Variable Model Research Articles

Evaluating methods for estimating influence effects in social networks: a simulation study

The contagion effect, also known as the peer effect, plays a significant role in social networks. It refers to the phenomenon in which one person or group can influence the behavior of other individuals with whom they share social connections. However, accurately estimating the contagion effect is challenging due to the confounding impact of peer selection. Previous studies have shown that this can be viewed as a problem of omitted variable bias. In an attempt to address this issue, simulation studies were conducted to compare the effectiveness of various methodologies in estimating peer influence within social networks, including latent variable models and machine learning techniques. Our research demonstrates that the performance of various approaches vary depending on the social network scenario. Overall, the latent space approach exhibits the best performance. This analysis provides valuable insights for researchers studying estimation techniques and emphasizes the importance of understanding the strengths, limitations, and objectives of these methods before using them for inference-based estimation.

Soft sensor modeling method and application based on TSECIT2FNN-LSTM

To address the issue of low accuracy in soft sensor modeling of key variables caused by multi-variable coupling and parameter sensitivity in complex processes, this paper introduces a TSK-type-based self-evolving compensatory interval type-2 fuzzy Long short-term memory (LSTM) neural network (TSECIT2FNN-LSTM) soft sensor model. The proposed TSECIT2FNN-LSTM integrates the LSTM neural network with the interval type-2 fuzzy inference system to address long-term dependencies in sequence data by utilizing the gate mechanism of the LSTM neural network. The TSECIT2FNN-LSTM structure learning algorithm uses the firing strength of the network rule antecedent to decide whether to generate new rules to improve the rationality of the network structure. TSECIT2FNN-LSTM parameter learning utilizes the gradient descent method to optimize network parameters. However, unlike other interval type-2 fuzzy neural network gradient calculation processes, the error term in the LSTM node parameter gradient of TSECIT2FNN-LSTM is propagated backwards in the time dimension. Additionally, the error term is simultaneously transferred to the upper layer network to enhance network prediction accuracy and memory capabilities. The TSECIT2FNN-LSTM soft sensor model is utilized to predict the alcohol concentration in wine and the nitrogen oxide emission in gas turbines. Experimental results demonstrate that the proposed TSECIT2FNN-LSTM soft sensing model achieves higher prediction accuracy compared to other models.

Impact of the Preservation of Residual Kidney Function on Hemodialysis Survival: Results from the BISTRO Trial.

Preservation of residual kidney function (RKF) in dialysis patients has been associated with improved survival. RKF in the BISTRO trial was relatively well preserved and here we describe its association with survival during the trial and extended follow-up. RKF, measured as the average urea and creatinine clearance (GFR) or 24-hour urine volume was assessed at baseline, one, two and three months and three-monthly up to 2 years in incident haemodialysis patients. Time to event survival data or competing events (transplantation, modality change) were obtained for 50 months post enrolment via data linkage with the UK Renal Registry. Cox proportional hazards regression survival models, including those incorporating change in GFR from baseline as a time-varying variable and joint regression models for longitudinal and survival data (longitudinal models for GFR or urine volume) were used to explore the relationship of RKF preservation with survival. Analyses were adjusted for age, sex, comorbidity and ethnicity. 2919 measures of RKF were made in 387 patients from 32 UK dialysis units. Higher age and comorbidity score associated with increased mortality in all models. Baseline GFR reduced the risk of death (Hazard Ratio: 0.918 95%CI: 0.844, 0.999) per ml/min/1.73m2. A greater fall in GFR and urine volume from baseline was associated with a non-significant increased risk of death as visualised on spline plots. In the joint survival models higher GFR (adjusted HR: 0.88 95%CI 0.80, 0.97) or urine volume (adjusted HR: 0.75 95%CI 0.57, 0.95 per L) at any time point associated with better survival. Lower RKF during the first two years of haemodialysis is associated with an increased death risk for up to 50 months following dialysis initiation. This adds to a growing body of evidence that interventions to preserve RKF should be developed and tested in clinical trials.

