Facilitators of and barriers to County Behavioral Health System Transformation and Innovation: an interview study.

BACKGROUND: Inadequate and inequitable access to quality behavioral health services and high costs within the mental health systems are long-standing problems. System-level (e.g., fee-for-service payment model, lack of a universal payor) and individual factors (e.g., lack of knowledge of existing resources) contribute to difficulties in accessing resources and services. Patients are underserved in County behavioral health systems in the United States. Orange County's (California) Behavioral Health System Transformation project sought to improve access by addressing two parts of their system: developing a template for value-based contracts that promote payor-agnostic care (Part 1); developing a digital platform to support resource navigation (Part 2). Our aim was to evaluate facilitators of and barriers to each of these system changes. METHODS: We collected interview data from County or health care agency leaders, contracted partners, and community stakeholders. Themes were informed by the Consolidated Framework for Implementation Research. RESULTS: Five themes were identified related to behavioral health system transformation, including 1) aligning goals and values, 2) addressing fit, 3) fostering engagement and partnership, 4) being aware of implementation contexts, and 5) promoting communication. A lack of fit into incentive structures and changing state guidelines and priorities were barriers to contract development. Involving diverse communities to inform design and content facilitated the process of developing digital tools. CONCLUSIONS: The study highlights the multifaceted factors that help facilitate or hinder behavioral health system transformation, such as the need for addressing systematic and process behaviors, leveraging the knowledge of leadership and community stakeholders, fostering collaboration, and adapting to implementation contexts.

Establishment of pregnancy-specific lipid reference intervals in pregnant women in a single-centre and assessment of the predictive value of early lipids for gestational diabetes mellitus: a prospective cohort study.

INTRODUCTION: This study was aimed at establishing a pregnancy-specific lipid reference interval (RI) in pregnant women in a single-centre in the Beijing area of China, simultaneously exploring the predictive value of lipid levels in early pregnancy for gestational diabetes mellitus (GDM). MATERIAL AND METHODS: From October 2017 to August 2019, Peking University International Hospital established records for 1588 pregnant women, whose lipid profiles were determined during the first and third trimesters. The Hoffmann technique was used to calculate gestation-specific lipid RI. The 95% reference range for gestational lipids was also estimated for 509 healthy pregnant women screened according to the Clinical and Laboratory Standards Institute guideline. Multivariate logistic regression analysis was used to calculate odds ratios (OR) and their 95% confidence interval (CI), and the receiver operating characteristic (ROC) curve was applied to assess the predictive value of lipids in the first trimester for the diagnosis of GDM. RESULTS: Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels were significantly higher in the third trimester (p < 0.05). Hoffmann technique RI of the lipid profiles and the 95% reference range of the lipid profiles in healthy pregnant women did not differ statistically (p > 0.05). TC, TG, and LDL-C levels were higher in the GDM group in the first trimester (p < 0.05), and the risk of GDM was 2.1 times higher in women with higher TG (95% CI: 1.13-3.77, p < 0.05). The optimal ROC cut-off for TG to predict GDM was 2.375 mmol / L, and the area under the ROC curve was 0.622 (95% CI: 0.592-0.751), with a sensitivity of 73.7% and a specificity of 59.3%. CONCLUSIONS: This study established pregnancy-specific lipid RI for pregnant women in a single centre in the Beijing area of China. Pregnant women with TG ≥ 2.375 mmol/L in the first trimester were at significantly increased risk for GDM.

Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration.

