Abstract Educational researchers and policymakers around the world have a strong interest in understanding the underlying reasons for the remarkable academic achievement of Chinese students in the Programme for International Student Assessment (PISA). Although teachers have a significant impact on student achievement, empirical evidence on teaching effectiveness in the Chinese education system has been scarce. This study uses the PISA 2012 Shanghai-China data and employs both multilevel models and quantile regression models to investigate effective teaching factors and their differential effects for students at different mathematics achievement levels. The results reveal the importance of cognitive activation and disciplinary climate as consistent, significant, positive predictors of secondary-school students' mathematics achievement in Shanghai, China. In addition, the positive relationships were more pronounced for low achievers. In contrast, student orientation and formative assessment demonstrate significant negative associations with mathematics achievement. Moreover, for student orientation, the negative association was larger for lower achievers whereas for formative assessment, the negative association was consistent except for very low or very high achievers.

Abstract Similarly to other abalone species, the ormer ( Haliotis tuberculata ), considered a delicacy, faced multiple anthropogenic pressures, including overfishing from legal and illegal harvesting. While the ormer is extensively studied in aquaculture, limited information has been available on the status of the wild population H. tuberculata in France since the Vibryo harveyi pandemic occurring in the late 1990s. Such a lack of data may contribute in the gradual shift in the perception of stock changes, known as the Shifting Baseling Syndrome (SBS). To address this gap, we combined historical monitoring data collected in the 1980s in the Emerald Coast (North Brittany; France) with recent participatory science diving surveys conducted by a local NGO in the same area. Using quantile regression and mixed models, we investigated changes in density and size structure over three decades, and further explored the ecology of H. tuberculata . Results showed a size-depth relationship, suggesting an age-related vertical distribution. Models indicated no change in density between the two periods, highlighting population recovery from the pandemic that cause high mortality in the region. However, mean individual size declined by approximately 2 cm compared to the 1980s, mainly related to the decline of individuals larger than the legal catch size (9 cm). Such changes could potentially result from anthropogenic pressures including overfishing, ocean warming or acidification. These preliminary findings highlight the interest of combining participatory science initiative along with historical records to tackle shift baseline syndrome, and to inform conservation and management strategies for overlooked and exploited marine species.

Abstract South Africa is one of the countries in the Global South that has been severely affected by the COVID-19 pandemic due to its preexisting socioeconomic challenges, including high unemployment, inequality, and widespread poverty. However, the government implemented social assistance programs, such as the COVID-19 Social Relief of Distress (SRD) grant, to address this crisis, specifically among working adults experiencing economic hardship. This study examines the effect of the COVID-19 SRD grant on household income and expenditure patterns from 2020 to 2023, employing fixed effects (FE) and quantile regression models. The results reveal a significant negative relationship between the SRD grant and total household income. Quantile regression further demonstrates that the grant’s impact is most pronounced among the poorest households underscoring its effectiveness in targeting low-income groups. In terms of expenditure, the grant significantly increases spending on basic necessities (lowest expenditure category) while reducing discretionary spending (median expenditure category), reflecting households’ prioritization of essential needs. These findings highlight the dual role of the SRD grant as a critical safety net for immediate relief and a mechanism for reallocating household resources toward essential goods. However, the negative effect on total household income suggests that the grant alone is insufficient to address structural economic challenges. This study provides critical insights for policymakers, emphasizing the need for integrated social protection strategies that combine immediate relief with sustainable livelihoods and economic recovery.