Weighing user's privacy calculus on personal information disclosure: the moderating effect of social media identification

PurposeWith the widespread collection and utilization of user data, privacy security has become a crucial factor influencing online engagement. In response to the growing concern about privacy security issues on social media, this research aims to examine the key causes of social media users' privacy calculus and how the balance between perceived privacy risks and benefits affects users' privacy concerns and their subsequent willingness to disclose personal information.Design/methodology/approachThe characteristics of the privacy calculus were extracted through partially structured interviews. A research model derived from privacy calculus theory was constructed, and latent variable modeling was employed to validate the proposed hypotheses.FindingsInformation sensitivity, experiences of privacy violations, social influence and the effectiveness of privacy policies influence users' privacy calculus. Privacy risk positively influences privacy concerns. Personal information disclosure willingness is positively influenced by privacy benefits and negatively influenced by privacy concerns, with both paths moderated by social media identification.Originality/valueThis study explores the key antecedents of users' privacy calculus and how these factors influence privacy concerns and subsequent willingness to disclose information on social media. It offers new insights into the privacy paradox observed within social media by validating the moderating role of social media identification on users' information disclosure willingness.

MCMC stopping rules in latent variable modelling.

Bayesian analysis relies heavily on the Markov chain Monte Carlo (MCMC) algorithm to obtain random samples from posterior distributions. In this study, we compare the performance of MCMC stopping rules and provide a guideline for determining the termination point of the MCMC algorithm in latent variable models. In simulation studies, we examine the performance of four different MCMC stopping rules: potential scale reduction factor (PSRF), fixed-width stopping rule, Geweke's diagnostic, and effective sample size. Specifically, we evaluate these stopping rules in the context of the DINA model and the bifactor item response theory model, two commonly used latent variable models in educational and psychological measurement. Our simulation study findings suggest that single-chain approaches outperform multiple-chain approaches in terms of item parameter accuracy. However, when it comes to person parameter estimates, the effect of stopping rules diminishes. We caution against relying solely on the univariate PSRF, which is the most popular method, as it may terminate the algorithm prematurely and produce biased item parameter estimates if the cut-off value is not chosen carefully. Our research offers guidance to practitioners on choosing suitable stopping rules to improve the precision of the MCMC algorithm in models involving latent variables.

A comprehensive evaluation of factors influencing offsite construction and BIM integration in the construction industry

PurposeThe construction industry's transformation of prefabrication into large-scale offsite manufacturing continues to grow. While the Offsite Construction (OSC) transition has brought positive changes to the industry, it also presents various challenges. Literature suggests that to address the challenges, advanced technologies such as Building Information Modeling (BIM) can be applied for effective mitigation. Nonetheless, the combined application of BIM and OSC is in its infancy and faces challenges in many countries. This study aims to investigate the factors influencing OSC and BIM integration, including challenges and strategies within the New Zealand construction industry.Design/methodology/approachThis study explores which specific factors impact integration positively or negatively through a comprehensive literature review and confirmatory survey. Factor Analysis (FA) was used as the data analysis method, resulting in eight groups of underlying factors. Next, three Structural Equation Models (SEM) were developed and tested to analyze the impact of latent and observed variables on OSC and BIM integration.FindingsSignificant underlying factors affecting OSC and BIM integration were identified, and recommendations to enhance integration were provided. Specifically, the findings demonstrated that three underlying factors for challenges, two for strategies and three for integration were identified through EFA and confirmed by CFA. This step was followed by SEM, which employed three different path analysis models for integration variables. It clarified that not all variables have similar impacts on integration; some exhibit significant positive or negative effects, while others are not significant at all.Originality/valueThe identified challenges and strategies for OSC and BIM integration and their impact on integration factors can contribute to developing standardized integration guidelines and frameworks to assist stakeholders in focusing on their specific integration goals.

Forecasting Non-stationary Time Series Using Deep Learning in a Fuzzy Time Series Framework and its Application to Stock Markets

Non-stationary time series prediction is challenging due to its dynamic and complex nature. Fuzzy time series models offer a promising solution for forecasting such data, but a key challenge lies in partitioning the universe of discourse, which significantly impacts forecasting accuracy. Traditional fuzzy time series models often use equal-length interval partitioning, which is more suited for stationary data and limits their adaptability to non-stationary time series. This paper introduces a novel variable-length interval partitioning method designed specifically for non-stationary time series. The developed method combines a Long Short-Term Memory (LSTM) Autoencoder with K-means clustering, enabling dynamic, data-driven partitioning that adapts to the changing characteristics of the data. The LSTM Autoencoder encodes the time series, which is clustered using K-means, and intervals are defined based on cluster centers. Furthermore, the Variable Length Interval Partitioning-based Fuzzy Time Series model (VLIFTS) is developed by incorporating this partitioning method and the concepts of Markov chain and transition probability matrix. In this model, fuzzy sets are viewed as states of a Markov chain, and transition probabilities are used in the forecasting phase. The model is validated on stock market indices Nifty 50, NASDAQ, S&P 500, and Dow Jones. Stationarity and heteroscedasticity are tested using Augmented Dickey-Fuller (ADF) and Levene's tests respectively. Statistical forecast accuracy metrics Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE) show that VLIFTS significantly improves forecasting accuracy over traditional models. This hybrid approach enhances fuzzy time series modelling and can be applied to various non-stationary time series forecasting problems.