This study delves into the potential therapeutic benefits of Fufang Sanling Granules for kidney cancer, focusing on their active components and the underlying mechanisms of their interaction with cancer-related targets. By constructing a drug-active component-target network based on eight herbs, key active compounds such as kaempferol, quercetin, and linolenic acid were identified, suggesting their pivotal roles in modulating immune responses and cellular signaling pathways relevant to cancer progression. The research further identified 51 central drug-disease genes through comprehensive bioinformatics analyses, implicating their involvement in crucial biological processes and pathways. A novel risk score model, encompassing six genes with significant prognostic value for renal cancer, was established and validated, showcasing its effectiveness in predicting patient outcomes through mutation analysis and survival studies. The model's predictive power was further confirmed by its ability to stratify patients into distinct risk groups with significant survival differences, highlighting its potential as a prognostic tool. Additionally, the study explored the relationship between gene expression within the identified black module and the risk score, uncovering significant associations with the extracellular matrix and immune infiltration patterns. This reveals the complex interplay between the tumor microenvironment and cancer progression. The integration of the risk score with clinical parameters through a nomogram significantly improved the model's predictive accuracy, offering a more comprehensive tool for predicting kidney cancer prognosis. In summary, by combining detailed molecular analyses with clinical insights, this study presents a robust framework for understanding the therapeutic potential of Fufang Sanling Granules in kidney cancer. It not only sheds light on the active components and their interactions with cancer-related genes but also introduces a reliable risk score model, paving the way for personalized treatment strategies and improved patient management in the future.

Comprehensive risk assessment of typhoon disasters in China's coastal areas based on multi-source geographic big data.

Typhoons can bring substantial casualties and economic ramifications, and effective prevention strategies necessitate a comprehensive risk assessment. Nevertheless, existing studies on its comprehensive risk assessment are characterized by coarse spatial scales, limited incorporation of geographic big data, and rarely considering disaster mitigation capacity. To address these problems, this study combined multi-source geographic big data to develop the Comprehensive Risk Assessment Model (CRAM). The model integrated 17 indicators from 4 categories of factors, including exposure, vulnerability, hazard, and mitigation capacity. A subjective-objective combination weighting method was introduced to generate the indicator weights, and comprehensive risk index of typhoon disasters was calculated for 987 counties along China's coastal regions. Results revealed a pronounced spatial heterogeneity of the comprehensive typhoon risk, which exhibited an overall decreasing trend from the southeast coastal areas toward the northwest inland territories. 61.7 % of the counties exhibited a medium-to-high level of comprehensive risk, and counties with very-high risks are predominantly concentrated in the Shandong Peninsula, Yangtze River Delta, Hokkien Golden Triangle, Greater Bay Area, Leizhou Peninsula, and Hainan Province, mainly due to high exposure and hazard factors. The correlation coefficient between the risk assessment results and typhoon-induced direct economic losses reached 0.702, indicating the effectiveness and reliability of the CRAM. Meanwhile, indicators from intrinsic attributes of typhoons and geographic big data had pronounced importance, and regional mitigation capacity should be improved. Our proposed method can help to scientifically understand spatial patterns of comprehensive risk and mitigate the effects of typhoon disasters in China's coastal regions.

Assessment of brain structure and volume reveals neurodevelopmental abnormalities in preterm infants with low-grade intraventricular hemorrhage.

There is increasing evidence of abnormal neurodevelopmental outcomes in preterm infants with low-grade intraventricular hemorrhage (IVH). The purpose of the study was to explore whether brain microstructure and volume are associated with neuro-behavioral outcomes at 40 weeks corrected gestational age in preterm infants with low-grade IVH. MR imaging at term-equivalent age (TEA) was performed in 25 preterm infants with mild IVH (Papile grading I/II) and 40 control subjects without IVH. These subjects all had neonatal behavioral neurological assessment (NBNA) at 40 weeks' corrected age. Microstructure and volume evaluation of the brain were performed by using diffusion kurtosis imaging (DKI) and Synthetic MRI. Correlations among microstructure parameters, volume, and developmental outcomes were explored by using Spearman's correlation. In preterm infants with low-grade IVH, the volume of brain parenchymal fraction (BPF) was reduced. In addition, mean kurtosis (MK), fractional anisotropy (FA), radial kurtosis (RK), axial kurtosis (AK) in several major brain regions were reduced, while mean diffusivity (MD) was increased (P < 0.05). BPF, RK in the cerebellum, MK in the genu of the corpus callosum, and MK in the thalamus of preterm infants with low-grade IVH were associated with lower NBNA scores (r = 0.831, 0.836, 0.728, 0.772, P < 0.05). DKI and Synthetic MRI can quantitatively evaluate the microstructure alterations and brain volumes in preterm infants with low-grade IVH, which provides clinicians with a more comprehensive and accurate neurobehavioral assessment of preterm infants with low-grade IVH.

