocorticoid therapy, and immunosuppressant treatment yielded AUC values of 0.95, 0.96, and 1, respectively, indicating high specificity. Drawing from these insights, we pioneered the development of an innovative decision support model for IgAN treatment. This model demonstrated the ability to make medical decisions comparable to those by experienced nephrologists, enabling the customization of personalized disease management strategies. ConclusionOur system accurately predicted IgAN prognosis and evaluated various treatment efficacies, aiding physicians in devising optimal therapeutic strategies for patients.

Tree belowground biomass in Congo Basin forests: allometric equations and scaling with aboveground biomass

Abstract Many allometric models to predict tree aboveground biomass have been developed in tropical moist forests, but few models are available for tree belowground biomass. Theory predicts that belowground biomass scales in an isometric way with aboveground biomass. Estimates of belowground biomass could then be derived from aboveground biomass using the root:shoot ratio. Using a dataset of 118 tropical trees for which both aboveground and belowground biomass and other tree and species characteristics were measured in Gabon and Cameroon, we found a near isometric, yet significantly allometric, relationship between belowground biomass (B, in kilograms) and aboveground biomass (A, in kilograms): B = 0.324 A0.939. The root:shoot ratio was 0.20–0.22, regardless of tree size. An efficient model to predict belowground biomass from tree diameter (D, in centimeters), height (H, in meters) and wood density (ρ, in grams per cubic centimeter) was B = 0.0188 (ρD2H)0.977. A significant residual effect of species and leaf habit was found in this model, indicating that further tree and species characteristics are likely to explain additional variation in belowground biomass. Yet, the future development of belowground allometric models can benefit from the many models already developed for aboveground biomass. On the basis of this unprecedented sampling effort on tree belowground biomass in the dense tropical forests of the Congo Basin, we conclude that the scaling of belowground biomass with aboveground biomass should be the relationship to focus on.

Machine Learning Method to Explore the Correlation between Fly Ash Content and Chloride Resistance.

Chloride ion corrosion has been considered to be one of the main reasons for durability deterioration of reinforced concrete structures in marine or chlorine-containing deicing salt environments. This paper studies the relationship between the amount of fly ash and the durability of concrete, especially the resistance to chloride ion erosion. The heat trend map of total chloride ion factor correlation displayed that the ranking of factor correlations was as follows: sampling depth > cement dosage > fly ash dosage. In order to verify the effect of fly ash dosage on chloride ion resistance, three different machine learning algorithms (RF, GBR, DT) are employed to predict the total chloride content of fly ash proportioned concrete with varying admixture ratios, which are evaluated based on R2, MSE, RMSE, and MAE. The results predicted by the RF model show that the threshold of fly ash admixture in chlorinated salt environments is 30-40%. Replacing part of cement with fly ash in the mixture of concrete below this threshold of fly ash, it could change the phase structure and pore structure, which could improve the permeability of fly ash concrete and reduce the content of free chloride ions in the system. Machine learning modeling using sample data can accurately predict concrete properties, which effectively reduce engineering tests. The development of machine learning models is essential for the decarbonization and intelligence of engineering.

Abstract A050: MicroRNA as biomarkers in early detection and personalized treatment in ovarian cancer: Development of a personalized prevention consortium

Abstract Epithelial ovarian cancer (EOC) has poor survival given no effective early detection strategies and non-specific early disease symptoms. We have formed a consortium with European and American partners to provide the necessary validation of a microRNA (miRNA) panel for earlier detection of EOC using serum samples and data from prospective cohorts, paired with clinical data and biospecimens. Our objectives are to validate a serologic miRNA panel that, together with CA125, would have sufficient diagnostic discrimination to be used as a tool that, complementary to imaging, would allow early identification and direction of patients with suspected malignancy to personalized gyn-oncological care. We will validate serum miRNA profiles as biomarkers for early detection in samples collected from cases ⇐3 years prior to EOC diagnosis and matched controls and characterize sources of variation in miRNA profiles and their impact on discrimination. Evaluate miRNA profiles in the clinical setting and the discrimination between EOC cases and women with (i) benign ovarian tumors in blood samples collected prior to diagnostic surgery and (ii) underlying comorbidities (including other cancer types). Further, we will evaluate serum miRNA profiles as predictors of recurrence and will compare tumor tissue and serum miRNA profiles. Develop and optimize protocols for data integration and methods for algorithm development. Direct in silico validation, including miRNA and CA125 levels, and clinical and epidemiologic data. Address Ethical, Legal and Social Aspects (ELSA), including activities in key stakeholder groups. Challenges in health data privacy and sharing, including for development of an artificial intelligence model, will be addressed, as will the definition of a “healthy” population with respect to miRNA profiles for EOC. The consortium was launched in 2023, with the first findings expected in 2024. Beyond the initial direct aims of the consortium, expanded aims on further promising markers are envisaged. Citation Format: Renée Turzanski Fortner, Hilde Langseth. MicroRNA as biomarkers in early detection and personalized treatment in ovarian cancer: Development of a personalized prevention consortium [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A050.