Participation in artisanal diamond mining and food security: A case study of Kasai Oriental in DR Congo

Abstract The high prevalence of food insecurity and malnutrition in Kasai Oriental, Democratic Republic of the Congo, prompted an investigation into the complex relationship between household livelihood activities and food security. Despite many rural households relying on subsistence farming, this alone may not ensure improved food security in Kasai Oriental. Consequently, non-farm sectors like artisanal mining offer a potential solution to address food insecurity among rural households. The aim of this study is to explore the association between engagement in artisanal diamond mining, food insecurity, and children’s nutritional status in Kasai Oriental. The research utilizes household cross-sectional data collected between November and December 2022. Fixed effects and Instrumental variable models were employed to address household heterogeneity and potential endogeneity related to participation in artisanal diamond activities. The regression results reveal a significant relationship between participation in artisanal diamond mining and food security. This implies that participation increases households’ cash, enabling them to access sufficient food and potentially mitigating the risk of falling into food insecurity. However, involvement in artisanal mining has not shown a significant association with children’s malnutrition. These findings call for further research on “hidden hunger.” Policies aiming to encourage and formalize artisanal diamond mining should integrate specific extension services and inform rural households about hidden hunger.

A proposed model to measure the maturity level for social, organizational and technological determinant factors of social entrepreneurships

Purpose The primary aim of this study is to create a comprehensive model quantifying the maturity levels of key determinants crucial for the sustained success of public sector entrepreneurship (PSE) [social entrepreneurship (SE)]. By integrating social, organizational and technological elements with Max-Neef's human needs, the model offers a holistic perspective for SE measurement. Notably, it emphasizes Max-Neef's existential needs (being, having, doing, interacting) to analyze variables crucial for sustainability, from diverse perspectives within SE. Design/methodology/approach This study presents a quantitative approach to develop a maturity model for assessing SE based on Max-Neef's human needs. The model integrates social, organizational and technological dimensions, using K-means algorithm for data processing. A panel of experts assigns weights to variables, and the maturity levels are adapted from the Capability Maturity Model. Data from 47 SEs in Ecuador's Zone 6 inform model construction, validated with two textile sector SEs. The methodology comprises three stages: variable selection, model construction and nonprobabilistic sample validation in Zone 6. Findings The research yields a maturity model enabling the quantification of PSE entrepreneurship maturity over time. It incorporates social, organizational and technological variables with Max-Neef's human needs, providing a broader perspective for SE evaluation. The model allows assessing the maturity level of each determinant, performance per variable in a Likert Scale, and facilitates comparisons between individual entrepreneurship and the overall population or sample. Visualization is enhanced through a color scale based on the Capability Maturity Model. Research limitations/implications Limitations may stem from the nonprobabilistic convenience sample used for validation, potentially affecting the generalizability of findings. The model's application is specific to Zone 6 of Ecuador and may require adjustments for broader contexts. Although the K-means algorithm aids data processing, its limitations should be considered. Future research could explore the model's applicability in diverse geographical and sectoral settings for a more comprehensive understanding. Practical implications This research has led to the development of a model that quantifies the maturity level of key determinants crucial for the long-term sustainability of PSE. This model integrates elements from social, organizational and technological dimensions, offering a comprehensive framework for assessing SE. Social implications This model offers valuable insights for the development and sustainability of social entrepreneurship by providing a structured approach to measure and enhance key social, organizational and technological determinants. By integrating Max-Neef's framework of human needs, the model helps social enterprises better align their efforts with fundamental human needs, ensuring that initiatives not only address societal challenges effectively but also contribute to long-term social well-being. This approach encourages the development of more resilient and impactful social enterprises that are capable of adapting to diverse socioeconomic contexts. Originality/value The model's uniqueness lies in its integration of social, organizational and technological variables with Max-Neef's human needs, offering a nuanced assessment of PSE entrepreneurship determinant factors from a human perspective. By applying existential needs (being, having, doing, interacting) as analysis levels, the model provides a distinctive and insightful approach to evaluating the impact of key variables. Its value lies in identifying strengths and weaknesses in each entrepreneurship, guiding support strategies and informing discussions for constructing effective public policies.