Green technology innovation and regional carbon emissions: analysis based on heterogeneous treatment effect modeling.

The impact of green technology innovation on regional carbon emissions has been a contentious issue in academic research. In this study, we attempt to analyze the influence of green technology innovation on regional carbon emissions using panel data from 28 Chinese provinces for the period of 2007-2020. Utilizing a heterogeneous treatment effect model, we systematically examine the effects of green technology innovation on regional carbon emissions. Firstly, we conduct a feature selection analysis on the factors influencing regional carbon emissions using causal inference methods based on machine learning. Subsequently, we explore the conditional and marginal treatment effects of green technology innovation on regional carbon emissions using the heterogeneous treatment effect model. Finally, we investigate the dynamic effects of green technology innovation on regional carbon emissions across different periods. Empirical results indicate that firstly, green technology innovation indirectly reduces regional carbon emissions by promoting energy efficiency improvement; secondly, the impact of green technology innovation on carbon emissions exhibits significant regional heterogeneity, with the largest effect observed in the eastern region, followed by the western region and the smallest effect in the central region; thirdly, at a significance level of 5%, green technology innovation has a direct inhibitory effect on carbon emissions in certain regions.

Go for Economic Transformation and Development in China: Financial Development, Higher Education, and Green Technology Evolution.

Technology innovation is the key driving force in achieving economic transformation and development. Financial development and the expansion of higher education can promote technological progress primarily by easing financing constraints and improving the level of human capital. This study examines the impact of financial development and higher education expansion on green technology innovation. It conducts an empirical analysis by constructing a linear panel model and a nonlinear threshold model. The present study sample is based on the urban panel data of China from 2003-2019. (1) Financial development can significantly promote the expansion of higher education. (2) The expansion of higher education can improve energy and environment-based technological progress. (3) Financial development can both directly and indirectly promote green technology evolution by expanding higher education. The joint financial development and higher education expansion can significantly empower green technology innovation. (4) In the process of promoting green technology innovation, financial development has a non-linear influence on it, with higher education as the threshold. The effect of financial development on green technology innovation varies according to the degree of higher education. Based on these findings, we put forward policy proposals for green technology innovation to promote economic transformation and development in China.

Attention network training promotes selective attention of children with low socioeconomic status.

The objectives of this study were to evaluate the difference of selective attention efficiency between children with low and high socioeconomic status (SES) and the promotional effect of attention network training (an attention network test was used as the training task) on selective attention in children with the low SES. A total of 139 10- to 12-year-old children participated in two experiments (71 in Experiment 1 and 68 in Experiment 2). The results suggest that selective attention and switch ability of children with high SES are better than those of children with low SES. After attention network training, selective attention, switch ability, and working memory of low-SES children improved significantly. The findings provide evidence that attention network training could enhance selective attention in low-SES children and that the beneficial training effect could also transfer to switch ability and working memory. The research may provide a promising method to compensate cognitive delay of low-SES children.

Quantitative Relaxometry Assessment of Brain Microstructural Abnormality of Preschool Children With Autism Spectrum Disorder With Synthetic Magnetic Resonance Imaging.

