Conventional Model Research Articles

Probabilistic Forecasting of Regional Solar Power Incorporating Weather Pattern Diversity

Power grid stability depends on the ability to forecast solar power generation at the regional level. Most previous research on probabilistic forecasting has focused on the use of machine learning to predict the output of individual solar power plants rather than regional solar power generation, and few studies have considered the effects of seasonal weather patterns at the regional level. In this study, climate and geographic data were collected from 83 weather stations between 2019 and 2021 for use in developing a probabilistic model by which to predict regional solar power generation. The results of weather pattern analysis based on unsupervised machine learning and ensemble voting were used to build a probabilistic quantile regression model for the short-term prediction of regional solar power generation capacity. The efficacy of the model was assessed using data from 48 solar PV power plants, which included four sub-datasets pertaining to four target regions. Highly accurate results were consistently obtained across all regions in both the winter and summer seasons. The proposed probabilistic model outperformed conventional deterministic model by 6.55% and conventional probabilistic model by 4.03% in terms of total normalized mean absolute error (NMAE). Prediction intervals generated by the proposed model could be used as input parameters for regional power dispatch decisions.

The Presence of Chaos in a Viscoelastic Harmonically Forced Von Mises Truss

Abstract This work investigates how viscoelasticity affects the dynamic behavior of a lumped-parameter model of a bistable von Mises truss. The system is controlled by a linear first-order equation and a second-order nonlinear Duffing equation with a quadratic nonlinearity that governs mechanical behavior. The second-order equation controls mechanical oscillations, while the linear first-order equation controls viscoelastic force evolution. Combined, the two equations form a third-order jerk equation that controls system dynamics. Viscoelasticity adds time scales and degrees-of-freedom to material behavior, distinguishing it from viscosity-only systems. Due to harmonic excitation, the system exhibits varied dynamic responses, from periodic to quasi-periodic to chaotic. We explore the dynamics of a harmonically forced von Mises truss with viscous damping to address this purpose. We demonstrate this system's rich dynamic behavior due to driving amplitude changes. This helps explain viscoelastic system behavior. A viscoelastic unit replaces the viscous damper, and we show that, although viscous damping merely changes how fast the trajectory decays to an attractor, viscoelasticity modifies both the energy landscape and the rate of decay. In a conventional linear solid model, three viscoelastic parameters control the system's behavior instead of one, as in pure viscous damping. This adds degrees-of-freedom that affect system dynamics. We present the parameter space for chaotic behavior and the shift from regular to irregular motion. Finally, Melnikov's criteria identify the regular-chaotic threshold. The system's viscous and elastic components affect the chaotic threshold amplitude

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

Imbalance data: The application of RUS FCM K-RBFNN Smote with XGBoost in the elderly well-being identification

Imbalanced data is a serious binary classification difficulty in forecasting the well-being of the elderly. This paper improves the Smote algorithm from the algorithm and sample dimensions to tackle the issue of imbalanced distribution of questionnaire data. The k-means Smote is combined with RBFNN as K-RBFNN Smote in the algorithm dimension and add FCM link to resample the minority set in the sample dimension as FCM K-RBFNN Smote. In order to improve the generalization of models, the RUS module is added to the algorithm. Experiments are carried out on four improved Smote technologies and two existing Smote technologies combined with XGBoost, which is superior than the other five conventional classification models. The experimental results indicate that the performance order is RUS FCM K-RBFNN Smote &gt; K-RBFNN Smote &gt; FCM K-RBFNN Smote &gt; RUS K-RBFNN Smote &gt; K-Means Smote &gt; FCM Smote. The RUS FCM K-RBFNN method has been identified as the optimal approach for enhancing performance, resulting in a 98.58% accuracy rate. In conclusion, Smote algorithm undergoes the implementation of K-RBFNN shows greater performance and the enhancement of FCM and RUS relies on the structure of sampling.

Monkeypox disease recognition model based on improved SE-InceptionV3

In the wake of the global spread of monkeypox, accurate disease recognition has become crucial. This study introduces an improved SE-InceptionV3 model, embedding the SENet module and incorporating L2 regularization into the InceptionV3 framework to enhance monkeypox disease detection. Utilizing the Kaggle monkeypox dataset, which includes images of monkeypox and similar skin conditions, our model demonstrates a noteworthy accuracy of 96.71% on the test set, outperforming conventional methods and deep learning models. The SENet module’s channel attention mechanism significantly elevates feature representation, while L2 regularization ensures robust generalization. Extensive experiments validate the model’s superiority in precision, recall, and F1 score, highlighting its effectiveness in differentiating monkeypox lesions in diverse and complex cases. The study not only provides insights into the application of advanced CNN architectures in medical diagnostics but also opens avenues for further research in model optimization and hyperparameter tuning for enhanced disease recognition.

Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning

As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metric to infusing advanced human virtues such as fairness, accountability, transparency, and unbiasedness. Recently, techniques in Explainable Artificial Intelligence (XAI) have been attracting considerable attention and have tremendously helped Machine Learning (ML) engineers in understand AI models. However, at the same time, we started to witness the emerging need beyond XAI among AI communities; based on the insights learned from XAI, how can we better empower ML engineers in steering their DNNs so that the model’s reasonableness and performance can be improved as intended? This article provides a timely and extensive literature overview of the field Explanation-Guided Learning (EGL), a domain of techniques that steer the DNNs’ reasoning process by adding regularization, supervision, or intervention on model explanations. In doing so, we first provide a formal definition of EGL and its general learning paradigm. Second, an overview of the key factors for EGL evaluation, as well as summarization and categorization of existing evaluation procedures and metrics for EGL are provided. Finally, the current and potential future application areas and directions of EGL are discussed, and an extensive experimental study is presented aiming at providing comprehensive comparative studies among existing EGL models in various popular application domains, such as Computer Vision and Natural Language Processing domains. Additional resources related to event prediction are included in the article website: https://kugaoyang.github.io/EGL/

Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits.

Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

Improving student higher order thinking skills using SynecticHOTS-oriented learning model

The achievement of students’ learning outcomes in physics lessons has a low tendency, this is due to various factors, one of which is higher-order thinking skills (HOTS) based learning that has not been fully implemented in the learning process and assessment. This study aimed to determine the differences in the Synectic-HOTS learning model in physics compared to conventional learning models. This study used a quasi-experimental study with a pre-test–post-test nonequivalent control group design. The instrument used in this study is a two-tier test question that contains 20 items of HOTS aspect. The prerequisite test showed that the data was not homogeneous, while the results of the data homogeneity test were stated to be homogeneous. Therefore, a non-parametric test was carried out using the Mann-Whitney U-test. Based on the results of the data analysis, the Asymp. Sig. (2-tailed) for the pre-test was 0.111 and the post-test was 0.001 (p=.000&amp;lt;05). The effect size calculation shows that the Synectic learning model is effective in the medium category. These results indicate that the application of the SynecticHOTS model significantly improves HOTS when compared to conventional learning models. This work can open up insights to teachers about alternative, creative, and effective learning models to improve higher-order thinking skills and student learning outcomes.

758 An Appraisal of the Quality of Development and Reporting of Predictive Models in Neurosurgery

INTRODUCTION: Overwhelming evidence indicates that the quality of reporting of predictive models is poor, often being confounded by small datasets, inappropriate statistical methods, a lack of validation, and reproducibility. METHODS: Articles reporting prediction models published in the top five neurosurgery journals by SJR2-rank (Neurosurgery, Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of NeuroInterventional Surgery, and Journal of Neurology, Neurosurgery, and Psychiatry) between January 1st, 2018, and January 1st, 2023, were identified via a PubMed search strategy that combined terms related to machine learning and prediction models. The identified original research articles were analyzed against the TRIPOD criteria. RESULTS: 110 articles were included and analyzed against the TRIPOD checklist. Among the articles screened, the median compliance was 57.4% (50.0-66.7%). Models utilizing machine learning-based models exhibited lower compliance on average compared to conventional learning-based models (57.1%, 50.0-66.7% vs. 68.1%, 50.2-68.1%, p = 0.472). Among the TRIPOD criteria, the lowest compliance was observed in blinding the assessment of predictors and outcomes (n = 7, 12.7% and n = 10, 16.9%, respectively), including an informative title (n = 17, 15.6%) and reporting model performance measures including confidence intervals (n = 27, 24.8%). Few studies provided sufficient information to allow for the external validation of results (n = 26, 25.7%). CONCLUSIONS: Published machine learning models predicting outcomes in neurosurgery often fall short of meeting the established guidelines laid out by TRIPOD for their development, validation, and reporting. This lack of compliance may indicate the extent to which these models have been subjected to external validation or adopted into routine clinical practice in neurosurgery.

