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The rapid advancement of IoT technology presents transformative opportunities across various sectors, with education being a prominent beneficiary. Smart classrooms, a product of IoT integration, are being widely adopted to create technology-enhanced, student-centric learning environments that cater to students' information literacy needs, particularly during events like pandemics. This widespread adoption generates substantial amounts of educational data, commonly known as big data, necessitating innovative solutions for analysis and utilization. To solve these challenges, this paper proposes utilizing the Apriori algorithm—a data mining technique renowned for uncovering valuable patterns and associations within extensive datasets. This paper evaluates the impact of various information resources with differing quality, considering individuals' information literacy skills. Utilizing data mining techniques, it delves into university students' information literacy data, integrating it with the university library resources to establish a data-driven information literacy education model. It then focuses on criteria, components, and effective methods for instructing college students in information literacy. Finally, a diverse group of students, from first-year undergraduates to doctoral candidates at a specific university, is studied for their engagement in information literacy instruction. Based on the experimental findings, sophomore students exhibited the highest level of participation at 75.9% accuracy, while postgraduate students received more information literacy training than undergraduates and Ph.D. students. When comparing this method to others, such as SVM, KNN, LR, RF, and DT, it achieved superior performance. Additionally, the quality of information literacy training in university libraries was assessed through three dimensions: student learning, behavior, and achievements. Only junior, senior, and first-year graduate students scored above 4, with scores of 4.18, 4.15, and 4.26, respectively. [ABSTRACT FROM AUTHOR]

In today's global landscape, efficient logistics management is crucial for fostering sustainable development across industries. Integrating big data analytics into logistics operations has emerged as a transformative approach to boost efficiency, minimize waste, and mitigate environmental impact. This paper aims to explore the significance of utilizing big data analytics to optimize logistics management for sustainable development. The current challenges in logistics management primarily revolve around four key aspects: the efficiency of goods delivery, raw material costs, one-stop solutions, and service evaluation. These challenges persist in traditional logistics management due to their strong independence. Therefore, this paper is devoted to the optimization of logistics flow management through the lens of sustainable development and ecological cycles. Initially, by considering the Sustainable Development logistics management mode and survey data from 302 logistics enterprises, improvements are made in the four aspects. Subsequently, a four-element analysis function is designed, comprising factors for efficiency improvement, cost reduction, comprehensive optimization, and service enhancement. This function, which aims to assess logistics operations thoroughly, is a crucial component of the research. It includes components for enhancing services, cutting costs, improving efficiency, and conducting comprehensive optimization. MATLAB 21.0 b software is utilized for model analysis, primarily focusing on Volar model area analysis. The aim is to identify and categorize new optimization directions for SD logistics management mode based on the collected data and analyzed samples. The effectiveness of the SD logistics management mode is verified by observing model results and trends. Ultimately, the paper demonstrates the relevance of optimizing logistics management mode by significantly improving the efficiency of goods delivery, reducing raw material costs, enhancing one-stop solutions, and evaluating service-sharing information. [ABSTRACT FROM AUTHOR]

Sports effect evaluation is the main module of modern information construction. To improve the accuracy of sports effect evaluation and enhance the training level of athletes, this paper aims to conduct in-depth research on sports effect evaluation based on feature selection under the background of big data. Firstly, a sports evaluation system is designed based on the Window Presentation Foundation (WPF) that adopts a Client–Server (C/S) mode. The overall architecture of the system is proposed by expounding the functional modules and the schema of information tables in the database. To assess the impact of sports, the data mining algorithm and big data analytics technique called Random Forest (RF) is used as the core evaluation system. With the help of information gain, feature selection is performed, based on which decision trees are constructed by generating branch nodes. The random forest is created by combining all the decision trees, which helps in improving the generalization. The proposed method is experimentally verified and it is found that the overall stability of the effect evaluation system is good and it effectively improves the efficiency and accuracy of sports effect evaluation, thus enhancing the level of athletes' training. The proposed sports effect evaluation method performs excellently in the experimental validation, as seen by the remarkable F1 Score of 85.32%, average accuracy of 84.28%, precision of 86%, and recall of 82.28%. In comparison, this performs noticeably better than current approaches, proving its improved efficacy in assessing the effects of sports and confirming its ability to improve athlete training proficiency. [ABSTRACT FROM AUTHOR]