OBJECTIVE: This study aimed to perform an assessment of brain microstructure in children with autism aged 2 to 5 years using relaxation times acquired by synthetic magnetic resonance imaging. MATERIALS AND METHODS: Thirty-four children with autism spectrum disorder (ASD) (ASD group) and 17 children with global developmental delay (GDD) (GDD group) were enrolled, and synthetic magnetic resonance imaging was performed to obtain T1 and T2 relaxation times. The differences in brain relaxation times between the 2 groups of children were compared, and the correlation between significantly changed T1/T2 and clinical neuropsychological scores in the ASD group was analyzed. RESULTS: Compared with the GDD group, shortened T1 relaxation times in the ASD group were distributed in the genu of corpus callosum (GCC) ( P = 0.003), splenium of corpus callosum ( P = 0.002), and right thalamus (TH) ( P = 0.014), whereas shortened T2 relaxation times in the ASD group were distributed in GCC ( P = 0.011), left parietal white matter ( P = 0.035), and bilateral TH (right, P = 0.014; left, P = 0.016). In the ASD group, the T2 of the left parietal white matter is positively correlated with gross motor (developmental quotient [DQ] 2) and personal-social behavior (DQ5), respectively ( r = 0.377, P = 0.028; r = 0.392, P = 0.022); the T2 of the GCC was positively correlated with DQ5 ( r = 0.404, P = 0.018); and the T2 of the left TH is positively correlated with DQ2 and DQ5, respectively ( r = 0.433, P = 0.009; r = 0.377, P = 0.028). All significantly changed relaxation values were not significantly correlated with Childhood Autism Rating Scale scores. CONCLUSIONS: The shortened relaxometry times in the brain of children with ASD may be associated with the increased myelin content and decreased water content in the brain of children with ASD in comparison with GDD, contributing the understanding of the pathophysiology of ASD. Therefore, the T1 and T2 relaxometry may be used as promising imaging markers for ASD diagnosis.

Government intervention, industrial structure, and energy eco-efficiency: an empirical research on new energy demonstration in cities.

This study investigates the relationships among government intervention, industrial structure, and energy eco-efficiency (EE). Energy eco-efficiency was measured based on a non-radial directional distance function for 236 cities in China from 2005 to 2019. Additionally, the difference-in-difference model (DID) method and spatial econometric models were used to analyse the impact of government intervention and industrial structure on energy eco-efficiency and their spatial spill-over effects. Government intervention includes fiscal expenditures and policy orientation for new energy demonstration construction. Our results indicate that: China's EE has a fluctuating upward trend and increased 17.85% in the period, and its spatial distribution imbalance gradually developed into a regional distribution balance. Moreover, government intervention and adjustment of the industrial structure improved urban energy eco-efficiency by 7.43% and 0.92%, respectively, which also has spatial spill-over effects in neighbouring regions. Furthermore, economic development, technological innovation, and foreign direct investment enable EE. However, urbanisation hinders the improvement of energy eco-efficiency. Finally, heterogeneity analysis showed that the policy of the new energy demonstration city has better effects on eastern and western cities in promoting EE.

The incidence, risk factors, management and prognosis of postoperative systemic complications after ophthalmic surgery: a retrospective study at a tertiary, academic referral hospital over a decade.

PURPOSE: To explore the incidence, risk factors, management and prognosis of systemic complications after ophthalmic surgeries. METHODS: A retrospective review of hospitalized patients undergoing ophthalmic surgeries between 2012 and 2022 at Peking Union Medical College Hospital was performed to summarize and analyse the postoperative systemic complications. Multivariate logistic and linear regression analyses were conducted to clarify the risk factors of postoperative systemic complications and factors associated with the severity of adverse events. RESULTS: A total of 34,841 patients underwent inpatient ophthalmic surgery, among which 162 systemic complications occurred in 150 patients during postoperative hospitalization. The overall incidence rate was 0.4%, with cardiovascular events (48.1%), digestive events (13.6%) and fever (12.3%) being the leading causes. About 17.3% of the cases had conditions improved after observation, 19.1% after symptomatic treatment, 54.9% had consultation with specific intervention and 8.6% were transferred to the corresponding departments for specialized treatment. For the prognosis, 93.8% had condition improved, 5.6% chose voluntary discharge without improvement, and one patient died of respiratory failure caused by postoperative pulmonary infection. The worse ADL (activities of daily living) grading, indication of primary intraocular lymphoma or intraocular tumour, surgery of simple pars plana vitrectomy (PPV), PPV with silicone oil tamponade, PPV with gas tamponade, general anaesthesia, history of diabetes mellitus (DM), chronic heart failure and digestive system disease were the risk factors positively correlated with postoperative systemic complications (p < .05). The worse ADL grading, history of DM and respiratory system disease were also positively correlated with the severity of the adverse events (p < .05). CONCLUSIONS: The incidence of postoperative systemic complications was low among patients undergoing ophthalmic surgery, most were mild and could be relieved after observation, symptomatic or specialist consultation. Patients with worse ADL and history of DM should be paid extra attention.

