How do digital lives affect resident mental health in the digital era? Empirical evidence based on Chinese general social survey

Having good mental health means we are better able to connect, function, cope and thrive. The widespread application of digital technology in daily life provides new ways and promising tools for residents to maintain their mental health. Given the importance of mental health for everyone, and the fact that mental health problems are prevalent worldwide, this study discusses how digital lives affects the mental health of residents. The results suggest that digital lives are significantly and positively associated with mental health. Mechanisms analysis identifies personal perceptions (self-rated physical exercise and subjective wellbeing) as the important paths for digital lives to promote mental health, while social perceptions (social trust and social fairness) play a suppressing effect on the relationship between them. The results of further discussion show that the degree of the influence of digital lives on mental health of individuals is heterogeneous among different regions. Due to the difference in development level, the positive impact of digital lives is greater in urban areas than in rural areas, and it is stronger in western regions than in eastern and central regions. This study enriches the nascent research stream of digitalization, explores new paths of harnessing digital technologies for mental health, and offers useful insights for the government to guide them in formulating digital development strategies and achieving the Healthy China Strategy

Probing Supersymmetric Black Holes with Surface Defects

It has long been conjectured that the large $N$ deconfinement phase transition of $\mathcal{N}=4$ ${\rm SU}(N)$ super-Yang-Mills corresponds via AdS/CFT to the Hawking-Page transition in which black holes dominate the thermal ensemble, and quantitative evidence of this has come through the recent matching of the superconformal index of ${1\over 16}$-BPS states to the supersymmetric black hole entropy. We introduce the half-BPS Gukov-Witten surface defect as a probe of the superconformal index, which also serves as an order parameter for the deconfinement transition. This can be studied directly in field theory as a modification of the usual unitary matrix model or in the dual description as a D3-brane probe in the background of a (complex) supersymmetric black hole. Using a saddle point approximation, we determine our defect index in the large $N$ limit as a simple function of the chemical potentials and show independently that it is reproduced by the renormalized action of the brane in the black hole background. Along the way, we also comment on the Cardy limit and the thermodynamics of the D3-brane in the generalized ensemble. The defect index sharply distinguishes between the confining and the deconfining phases of the gauge theory and thus is a supersymmetric non-perturbative order parameter for these large $N$ phase transitions which deserves further investigation. Finally, our work provides an example where the properties of a black hole coupled to an external system can be analyzed precisely.Comment: 51 pages + appendices, 7 figure

Are Large Language Models Good Fact Checkers: A Preliminary Study

Recently, Large Language Models (LLMs) have drawn significant attention due to their outstanding reasoning capabilities and extensive knowledge repository, positioning them as superior in handling various natural language processing tasks compared to other language models. In this paper, we present a preliminary investigation into the potential of LLMs in fact-checking. This study aims to comprehensively evaluate various LLMs in tackling specific fact-checking subtasks, systematically evaluating their capabilities, and conducting a comparative analysis of their performance against pre-trained and state-of-the-art low-parameter models. Experiments demonstrate that LLMs achieve competitive performance compared to other small models in most scenarios. However, they encounter challenges in effectively handling Chinese fact verification and the entirety of the fact-checking pipeline due to language inconsistencies and hallucinations. These findings underscore the need for further exploration and research to enhance the proficiency of LLMs as reliable fact-checkers, unveiling the potential capability of LLMs and the possible challenges in fact-checking tasks

Co-benefits of the National Key Ecological Function Areas in China for carbon sequestration and environmental quality

IntroductionThe National Key Ecological Functional Areas (NKEFAs) are location-oriented ecological engineering of China, which rely on the main functional area planning. The co-benefits of ecological product supply and ecological environment improvement of NKEFAs has not been fully assessed in the literature.MethodsNKEFAs is considered a quasi-natural experiment, and the time-varying difference-in-differences (DID) model is used to assess the impact of NKEFAs on carbon sequestration (CS) and environmental quality (EQ) based on the panel data of 330 cities in China from 2001 to 2019. Then, we explore whether the co-benefits of ecological product supply and eco-environment protection can be achieved.Results and discussionNKEFAs can enhance CS and EQ and thus achieve co-benefits for both. NKEFAs can achieve the co-benefits of CS and EQ through territory spatial allocation and labor force aggregation, but industrial structure upgrading only positively mediates the impact of NKEFAs on CS. The co-benefits of CS and EQ are heterogeneous across functional area types, geospatial locations, and quantiles, while only CS at windbreak-sand fixation area, northwestern region, and low quantile regions is enhanced. This study makes a theoretical and methodological contribution to the existing literature on the policy effect assessment of ecological engineering. It also provides a comprehensive framework for evaluating the ecological effects of relevant policies in other countries by integrating the co-benefits of ecological products and eco-environment, analyzing regional heterogeneity, and exploring the underlying mechanisms

PAHs in the North Atlantic Ocean and the Arctic Ocean: Spatial Distribution and Water Mass Transport

In the Arctic Ocean, it is still unclear what role oceanic transport plays in the fate of semivolatile organic compounds. The strong-stratified Arctic Ocean undergoes complex inputs and outputs of polycyclic aromatic hydrocarbons (PAHs) from the neighboring oceans and continents. To better understand PAHs’ transport processes and their contribution to high-latitude oceans, surface seawater, and water column, samples were collected from the North Atlantic Ocean and the Arctic Ocean in 2012. The spatial distribution of dissolved PAHs (∑9PAH) in surface seawater showed an “Arctic Shelf \u3e Atlantic Ocean \u3e Arctic Basin” pattern, with a range of 0.3–10.2 ng L−1. Positive matrix factorization modeling results suggested that vehicle emissions and biomass combustion were the major PAHs sources in the surface seawater. According to principal component analysis, PAHs in different water masses showed unique profiles indicating their different origins. Carried by the Norwegian Atlantic Current (0–800 m) and East Greenland Current (0–300 m), PAH individuals’ net transport mass fluxes ranged from −4.4 ± 1.7 to 53 ± 39 tons year−1 to the Arctic Ocean. We suggested the limited contribution of ocean currents on PAHs’ delivery to the Arctic Ocean, but their role in modulating PAHs’ air–sea interactions and other biogeochemical processes needs further studies

AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data

Recently, the utilization of extensive open-sourced text data has significantly advanced the performance of text-based large language models (LLMs). However, the use of in-the-wild large-scale speech data in the speech technology community remains constrained. One reason for this limitation is that a considerable amount of the publicly available speech data is compromised by background noise, speech overlapping, lack of speech segmentation information, missing speaker labels, and incomplete transcriptions, which can largely hinder their usefulness. On the other hand, human annotation of speech data is both time-consuming and costly. To address this issue, we introduce an automatic in-the-wild speech data preprocessing framework (AutoPrep) in this paper, which is designed to enhance speech quality, generate speaker labels, and produce transcriptions automatically. The proposed AutoPrep framework comprises six components: speech enhancement, speech segmentation, speaker clustering, target speech extraction, quality filtering and automatic speech recognition. Experiments conducted on the open-sourced WenetSpeech and our self-collected AutoPrepWild corpora demonstrate that the proposed AutoPrep framework can generate preprocessed data with similar DNSMOS and PDNSMOS scores compared to several open-sourced TTS datasets. The corresponding TTS system can achieve up to 0.68 in-domain speaker similarity