Explore the vast range of titles available.

This paper sets university students using Tik Tok APP as the research object, combining theoretical basis with empirical research. Based on use and gratification approach, questionnaire and interview ithe core to convey this research. Analysis of characteristics of Tik Tok lay a foundation for the research as well. At the same time, according to the specific use behavior of college students from Beijing area, the reasons and influences of use of Tik Tok by college students will be analyzed. This paper aims to help college students rationally deal with their own use of Tik Tok and provide suggestions for the further development of Tik Tok in the future.

Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining macroscopic material properties. Here, we develop a graph neural network (GNN) model for obtaining an embedding of polycrystalline microstructure which incorporates not only the physical features of individual grains but also their interactions. The embedding is then linked to the target property using a feed-forward neural network. Using the magnetostriction of polycrystalline Tb0.3Dy0.7Fe2 alloys as an example, we show that a single GNN model with fixed network architecture and hyperparameters allows for a low prediction error of ~10% over a group of remarkably different microstructures as well as quantifying the importance of each feature in each grain of a microstructure to its magnetostriction. Such a microstructure-graph-based GNN model, therefore, enables an accurate and interpretable prediction of the properties of polycrystalline materials.

The proliferation of next-generation renewable energy systems has driven widespread adoption of electronic devices and nonlinear loads, causing grid distortion that degrades waveform quality in grid-forming (GFM) converters. Additionally, unbalanced grid faults exacerbate overcurrent risks and transient stability challenges when employing conventional virtual impedance strategies. While existing studies have separately examined these challenges, few have comprehensively addressed non-ideal grid conditions. To bridge this gap, a novel control strategy is proposed that reshapes the output current waveforms and enhances transient stability in GFM converters under such conditions. First, a sliding mode controller with an improved composite reaching law to achieve rapid reference tracking while eliminating chattering is designed. Second, a multi-quasi-resonance controller incorporating phase compensation is introduced to suppress harmonic distortion in the converter output current. Third, an individual-phase fuzzy adaptive virtual impedance strategy dynamically reshapes the current amplitude during unbalanced faults and improves the system’s transient stability. Validated through PSCAD/EMTDC simulations and hardware-in-the-loop experiments, the proposed strategy demonstrates superior transient stability and fault ride-through capability compared to state-of-the-art methods, ensuring reliable GFM converter operation under severe harmonic and unbalanced grid conditions.

Deep-learning-based ship detection methods in Synthetic Aperture Radar (SAR) imagery are a current research hotspot. However, these methods rely on high-quality images as input, and in practical applications, SAR images are interfered with by speckle noise, leading to a decrease in image quality and thus affecting detection accuracy. To address this problem, we propose a unified framework for ship detection that incorporates a despeckling module into the object detection network. This integration is designed to enhance the detection performance, even with low-quality SAR images that are affected by speckle noise. Secondly, we propose a Multi-Scale Window Swin Transformer module. This module is adept at improving image quality by effectively capturing both global and local features of the SAR images. Additionally, recognizing the challenges associated with the scarcity of labeled data in practical scenarios, we employ an unlabeled distillation learning method to train our despeckling module. This technique avoids the need for extensive manual labeling and making efficient use of unlabeled data. We have tested the robustness of our method using public SAR datasets, including SSDD and HRSID, as well as a newly constructed dataset, the RSSDD. The results demonstrate that our method not only achieves a state-of-the-art performance but also excels in conditions with low signal-to-noise ratios.

The prevalence of learning disabilities (LD) among children is a critical public health issue; however, recent LD prevalence estimates among children and adolescents aged 6–17 years, as reported by the National Survey of Children’s Health (NSCH), remain largely unexplored. Data for this population-based cross-sectional study were obtained from NSCH to estimate the prevalence of LD diagnosis among U.S. children at both national and state levels, and to inspect the 8-year trends in these estimates from 2016 to 2023. Among 221,244 U.S. children, 20,644 had a history of LD diagnosis, with a weighted prevalence of 8.85% (95% CI  = 8.59–9.10). Of these, 19,289 were currently diagnosed with LD, yielding a weighted prevalence of 8.26% (95% CI  = 8.01–8.51). From 2016 to 2023, the prevalence of ever-diagnosed LD increased from 7.86% to 9.15%, while that of current-diagnosed LD rose from 7.32% to 8.66%, representing relative increases of 16.4% and 18.3%, respectively. The state with the highest prevalence (New Hampshire) had twice that of the state with the lowest prevalence (Utah). This study highlights a critical escalation in LD prevalence among U.S. children and adolescents between 2016 and 2023. Comprehensive screening and support programs must be implemented to enhance early identification and intervention.

