Does Housework Help Improve Academic Performance? An Empirical Analysis on the Influence of Participation in Housework on Academic Performance of Primary and Middle School Students

At present, even if the education on hard-working spirit has been emphasized increasingly as an important part of practical education in China’s education policy, the reality is still far from satisfactory, because many parents do not provide their children with sufficient opportunities to do housework. Previous studies have indicated that the empirical analysis remains to be improved in terms of the relationship between housework and the development of primary and junior high school students. Based on data from the 2020 Monitoring of Students' Academic Quality in Basic Education in Jiangsu Province Study, this study investigates the influence of primary and secondary school students’ participation in housework on academic performance by using OLS regression and Coarsened Exact Matching (CEM). The results show that the current proportion of primary and junior high school students involved in housework is not high; however, participating in housework frequently will positively affect the academic performance of primary and junior high school students. Participation in housework in primary school has a greater positive impact on academic performance than that in junior high school. In addition, since excessive academic burden is the main factor hindering primary and junior high school students from being involved in housework, it is necessary to strengthen the publicity of education on hard-working spirit to help people know its importance. Also, we suggest the burden on schoolwork should be reduced to in order to promote more diversified housework related educational opportunities for students

OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation

Light field (LF) depth estimation is a crucial task with numerous practical applications. However, mainstream methods based on the multi-view stereo (MVS) are resource-intensive and time-consuming as they need to construct a finer cost volume. To address this issue and achieve a better trade-off between accuracy and efficiency, we propose an occlusion-aware cascade cost volume for LF depth (disparity) estimation. Our cascaded strategy reduces the sampling number while keeping the sampling interval constant during the construction of a finer cost volume. We also introduce occlusion maps to enhance accuracy in constructing the occlusion-aware cost volume. Specifically, we first obtain the coarse disparity map through the coarse disparity estimation network. Then, the sub-aperture images (SAIs) of side views are warped to the center view based on the initial disparity map. Next, we propose photo-consistency constraints between the warped SAIs and the center SAI to generate occlusion maps for each SAI. Finally, we introduce the coarse disparity map and occlusion maps to construct an occlusion-aware refined cost volume, enabling the refined disparity estimation network to yield a more precise disparity map. Extensive experiments demonstrate the effectiveness of our method. Compared with state-of-the-art methods, our method achieves a superior balance between accuracy and efficiency and ranks first in terms of MSE and Q25 metrics among published methods on the HCI 4D benchmark. The code and model of the proposed method are available at https://github.com/chaowentao/OccCasNet

LFSRDiff: Light Field Image Super-Resolution via Diffusion Models

Light field (LF) image super-resolution (SR) is a challenging problem due to its inherent ill-posed nature, where a single low-resolution (LR) input LF image can correspond to multiple potential super-resolved outcomes. Despite this complexity, mainstream LF image SR methods typically adopt a deterministic approach, generating only a single output supervised by pixel-wise loss functions. This tendency often results in blurry and unrealistic results. Although diffusion models can capture the distribution of potential SR results by iteratively predicting Gaussian noise during the denoising process, they are primarily designed for general images and struggle to effectively handle the unique characteristics and information present in LF images. To address these limitations, we introduce LFSRDiff, the first diffusion-based LF image SR model, by incorporating the LF disentanglement mechanism. Our novel contribution includes the introduction of a disentangled U-Net for diffusion models, enabling more effective extraction and fusion of both spatial and angular information within LF images. Through comprehensive experimental evaluations and comparisons with the state-of-the-art LF image SR methods, the proposed approach consistently produces diverse and realistic SR results. It achieves the highest perceptual metric in terms of LPIPS. It also demonstrates the ability to effectively control the trade-off between perception and distortion. The code is available at \url{https://github.com/chaowentao/LFSRDiff}

Plasma lensing interpretation of FRB 20201124A bursts at the end of September 2021

When the radio photons propagate through a non-uniform electron density volume, the plasma lensing effect can induce an extreme magnification to the observed flux at certain frequencies. Because the plasma lens acts as a diverging lens, it can extremely suppress the observed flux when aligned with source. These two properties can theoretically cause a highly magnified Fast Radio Burst (FRB) to faint or even disappear for a period of time. In this paper, we interpret that the significant increase in burst counts followed by a sudden quenching in FRB 20201124A in September 2021 can be attributed to plasma lensing. Based on the one-dimensional Gaussian lens model, we search for double main-peak structures in spectra just before its extinction on September 29, 2021. After the de-dispersion and de-scintillation procedures, we find eight bursts with double main-peaks at stable positions. There are three parameters in our modelling, the height and width of the one-dimension Gaussian lens and its distance to the source. We reformulate them as a combined parameter $\mathrm{P}_0 \propto \left ( \frac{a}{\mathrm{AU}}\right )\sqrt{\frac{\mathrm{kpc}}{D_{\mathrm{LS}}} \frac{\mathrm{pc}\;\mathrm{cm}^{-3}}{N_0} }$. The frequency spectra can give an accurate estimation of $\mathrm{P}_0$ corresponding to $\left ( \frac{a}{\mathrm{AU}}\right )\sqrt{\frac{\mathrm{kpc}}{D_{\mathrm{LS}}} \frac{\mathrm{pc}\;\mathrm{cm}^{-3}}{N_0} } \approx 28.118$, while the time of arrival only give a relatively loose constraint on $a^2/D_{\mathrm{LS}}$. Comparing with the observation dynamic spectra, we suggest that for a plasma lens in host galaxy, e.g., $D_{\mathrm{LS}}\approx 1\mathrm{kpc}$, the width of lens can not be larger than $40\mathrm{AU}$. At last, we estimate the relative transverse motion velocity between the lens and source, $v\approx98\left(\frac{a}{\mathrm{AU}}\right)\mathrm{km/s}$.Comment: 9 pages, 12 figures. Comments are welcom

Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models

When a damaging earthquake occurs, immediate information about casualties is critical for time-sensitive decision-making by emergency response and aid agencies in the first hours and days. Systems such as Prompt Assessment of Global Earthquakes for Response (PAGER) by the U.S. Geological Survey (USGS) were developed to provide a forecast within about 30 minutes of any significant earthquake globally. Traditional systems for estimating human loss in disasters often depend on manually collected early casualty reports from global media, a process that's labor-intensive and slow with notable time delays. Recently, some systems have employed keyword matching and topic modeling to extract relevant information from social media. However, these methods struggle with the complex semantics in multilingual texts and the challenge of interpreting ever-changing, often conflicting reports of death and injury numbers from various unverified sources on social media platforms. In this work, we introduce an end-to-end framework to significantly improve the timeliness and accuracy of global earthquake-induced human loss forecasting using multi-lingual, crowdsourced social media. Our framework integrates (1) a hierarchical casualty extraction model built upon large language models, prompt design, and few-shot learning to retrieve quantitative human loss claims from social media, (2) a physical constraint-aware, dynamic-truth discovery model that discovers the truthful human loss from massive noisy and potentially conflicting human loss claims, and (3) a Bayesian updating loss projection model that dynamically updates the final loss estimation using discovered truths. We test the framework in real-time on a series of global earthquake events in 2021 and 2022 and show that our framework streamlines casualty data retrieval, achieving speed and accuracy comparable to manual methods by USGS.Comment: 10 pages, 8 figure

Essential role of liquid phase on melt-processed GdBCO single-grain superconductors

RE-Ba-Cu-O (RE denotes rare earth elements) single-grain superconductors have garnered considerable attention owning to their ability to trap strong magnetic field and self-stability for maglev. Here, we employed a modified melt-growth method by adding liquid source (LS) to provide a liquid rich environment during crystal growth. It further enables a significantly low maximum processing temperature (Tmax) even approaching peritectic decomposition temperature. This method was referred as the liquid source rich low Tmax (LS+LTmax) growth method which combines the advantage of Top Seeded Infiltration Growth (TSIG) into Top Seeded Melt-texture Growth (TSMG). The LS+LTmax method synergistically regulates the perfect appearance and high superconducting performance in REBCO single grains. The complementary role of liquid source and low Tmax on the crystallization has been carefully investigated. Microstructure analysis demonstrates that the LS+LTmax processed GdBCO single grains show clear advantages of uniform distribution of RE3+ ions as well as RE211 particles. The inhibition of Gd211 coarsening leads to improved pining properties. GdBCO single-grain superconductors with diameter of 18 mm and 25 mm show maximum trapped magnetic field of 0.746 T and 1.140 T at 77 K. These trapped fields are significantly higher than those of conventional TSMG samples. Particularly, at grain boundaries with reduced RE211 density superior flux pinning performance has been observed. It indicates the existence of multiple pinning mechanisms at these areas. The presented strategy provides essential LS+LTmax technology for processing high performance single-grain superconductors with improved reliability which is considered important for engineering applications

Riemannian Surface on Carbon Anodes Enables Li-Ion Storage at −35 °C

Since sluggish Li$^{+}$ desolvation leads to severe capacity degradation of carbon anodes at subzero temperatures, it is urgently desired to modulate electron configurations of surface carbon atoms toward high capacity for Li-ion batteries. Herein, a carbon-based anode material (O-DF) was strategically synthesized to construct the Riemannian surface with a positive curvature, which exhibits a high reversible capacity of 624 mAh g$^{-1}$ with an 85.9% capacity retention at 0.1 A g$^{-1}$ as the temperature drops to −20 °C. Even if the temperature drops to −35 °C, the reversible capacity is still effectively retained at 160 mAh g$^{-1}$ after 200 cycles. Various characterizations and theoretical calculations reveal that the Riemannian surface effectively tunes the low-temperature sluggish Li$^{+}$ desolvation of the interfacial chemistry via locally accumulated charges of non-coplanar sp$^{x}$ (2 < x < 3) hybridized orbitals to reduce the rate-determining step of the energy barrier for the charge-transfer process. Ex-situ measurements further confirm that the sp$^{x}$-hybridized orbitals of the pentagonal defect sites should denote more negative charges to solvated Li$^{+}$ adsorbed on the Riemannian surface to form stronger Li–C coordinate bonds for Li$^{+}$ desolvation, which not only enhances Li-adsorption on the curved surface but also results in more Li$^{+}$ insertion in an extremely cold environment