Improved Position Sensorless Resonant Frequency Tracking Control Method for Linear Oscillatory Machine

To improve the reliability of control system and obtain the maximum output efficiency, it is very necessary to carry out the position sensorless piston stroke control and the resonance frequency tracking control for linear oscillatory machine (LOM) at the same time. The conventional resonant frequency tracking control method based on model reference adaptive system (MRAS) has the problem of low estimation accuracy caused by inaccurate model parameters. In order to obtain more accurate resonant frequency, an improved position sensorless resonant frequency tracking control method is proposed in this article. In this method, the adjustable model is constructed based on the mechanical dynamics equation of LOM to track the system resonant frequency. A hybrid terminal sliding-mode observer (HTSMO) is designed to replace the conventional reference model, which can obtain more accurate velocity reference signal for adjustable model by introducing the current error feedback link, thereby further improving the resonant frequency tracking accuracy. Moreover, a second-order generalized integrator (SOGI) is introduced to filter the estimated velocity signal of HTSMO. The filtered velocity signal is not only used for the adjustable model to track the resonant frequency, but also used to obtain the smooth piston stroke signal for the stroke closed-loop control. Comprehensive simulation and experimental results have proved the effectiveness and the superiority of the proposed method.

Sentiment analysis with hotel customer reviews using FNet

Recent research has focused on opinion mining from public sentiments using natural language processing (NLP) and machine learning (ML) techniques. Transformer-based models, such as bidirectional encoder representations from transformers (BERT), excel in extracting semantic information but are resourceintensive. Google’s new research, mixing tokens with fourier transform, also known as FNet, replaced BERT’s attention mechanism with a non-parameterized fourier transform, aiming to reduce training time without compromising performance. This study fine-tuned the FNet model with a publicly available Kaggle hotel review dataset and investigated the performance of this dataset in both FNet and BERT architectures along with conventional machine learning models such as long short-term memory (LSTM) and support vector machine (SVM). Results revealed that FNet significantly reduces the training time by almost 20% and memory utilization by nearly 60% compared to BERT. The highest test accuracy observed in this experiment by FNet was 80.27% which is nearly 97.85% of BERT’s performance with identical parameters.

Data-Driven Dynamic Inversion Method for Complex Fractures in Unconventional Reservoirs

Abstract Hydraulic fracturing is a crucial technology for enhancing the recovery of oil and gas from unconventional reservoirs. Accurately describing fracture morphology is essential for accurately predicting production dynamics. This article proposes a new fracture inversion model based on dynamic data-driven methods, which is different from the conventional linear elastic fracture mechanics model. This method eliminates the need to consider complex mechanical mechanisms, resulting in faster simulation speeds. In the model, the fracture morphology is constrained by combining microseismic data and fracturing construction data, and the fracture tip propagation domain is introduced to characterize the multi-directionality of fracture propagation. The simulated fracture exhibits a multi-branch fracture network morphology, aligning more closely with geological understanding. In addition, the influence of microseismic signal intensity on the direction of fracture propagation is considered in this study. The general stochastic approximation (GSA) algorithm is employed to optimize the direction of fracture propagation. The proposed method is applied to both the single-stage fracturing model and the whole well fracturing model. The research findings indicate that in the single-stage fracturing model, the inverted fracture morphology aligns closely with the microseismic data, with a fitting rate of the fracturing construction curve exceeding 95%, and a microseismic data fitting rate exceeding 93%. In the whole well fracturing model, a total of 18 sections were inverted. The fitting rate between the overall fracture morphology and the microseismic data reached 90%. The simulation only took 5 minutes, demonstrating high computational efficiency and meeting the needs of large-scale engineering fracture simulation. This method can effectively support geological modeling and production dynamic prediction.

Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment

Multi-hop fact verification aims to detect the veracity of the given claim by integrating and reasoning over multiple pieces of evidence. Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets. Among the various debiasing works, the causal inference-based methods become popular by performing theoretically guaranteed debiasing such as casual intervention or counterfactual reasoning. However, existing causal inference-based debiasing methods, which mainly formulate fact verification as a single-hop reasoning task to tackle shallow bias patterns, cannot deal with the complicated bias patterns hidden in multiple hops of evidence. To address the challenge, we propose Causal Walk, a novel method for debiasing multi-hop fact verification from a causal perspective with front-door adjustment. Specifically, in the structural causal model, the reasoning path between the treatment (the input claim-evidence graph) and the outcome (the veracity label) is introduced as the mediator to block the confounder. With the front-door adjustment, the causal effect between the treatment and the outcome is decomposed into the causal effect between the treatment and the mediator, which is estimated by applying the idea of random walk, and the causal effect between the mediator and the outcome, which is estimated with normalized weighted geometric mean approximation. To investigate the effectiveness of the proposed method, an adversarial multi-hop fact verification dataset and a symmetric multi-hop fact verification dataset are proposed with the help of the large language model. Experimental results show that Causal Walk outperforms some previous debiasing methods on both existing datasets and the newly constructed datasets. Code and data will be released at https://github.com/zcccccz/CausalWalk.

