Self-perception evolution among university student TikTok users: evidence from China

The effects of short movies on social media platforms are gaining worldwide popularity and are now attracting global academic attention. Employing self-perception theory and qualitative research methodology, the study examines the influence of short video applications (TikTok) on app-user engagement and evaluates the self-perceived cognitive psychological understanding of Chinese university students. The findings show that identity, attitude change, emotional perception, and civic engagement are the most influential aspects of Chinese youths’ self-perceptions. Furthermore, the positive and negative correlated components influence the distribution of short video values. Such tactical use of personality construction contributes to the present psychological research of Chinese university students

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance

Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

Introduction. Switchingfrom polluting (e.g. wood, crop waste, coal)to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions.While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods.We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study.We assessed household-level primary cooking fuel switching during a median of 10 years offollow up (∼2005–2015).We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households(12 369)reported changing their primary cookingfuels between baseline andfollow up surveys. Of these, 61% (7582) switchedfrom polluting (wood, dung, agricultural waste, charcoal, coal, kerosene)to clean (gas, electricity)fuels, 26% (3109)switched between different polluting fuels, 10% (1164)switched from clean to polluting fuels and 3% (522)switched between different clean fuels

Graph Mixed Random Network Based on PageRank

In recent years, graph neural network algorithm (GNN) for graph semi-supervised classification has made great progress. However, in the task of node classification, the neighborhood size is often difficult to expand. The propagation of nodes always only considers the nearest neighbor nodes. Some algorithms usually approximately classify by message passing between direct (single-hop) neighbors. This paper proposes a simple and effective method, named Graph Mixed Random Network Based on PageRank (PMRGNN) to solve the above problems. In PMRGNN, we design a PageRank-based random propagation strategy for data augmentation. Then, two feature extractors are used in combination to supplement the mutual information between features. Finally, a graph regularization term is designed, which can find more useful information for classification results from neighbor nodes to improve the performance of the model. Experimental results on graph benchmark datasets show that the method of this paper outperforms several recently proposed GNN baselines on the semi-supervised node classification. In the research of over-smoothing and generalization, PMRGNN always maintains better performance. In classification visualization, it is more intuitive than other classification methods

A biofilter for treating toluene vapors: performance evaluation and microbial counts behavior

A lab-scale biofilter packed with mixed packing materials was used for degradation of toluene. Different empty bed residence times, 148.3, 74.2 and 49.4 s, were tested for inlet concentration ranging from 0.2 to 1.2 g/m3. The maximum elimination capacity of 36.0 g/(m3 h) occurred at an inlet loading rate of 45.9 g/(m3 h). The contribution of the lower layer was higher than other layers and always had the highest elimination capacity. The carbon dioxide production rate and distribution of micro-organisms followed toluene elimination capacities. The results of this study indicated that mixed packing materials could be considered as a potential biofilter carrier, with low pressure drop (less than 84.9 Pa/m), for treating air streams containing VOCs

FMCW Radar-Based Human Sitting Posture Detection

Sitting posture is closely related to our health. Poor sitting posture can cause various diseases and harm our physical health. Current methods to detect sitting posture include machine vision, wearable sensors, and pressure sensors. However, these methods have problems with respect to privacy, inconvenience, and cost. In this work, we proposed the use of frequency-modulated continuous wave radar (FMCW) for detecting human sitting posture, which employs wireless signal transmission to enable non-contact detection, protect privacy, and reduce costs. First, the range fast Fourier transform (FFT) and Doppler FFT of the radar’s intermediate frequency (IF) signals are performed to obtain range and Doppler feature information for different sitting postures. Second, to overcome the problem of range FFT bin offset, a single target angle measurement method is proposed to obtain angle features. Subsequently, we constructed various combinations of features to explore the influence of different combinations of features on the detection of posture while sitting. And we used five machine learning algorithms to perform sitting posture detection experiments. Finally, we conducted sedentary experiments in an office setting and provided sitting history records. The experimental results demonstrate that the method we proposed can identify five distinct sitting postures with an average accuracy of 98.07%

