Research on the Internet Behavior of Teenagers in Xi'an and Its Parental Intervention Mechanism

With the development of modern Internet technology, China's Internet services are playing an increasingly important role in youth theme education and youth growth and development. People's understanding of the growth and development of adolescents is limited to their physical fitness and intellectual development, and less attention is paid to the mental health of adolescents. To this end, this article plans to use a questionnaire survey method to conduct research. Through the survey, we can promote the continued development of the mental health of teenagers, so that more teenagers and parents can get better communication, so that teenagers can have a better growth environment, and more Good healthy growth and happy development

A Study on Satisfaction and Willingness to Continuously Participate in Business Class Virtual Simulation Competitions -- An Empirical Analysis Based on Technology Acceptance Model (TAM)

The combination of virtual simulation technology and innovation and entrepreneurship education can achieve the role of interaction between the environment and the real environment during students’ virtual simulation, which can attract active participation of users and help college students understand the risks and opportunities faced by enterprises in the process of operation and growth, so as to improve the ability of enterprise operation and management, and deepen students’ understanding of theory and practical ability. In this paper, we construct a technology acceptance model (TAM) through seven dimensions: perceived usefulness, perceived ease of use, external environment, teacher guidance, willingness to participate, satisfaction, and willingness to continue to use, and investigate students’ satisfaction and willingness to continue to participate in the virtual simulation competition in some universities. The results of the data analysis show that the satisfaction and willingness to continue to participate in the virtual simulation competition play a good role

Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning

Spatio-temporal (ST) prediction is an important and widely used technique in data mining and analytics, especially for ST data in urban systems such as transportation data. In practice, the ST data generation is usually influenced by various latent factors tied to natural phenomena or human socioeconomic activities, impacting specific spatial areas selectively. However, existing ST prediction methods usually do not refine the impacts of different factors, but directly model the entangled impacts of multiple factors. This amplifies the modeling complexity of ST data and compromises model interpretability. To this end, we propose a multi-factor ST prediction task that predicts partial ST data evolution under different factors, and combines them for a final prediction. We make two contributions to this task: an effective theoretical solution and a portable instantiation framework. Specifically, we first propose a theoretical solution called decomposed prediction strategy and prove its effectiveness from the perspective of information entropy theory. On top of that, we instantiate a novel model-agnostic framework, named spatio-temporal graph decomposition learning (STGDL), for multi-factor ST prediction. The framework consists of two main components: an automatic graph decomposition module that decomposes the original graph structure inherent in ST data into subgraphs corresponding to different factors, and a decomposed learning network that learns the partial ST data on each subgraph separately and integrates them for the final prediction. We conduct extensive experiments on four real-world ST datasets of two types of graphs, i.e., grid graph and network graph. Results show that our framework significantly reduces prediction errors of various ST models by 9.41% on average (35.36% at most). Furthermore, a case study reveals the interpretability potential of our framework

Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts

This study investigates the behaviors of Large Language Models (LLMs) when faced with conflicting prompts versus their internal memory. This will not only help to understand LLMs' decision mechanism but also benefit real-world applications, such as retrieval-augmented generation (RAG). Drawing on cognitive theory, we target the first scenario of decision-making styles where there is no superiority in the conflict and categorize LLMs' preference into dependent, intuitive, and rational/irrational styles. Another scenario of factual robustness considers the correctness of prompt and memory in knowledge-intensive tasks, which can also distinguish if LLMs behave rationally or irrationally in the first scenario. To quantify them, we establish a complete benchmarking framework including a dataset, a robustness evaluation pipeline, and corresponding metrics. Extensive experiments with seven LLMs reveal their varying behaviors. And, with role play intervention, we can change the styles, but different models present distinct adaptivity and upper-bound. One of our key takeaways is to optimize models or the prompts according to the identified style. For instance, RAG models with high role play adaptability may dynamically adjust the interventions according to the quality of retrieval results -- being dependent to better leverage informative context; and, being intuitive when external prompt is noisy

AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction

Air quality prediction and modelling plays a pivotal role in public health and environment management, for individuals and authorities to make informed decisions. Although traditional data-driven models have shown promise in this domain, their long-term prediction accuracy can be limited, especially in scenarios with sparse or incomplete data and they often rely on black-box deep learning structures that lack solid physical foundation leading to reduced transparency and interpretability in predictions. To address these limitations, this paper presents a novel approach named Physics guided Neural Network for Air Quality Prediction (AirPhyNet). Specifically, we leverage two well-established physics principles of air particle movement (diffusion and advection) by representing them as differential equation networks. Then, we utilize a graph structure to integrate physics knowledge into a neural network architecture and exploit latent representations to capture spatio-temporal relationships within the air quality data. Experiments on two real-world benchmark datasets demonstrate that AirPhyNet outperforms state-of-the-art models for different testing scenarios including different lead time (24h, 48h, 72h), sparse data and sudden change prediction, achieving reduction in prediction errors up to 10%. Moreover, a case study further validates that our model captures underlying physical processes of particle movement and generates accurate predictions with real physical meaning.Comment: Accepted by the 12th International Conference on Learning Representations (ICLR 2024