Hierarchical Bayesian Modelling of Macroeconomic Variables in Ghana

Abstract This study analyzed the impact of macroeconomic variables (manufacturing, real exchange rate, government expenditure, and gross fixed capital formation) on GDP growth in Ghana. Utilizing secondary data from the World Development Indicators of the World Bank (1991–2021), we employed a hierarchical Bayesian linear model with interaction effects to assess these relationships. The results indicate that the real exchange rate, manufacturing, and government expenditure have a positive influence on GDP growth, while gross fixed capital formation exhibits a moderately negative effect. To enhance economic growth, it is crucial to optimize capital investments, bolster export competitiveness through targeted policies, and invest in manufacturing innovation. These findings offer actionable insights for policymakers aiming to stimulate economic growth in Ghana.

Large vessel occlusion mediated fluid attenuated inversion recovery signal intensity ratio is associated with stroke within 4.5 h.

The primary objective was to investigate the value of the fluid attenuated inversion recovery (FLAIR) signal intensity ratio (SIR) in identifying stroke within 4.5 h. The secondary objective was to ascertain whether large vessel occlusion (LVO) mediated the relationship between the SIR and stroke within 4.5 h. We analyzed 633 acute stroke patients within 24 h of clear symptom onset. The SIR and DWI-FLAIR mismatch were evaluated. First, we determined whether demographic variables, vascular risk factors and LVO were related to stroke within 4.5 h with multivariate logistic regression analyses and stratified regression analysis. Next, we used mediation analysis to determine whether LVO explained the association between SIR and stroke within 4.5 h. Finally, we used receiver operating characteristic (ROC) analysis to assess the value of SIR, independent variable, and multiparameter models in identifying stroke within 4.5 h and compared with DWI-FLAIR mismatch. Hyperlipemia, LVO and SIR were associated with stroke within 4.5 h. Mediation analysis revealed that LVO partially mediated the relationship between SIR and stroke within 4.5 h (p < 0.001). The multiparameter model (hyperlipemia, LVO and SIR) showed significantly improved performance (AUC 0.869) in identifying stroke within 4.5 h over DWI-FLAIR mismatch (0.684), hyperlipemia (0.632), LVO (0.667) and SIR (0.773) models. SIR is associated with stroke within 4.5 h, and LVO partially mediates this relationship. A multiparameter model combining hyperlipemia, LVO and SIR can more accurately identify stroke within 4.5 h than individual parameter models.

Adaptive impedance control method for manipulator based on radial basis function

Purpose In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments. Design/methodology/approach The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property. Findings Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%. Originality/value This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

Функциональная модель психологической службы в системе образования: направления деятельности и ее организационно-правовое регулирование

&lt;p&gt;The article presents the results of a monitoring study of the functional model of psychological service in the system of general education and secondary vocational education. The purpose of the monitoring is to study variable approaches to the implementation of psychological and pedagogical support in various regions of the Russian Federation. The study was conducted from March to April 2024 in 8 constituent entities of the Russian Federation, and involved educational psychologists (educational psychologists) from preschool, general education and professional educational organizations. The total sample size was 2288 people. The article discusses the analysis of the results obtained in accordance with the identified functional blocks: organizational and legal regulation of psychological and pedagogical support activities and areas of activity of psychological services in the system of general education and secondary vocational education. The study made it possible to comprehensively present the current state of the psychological service, its development resources and areas of improvement. This study will become the basis for further development of a draft functional model of psychological service in the system of general education and secondary vocational education, as well as variable models of its organization.&lt;/p&gt;

A-321 Rethinking the Role of Race in Prenatal AFP Screening for Open Neural Tube Defects