[Source Analysis and Health Risk Assessment of Heavy Metals in the Groundwater of Shijiazhuang, a Typical City in North China Plain].

Increasing attention has been paid to the heavy metal pollution in groundwater. The source analysis and risk assessment of heavy metals will provide data and method support for the targeted control of heavy metal pollution in groundwater. In this study, 20 sampling sites were selected in Shijiazhuang City. The APCS-MLR model and health risk model were applied to analyze and evaluate the pollution sources and health risks of 10 types of heavy metals in the groundwater of Shijiazhuang. The results showed that ① the mean concentration of heavy metals in groundwater followed the order of Fe>Zn>Mn>Cu>Al>Pb>Cr>As>Cd>Hg, and the mean ρ(Fe) and ρ(Pb) were 260.3 µg·L-1 and 10.01 µg·L-1, respectively. According to the results of the single factor and Nemerow index, Pb, Fe, and Cd primarily contributed to the heavy metal pollution in the groundwater. ② The concentration of heavy metals ranged from 47.30 to 2560 µg·L-1. In terms of spatial distribution, the highest concentration appeared at S3 (2560 µg·L-1), whereas the lowest concentration was at S9 (47.30 µg·L-1). ③ Source analysis results showed that industrial and agricultural activities, transportation emission, and geological background were the major heavy metal sources, among which the contribution of industrial and agricultural activities was the highest (47.83%). ④ The industrial-agricultural activities posed a potential threat to adults (HI>1); however, the non-cancer and the cancer risks of other sources for both adults and children were at an acceptable level (HI<1) and potential threat level, respectively; industrial-agricultural activities were the major source of non-cancer (adults:52.46%, children:52.45%) and cancer risks (adults:65.22%, children:65.69%), among which Cd and As showed high cancer risk. Therefore, to ensure the safety of the groundwater environment, strictly controlling the pollution sources and further strengthening the risk control of heavy metal pollution in groundwater are necessary.

Assessment of vaccinations and breakthrough infections after adjustment of the dynamic zero-COVID-19 strategy in China: an online survey.

Coronavirus disease 2019 (COVID-19) cases in China has grown rapidly after adjustment of the dynamic zero-COVID-19 strategy. However, how different vaccination states affect symptoms, severity and post COVID conditions was unclear. Here, we used an online questionnaire to investigate the infection status of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among 11,897 participants, with 55.55% positive and 28.42% negative. The common COVID-19 symptoms were fatigue (73.31%), cough (70.02%), fever (65.25%) and overall soreness (58.64%); self-reported asymptomatic infection accounted for 0.7% of participants. The persistent symptoms at 1 month after infection included fatigue (48.7%), drowsiness (34.3%), cough (30.1%), decreased exercise ability (23.1%) and pharyngeal discomfort (19.4%), which was reduced by more than 200% at 2 months. Participants with complications such as chronic obstructive pulmonary disease, respiratory diseases, diabetes, hypertension, etc. have a higher proportion of hospitalization and longer recovery time (p < = 0.01). Multiple vaccination statuses reduced the infection (p < 0.001) and severity rates (p = 0.022) by varying degrees as well as reduced the risk of high fever (>39.1 °C), chills, diarrhea and ageusia/anosmia, respectively (p < 0.05). Vaccination may enhance some upper respiratory symptoms, including sore throat, nasal congestion and runny nose, respectively (p < 0.05). Participants who had been vaccinated within 3 months were better protected by helping reduce their risk of overall soreness, chills and ageusia/anosmia, respectively (p < 0.05). In conclusion, our work has updated the epidemic characteristics of the breakthrough infection (BTI) wave after the dynamic zero-COVID-19 strategy, providing data and insights on how different vaccination statuses affect COVID-19 symptoms and disease prognosis.

