Dynamic asymmetric spillovers and connectedness between Chinese sectoral commodities and industry stock markets.

This study investigates the dynamic and asymmetric propagation of return spillovers between sectoral commodities and industry stock markets in China. Using a daily dataset from February 2007 to July 2022, we employ a time-varying vector autoregressive (TVP-VAR) model to examine the asymmetric return spillovers and dynamic connectedness across sectors. The results reveal significant time-varying spillovers among these sectors, with the industry stocks acting as the primary transmitter of information to the commodity market.

Income composition inequality of Chinese residents and fiscal redistribution effect: An empirical analysis on individual income tax and transfer system.

Based on the data of China Family Panel Studies (CFPS), this paper decomposed Chinese residents' income into labor income and capital income by income source, and measured the income inequality and income composition inequality of Chinese residents during 2010-2018. We take the Gini coefficient as a measure of inequality and, by decomposing it by income source, analyze the absolute and relative marginal effects of capital income and labor income on the overall income inequality. On this basis, this paper discusses the redistributive effect of financial instruments such as personal income tax and transfer payment on income inequality and income composition inequality.

Mpox patients' experience from infection to treatment and implications for prevention and control: A multicenter qualitative study in China.

Monkeypox (mpox), a viral zoonotic disease, is spreading worldwide. However, evidence that informs prevention and control strategies in the Asia Pacific Region is very limited. Our study aims to investigate the experiences of mpox patients from infection to treatment to provide scientific basis for the prevention and control.

Prepubescent Electronic Cigarette Exposure Affects Sexual Motivation and Puberty in Female But Not Male Long-Evans Rats.

Introduction: A method for delivering vaporized nicotine to animals has been developed using e-cigarette devices. The present experiment was designed to measure the effects of e-cigarette nicotine on pubertal onset and development of reproductive behavior in female and male Long-Evans rats.

Aim And Methods: Rats received daily 10-min sessions of electronic-cigarette vaporized nicotine (5% Virginia Tobacco JUUL Pods) or room air in a whole-body exposure chamber (postnatal day 28-31).

The spatiotemporal pattern of surface ozone and its impact on agricultural productivity in China.

The slowing of agricultural productivity growth globally over the past two decades has brought a new urgency to detect its drivers and potential solutions. We show that air pollution, particularly surface ozone (O), is strongly associated with declining agricultural total factor productivity (TFP) in China. We employ machine learning algorithms to generate estimates of high-resolution surface O concentrations from 2002 to 2019.

How to measure earnings surprises: Based on revised market reaction.

We investigate the robustness of earnings surprise measures in the context of a revised market reaction. While existing literature suggests that financial anomalies may distort cumulative abnormal returns (CAR) during annual announcements, our research proves that a revised market reaction offers a more accurate reflection of investor reactions to earnings correction. Specifically, we introduce an innovative adjustment to CAR using stock price jumps, and prove that the fraction of misses on the same side (FOM) provides a superior measure of earnings surprises.

Public perceptions of air pollution and its impacts on fertility desire: a nationwide study in China.

Over the past few years, there has been a significant focus on air pollution due to its various detrimental effects on human health. However, its influence on people's tendency to have children remains uncertain, as only a few studies have examined the correlation between public perception of air pollution and the desire to start a family. This article introduces a theoretical framework utilizing a two-stage interval iteration model to explore the connection between children's relative utility and the perception of air pollution.

[Identification of Q-markers for Schisandrae Sphenantherae Fructus in treating drug-induced liver injury based on network pharmacology, fingerprint and quantitative analysis].

This study aims to establish the ultra-performance liquid chromatography(UPLC) fingerprint and multi-indicator quantitative analysis method for Schisandrae Sphenantherae Fructus(SSF) and to screen out the potential quality markers(Q-markers) of hepatoprotection based on network pharmacology. The similarity analysis was performed using the Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System, which showed that the similarity of the fingerprints of 15 samples from different regions ranged from 0.981 to 0.

Community engagement and loneliness in older adults in China: mediation effects of social support in the wake of COVID-19.

Introduction: Loneliness is a key indicator of well-being in older adults. Drawing from the ecological model of aging, the active aging perspective, and the convoy model of social relations, this study investigates the extent community engagement influences loneliness and whether the relationship is mediated by social support during the COVID-19 pandemic.