Abstract Background The quantification of alpha-fetoprotein (AFP) in maternal serum is commonly used in prenatal screening to estimate the risk of fetal open neural tube defects (ONTDs). This involves comparing a patient’s measured AFP concentration to the median AFP for women of similar gestational age and weight, reported in AFP multiples of the median (MoM). Current guidelines recommend establishing population medians separately grouped by patients’ race, based on historical reports documenting differences in median AFP concentrations across different populations. We aimed to reevaluate this practice of race based ONTD risk stratification given the limited and questionable historical evidence as well as the small observed differences in AFP concentrations in our practice and in a recent report. Methods We collected maternal serum AFP screening data for patients between 12/30/19 and 2/9/24. Informed by the statistical methods used for clinical risk stratification, we developed two log-linear multiple variable regression models for estimating AFP: gestational age (GA), weight, and interaction terms (Model 1) and Model 1 predictors, race, and interaction terms (Model 2). AFP MoMs were calculated by dividing the measured AFP concentration by the model-estimated AFP. Results We analyzed maternal ONTD screening for 7,702 patients with singleton pregnancies. The current clinical process uses race and ethnicity as documented in the electronic health record, which was categorized as Black (N = 2,360), White (N = 3,734), Hispanic (N = 209), Asian (N = 641), and Other (N = 758). The addition of race in Model 2 included statistically significant predictors (p &amp;lt; 0.001) for Black (β = 0.2), Asian (β = 0.05), and Black:weight interaction (β = -5.9 x 10-4). Between Model 1 and Model 2, the mean change in AFP MoMs was -0.05, +0.03, 0.0, -0.02, and +0.01 for Black, White, Hispanic, Asian, and Other patients, respectively. 95% of Black patients had a change &amp;lt; 0.12 in AFP MoM and &amp;gt; 99% of all other patients had a change &amp;lt; 0.1. Using the clinical interpretative threshold, 56 (0.7%) results were considered elevated in both models, 4 (0.05%) elevated in Model 1 only (3 Black, 1 Other), and 4 (0.05%) elevated in Model 2 only (3 White, 1 Hispanic). Chart review of the 8 discordant interpretations showed small changes in MoMs (mean absolute difference = 0.09) and no cases of ONTDs. Conclusions In our health system’s population, maternal race appears to have a small statistically significant association with maternal AFP concentration, even after adjustment for weight and maternal GA. However, preliminary analyses indicate that this difference has a small impact on the clinical interpretation of these studies, with most patients classified similarly when race is not accounted for in the calculation. Further investigation is needed to clarify whether this small impact on estimation of risk warrants the continued inclusion of race in NTD screening, as well as whether other clinical, social, or community factors available for inclusion in this clinical risk stratification model would be better predictors than the poorly defined and discriminatory concept of race.

Dynamic uncertainty evaluation of cylindricity error based on Bayesian deep neural network propagation method

Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable.

The JCSS probabilistic model Code, future developments

To assess and verify the reliability of structures, reliability based building codes allow for the application of full-probabilistic methods and semi-probabilistic methods (i.e. the partial factor method). In principle, both methods should be equivalent and lead to (approximately) the same reliability level. Therefore partial factors should be as much as possible determined based on a probabilistic background and calibration exercises. On the other hand, as the probabilistic design method may be considered as more rational and consistent than the partial factor design, there is a tendency to use probabilistic methods directly in the assessment of special of important new structures and also in the assessment of existing structures. In both the calibration exercise and in the full probabilistic assessment of structures, we face the problem that many assumptions have to be made. In particular in regard to the statistical modelling of random variables and in regard to accepted approximative methods of calculation. This often brings the engineer to a challenging position. In the past years the JCSS probabilistic model code (PMC) has served as an often-consulted operational code for this purpose. In the present paper, the JCSS PMC and its future developments are presented and discussed.

Investigation of essential parameters for the design of offshore wind turbine based on structural reliability

The probabilistic-based design method is gradually gaining attention in the wind industry because it provides more accurate modeling of uncertainty variables than that of traditional methods. Unfortunately, the numerous uncertainty variables involved in structural design are major obstacles to the successful application of this method. Therefore, this study presents a sensitivity analysis (SA) of a benchmark monopile offshore wind turbine (OWT) to screen the top-ranking variables from the viewpoint of reliability. Primarily, a comprehensive reliability SA framework of OWT is proposed, in which a novel measurement of soil uncertainties is conducted using quantitative analysis from the perspective of soil structure interaction (SSI). Subsequently, a reliability SA is conducted to explore the crucial variables influencing the structural safety from the uncertain clusters. The results indicate that Young's modulus, structural geometry, and SSI have significant effects on the structural reliability of excessive vibration failure. The hydrodynamic and aerodynamic load variables exhibit the most prominent influence on excessive deflection failure. Additionally, the SSI uncertainties exhibit a non-negligible effect in affecting the structural reliability, i.e., the lateral bending stiffness shows more sensitivity to the normal operation cases, whereas the impact of joint stiffness is more remarkable in parked scenarios.