A step toward inclusive green growth: can digital finance be the main engine?

Inclusive green growth (IGG) has become a worldwide consensus to achieve the target of sustainable development goals. Although the prominent role of digital finance (DF) against the pandemic has drawn considerable attention from policymakers, its plausible effect on IGG and underlying mechanisms have not been distinctly explored in academia. The aim of the study is to explore the causal effect of DF on IGG based on prefecture city-level data from 2011 to 2019 in China. To this end, we employed the non-radial direction distance function approach within the global production technology to evaluate the aggregate IGG performance and its three sub-dimensions. The empirical results demonstrate that DF exerts a significant promotional effect on urban IGG. This finding continues to survive in an extensive set of robustness checks using an alternative dependent variable, model specifications, instrumental variable, and difference-in-difference approaches to address the endogeneity concerns. Meanwhile, sub-dimensional regressions show that this positive effect is driven predominantly by the scale economy of DF, while the depth of usage and digitalization playing a minor role. Moreover, we uncover that DF enhances IGG by leveraging greater marginal product of labor rather than capital, improving environmental externalities, increasing fuller employment, and reducing rural-urban income inequality. However, we also reveal the dark side of DF on imbalanced regional development. The promotional effect of DF on IGG is only prominent for cities with better inherent comparative advantages, and we are thus likely to see a widening digital divide resulting from the "Matthew effect" on regional disparity without timely policy interventions.

Spatio-temporal forecasting using wavelet transform-based decision trees with application to air quality and covid-19 forecasting.

We develop a new method that combines a decision tree with a wavelet transform to forecast time series data with spatial spillover effects. The method can not only improve prediction but also give good interpretability of the time series mechanism. As a feature exploration method, the wavelet transform represents information at different resolution levels, which may improve the performance of decision trees. The method is applied to simulated data, air pollution and COVID time series data sets. In the simulation, Haar, LA8, D4 and D6 wavelets are compared, with the Haar wavelet having the best performance. In the air pollution application, by using wavelet transform-based decision trees, the temporal effect of air quality index including autoregressive and seasonal effects can be described as well as the spatial correlation effect. To describe the spillover spatial effect in contiguous regions, a spatial weight is constructed to improve the modeling performance. The results show that air quality index has autoregressive, seasonal and spatial spillover effects. The wavelet transformed variables have a better forecasting performance and enhanced interpretability than the original variables. For the COVID time series of cumulative cases, spatial weighted variables are not selected which shows the lock-down policies are truly effective.

A two-stage interval-valued carbon price forecasting model based on bivariate empirical mode decomposition and error correction.

Economic development has brought about global greenhouse gas emissions and, thus, global climate change, a common challenge worldwide and urgently needs to be addressed. Accurate carbon price forecasting plays a pivotal role in providing a reasonable basis for carbon pricing and ensuring the healthy development of carbon markets. Therefore, this paper proposes a two-stage interval-valued carbon price combination forecasting model based on bivariate empirical mode decomposition (BEMD) and error correction. In Stage I, the raw carbon price and multiple influencing factors are decomposed into several interval sub-modes by BEMD. Then, we select artificial intelligence-based multiple neural network methods such as IMLP, LSTM, GRU, and CNN to conduct combination forecasting for interval sub-modes. In Stage II, the error generated in Stage I is calculated, and LSTM is used to predict the error; then, the error forecasting result is added to the first stage result to obtain the error-corrected forecasting result. Taking the carbon trading prices of Hubei, Guangdong, and the national carbon market, China, as the research object, the empirical analysis proves that the combination forecasting of interval sub-modes of Stage I outperforms the single forecasting method. In addition, the error correction technique in Stage II can further improve the forecasting accuracy and stability, which is an effective model for interval-valued carbon price forecasting. This study can help policymakers formulate regulatory policies to reduce carbon emissions and help investors avoid risks.