Rapid Determination of Geniposide and Baicalin in Lanqin Oral Solution by Near-Infrared Spectroscopy with Chemometric Algorithms during Alcohol Precipitation

The selection of key variables is an important step that improves the prediction performance of a near-infrared (NIR) real-time monitoring system. Combined with chemometrics, NIR spectroscopy was employed to construct high predictive accuracy, interpretable models for the rapid detection of the alcohol precipitation process of Lanqin oral solution (LOS). The variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) was innovatively introduced into the variable screening process of the model of geniposide and baicalin. Compared with the commonly used synergy interval partial least squares regression, competitive adaptive reweighted sampling, and random frog, VCPA-IRIV achieved the maximum compression of variable space. VCPA-IRIV-partial least squares regression (PLSR) only needs to use about 1% of the number of variables of the original data set to construct models with Rp values greater than 0.95 and RMSEP values less than 10%. With the advantages of simplicity and strong interpretability, the prediction ability of the PLSR models had been significantly improved simultaneously. The VCPA-IRIV-PLSR models met the requirements of rapid quality detection. The real-time detection system can help researchers to understand the quality rules of geniposide and baicalin in the alcohol precipitation process of LOS and provide a reference for the optimization of a LOS quality control system

Modification of glycosylation mediates the invasive properties of murine hepatocarcinoma cell lines to lymph nodes.

Among the various posttranslational modification reactions, glycosylation is the most common, and nearly 50% of all known proteins are thought to be glycosylated. In fact, changes in glycosylation readily occur in carcinogenesis, invasion and metastasis. This report investigated the modification of glycosylation mediated the invasive properties of Hca-F and Hca-P murine hepatocarcinoma cell lines, which have high, low metastatic potential in the lymph nodes, respectively. Analysis revealed that the N-glycan composition profiling, expression of glycogenes and lectin binding profiling were different in Hca-F cells, as compared to those in Hca-P cells. Further analysis of the N-glycan regulation by tunicamycin (TM) application or PNGase F treatment in Hca-F cells showed partial inhibition of N-glycan glycosylation and decreased invasion both in vitro and in vivo. We targeted glycogene ST6GAL1, which was expressed differently in Hca-F and Hca-P cells, and regulated the expression of ST6GAL1. The altered levels of ST6GAL1 were also responsible for changed invasive properties of Hca-F and Hca-P cells both in vitro and in vivo. These findings indicate a role for glycosylation modification as a mediator of tumor lymphatic metastasis, with its altered expression causing an invasive ability differentially

Cell-Wall-Degrading Enzymes Required for Virulence in the Host Selective Toxin-Producing Necrotroph Alternaria alternata of Citrus

The necrotrophic fungal pathogen Alternaria alternata attacks many citrus species, causing brown spot disease. Its pathogenic capability depends primarily on the production of host-selective ACT toxin. In the current study a Ste12 transcription factor was characterized to be required for conidial formation and the production of cell-wall-degrading enzymes (CWDEs) in the tangerine pathotype of A. alternata. The Ste12 deficiency strain (ΔSte12) retained wild-type growth, ACT toxin production, and sensitivity to oxidative and osmotic stress. However, pathogenicity tests assayed on detached Dancy leaves revealed a marked reduction in virulence of ΔSte12. Transcriptome and quantitative RT-PCR analyses revealed that many genes associated with Carbohydrate-Active Enzymes (CAZymes) were downregulated in ΔSte12. Two cutinase-coding genes (AaCut3 and AaCut7) regulated by Ste12 were individually and simultaneously inactivated. The AaCut3 or AaCut7 deficiency strain unchanged in cutinase activities and incited wild-type lesions on Dancy leaves. However, the strain carrying an AaCut3 AaCut7 double mutation produced and secreted significantly fewer cutinases and incited smaller necrotic lesions than wild type. Not only is the host-selective toxin (HST) produced by A. alternata required for fungal penetration and lesion formation, but so too are CWDEs required for full virulence. Overall, this study expands our understanding of how A. alternata overcomes citrus physical barriers to carry out successful penetration and colonization