Modeling of primary breakup considering turbulent nozzle flow, internal turbulence and surface instability of liquid jet using turbulence decay theory

In heat engines utilizing fuel injection, the processes of atomization and spray formation have a significant impact on the combustion process, thereby determining both efficiency and emission characteristics. Accurate prediction and control of spray formation in fuel injection systems play a key role in improving the efficiency and environmental performance of thermal engines, especially with the emergence of carbon-neutral fuels. To achieve accurate prediction of spray mixture formation, it is imperative to refine the atomization model for the liquid jet within numerical simulations. This requires a phenomenological representation of the atomization process that avoids reliance on computational constants obtained from spray experimental results. Consequently, the present study attempts to mathematically model the turbulent nozzle flow and liquid jet atomization process, leading to the development of a novel primary breakup model. The construction of the primary breakup model involves an analysis of the turbulence at the nozzle inlet. By merging this turbulence with the turbulence resulting from wall shear flow within the nozzle, the model provides insight into the internal turbulence and surface instability of the liquid jet, encompassing the turbulence spectrum. Consequently, the influence of nozzle length on the turbulent flow within the nozzle can be understood, and the droplet formation characteristics of the liquid jet can be predicted along with its multi-wavelength dispersion characteristics. The model effectively captures the experimental results in terms of breakup length and droplet dispersion characteristics, thus adding a higher level of accuracy to numerical simulations. Ultimately, the in-depth study of this model, coupled with its comparison with experimental results, enhances the understanding of the liquid jet atomization process.

Relationship among runoff, soil erosion, and rill morphology on slopes of overburdened stockpiles under simulated rainfall

Overburdened stockpiles are difficult to achieve natural stability in a short period time because of their complex and diverse composition of materials and significant differences in physical and chemical properties, causing numerous cases of water loss and soil loss events. We conducted a series of experiments at different rainfall intensities (30, 60, 90, and 120 mm h−1) and different gravel contents (0 %, 10 %, 20 %, 30 %, and 40 %) to study the runoff, soil erosion, and rill morphology characteristics and relationship among three of overburdened stockpiles in production and construction projects with simulated rainfall. The runoff rate and soil loss rate basically showed a trend of increasing and then decreasing with gravel content and lower gravel content had a certain degree of inhibition on the flow velocity. 20 %, 0 %, 0 %, and 20 % gravel contents had the most significant inhibition on the flow velocity at 30, 60, 90, and 120 mm h−1 rainfall intensities, respectively. Flow shear stress, stream power, unit energy of water cross-section, maximum rill length, width and depth all increased with increasing rainfall intensity. The stream power, unit stream power, and unit energy of water cross-section were larger at higher gravel content. The maximum rill length and depth were positively correlated with gravel content, which were the minimum at 0 % gravel content and all rainfall intensities. Gravel content played a more important role in the runoff, soil loss, and rill morphology. The relationship between runoff rate and soil loss rate, flow velocity and rill morphology parameters were all expressed as linear functions, and flow velocity and soil loss rate was quadratic function, with a correlation coefficient of 0.339. In contrast, runoff rate and rill morphology parameters, soil loss rate and rill morphology parameters, erosion dynamics parameters and rill morphology parameters were all expressed as power functions. In addition, the runoff rate can better describe the process of soil loss (R2 = 0.798, P < 0.05). The unit energy of water cross-section was more suitable to describe the development of maximum rill length and depth (with rill length was R2 = 0.781, and with rill depth was R2 = 0.723, P < 0.05). At the same time, the stream power was more suitable to describe the development of maximum rill width on the slope of overburdened stockpiles (R2 = 0.841, P < 0.05). These results contributed to preventing soil loss on overburdened stockpiles and helping the development of estimation models for soil erosion.