Methods: Data was collected from 1,067 retired older adults in a cross-sectional design in Chengdu, China in 2022.

Toward a low carbon path: Do E-commerce reduce CO emissions? Evidence from China.

To address global climate change, achieving carbon peak and carbon neutrality has become a global consensus. However, the means to simultaneously achieve carbon reduction and promote green economic development, particularly in developing countries, require further investigation. This study evaluates the impact of e-commerce on CO emissions.

Generalized robust loss functions for machine learning.

Loss function is a critical component of machine learning. Some robust loss functions are proposed to mitigate the adverse effects caused by noise. However, they still face many challenges.

How does early-life famine experience influence household energy transition?

The transition of households towards cleaner energy is crucial for achieving sustainable development goals. However, the impacts and associated mechanisms of early-life experiences on household energy transition have not been considered. Based on data from the 2015 Chinese General Social Survey, this study aimed to investigate whether people experiencing China's Great Famine (1959-1961) in their early life promoted household energy transition in adulthood.

Robust meta gradient learning for high-dimensional data with noisy-label ignorance.

Large datasets with noisy labels and high dimensions have become increasingly prevalent in industry. These datasets often contain errors or inconsistencies in the assigned labels and introduce a vast number of predictive variables. Such issues frequently arise in real-world scenarios due to uncertainties or human errors during data collection and annotation processes.

Psychological constraint on unethical behavior in team-based competition.

A growing body of research contributes to our knowledge about unethical behavior. However, very little is known about how group-based competition shape members' unethical behavior. Building on social learning theory, we conducted three studies to reveal how group-based competition may affect individual's unethical behavior for their team.

A Single 10-Minute E-cigarette Vapor Exposure Reduces Tidal Volume and Minute Ventilation in Normoxia and Normobaric Hypoxia in Adult Rats.

The present study investigated the effects of a single 10-minute exposure to e-cigarette vapor on ventilation in adult male Long-Evans rats. Ventilation was recorded using awake, unrestrained whole-body plethysmography. Baseline recordings were taken the day before full-body exposure to either room air (n = 9; air control group) or e-cigarette vapor (n = 9; treatment group).

The impact of Public Health Emergency (PHE) on the news dissemination strength: Evidence from Chinese-Speaking Vloggers on YouTube.

News dissemination plays a vital role in supporting people to incorporate beneficial actions during public health emergencies, thereby significantly reducing the adverse influences of events. Based on big data from YouTube, this research study takes the declaration of COVID-19 National Public Health Emergency (PHE) as the event impact and employs a DiD model to investigate the effect of PHE on the news dissemination strength of relevant videos. The study findings indicate that the views, comments, and likes on relevant videos significantly increased during the COVID-19 public health emergency.

Effectiveness of a government-led, multiarm intervention on early childhood development and caregiver mental health: a study protocol for a factorial cluster-randomised trial in rural China.

Introduction: The high incidences of both the developmental delay among young children and the mental health problems of their caregivers are major threats to in low-income and middle-income countries. Parental training interventions during early childhood have been shown to benefit early development, yet evidence on strategies to promote caregiver mental health remains limited. In addition, evidence on the optimal design of scalable interventions that integrate early child development and maternal mental health components is scarce.

Synergistic integration of digital twins and sustainable industrial internet of things for new generation energy investments.

Introduction: This study aims to identify optimal digital twin policies for enhancing renewable energy projects. Through a comprehensive analysis, the research evaluates the potential of digital twins in the renewable energy sector while considering triple bottom line perspectives.

Objectives: The study's main goal is to prioritize digital twin policies that can effectively boost renewable energy projects.

A Framework for Enhancing Stock Investment Performance by Predicting Important Trading Points with Return-Adaptive Piecewise Linear Representation and Batch Attention Multi-Scale Convolutional Recurrent Neural Network.

Efficient stock status analysis and forecasting are important for stock market participants to be able to improve returns and reduce associated risks. However, stock market data are replete with noise and randomness, rendering the task of attaining precise price predictions arduous. Moreover, the lagging phenomenon of price prediction makes it hard for the corresponding trading strategy to capture the turning points, resulting in lower investment returns.