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The level of difficulty of mathematical test items is a critical aspect for evaluating test quality and educational outcomes. Accurately predicting item difficulty during test creation is thus significantly important for producing effective test papers. This study used more than ten years of content and score data from China's Henan Provincial College Entrance Examination in Mathematics as an evaluation criterion for test difficulty, and all data were obtained from the Henan Provincial Department of Education. Based on the framework established by the National Center for Education Statistics (NCES) for test item assessment methodology, this paper proposes a new framework containing eight features considering the uniqueness of mathematics. Next, this paper proposes an XGBoost-based SHAP model for analyzing the difficulty of mathematics tests. By coupling the XGBoost method with the SHAP method, the model not only evaluates the difficulty of mathematics tests but also analyzes the contribution of specific features to item difficulty, thereby increasing transparency and mitigating the "black box" nature of machine learning models. The model has a high prediction accuracy of 0.99 for the training set and 0.806 for the test set. With the model, we found that parameter-level features and reasoning-level features are significant factors influencing the difficulty of subjective items in the exam. In addition, we divided senior secondary mathematics knowledge into nine units based on Chinese curriculum standards and found significant differences in the distribution of the eight features across these different knowledge units, which can help teachers place different emphasis on different units during the teaching process. In summary, our proposed approach significantly improves the accuracy of item difficulty prediction, which is crucial for intelligent educational applications such as knowledge tracking, automatic test item generation, and intelligent paper generation. These results provide tools that are better aligned with and responsive to students' learning needs, thus effectively informing educational practice. [ABSTRACT FROM AUTHOR]

This correction notice provides updates and corrections to a research paper on deep machine learning model-based cyber-attacks detection in smart power systems. The paper proposes a deep learning-based attack detection model that addresses system disturbances caused by natural events and cyber-attacks. It discusses the use of principal component analysis (PCA) for feature selection and the potential of deep learning-based and decision tree classifiers for detecting cyber-attacks in intelligent energy grids. The notice also includes updates to references, clarifications on the experimental data set, and feature selection based on PCA. The study evaluates the effectiveness of the proposed model using various assessment indexes and compares it to conventional methods, concluding that the proposed model is effective in detecting cyber-attacks and demonstrates good performance in detecting destructive attacks. [Extracted from the article]

This document is a correction notice for a paper titled "An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach." The correction addresses an error in the affiliation of one of the authors, Ehsan Nazemi. The updated affiliation should include the Institute of Fundamental and Applied Sciences, Duy Tan University, Ho Chi Minh City, Vietnam. Additionally, there was an error in the Introduction section where a paragraph with unexplained references was mistakenly copied from one of the author's theses. This paragraph has been removed, and the order of references has been adjusted accordingly. The authors assure that these corrections do not affect the scientific conclusions of the paper. [Extracted from the article]

This paper introduces a novel classification method for multi-class multi-label datasets, named multi-class multi-label logistic model tree (MMLMT). Our approach supports multi-label learning to predict multiple class labels simultaneously, thereby enhancing the model's capacity to capture complex relationships within the data. The primary goal is to improve the accuracy of classification tasks involving multiple classes and labels. MMLMT integrates the logistic regression (LR) and decision tree (DT) algorithms, yielding interpretable models with high predictive performance. By combining the strengths of LR and DT, our method offers a flexible and powerful framework for handling multi-class multi-label data. Extensive experiments demonstrated the effectiveness of MMLMT across a range of well-known datasets with an average accuracy of 85.90%. Furthermore, our method achieved an average of 9.87% improvement compared to the results of state-of-the-art studies in the literature. These results highlight MMLMT's potential as a valuable approach to multi-label learning. [ABSTRACT FROM AUTHOR]

