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

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}

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

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

Research on Service Recommendation Method of Multi-network Hybrid Embed-ding Learning

The network embedding method can map the network nodes to a low-dimensional vector space and ext-ract the feature information of each node effectively. In the field of service recommendation, some studies show that the introduction of network embedding method can effectively alleviate the problem of data sparsity in the recom-mendation process. However, the existing network embedding methods are mostly aimed at a specific structure of the network, and do not cooperate with a variety of relationship networks from the source. Therefore, this paper proposes a service recommendation method based on multi-network hybrid embedding (MNHER), which maps mul-tiple relational networks to the same vector space from vertical and parallel perspectives. Firstly, the social network of users, the shared network of service tags and the user-service heterogeneous information network are constructed. Then, the hybrid embedding method proposed in this paper is used to obtain the embedding vector of users and services in the same vector space. Finally, the service recommendation is made to target users based on the embed-ding vector of users and services. In this paper, the random walk method is further optimized to extract and retain the characteristic information of the original network more effectively. In order to verify the effectiveness of the method proposed in this paper, it is compared with a variety of representative service recommendation methods on three public datasets, and the F-measure values of the service recommendation methods based on single relational network and simply fused multi-relational network are improved by 21% and 15%, respectively. It is proven that the method of multi-network hybrid embedding can effectively coordinate multi-relationship network and improve the quality of service recommendation