This study presents a comprehensive analysis of the impact of the intensity of research and development (R&D) costs on the financial performance of Russian oil and gas companies, including in the context of external sanctions pressure. To conduct an empirical analysis, a data panel was created covering 112 companies in the industry for the period from 2017 to 2023. For the econometric assessment, an improved two-step model based on the CDM approach (Crépon – Duguet – Mairesse) [1] is used, which allows solving the problem of endogeneity. At the first stage, the key determinants of the intensity of R&D costs, including return on assets, company size, and debt burden, are determined using a fixed-effect panel regression. At the second stage, the R&D intensity values predicted at the first step are used as an independent variable in the quantile regression model. This method allows us to analyze the impact of investments in innovation on the gross margin of companies with different levels of profitability (different distribution quantiles) and with time lags from 1 to 3 years. The results obtained demonstrate that an increase in the intensity of R&D costs has a statistically significant and positive impact on the financial performance of oil and gas companies within a year after investment, especially for firms with medium and high profitability. However, this effect does not persist in the medium term (with lags of 2 and 3 years). Such a rapid but short-term financial return indicates that until recently, R&D funds have been mainly used to purchase and implement ready-made imported technological solutions, rather than to create companies’ own breakthrough technologies. In addition, it was discovered that the inclusion of a company in the list of sanctioned entities is statistically significant and has a positive effect on its financial performance in the short term in certain groups in terms of profitability. The article makes up for the lack of empirical research on the financial impact of R&D in the domestic economy and highlights the vulnerability of the current innovation model of the sector.

Impaired kidney function has been linked to altered concentrations of blood biomarkers of Alzheimer disease (AD), but the underlying mechanisms and its potential role in dementia development remain poorly understood. We explored the associations between estimated glomerular filtration rate (eGFR), blood-based biomarkers of AD, and dementia development. Data were extracted from the Swedish National Study on Aging and Care in Kungsholmen, an ongoing longitudinal population-based study. Kidney function was assessed using eGFR based on serum creatinine. AD biomarkers (amyloid beta [Aβ42/40], phosphorylated tau [p-tau181 and p-tau217] and total tau [t-tau] proteins, neurofilament light chain [NfL], and glial fibrillary acidic protein [GFAP]) were measured from peripheral blood samples using the Simoa platform. Dementia was diagnosed according to DSM-IV criteria. Quantile regression models assessed the cross-sectional associations between eGFR and AD biomarkers; Cox regression models were used to examine the association of kidney function and biomarkers with incident dementia. At baseline, 2,279 dementia-free participants with available blood samples were included (median age 72 (interquartile range, 61-81) years; 62% female). Lower eGFR was associated with higher median z-score levels of all examined AD blood biomarkers, except Aβ42/40, following a nonlinear relationship. At eGFR = 30 mL/min/1.73 m2, estimated differences were as follows: p-tau181: β, 0.22 [95% CI 0.09-0.35]; p-tau217: β, 0.20 [95% CI 0.10-0.31]; t-tau: β, 0.24 [95% CI 0.05-0.42]; NfL: β, 0.88 [95% CI 0.80-0.95]; GFAP: β, 0.10 [95% CI 0.03-0.16]. During a mean follow-up period of 8.3 (SD, 4.3) years, 362 participants developed dementia. In multivariable-adjusted models, impaired kidney function (eGFR < 60 mL/min/1.73 m2) was not associated with an increased hazard of dementia compared with preserved kidney function (eGFR ≥ 60 mL/min/1.73 m2) (hazard ratio [HR], 0.93 [95% CI 0.72-1.21]). The relationship between increased (high vs low) NfL and dementia was stronger among individuals with impaired (vs preserved) kidney function (HR, 3.85 [95% CI 1.87-7.95] vs HR, 1.84 [95% CI 1.34-2.53], respectively). Impaired kidney function was associated with elevated circulating level of most AD blood biomarkers. However, the presence of impaired kidney function did not independently increase the risk of dementia but rather seemed to accelerate the clinical expression of underlying neurodegenerative pathology.

ABSTRACT Detecting heterogeneity across latent subgroups is essential for understanding complex data structures in fields such as economics, medicine and social sciences. Threshold quantile regression (TQR) with change‐plane structures provides a flexible framework for such heterogeneity, especially in the presence of heavy‐tailed errors. However, a key challenge in subgroup detection lies in the nonidentifiability of threshold parameters under the null hypothesis, which invalidates classical testing approaches. To address this, we propose a difference of quantile loss test (DQLT) that constructs a supremum‐type test statistic based on differences in quantile loss functions. We derive the asymptotic distributions of the proposed test statistic under both the null and local alternative hypotheses and a bootstrap procedure is introduced to compute ‐values, effectively. The performance of our method is evaluated through extensive simulation studies, demonstrating well‐controlled type‐I error under the null hypothesis and enhanced power under the alternative hypotheses. We further apply the DQLT method to CHARLS 2015 and Boston housing datasets, illustrating its effectiveness in identifying subgroups with heterogeneous covariate effects.

