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Background Previous studies have shown that prolonged time spent on screen-based sedentary behavior was significantly associated with lower health status in children, independent of physical activity levels. The study aimed to explore the individual and environmental correlates of screen time (ST) among 8–19-year-old students in China. Methods The study surveyed ST using a self-administered questionnaire in Chinese students aged 8–19 years; 1063 participants were included in the final analysis. Individual and environmental correlates of ST were assessed using a mixed-effects model (for continuous outcome variables) and multiple logistic regression model (for binary outcome variables). Results Prolonged ST was observed in 14.7% of boys and 8.9% of girls. Of the ST, weekend and mobile phone/tablet use represented 80% and 40%, respectively. A positive relationship was observed between media accessibility and ST in both boys and girls ( p < 0.05), whereas the presence of parents/others while using screens was a negative factor for longer ST ( p < 0.05). Among the assessed correlates, access to a television (TV) in students’ bedrooms was associated with prolonged total and weekend ST ( p < 0.05 and p < 0.001, respectively). However, spending time on a mobile phone/tablet or a computer rather than viewing a TV, along with increased media accessibility, increased ST. Conclusions These results indicate that greater media accessibility was positively associated and the presence of parents/others was negatively associated with prolonged ST in both Chinese boys and girls. Development of new and effective strategies against prolonged ST are required, especially for small screen device-based ST on weekends.

Multi-fidelity optimization (MFO) has received extensive attentions in engineering design, which resorts to augmenting the small number of expensive high-fidelity (HF) samples by a large number of low-fidelity (LF) but cheap samples to improve the optimization performance. A key factor that influences the effectiveness of MFO is how to adaptively assign samples for HF and LF simulations in the iteration process. To address such sample assignment issue in MFO, we propose a new infill criterion named Filter-GEI, which imposes an adaptive filter function on top of the generalized expected improvement (GEI) acquisition function. In particular, by taking the correlations between HF and LF models into account, the Filter-GEI can efficiently allocate HF and LF samples to achieve a good balance in between the local and global search. Furthermore, considering parallel computing, the Filter-GEI infills multiple HF and LF samples in each iteration, which can further improve its efficiency as computing power increases. Through tests on five mathematical toy problems and one engineering problem for the turbine blade design, the effectiveness of the proposed algorithm has been well demonstrated.

Precise on‐orbit identification of solar radiation pressure is essential for space‐based gravitational wave detection spacecraft to enter science mode and achieve mission objectives. However, separating subtle environmental variations from non‐stationary system noise remains challenging. This letter proposes a robust identification framework that integrates deep multi‐period feature extraction with density‐based outlier rejection. Validated using a dataset generated by a high‐fidelity gravitational wave spacecraft formation flight simulation system, the proposed method achieves a vector identification error of 1.63% under an optimal observation window of , demonstrating superior performance and robustness for drag‐free control applications.

With concerns of diminishing fossil fuel reserves and environmental deterioration, great efforts have been made to explore novel approaches of efficiently utilizing bio-renewable feedstocks to produce chemicals and fuels. 5-Hydroxymethylfurfural（HMF）,generated from dehydration of six-carbon ketose, is regarded as a primary and versatile renewable building block to realize the goal of production of these high valued products from renewable biomass resources transformation. In this review, we summarize the recent advances via green routes in the heterogeneous reaction system for the catalytic production of HMF from glucose conversion, and emphasize reaction pathways of these reaction approaches based on the fundamental mechanistic chemistry as well as highlight the challenges（such as separation and purification of products, reusing and regeneration of catalyst, recycling solvent） in this field.

PM 2.5 poses significant public health risks, with its sources and composition exhibiting pronounced spatial heterogeneity. While extensive research has focused on heavily polluted regions in northern China, the pollution structure of Jiangxi Province remains understudied. This study investigates the chemical composition and source apportionment of PM 2.5 during a severe regional haze episode (25 December 2023–20 January 2024) across six cities in Jiangxi Province: Nanchang, Jiujiang, Pingxiang, Ji’an, Xinyu. Observed PM 2.5 concentrations ranged from 44.1 to 76.6 μg/m 3 , dominated by water-soluble ions, organic matter (OM), and carbonaceous aerosols. Spatial analysis revealed a pollution hotspot centered on Nanchang and Jiujiang, characterized by distinct gradients in SNA (SO 4 2− , NO 3 − , NH 4 + ) and OM. Based on local emission patterns and topographic features and the component concentration differences of PM 2.5 , we speculate that there are three regional patterns: (1) Northern cities, characterized by high loadings of NO 3 − (industrial), OM (VOCs-derived), and SO 4 2− (promoted by lake air masses with high humidity); (2) Central cities, dominated by local agricultural NH 4 + and conversion from industrial gaseous sources precursors enhanced by local photochemistry; (3) Southern Jiangxi, where vehicular NOx-to-NO 3 − conversion predominated, exacerbated by topographic stagnation from the Nanling Mountains. Positive Matrix Factorization (PMF) resolved city-specific sources: secondary formation and combustion in Nanchang; industrial and vehicular emissions in Jiujiang; agricultural NH 4 + and traffic in Pingxiang; mixed industrial-traffic sources in Ji’an; and vehicle-derived NO 3 − with dust in Ganzhou. These findings underscore spatiotemporal heterogeneity in energy structures and regional transport pathways, providing a scientific basis for region-specific PM 2.5 control strategies in Jiangxi Province, China.