Abstract 119: A non-surgical method for developing a pre-clinical orthotopic mouse model for colorectal cancer

Abstract Recapitulating physiologically relevant tumor microenvironment is important yet a significant challenge in modeling colorectal cancer (CRC) for basic and translational studies. Among the different methods, using the cecal-wall injection technique is the most common way to create animal models of colorectal cancer (CRC). However, this approach requires complex surgery, and its unnatural placement creates a significant obstacle for developing treatments. Here, we report a non-surgical procedure for establishing orthotopic CRC mouse models, reflecting the appropriate tumor microenvironment to better model the disease development and facilitate translational studies including drug identification and validation. In this procedure, anesthesia is induced in overnight fasted study immunocompetent or immunodeficient mice while placed on a thermostat-controlled heating blanket. A specialized catheter is inserted and distended into the mouse colon to gently block the colon and ensure the localization for tumor development. Hereafter, the colonic epithelium is treated with proteases and mechanical abrasion to cause mild disruption. Following the local gut inflammation, the targeted colon region is incubated with tumor cells, leading to the adhesion of tumor cells to the colonic epithelium. Based on the animal background and the cell line used, the tumor developed within 3-4 weeks. The stages of tumor development at different time points were successfully confirmed using hematoxylin and eosin stain. Tumor microenvironment data revealed a significant increase in the immune cell infiltration specifically CD4+ T-cells and tumor-associated macrophages in the non-surgical procedure as compared to the cecal-wall injection. Furthermore, a significant increase in the PD-1 expression of the CD8+ T-cells in the cecal-wall injection as compared to the non-surgical procedure, suggests that the precise location of the tumor site may exert a substantial influence on pre-clinical therapeutic outcomes of immune-checkpoint inhibitors, thereby potentially hampering the clinical efficacy of drug treatments. Additional adaptations of this procedure may include: i) tumor development in different mouse strains, ii) local delivery of adeno-Cre in transgenic mice for CRC, and iii) delivering tumor organoids and patient-derived xenografts in immuno-deficient mice. Overall, our non-surgical procedure overcomes the technical difficulties of surgery and offers several advantages like ease of handling, cost-effectiveness, pain-free with nearly no procedure-associated mortality and adaptability to several strains. In conclusion, this procedure provides novel and promising alternative to conventional surgical orthotopic CRC mouse models for therapeutic interventions. Citation Format: Naresh Singh, Samantha Sharma, Kevin Van Der Jeught, Zhuolong Zhou, Xinna Zhang, Xiongbin Lu. A non-surgical method for developing a pre-clinical orthotopic mouse model for colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 119.

Explainable machine learning for predicting stomatal conductance across multiple plant functional types

Stomatal conductance (gs) is a key leaf-level function controlling water, carbon, and energy exchange between vegetation and the surrounding environment. Conventionally, semi-empirical models have been used to model gs, but these models require re-parameterization as ecosystems undergo phenological changes over the growing season. In contrast, machine learning (ML) models offer a potential path to overcome this problem but are less interpretable than process-based models. This study explores ML as an approach to develop flexible and robust models of gs for a range of plant functional types (PFTs), including C3 crops, C3 grasses, shrubs, and tree species across different continents. An explainable machine-learning approach (eXML) was used here to provide novel interpretations and insights into the ML model formulations and relative predictor importance. We contrast the performance of three ML architectures: extreme gradient boosting, random forests, and neural networks. Models were developed and examined using many combinations of environmental and physiological predictors. The results demonstrated that ML models significantly outperform conventional semi-empirical models in predicting gs responses to the environment, while not requiring re-parameterization as is required in the semi-empirical paradigm. Particular focus is placed on models formulated around predictor sets that are: (a) relevant to gs estimation in modern terrestrial biophysical simulation models, and (b) composed of variables describing environmental and physiological drivers that can be remotely sensed non-invasively. “Generalized” models developed using data from all four PFTs demonstrated strong predictive performance using only three predictor variables, capturing 63–80 % of the variability in stomatal conductance across all ML architectures. Four predictor variables resulted in models capturing 79–83 % of gs variability, and models developed using all five predictor variables examined here were able to capture as much as 87 % of gs variability across all PFTs. Uncertainty in gs predictions was quantified using quantile regression. Shapley additive explanations was applied to unravel instance-based positive and negative contributions of environmental and physiological predictors to gs modeling, while illustrating that the models are consistent with the underlying ecophysiology. This work demonstrates the power of ML to introduce a new paradigm in the simulation of highly dynamic ecophysiological processes critical to environmental prediction.