A robust cross-tissue DNA methylation model for forensic age estimation from oral samples.

Stratafix vs Vicryl suture for hysterotomy closure in scheduled cesarean deliveries: a randomized clinical trial.

Emotional distress in adolescents in 2018 and 2022: a comparison of cross-sectional national probabilistic samples from six countries.

Background Managing chronic illness effectively depends not only on treatment availability but also on patients’ ability to adhere to prescribed medications. Objectives This study examined the factors influencing medication adherence among Jordanian adults with long-term conditions, using both traditional regression and machine learning methods. Method In this cross-sectional study, patients with chronic conditions completed an online survey that assessed demographic, clinical and behavioural variables, including Health Literacy Questionnaire (HLQ-12) and adherence (MARS-5). Quantile regression and machine learning models were applied. Results A total of 981 patients (63.1% females) were enrolled in the study. Quantile regression showed that higher health literacy, a diagnosis of diabetes or cardiovascular disease, and fewer prescribed medications were positively associated with adherence. In contrast, being married or having public, military or no insurance was linked to lower adherence scores. The Random Forest model achieved the highest predictive accuracy (R 2 = 0.38), and SHAP analysis identified health literacy, disease duration and age as the most influential features. Conclusions These findings highlight the need for targeted interventions that address both individual understanding and structural challenges, such as insurance type and treatment complexity. Improving health literacy, simplifying medication regimens, and ensuring equitable healthcare access may help support better adherence in this population. The use of explainable machine learning, alongside conventional statistical approaches, offers new opportunities to improve the understanding and prediction of adherence behaviours in resource-constrained settings.

Environmental degradation significantly hampers economic sustainability by causing severe economic problems, prompting researchers to focus on environmental sustainability. Global economies aim to achieve net-zero emissions by 2060 to address climate change issues. Achieving this by 2050 depends on the transition from fossil fuels to renewable energy. Recently, the circular economy (CE) has emerged as a key concept for expediting sustainability. Prior research has emphasized CE practices in the corporate sector, overlooking the public sector. This study uses panel IFE and D-CCE tests to analyze the impact of CE and public management on CO2 emissions in E-7 economies from 2000 to 2023, considering GDP per capita, urbanization, digital technologies, and energy transition. It examines long-term cointegration and heterogeneity among variables and employs a panel quantile regression (PQR) model to validate the findings. The results show that digital technology, CE, and public management significantly reduce carbon emissions, while GDP per capita, urbanization, and energy transition are crucial for long-term CO2 emissions. Further analysis indicates that public management mediates the negative impact of digital technology, CE, and energy transition on carbon emissions in E-7 economies. These findings provide policymakers with insights into managing CE, energy transition, digital technology, and public administration to reduce CO2 emissions without hindering economic growth and sustainable development. The practical implications concerning carbon neutrality and structural transitions are also discussed.

Oral participation in class reflects academic engagement, but decreases when faced with challenging content such as statistics, which is often linked to student anxiety. This study modeled the relationship between anxiety about asking for help in statistics class and oral participation, considering the role of affective learning toward the content and the teacher, participation grades, and sociodemographic variables. Using quantile regression models, 721 Colombian students (383 women) in secondary school (42.7%) and high school (57.3%) were evaluated. The results showed that anxiety inhibits oral participation, while affection for the subject content stimulates it. Although no explicit moderating effects were found, the impact of anxiety varied according to the level of affect for the content. Grades did not encourage participation, which did differ according to gender and age. The findings highlight the importance of designing pedagogical strategies that include affective learning to reduce statistical anxiety and encourage class participation.