Measurement invariance in the assessment of parenting practices: A cross-cultural comparison of China and the United States.

Assessing parenting practices in a culturally informed manner is critical to clinical practice when working with families. Although many parenting measures have been translated into Chinese, limited evidence for measurement invariance is available. The present study aims to assess the measurement invariance of positive and negative parenting practices across Mandarin-speaking families living in Mainland China and English-speaking families living in the United States. Three thousand seven parents of children ages 6-12 years (770 English-speaking: parent Mage = 35.15 years, SD = 7.96; child Mage = 9.50 years, SD = 4.27; 2,237 Chinese-speaking: parent Mage = 38.46 years, SD = 4.42; child Mage = 9.40 years, SD = 1.78) completed the Multidimensional Assessment of Parenting Scale as a part of two separate research protocols. Multiple-group confirmatory factor analyses (CFAs) were used, and the source of invariance at the factor and item levels was examined. CFA revealed that a seven-factor solution was feasible across both samples, as evidenced by configural and metric invariance. We found a lack of scalar invariance; thus, we constructed a partial scalar invariance model and presented latent means, correlations, and variances of the seven subscales. Item-level parameter estimates and content analyses revealed potentially different item interpretations of the measure. The lack of scalar invariance suggests that researchers should not use mean differences (e.g., from simple t tests) for cross-cultural comparisons using common parenting questionnaires. Instead, we recommend analyzing data utilizing latent variable modeling (e.g., structural equation modeling) and future directions for improving measures as part of larger efforts for promoting inclusive parenting science. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Environmental management through examining the technical factors of carbon emissions in South Asian economies.

COP27 has provided a new direction in overcoming the issue of climate change. In the world of growing environmental degradation and climate change issues, the South Asian economies are playing a vital role in tackling these issues. Still, the literature focuses on industrialized economies while ignoring the newly emerging economies. This study evaluates the technological factors' influence on carbon emission in the four small South Asian economies (Sri Lanka, Bangladesh, Pakistan, and India) from 1989 to 2021. This study found the long-run equilibrium relationship between the variables using second-generation estimating tools. Employing the non-parametric and robust parametric approach, this study found that economic performance and development are substantial emissions drivers. In contrast, energy technology and tech innovation are the region's key environmental sustainability factors. Further, the study found that trade positively yet insignificantly impacts pollution. This study suggests further investment in energy technology and technological innovation to improve the production of energy-efficient products and services in these emerging economies.

Assessment of left ventricular energy loss in patients with mild coronary artery stenosis by using vector flow mapping combined with exercise stress echocardiography.

OBJECTIVES: To evaluate the left ventricular energy loss (EL), energy loss reserve (EL-r), and energy loss reserve rate in patients with mild coronary artery stenosis by using vector flow mapping (VFM) combined with exercise stress echocardiography. METHODS: A total of 34 patients (case group) with mild coronary artery stenosis and 36 sex and age matched patients (control group) without coronary artery stenosis according to coronary angiogram were prospectively enrolled. The total energy loss (ELt), basal segment energy loss (ELb), middle segment energy loss (ELm), apical segment energy loss (ELa), energy loss reserve (EL-r), and energy loss reserve rate were recorded in the isovolumic systolic period (S1), rapid ejection period (S2), slow ejection period (S3), isovolumic diastolic period (D1), rapid filling period (D2), slow filling period (D3), and atrial contraction period (D4). RESULTS: Compared with the control group, some of the EL in the resting case group were higher; some of the EL in the case group were lower after exercise, and those during D1 ELb and D3 ELb were higher. Compared with the resting state, the total EL and the EL within the time segment in the control group were higher after exercise, except during D2 ELb. In the case group, except for during D1 ELt, ELb and D2 ELb, the total and segmental EL of each phase was mostly higher after exercise (p < .05). Compared with the control group, most of the EL-r and EL reserve rates in the case group were lower (p < .05). CONCLUSION: The EL, EL-r, and energy loss reserve rate have a certain value in the evaluation of cardiac function in patients with mild coronary artery stenosis.