The exponential growth of urban data presents significant challenges in efficiently analyzing and gaining actionable insights for urban planning and design. This paper proposes a big data analytics framework using MapReduce-based parallel FP-growth (MP-PFP) algorithm leveraging tools like Hadoop, MapReduce, and distributed crawlers to uncover patterns and trends from large-scale, heterogeneous urban datasets. A key contribution is the integration of diverse data types, from socio-economic datasets to environmental parameters, into a consistent analysis framework. The methodology employs frequent pattern mining algorithms on a scalable analytics platform to process behavior data and derive planning directives. Additionally, data visualization and parametric analysis techniques transform raw statistics into interactive 3D landscape representations that expose environmental site attributes. Specifically, the MapReduce capabilities enable distributed parallel processing of vast urban data volumes, ensuring speed and efficiency. The data visualization module creates immersive VR representations of urban landscapes, allowing interactive modifications. Advanced simulation techniques are incorporated to model the impact of planning directives on multiple landscape attributes. The framework is designed as a scalable, customizable solution that can integrate diverse urban data sources with customizable analytics, modeling and visualization modules through APIs. Comparative evaluations demonstrate a classification accuracy improvement from 68 to 93% over prevailing approaches. The framework has proven superior in data integration, real-time responsiveness, and accurately modeling the dynamic complexities of urban landscapes. The quantifiable simulations empower designers to make more informed planning decisions aligned with community needs. Despite ongoing data accuracy and privacy concerns, the methodology shows promising capabilities in harnessing urban big data to drive intelligent, sustainable urban development through its integration of data-driven insights, computational analysis, and interactive visualization. It brings impactful innovations to the future of urban informatics and planning. [ABSTRACT FROM AUTHOR]

In recent years, technological advancements have been replicated in various industries, including sports medicine. Recent developments, such as big data analytics and data mining, which have revolutionized medical services in sports, are apparent in this transformation. This technological shift is motivated by the need to enhance athletic performance, prevent injuries, and offer individualized health advice. Modern lifestyles have simultaneously increased people's attention to their health, creating a demand for better medical services. However, China's ability to provide superior medical care needs to be improved due to a lack of medical resources and an ever-increasing patient population. To address these challenges, this research paper presents an integrated framework that leverages Spark-based big data analytics and the XGBoost algorithm. The framework aims to provide a robust sports medical service encompassing real-time health monitoring and data-driven insights. Powered by the formidable distributed computing platform Spark, it adeptly manages extensive sports data generated during training and events, facilitating instant health evaluations. Incorporating the XGBoost algorithm for data mining amplifies health prediction and recommendation capabilities. Renowned for its predictive prowess, XGBoost excels in discerning intricate sports data patterns and trends. Its proficiency in tackling intricates feature selection and modeling tasks ensures precision and actionable insights. Empirical findings underscore substantial enhancements in sports medical services. When applied to chronic disease datasets, the XGBoost algorithm garnered an impressive 93% trust rate. In contrast to conventional methods like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), Naïve Bayes (NB), and Logistic Regression (LR), the proposed framework consistently outperforms these established techniques. This remarkable performance underscores the transformative potential of the integrated framework in revolutionizing sports medical services. [ABSTRACT FROM AUTHOR]

Adolescent mental health issues like depression and anxiety are rising among adolescents worldwide, leading to serious long-term consequences if left untreated. However, stigma and lack of resources delay access to timely screening and care. Traditional subjective methods fail to detect early warning signs and risk factors. This paper develops a novel hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model to enable early warning of common mental health risks like depression in teenagers. The model is trained on a large clinical dataset with over 50,000 adolescents encompassing electronic health records and neuroimaging data. This allows discovering subtle predictive patterns within clinical encounters and brain structure or function at the population level. The methodology centers on a tailored CNN and LSTM architecture to comprehensively model the multivariate clinical time series data, combining CNN spatial feature learning with LSTM temporal sequence modeling. The hybrid approach leverages complementary techniques to analyze the neuroimaging and electronic health record data. The model achieves an accuracy of 95%, AUC of 97%, precision of 94%, recall of 91%, and F1 score of 92% on held-out test data, significantly outperforming prior state-of-the-art models which had lower accuracy. Detailed predictive feature analysis and clinical validation confirm the model's utility for early mental health risk screening and targeted intervention in teenagers. Overall, the study demonstrates an effective approach to integrating big data, deep learning and rigorous evaluation for developing more accurate, explainable and useful models to support adolescent mental healthcare through data-driven early warning systems. [ABSTRACT FROM AUTHOR]