Towards Wireless Characterization of Solvated Ions with Uncoated Resonant Sensors

Uncoated resonant sensors are presented here for wireless monitoring of solvated ions, with progress made toward monitoring nitrates in agricultural runoff. The sensor, an open-circuit Archimedean coil, is wirelessly interrogated by a portable vector network analyzer (VNA) that monitors the scattering parameter response to varying ionic concentrations. The sensor response is defined in terms of the resonant frequency and the peak-to-peak amplitude of the transmission scattering parameter profile (|S21|). Potassium chloride (KCl) solutions with concentrations in the range of 100 nM – 4.58 M were tested on nine resonators having different length and pitch sizes to study the effect of sensor geometry on its response to ion concentration. The resonant sensors demonstrated an ion-specific response, caused by the variations in the relative permittivity of the solution, which was also a function of the resonator geometry. A lumped circuit model, which fit the experimental data well, confirms signal transduction via change in solution permittivity. Also, a ternary ionic mixture (composed of potassium nitrate (KNO3), ammonium nitrate (NH4NO3), and ammonium phosphate (NH4H2PO4)) response surface was constructed by testing 21 mixture variations on three different sensor geometries and the phase and magnitude of scattering parameters were monitored. It was determined that the orthogonal responses presented by resonant sensor arrays can be used for quantifying levels of target ions in ternary mixtures. Applications of these arrays include measuring the concentration of key ions in bioreactors, human sweat, and agricultural waters. Preliminary results are shown for calibration standards and real waterway samples in Iowa, USA

Facile Construction of Polypyrrole Microencapsulated Melamine-Coated Ammonium Polyphosphate to Simultaneously Reduce Flammability and Smoke Release of Epoxy Resin

A unique mono-component intumescent flame retardant, named PPy-MAPP, of which melamine-coated ammonium polyphosphate (MAPP) was microencapsulated by polypyrrole (PPy), was synthesized and carefully characterized. The obtained PPy-MAPP was applied to epoxy resin (EP) for obtaining flame-retarded EP composites. The results show that PPy-MAPP imparts better flame retardancy and smoke suppression properties to EP compared to the same addition of MAPP. The EP composite with 15 wt% PPy-MAPP easily passes the UL94 V-0 rating and achieves an LOI value of 42.4%, accompanied by a 61.9% reduction in total heat release (THR) and a 73.9% reduction in total smoke production (TSP) when compared with pure EP. The char residue analysis shows that PPy-MAPP can promote a generation of more phosphorus-rich structures in the condensed phase that improve the integrity and intumescence of char against fire. The mechanical test indicates that PPy-MAPP has a less negative effect on the tensile strength and elastic modulus of epoxy resin due to the good compatibility between PPy-MAPP and the EP matrix, as supported by differential scanning calorimetry (DSC) analyses. In this paper, these attractive features of PPy-MAPP provide a new strategy to prepare satisfactory flame retardant and super flame retarding EP composites

Effects of Boron Nitride Coatings at High Temperatures and Electromagnetic Wave Absorption Properties of Carbon Fiber-Based Magnetic Materials

An electromagnetic (EM) wave-absorbing material with a three-layer structure is prepared by depositing magnetic particles and a high-temperature resistant coating on the surface of the carbon fiber (CF) with in situ hybridization. Accordingly, the structure, chemical composition, morphology, high-temperature resistance, EM characteristics, and EM wave absorption of the composite materials were analyzed. The composite materials contained CFs, and the magnetic particles, such as Fe3O4, NiFe2O4, CoFe2O4, and Ni3Fe, distributed along the axial direction of the fiber, while boron nitride (BN) existed in the outermost coating layer. This preparation method improves the oxidation resistance and EM wave absorption performance of the CF. When the concentrations of the metal salt solution and the original BN solution are 0.625×1.5 mol L-1 [nFeCl3: nCoSO4: nNiSO4=2:2:1] and 4 mol L-1 [nH3BO3:nCONH22=1:3], respectively, the thermal decomposition temperature of the prepared CF/1.5FeCoNi/2BN is increased from 450°C to 754°C. In the frequency range of 10.6–26 GHz, the EM wave loss is less than −10 dB (the bandwidth spans 15.4 GHz). The CF-based composite material prepared in this study has the characteristics of light weight, wide absorption band, and strong oxidation resistance and constitutes the reference basis for the study of other high-temperature, EM wave-absorbing materials