Survival time prediction in patients with high-grade serous ovarian cancer based on 18F-FDG PET/CT- derived inter-tumor heterogeneity metrics

BackgroundThe presence of heterogeneity is a significant attribute within the context of ovarian cancer. This study aimed to assess the predictive accuracy of models utilizing quantitative 18F-FDG PET/CT derived inter-tumor heterogeneity metrics in determining progression-free survival (PFS) and overall survival (OS) in patients diagnosed with high-grade serous ovarian cancer (HGSOC). Additionally, the study investigated the potential correlation between model risk scores and the expression levels of p53 and Ki-67.MethodsA total of 292 patients diagnosed with HGSOC were retrospectively enrolled at Shengjing Hospital of China Medical University (median age: 54 ± 9.4 years). Quantitative inter-tumor heterogeneity metrics were calculated based on conventional measurements and texture features of primary and metastatic lesions in 18F-FDG PET/CT. Conventional models, heterogeneity models, and integrated models were then constructed to predict PFS and OS. Spearman’s correlation coefficient (ρ) was used to evaluate the correlation between immunohistochemical scores of p53 and Ki-67 and model risk scores.ResultsThe C-indices of the integrated models were the highest for both PFS and OS models. The C-indices of the training set and testing set of the integrated PFS model were 0.898 (95% confidence interval [CI]: 0.881–0.914) and 0.891 (95% CI: 0.860–0.921), respectively. For the integrated OS model, the C-indices of the training set and testing set were 0.894 (95% CI: 0.871–0.917) and 0.905 (95% CI: 0.873–0.936), respectively. The integrated PFS model showed the strongest correlation with the expression levels of p53 (ρ = 0.859, p < 0.001) and Ki-67 (ρ = 0.829, p < 0.001).ConclusionsThe models based on 18F-FDG PET/CT quantitative inter-tumor heterogeneity metrics exhibited good performance for predicting the PFS and OS of patients with HGSOC. p53 and Ki-67 expression levels were strongly correlated with the risk scores of the integrated predictive models.

Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.

Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysis might improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty. Does machine learning perform better than traditional regression models for estimating the risk of revision for patients undergoing hip or knee arthroplasty? Eleven datasets from published studies from the Dutch Arthroplasty Register reporting on factors associated with revision or survival after partial or total knee and hip arthroplasty between 2018 and 2022 were included in our study. The 11 datasets were observational registry studies, with a sample size ranging from 3038 to 218,214 procedures. We developed a set of time-to-event models for each dataset, leading to 11 comparisons. A set of predictors (factors associated with revision surgery) was identified based on the variables that were selected in the included studies. We assessed the predictive performance of two state-of-the-art statistical time-to-event models for 1-, 2-, and 3-year follow-up: a Fine and Gray model (which models the cumulative incidence of revision) and a cause-specific Cox model (which models the hazard of revision). These were compared with a machine-learning approach (a random survival forest model, which is a decision tree-based machine-learning algorithm for time-to-event analysis). Performance was assessed according to discriminative ability (time-dependent area under the receiver operating curve), calibration (slope and intercept), and overall prediction error (scaled Brier score). Discrimination, known as the area under the receiver operating characteristic curve, measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities; a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. A scaled version of the Brier score, 1 - (model Brier score/null model Brier score), can be interpreted as the amount of overall prediction error. Using machine learning survivorship analysis, we found no differences between the competing risks estimator and traditional regression models for patients undergoing arthroplasty in terms of discriminative ability (patients who received a revision compared with those who did not). We found no consistent differences between the validated performance (time-dependent area under the receiver operating characteristic curve) of different modeling approaches because these values ranged between -0.04 and 0.03 across the 11 datasets (the time-dependent area under the receiver operating characteristic curve of the models across 11 datasets ranged between 0.52 to 0.68). In addition, the calibration metrics and scaled Brier scores produced comparable estimates, showing no advantage of machine learning over traditional regression models. Machine learning did not outperform traditional regression models. Neither machine learning modeling nor traditional regression methods were sufficiently accurate in order to offer prognostic information when predicting revision arthroplasty. The benefit of these modeling approaches may be limited in this context.