The Fibonacci sequence has broad applications in mathematics, where its inherent patterns and properties are utilized to solve various problems. The sequence often emerges in areas involving growth patterns, series, and recursive relationships. It is known for its connection to the golden ratio, which appears in numerous natural phenomena and mathematical constructs. In this research paper, we introduce new concepts of convergence and summability for sequences of real and complex numbers by using Fibonacci sequences, called Δ -Fibonacci statistical convergence, strong Δ -Fibonacci summability, and Δ -Fibonacci statistical summability. And, these new concepts are supported by several significant theorems, properties, and relations in the study. Furthermore, for this type of convergence, we introduce one-sided Tauberian conditions for sequences of real numbers and two-sided Tauberian conditions for sequences of complex numbers. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Attitude Control of UAVs with Search Optimization and Disturbance Rejection Strategies" published in the journal Mathematics. The correction involves the removal of an author, Dr. Chunbo Luo, from the authorship list. Dr. Luo did not consider his contribution sufficient for authorship and requested to be removed. The correction does not impact the scientific conclusions of the paper. The remaining authors are Wensheng Li, Fanke Yang, Liqiang Zhong, Hao Wu, Xiangyuan Jiang, and Andrei V. Chukalin. [Extracted from the article]

This correction notice addresses an omission in a previously published paper titled "Ricci Vector Fields Revisited." The authors apologize for not including acknowledgements in the original paper and express their gratitude for the support received from King Saud University in Riyadh, Saudi Arabia. The authors assure readers that this correction does not impact the scientific conclusions of the paper. The correction has been approved by the Academic Editor and the original publication has been updated. The authors of the paper are Hanan Alohali, Sharief Deshmukh, and Gabriel-Eduard Vîlcu. [Extracted from the article]

Financial forecasting involves predicting the future financial states and performance of companies and investors. Recent technological advancements have demonstrated that machine learning-based models can outperform traditional financial forecasting techniques. In particular, hybrid approaches that integrate diverse methods to leverage their strengths have yielded superior results in financial prediction. This study introduces a novel hybrid model, entitled EMD-TI-LSTM, consisting of empirical mode decomposition (EMD), technical indicators (TI), and long short-term memory (LSTM). The proposed model delivered more accurate predictions than those generated by the conventional LSTM approach on the same well-known financial datasets, achieving average enhancements of 39.56%, 36.86%, and 39.90% based on the MAPE, RMSE, and MAE metrics, respectively. Furthermore, the results show that the proposed model has a lower average MAPE rate of 42.91% compared to its state-of-the-art counterparts. These findings highlight the potential of hybrid models and mathematical innovations to advance the field of financial forecasting. [ABSTRACT FROM AUTHOR]

Assessment for learning (AFL) has been associated with curriculum and teaching reform for the past three decades. However, studies on undergraduate students' perceptions of their mathematics teachers' AFL practices are still very limited in the Chinese higher education context. This quantitative study investigated three independent variables—teacher formal feedback and support, interactive dialog and peer collaboration, and learning-oriented assessment—that influence undergraduate students' ability to take responsibility for their learning through the mediation of the factor of active engagement with subject matter in first-year mathematics courses. One hundred and sixty-eight students from a Chinese "double-first-class" university were recruited to provide valid questionnaire data using the convenience sampling method. Partial least-squares structural equation modeling (PLS-SEM) was used to analyze the data. The results showed that interactive dialog and peer collaboration, as well as learning-oriented assessment, have a direct effect on students' active engagement with the subject matter and an indirect effect on undergraduate students taking responsibility for their learning in first-year mathematics courses. In addition, learning-oriented assessment was the biggest factor influencing undergraduate students' ability to take responsibility for their learning in first-year mathematics courses. This study contributes by developing a conceptual model and providing new insights into Chinese higher education sectors on factors that can improve undergraduate students' ability to take responsibility for their learning. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "The Effect of Preloaded Compressive Stress and Curvature of Defect on Blast-Induced Fracture Behavior by Caustic and Numerical Models." The correction includes the addition of a missing citation and the correction of a mistake in Figure 2b. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original publication has been updated to reflect these corrections. [Extracted from the article]