Background The increasing prevalence of allergies and asthma has led to a growing global socioeconomic burden. Since the outbreak of the COVID-19 pandemic, the health and lifestyles of children and adolescents have changed dramatically. It’s unclear how this shift impacted allergy and asthma, with limited studies addressing this question. We aim to explore the difference of the prevalence of allergies and asthma among US children and adolescents during and before the COVID-19 pandemic using a nationally representative sample of US children and adolescents. Methods This cross-sectional study included 31,503 participants in the National Health Interview Survey (NHIS) between 2018 and 2021. Allergies and asthma were defined on an affirmative response in the questionnaire by a parent or guardian. Chi-square tests were used to compare baseline characteristics with allergies and asthma for categorical variables. Differences in prevalence during and before the COVID-19 pandemic were estimated with weighted logistic regression, adjusting for demographic factors. Interaction analyses explored variations across strata. Results In US children and adolescents aged 0 to 17, prevalence of any allergy was 26.1% (95% CI, 24.8%- 27.4%) in 2018 and 27.1% (95% CI, 25.9%- 28.2%) in 2021. Thereinto, in 2018, prevalence of respiratory allergies, food allergies and skin allergies were 14.0% (95% CI, 13.1%- 15.0%), 6.5% (95% CI, 5.8%- 7.1%) and 12.6% (95% CI, 11.6%- 13.5%), respectively, and in 2021, 18.8% (95% CI, 17.8%- 19.9%), 5.8% (95% CI, 5.2%- 6.4%) and 10.7% (95% CI, 9.9%- 11.5%), respectively. And prevalence of asthma was 11.1% (95% CI, 10.5%- 11.7%) in 2018–2019 and 9.8% (95% CI, 9.2%- 10.4%) in 2020–2021. Prevalence of respiratory allergies, skin allergies and asthma during and before the COVID-19 pandemic in children and adolescents had statistically significant differences. The differences persisted after adjusting for demographic and socioeconomic variables. Conclusion Prevalence of respiratory allergies increased and the prevalence of both skin allergies and asthma decreased among US children and adolescents during the COVID-19 pandemic compared with the pre-COVID-19 pandemic. Further research is required to explore the association between allergic diseases and the pandemic, with a particular emphasis on the impact of lifestyle changes resulting from measures to prevent COVID-19 infection.

China is among the countries most severely affected by storm surge disasters, with substantial economic losses and human casualties inflicted on its coastal areas. Computing inundation from storm surges for various typical return periods (TRPs) can serve as the scientific basis for preparing evacuation maps kin the case of storm surge disasters, conducting spatial planning of coastal cities, and formulating emergency plans. Zhoushan City in Zhejiang Province, China, was selected as the representative case for this study. A high-precision storm surge numerical model was established and historical observation data were used to calculate the probability distribution curves of extreme tidal levels at two representative tide stations within the study area. First, based on historical typhoon and extratropical-cyclone events affecting the study area, a dataset including weather events was used, and extreme tidal levels for the two representative tide stations were deployed to identify weather events that could cause a storm surge for different return periods. Numerical modeling was then used to generate the resulting inundation range and water depth distribution, establishing a method for calculating inundation from storm surge for various TRPs. The proposed method could easily be adopted in various coastal counties and serve as an effective tool for decision-making in storm surge disaster risk mitigation efforts.

The aim of this review is to explore the potential of new regenerative medicine approaches in the treatment of cholestatic liver fibrosis. Cholestatic liver diseases, such as primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and biliary atresia (BA), due to the accumulation of bile, often progress to liver fibrosis, cirrhosis, and liver failure. When the disease becomes severe enough to require liver transplantation. Deeply understanding the disease’s progression and fibrosis formation is crucial for better diagnosis and treatment. Current liver fibrosis treatments mainly target the root causes and no direct treatment method in fibrosis itself. Recent advances in regenerative medicine offer a potential approach that may help find the ways to target fibrosis directly, offering hope for improved outcomes. We also summarize, analyze, and discuss the current state and benefits of regenerative medicine therapies such as mesenchymal stem cell (MSC) therapy, induced pluripotent stem cells (iPSCs), and organoid technology, which may help the treatment of cholestatic liver diseases. Focusing on the latest research may reveal new targets and enhance therapeutic efficacy, potentially leading to more effective management and even curative strategies for cholestatic liver diseases.

Photovoltaic (PV) power stations tend to have a relatively weak infeed characteristic, unlike conventional synchronous generators. The limited overcurrent capability of power electronic devices and the controllability of grid-connected inverters mean that PV power stations will cause changes in the characteristics of faults on transmission lines. To analyze the adaptability of fault component distance protection on transmission lines connected to PV power stations, a unified phasor expression for the fault current of a PV power station side under various control strategies was deduced in this paper. This expression is then used to derive the equivalent impedance on the PV power station side and the additional impedance. The equivalent impedance and additional impedance are affected greatly by the active and reactive power commands, control targets, and fault conditions. These aspects of a PV power station may cause malfunctions, which can thereby reduce the reliability of fault component distance protection on transmission lines connected to PV power stations. A simulation model of a PV power station was established in PSCAD/EMTDC and the correctness of theoretical analysis was verified by the simulation results.

Decidualization of human endometrial stromal cells (hESCs) is essential for the maintenance of pregnancy, which depends on the fine-tuned regulation of hESCs survival, and its perturbation contributes to pregnancy loss. However, the underlying mechanisms responsible for functional deficits in decidua from recurrent spontaneous abortion (RSA) patients have not been elucidated. Here, we observed that JAZF1 was significantly downregulated in stromal cells from RSA decidua. JAZF1 depletion in hESCs resulted in defective decidualization and cell death through apoptosis. Further experiments uncovered G0S2 as a important driver of hESCs apoptosis and decidualization, whose transcription was repressed by JAZF1 via interaction with G0S2 activator Purβ. Moreover, the pattern of low JAZF1, high G0S2 and excessive apoptosis in decidua were consistently observed in RSA patients. Collectively, our findings demonstrate that JAZF1 governs hESCs survival and decidualization by repressing G0S2 transcription via restricting the activity of Purβ, and highlight the clinical implications of these mechanisms in the pathology of RSA. JAZF1 regulates human endometrial stromal cells survival and decidualization by restricting the activity of Purβ, which induces the expression of G0S2, a factor that is involved in the mitochondrial apoptotic pathway in recurrent spontaneous abortion.