Northwest Missouri State University

This study examines mathematics teachers’ engagement with counterintuitive problems through a structured four-stage process: intuitive responses, reasoning, calculation, and peer discussion. Using convenience sampling method, 60 mathematics teachers from Kazakhstan’s Zhetysu Region were selected for the study. Data were collected using an adapted set of four counterintuitive mathematics tasks. Results revealed that initial intuitive responses frequently led to systematic errors. Reasoning explanations were often only partially correct, indicating that intuitive responses were grounded in incomplete conceptual models. Formal calculations improved accuracy across most tasks, though the average speed problem remained especially resistant to resolution. Peer discussion proved to be the most effective intervention, leading to substantial gains in correctness, particularly for the most counterintuitive tasks. Comparative analysis across the four stages revealed distinct learning trajectories, with collaborative reasoning consistently yielding the largest improvements. Regression analysis demonstrated a moderate positive relationship between reasoning quality and solution accuracy. These findings show the pedagogical value of counterintuitive problems for improving reasoning, conceptual understanding, and collaborative learning in mathematics education.

Sports climbing, as both recreation and competition, has reached a level of popularity that demands physical and psychological strength, as well as a high level of commitment. This study expands upon widely accepted models of sports identity by testing the prediction of athletic identity from sports commitment and enjoyment, as well as from social constraints for climbers, a relationship well known in sports but rarely tested in climbing. A total of 131 climbers from 31 countries took part in a validated online survey. Using multiple regression and path analysis, the model explained 55% of the variance in athletic identity. Sports commitment was the strongest predictor ( r = .72, p < .05; β = .636), while sports enjoyment showed no significant direct effect. Furthermore, enjoyment indirectly influenced athletic identity through its impact on commitment and social constraints. The model explained 26% of the variance in sports commitment. Climbers who competed reported significantly higher levels of athletic identity, sports commitment, and social constraints than climbers who did not compete, while they reported similar levels of enjoyment. The implications of this study suggest that, inclimbing, identity is more strongly linked to commitment than to enjoyment, which could be important for developing athletes. Although this study was naturally limited by its cross-sectional design and reliance on self-reported data, it offers some possibilities and insights into how social and psychological factors influence athletic identity in less well-tried sports. There is practical value in the information provided for coaching and outdoor programs designed to foster deeper motivation and commitment among climbers.

Background: Understanding college students’ attitudes, beliefs, and behaviors related to the COVID-19 pandemic is important for public health and health promotion efforts on university campuses, as well as to inform decisions and practices of administrators. The purpose of this study was to extend existing research by investigating some COVID-related variables that have yet to be clearly studied, and by examining how student responses varied by age, sex, and race. Methods: University students (N=2764) attending five universities in Florida, Louisiana, Missouri, North Carolina, and Virginia completed an online anonymous survey between October and December, 2021. Survey items addressed personal COVID-19 history, perceptions of severity and susceptibility, engagement in COVID-19 preventing behaviors, COVID-19 vaccination status, and attitudes about COVID-related university policies. Results: More than half of students (63.1%) had not contracted COVID-19 at the time of data collection, but most (96.4%) knew someone who had. Over two-thirds of the sample (67.9%) had been vaccinated at the time of survey completion. Survey responses varied significantly by gender, age, and race/ethnicity, with female, older, and African American students expressing greater concern, more often engaging in COVID-preventing behaviors, getting vaccinated against the virus, and supporting college policies regarding mandated face masks and COVID-19 vaccination. Conclusion: This study highlights significant variations in college students’ attitudes and behaviors towards COVID-19, influenced by demographic factors such as age, sex, and race

To improve the identification of potential anomaly patterns in complex user behavior, this paper proposes an anomaly detection method based on a deep mixture density network. The method constructs a Gaussian mixture model parameterized by a neural network, enabling conditional probability modeling of user behavior. It effectively captures the multimodal distribution characteristics commonly present in behavioral data. Unlike traditional classifiers that rely on fixed thresholds or a single decision boundary, this approach defines an anomaly scoring function based on probability density using negative log-likelihood. This significantly enhances the model's ability to detect rare and unstructured behaviors. Experiments are conducted on the real-world network user dataset UNSW-NB15. A series of performance comparisons and stability validation experiments are designed. These cover multiple evaluation aspects, including Accuracy, F1-score, AUC, and loss fluctuation. The results show that the proposed method outperforms several advanced neural network architectures in both performance and training stability. This study provides a more expressive and discriminative solution for user behavior modeling and anomaly detection. It strongly promotes the application of deep probabilistic modeling techniques in the fields of network security and intelligent risk control.

Credit card cash-out methods have become increasingly complex, with new fraudulent transaction forms emerging continuously. Effective management is hindered by challenges in obtaining accurate transaction data during customer interactions, relying heavily on account-level cash-out label data. This study proposes a deep learning-based single-transaction security identification model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. A Spy mechanism is introduced for labeling sample data, extracting 1.2 million highly reliable positive samples and labeling 1.5 million negative samples from transaction data. The model leverages 50 key variables selected from 148 candidates, including customer attributes, credit utilization, and transaction preferences. The proposed CNN-LSTM hybrid model achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 6 \%}$</tex> accuracy in detecting fraudulent transactions, with a Population Stability Index (PSI) of 0.05 %, demonstrating its robust monitoring capabilities. This research provides an innovative method for enhancing transaction security in commercial bank credit card systems, addressing challenges in obtaining high-accuracy labeled data and improving the discriminative performance of deep learning algorithms.

Summary Increased interest in mountain tourism in the Bolivian Andes has contributed to the formation of “Cholitas Escaladoras”, a group of Indigenous women mountaineers scaling the highest peaks of the South American continent. Moreover, these women serve as mountain guides who help climbers and tourists from around the world to safely reach the highest peaks of Bolivia. At the same time, the Cholitas are acknowledging and addressing the effects of climate change on their mountains. Six women of Aymara and Quechua descent took part in this case study. In-depth semi-structured interviews were conducted to gain an understanding of Cholitas’ perspectives and motivations. The findings revealed that Cholitas Escaladoras’ competence in mountaineering, besides empowering them personally, also helped them to advocate for the protection of Bolivian glaciers heavily impacted by global warming. Cholitas’ expertise and growing popular media coverage provide them with a strong platform to raise awareness about climate change’s impact on high-altitude ecosystems. Their deep cultural connection to the mountains and Indigenous lands enhances their commitment to safeguarding these fragile environments. Information © The Authors 2025

In [1], the authors presented a class of sequences generated by powers of the k th-order Fibonacci recurrence relation and showed that the sequences converge. Precisely, the following result was shown: Suppose that k ∈ Z + and 0 &lt; p &lt; 1. Let x 1 , x 2 , …, x k be positive numbers. For n &gt; k , let (1) Then { x n } converges to .

This thorough article examines how important data processing is to contemporary analytics and corporate operations. The article examines important facets of data preprocessing, such as feature engineering strategies, transformation techniques, and data cleaning methodologies. It examines the effects of different tools and technologies, paying special attention to distributed computing frameworks and Python-based solutions. Through in-depth case studies and industry analysis, the article shows how systematic data processing techniques greatly enhance operational effectiveness, model accuracy, and business outcomes. The article also looks at new developments in machine learning and artificial intelligence applications for data preparation. It provides firms looking to improve their data processing skills with best practices and insights into prospects.