Neural operators have emerged as powerful deep learning frameworks for approximating solution operators of parameterized partial differential equations (PDE). However, current methods predominantly rely on multilayer perceptrons (MLPs) for mapping inputs to solutions, which impairs training robustness in physics-informed settings due to inherent spectral biases and fixed activation functions. To overcome the architectural limitations, we introduce the Physics-Informed Chebyshev Polynomial Neural Operator (CPNO), a novel mesh-free framework that leverages a basis transformation to replace unstable monomial expansions with the numerically stable Chebyshev spectral basis. By integrating parameter dependent modulation mechanism to main net, CPNO constructs PDE solutions in a near-optimal functional space, decoupling the model from MLP-specific constraints and enhancing multi-scale representation. Theoretical analysis demonstrates the Chebyshev basis's near-minimax uniform approximation properties and superior conditioning, with Lebesgue constants growing logarithmically with degree, thereby mitigating spectral bias and ensuring stable gradient flow during optimization. Numerical experiments on benchmark parameterized PDEs show that CPNO achieves superior accuracy, faster convergence, and enhanced robustness to hyperparameters. The experiment of transonic airfoil flow has demonstrated the capability of CPNO in characterizing complex geometric problems.

Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experiments, where we propose a novel group discussion framework to enrich the set of discussion mechanisms. Interestingly, our results show that a single-agent LLM with strong prompts can achieve almost the same performance as the best existing discussion approach on a wide range of reasoning tasks and backbone LLMs. We observe that the multi-agent discussion performs better than a single agent only when there is no demonstration in the prompt. Further study reveals the common interaction mechanisms of LLMs during the discussion.

In computer networking, network traffic refers to the amount of data transmitted in the form of packets between internetworked computers or Cyber-Physical Systems. Monitoring and analyzing network traffic is crucial for ensuring the performance, security, and reliability of a network. However, a significant challenge in network traffic analysis is to process diverse data packets including both ciphertext and plaintext. While many methods have been adopted to analyze network traffic, they often rely on different datasets for performance evaluation. This inconsistency results in substantial manual data processing efforts and unfair comparisons. Moreover, some data processing methods may cause data leakage due to improper separation of training and testing data. To address these issues, we introduce the NetBench, a large-scale and comprehensive benchmark dataset for assessing machine learning models, especially foundation models, in both network traffic classification and generation tasks. NetBench is built upon seven publicly available datasets and encompasses a broad spectrum of 20 tasks, including 15 classification tasks and 5 generation tasks. Furthermore, we evaluate eight State-Of-The-Art (SOTA) classification models (including two foundation models) and two generative models using our benchmark. The results show that foundation models significantly outperform the traditional deep learning methods in traffic classification. We believe NetBench will facilitate fair comparisons among various approaches and advance the development of foundation models for network traffic. Our benchmark is available at https://github.com/WM-JayLab/NetBench.

Two ways has been discussed to unlock the reasoning capability of a large language model. The first one is prompt engineering and the second one is to combine the multiple inferences of large language models, or the multi-agent discussion. Theoretically, this paper justifies the multi-agent discussion mechanisms from the symmetry of agents. Empirically, this paper reports the empirical results of the interplay of prompts and discussion mechanisms, revealing the empirical state-of-the-art performance of complex multi-agent mechanisms can be approached by carefully developed prompt engineering. This paper also proposes a scalable discussion mechanism based on conquer and merge, providing a simple multi-agent discussion solution with simple prompts but state-of-the-art performance.

Network traffic refers to the amount of data being sent and received over the Internet or any system that connects computers. Analyzing network traffic is vital for security and management, yet remains challenging due to the heterogeneity of plain-text packet headers and encrypted payloads. To capture the latent semantics of traffic, recent studies have adopted Transformer-based pretraining techniques to learn network representations from massive traffic data. However, these methods pre-train on data-driven tasks but overlook network knowledge, such as masking partial digits of the indivisible network port numbers for prediction, thereby limiting semantic understanding. In addition, they struggle to extend classification to new classes during fine-tuning due to the distribution shift. Motivated by these limitations, we propose \\Lens, a unified knowledge-guided foundation model for both network traffic classification and generation. In pretraining, we propose a Knowledge-Guided Mask Span Prediction method with textual context for learning knowledge-enriched representations. For extending to new classes in finetuning, we reframe the traffic classification as a closed-ended generation task and introduce context-aware finetuning to adapt to the distribution shift. Evaluation results across various benchmark datasets demonstrate that the proposed Lens~achieves superior performance on both classification and generation tasks. For traffic classification, Lens~outperforms competitive baselines substantially on 8 out of 12 tasks with an average accuracy of \\textbf{96.33\\%} and extends to novel classes with significantly better performance. For traffic generation, Lens~generates better high-fidelity network traffic for network simulation, gaining up to \\textbf{30.46\\%} and \\textbf{33.3\\%} better accuracy and F1 in fuzzing tests. We will open-source the code upon publication.