The Evaluation of using Learning Videos for High School Mathematics Curriculum Subjects with Flipped Classroom Models on Learning Achievement in the IVET University Semarang

This research deals with the development of learning video media for high school mathematics curricula using the ADDIE stage model. This model, as the name implies, consists of five main phases or stages, namely (A) analysis, (D) design, (D) development, (I) implementation, and (E) evaluation. The implementation and evaluation phase was carried out in this second year, with an expanded test carried out in the mathematics education study program at IVET University Semarang in a fun and enjoyable way, with a posttest and a user response questionnaire with the following results: Based on research at IVET University Semarang, the results of the posttest were processed using The t-test obtained a result of 0% &lt; 5%, then H0 was rejected. This means that we accept H1, which means that the results of learning mathematics with learning video media for high school mathematics curricula are better than conventional learning models, looking at the average learning achievement in the mean column, the group statistics table, it is obtained that the average of the experimental class was 85.50 while the average of the control class was 70.42. These results show that the learning outcomes of the experimental class were better than those of the control class. R Square value is 0.825 = 82.5%. This value means that the effect of virtual lab media on learning achievement is 82.5%. In comparison, 17.5% of learning achievement was influenced by other variables outside of the independent variables in this study.&#x0D; Keywords: the evaluation, learning videos, high school mathematics curriculum; flipped classroom models

Data-Driven Version of Multiway Soft Independent Modeling of Class Analogy (N-Way DD-SIMCA): Theory and Application.

One-class classification (OCC) is discussed in the framework of the measurement and processing of multiway data. Data-driven soft independent modeling of class analogy (DD-SIMCA) is applied in the following formats: (1) multiblock and (2) Tucker 3 N-way SIMCA, which are shown to be useful tools for solving classification tasks. A new decision rule for N-way DD-SIMCA is adopted based on the conventional two-way DD-SIMCA model. Multiblock SIMCA is shown to be useful for variable selection, and Tucker 3 SIMCA to select the optimal model complexity when applying multiway data decomposition and to assess the role of individual samples in the classification model. Both approaches, together with the two-way DD-SIMCA version applied to the unfolded data, are compared regarding the analysis of an experimental data set including genuine and adulterated blueberry extract samples. The latter were employed to produce matrix spectral-time data matrices per sample within a flow injection system, taking advantage of the spectral changes in the sample constituents as a function of the pH of the carrier phase. The need to employ the Tucker 3 model instead of a trilinear decomposition is supported by a discussion on the lack of the trilinearity property of the studied data.

A dual-reciprocity boundary element method based transient seepage model with nonlinear distributed point source dual-porosity gas reservoir

As a result of advancements in gas reservoir development technologies and a deeper understanding of seepage theory, numerous nonlinear seepage problems have emerged. Consequently, conventional analytical solution models are no longer applicable. Moreover, existing semi-analytical and numerical solution models suffer from issues like high computational complexity and limited flexibility, significantly constraining their practical utility. For this purpose, a gas reservoir transient seepage model is established based on the dual-reciprocity boundary element method (DRBEM). This model couples the wellbore storage coefficients with skin factor and introduces a novel dual-porosity transient interporosity seepage algorithm, enabling the resolution of transient seepage problems in dual-porosity gas reservoirs with nonlinear point source distributions. Additionally, it investigates the dynamic behavior of bottomhole pressure during the early production stage of gas wells, while capturing the dynamic pressure distribution within the seepage field. This approach offers the advantages of semi-analyticity, meshless, and eliminates the need for Laplace transforms, resulting in a significant reduction in computational complexity. Ultimately, the application of our model to well-pattern production with the production system transition issues, in conjunction with the log-log type curves of bottomhole pressure, and the dynamics of gas reservoir pressure, enables the analysis of interference characteristics between injection-production wells. This showcases the practical advantages of the model's application. The findings of this study provide valuable assistance for modeling and solving transient seepage problems in dual-porosity oil and gas reservoirs production. The application of DRBEM in unsteady-state diffusion, heat conduction, stress fields, and other potential field problems is relevant for reference.