This study investigates the heterogeneous impact of new quality productivity (NQP) on firm resilience from a factor intensity perspective-a critical gap in the literature on transitioning economies. Utilizing a panel dataset of 11,345 A-share listed firms from 2017-2023, we employ fixed-effects and quantile regression models to test the differential effects of NQP across industries. Results reveal that NQP primarily enhances firm resilience through improved rebound capacity (e.g., solvency and ROE), demonstrating limited effects on asset returns, thereby challenging the conventional view of NQP as a uniform driver of resilience. We identify significant industrial heterogeneity: the effect is strongest in technology-intensive sectors, moderate in labor-intensive sectors, and non-significant in asset-intensive sectors, likely due to external commodity price volatility. These findings highlight the necessity of factor-specific interventions, such as digital infrastructure for technology firms and workforce training for labor-intensive industries. This study contributes to the field by bridging the micro-macro divide in productivity literature and offers actionable insights for policymakers and firms to tailor resilience strategies based on factor endowments.

With the deepening of carbon peak and carbon neutrality (“dual carbon”) initiatives, corporate responsibility for environmental information disclosure has become imperative. However, due to imperfect laws and regulations, companies may have incentives to over-disclose environmental information, which could trigger stock price crashes. This study investigates the behavior of excessive environmental information disclosure among A-share listed companies in China. Using a sample of A-share firms that published social responsibility reports from 2015 to 2023, we employ threshold effect and quantile regression models to verify the presence of “greenwashing” components in environmental textual disclosures. A panel fixed-effects model is further adopted to examine the potential impact of excessive environmental information disclosure on stock price crash risk. The findings reveal that corporate environmental disclosures contain non-substantive, embellished content-indicative of greenwashing-and that such behavior significantly exacerbates stock price crash risk, particularly in manufacturing industries. The mechanism lies in the fact that excessive textual disclosure reduces information quality and transparency, thereby amplifying irrational investment behaviors. Conversely, effective environmental disclosure practices are shown to mitigate crash risk. Further analysis demonstrates that reducing ownership concentration, increasing managerial shareholding, and enhancing the role of independent directors in corporate governance can improve the quality of environmental disclosure and curb over-disclosure. This study provides a novel analytical perspective on environmental textual disclosure and offers practical insights for guiding rational investor decision-making.

This paper examines the impact of e-commerce adoption on earnings and incomedistribution among rural agricultural employers in Indonesia, both during and after the COVID19 pandemic. Using microdata from the National Labour Force Survey/Sakernas (2018–2024)and applying probit, OLS, Propensity Score Matching, and quantile regression models, weidentify the determinants of adoption and its impact on earnings. Adoption was strongly drivenby education, training, and enterprise characteristics, while older age and reliance on unpaidhousehold labor constrained uptake. Results show that e-commerce adopters earned substantiallyhigher than non-adopters (more than 30 percent) both during and after the pandemic, confirmingsustained income gains beyond the crisis. Quantile regressions reveal that the lowest-incomeemployers benefited most, with earnings gains exceeding 50 percent at the bottom quantileduring the pandemic. Although relative advantages shifted toward higher earners after thepandemic, large and significant effects remained for the lowest-income groups. These findingsindicate that e-commerce not only enhances market access but also contributes to improvingincome distribution. Policy interventions to strengthen digital literacy, rural infrastructure, andfinancial access are essential to preserve its inclusive role and ensure that vulnerable agriculturalemployers continue to benefit disproportionately.

Nitrates in drinking water, common in agricultural states like Iowa, pose health risks to young infants. This brief first reviews the plausible biological mechanisms linking nitrate exposure to brain and central nervous system (BCNS) cancers in the first year of life. We then linked historic water quality and cancer registry data at the county level to construct linear and quantile regression models estimating the association between a BCNS diagnosis and nitrate measures. We found significantly higher nitrate levels preceding pediatric zero-year-old BCNS cancer diagnoses. These findings support investigating early-life nitrate exposure as a potential risk factor for infant BCNS tumors.