Ambient air pollution and low birth weight in Brazil: A nationwide study of more than 10 million births between 2001 and 2018

Low birth weight (LBW) is a global health concern. While it is commonly associated with maternal health and behavior, exposure to ambient air pollution, can also play a role in contributing to LBW. In Brazil, where diverse environmental conditions and regional disparities exist, assessing the impact of ambient air pollution on LBW becomes particularly pertinent. To our knowledge, there is a gap in the existing literature, as no previous study has specifically investigated the relationship between ambient air pollution and LBW nationwide in Brazil. This study aims to fill this gap by examining the association between ambient air pollution and LBW in each trimester of pregnancy across the Brazilian states. In this work, birth data from January 1, 2001, to December 31, 2018 has been used. We utilized logistic regression models to estimate the odds ratio (OR) for low birth weight (LBW) associated with ambient air pollution (PM2.5, NO2, and O3) during each trimester of pregnancy (1st to 3rd trimester) across all 27 Brazilian states in our nationwide case-control study. We adjusted our model for several variables, including ambient temperature, relative humidity, and socioeconomic status (SES) variables at the individual level. We also conducted effect modification analyses by infant sex, mother's age, and the number of prenatal visits. Our study comprises over 10,213,144 birth records nationwide. Of these, 479,204 (4.92%) infants were included as cases of LBW. Our results indicate positive associations between PM2.5 and LBW, mainly in the Southern region. For example, in the state of Santa Catarina (South region), ORs were 1.003 (95% CI: 1.002, 1.004), 1.003 (95% CI: 1.002, 1.004), and 1.005 (95% CI: 1.003, 1.007) for the 1st, 2nd, and 3rd trimesters of exposure, respectively. NO2 had a robust association with LBW in the Northern and Northeastern states, including the state of Amapá (North region, where the Amazon Forest is located) with ORs of 1.377 (95% CI: 1.010, 1.878), 1.390 (95% CI: 1.020, 1.894), and 1.747 (95% CI: 1.297, 2.352) for the 1st, 2nd, and 3rd trimesters of exposure, respectively. Similarly, O3 had a robust association in the North and Midwest states, as observed in the state of Amapá with ORs of 1.033 (95% CI: 1.012, 1.054), and 1.033 (95% CI: 1.013, 1.053) for the 2nd, and 3rd trimesters, respectively. In the stratified analysis, boys were more vulnerable than girls, and the lower number of prenatal visits was associated with higher OR. Our findings are essential to the development of guidelines to prevent maternal exposure and protection of newborns in Brazil. This study provides valuable insights for region-specific strategies to improve maternal and neonatal health.

Optimal scheduling of zero-carbon park considering variational characteristics of hydrogen energy storage systems

Zero-carbon parks have broad prospects in carbon neutralization. As an energy hub, hydrogen energy storage plays an important role in zero-carbon parks. However, the nonlinear characteristics of hydrogen energy storage systems (HESSs) have a significant impact on the system economy. Therefore, considering the variable working condition characteristics of HESSs, a hybrid operation method is proposed for HESS, to support the efficient and economic operation of zero-carbon parks, by analyzing the operating principle of a zero-carbon park with HESS, the system structure framework and variable condition linearization model of the equipment in HESS are established. Moreover, considering the energy output characteristics of hydrogen energy storage equipment under variable working conditions, a multimodule hybrid operation strategy is proposed for electrolytic and fuel cells, effectively meeting the thermoelectric load demand of zero- carbon parks in different scenarios. Finally, the economy of the proposed hybrid operation strategy was verified in typical scenarios, using a zero-carbon park embedded with a HESS.

Sensitivity of Simulated Arctic Ocean Salinity and Strait Transport to Interannually Variable Hydrologic Model Based Runoff

AbstractAs the Arctic warms at an increased rate compared to the rest of the globe, freshwater runoff has been shown to be increasing into the Arctic Ocean. The effects of this contemporary increase in riverine freshwater into the Arctic Ocean are estimated from ocean model simulations, using two runoff data sets. One runoff data set is based on older climatological data, which has no inter‐annual variability after 2007 and as such does not represent the observed increases in river runoff into the Arctic. The other data set comes from a hydrological model developed for the Arctic drainage basin, which includes contemporary changes in the climate. At the Pan‐Arctic scale this new data set represents an approximately 11% increase in runoff, compared with the older climatological data. Comparing two ocean model runs forced with the different runoff data sets, overall changes in different freshwater markers across the basin were found to be between 5% and 10%, depending on the regions. The strongest increases were seen from the Siberian rivers, which in turn caused the strongest freshening in the Eastern Arctic. As the surface waters of the Arctic Ocean are sensitive to runoff, incorporating hydrological model data can help to better understand current changes and potential future impacts from increased runoff with climate change.