ABSTRACT It has become imperative to revisit the growing concern raised by financial inclusion and environmental issues globally, especially within low‐ and middle‐income economies such as the Sub‐Saharan African (SSA) region. Hence, this study aims to examine the impact of financial inclusion on environmental sustainability in SSA countries. The data was collected for the period between 1990 and 2023 and analyzed using the CS‐ARDL estimator, while the quantile regression model estimator served the purpose of robustness check. The findings revealed that financial institutions and access to financial markets worsen environmental sustainability in SSA countries. This suggests that financial inclusion can lead to ecological degradation. Again, the results indicate that population increase, energy use, and economic growth continue to be the major causes of ecological deterioration in SSA. However, renewable energy consumption promotes environmental sustainability in both the long and short term. The study recommends that SSA countries to drive a sustainable environment with increasing financial inclusion in the region is to integrate green finance into the financial systems, which means that financial institutions must launch credit products based on sustainability, promote green financing, sustainable investments, establish a stable regulatory framework for sustainable finance, promote family planning, sustainable urbanization policies, and comprehensive educational programs with the gains of solving attendant environmental issues within the region.

Ordinary linear regression is the most common approach for modeling relationships between continuous outcomes and explanatory variables in epidemiological research. However, this method relies on restrictive assumptions-normality, homoscedasticity, and linearity-that are often violated in real-world biomedical data. When these assumptions fail, mean-based estimates may obscure important heterogeneity across the outcome distribution. This study aims to illustrate the methodological and interpretive advantages of quantile regression over ordinary regression in the analysis of epidemiological data. Secondary data were derived from a cross-sectional study of 1415 healthy Greek adults aged 25-82 years. Body mass index (BMI) served as the outcome variable, while sex, age, physical activity, dieting status, and daily energy intake were considered predictors. Both ordinary and quantile regression models were applied to estimate associations between BMI and its determinants across the 25th, 50th, 75th, and 90th quantiles. Ordinary regression identified positive associations of BMI with age and energy intake and a negative association with physical activity. Quantile regression revealed that these relationships were not constant across the BMI distribution. The inverse association with physical activity intensified at higher quantiles, and the gender effect reversed direction at the upper tail, suggesting heterogeneity was not captured by mean-based models. Quantile regression provides a distribution-sensitive alternative to ordinary regression, offering insight into covariate effects across different points of the outcome distribution and serving as both a robust analytical tool and an educational framework for applied epidemiological research.

Background: Metabolic syndrome (MetS) is linked to brain degeneration and Alzheimer’s disease (AD). Women, especially during menopausal transition, show increased susceptibility to AD-related brain changes. This study investigated the sex-specific neurostructural impact of MetS on brain regions vulnerable to AD. Methods: This cross-sectional study analyzed data from 500 participants (303 women, 197 men) from the Baependi Heart Study cohort, Brazil. High-resolution T1-weighted MRI scans were used for volumetric analysis of AD-related regions of interest (ROIs). Non-parametric quantile regression models compared ROI volumes between MetS and Non-MetS groups, stratified by sex and age (median split), adjusting for age and education. Results: No significant differences in ROI volume were observed between the MetS and Non-MetS groups in men. In women, findings were age-dependent. The younger cohort (≤48 years) with MetS exhibited significantly smaller left hippocampal volume (p = 0.02) and a trend toward smaller left middle temporal gyrus volume (p = 0.05) compared to Non-MetS. The older cohort (>48 years) with MetS showed a significantly larger right amygdala volume (p < 0.001). Furthermore, age-related volume decline in the hippocampus and middle temporal gyrus was significant in Non-MetS women but not in women with MetS, suggesting that MetS may be a confounding factor in age-related neurodegeneration. Conclusions: MetS is associated with sex-specific alterations in AD-vulnerable brain structures. In women, MetS may influence medial temporal lobe atrophy pre-menopause, and is linked to amygdala enlargement post-menopause. These exploratory results generate the hypothesis that MetS may uniquely predispose women to AD-related neurodegeneration, which requires critical longitudinal confirmation.

Patterns of care and outcomes following external ventricular drain placement: Insights from the England HES administrative data set