Articles

The focus of this study was to determine the influence of religious literacy and multicultural teaching competence on the teaching self-efficacy of Religious and Moral Education (RME) teachers in public basic schools within the Kumasi Metropolis. The research employed a cross-sectional survey design with a sample of 308 RME teachers selected through the stratified sampling technique from 165 basic schools across 14 circuits. Data were collected using questionnaires and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings revealed that religious literacy significantly and positively influenced teachers’ self-efficacy (β = 0.487, p < 0.05), accounting for 23.7% of the variance. Similarly, multicultural teaching competence demonstrated a strong positive effect on teaching self-efficacy (β = 0.711, p < 0.05), explaining 50.6% of the variance. Finally, the study found that religious literacy and multicultural teaching competence together contributed 52.2% to RME teachers' teaching self-efficacy (SD = 0.692, p < 0.05, R² = 0.522). The study recommended that the Ghana Education Service (GES) and the National Council for Curriculum and Assessment (NaCCA) should design and mandate regular in-service training programmes focused specifically on religious literacy for RME teachers. Also, it was recommended that pre-service and in-service training should emphasise awareness of personal biases, deep knowledge of learners’ cultural and religious backgrounds, and practical skills for culturally responsive pedagogy.

Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

Mathematical modeling plays a central role in infectious disease epidemiology, shaping outbreak response strategies and informing public health policy. The COVID-19 pandemic demonstrated the value of these models but also exposed persistent limitations related to data fragility, lack of transparency, limited stakeholder engagement, and insufficient consideration of social and political contexts. Rather than critiquing modeling as a discipline, this perspective argues for a reorientation of infectious disease modeling toward a more responsive, equity-centered, and participatory paradigm. We propose a conceptual framework built on three interrelated principles: adaptability through real-time data integration, transparency via open-source and reproducible practices, and relevance through interdisciplinary and co-produced model design. Drawing on illustrative examples from COVID-19 and dengue control efforts, we highlight how integrating behavioral dynamics, local knowledge, and policy feedback can improve model usefulness and public trust. Reconceptualizing models as dynamic systems of inquiry rather than static forecasting tools can enhance decision-making and promote more equitable and effective responses to future public health emergencies.

<b>Background:</b> Building Information Modelling (BIM) improves safety planning in construction by enabling visualization and simulation to identify and reduce risks. However, its adoption in Nigeria is limited. This study examines the application of BIM in enhancing safety and environmental performance on construction sites in Nigeria. <b>Methodology:</b> A quantitative cross-sectional survey was conducted using a structured online questionnaire distributed to professionals in Nigeria’s construction industry. A purposive sampling method was employed to target respondents with relevant BIM experience. Data were analysed using SPSS version 28, applying descriptive statistics, chi-square tests, and logistic regression at a 5% significance level. <b>Result:</b> Findings show that BIM was fully adopted by 7.0% of organizations, with only 19.8% of respondents using it to identify safety hazards during planning. While 76.8% reported no notable safety benefit, 19.5% identified improved risk management as the key benefit. Most respondents (80.2%) reported no noticeable environmental benefits. Among those who did, improved energy efficiency was the most cited benefit (16.4%). Respondents with 10 or more years of experience were significantly more likely to report enhanced safety and environmental outcomes (AOR = 4.555; p = 0.003) and adequate BIM utilization (AOR = 3.255; p = 0.023). Those with intermediate BIM experience were also more likely to report high enhancement (AOR = 2.857; p = 0.039) and effective tool use (AOR = 2.881; p = 0.050). <b>Conclusion:</b> This study revealed that BIM has the potential to improve construction outcomes in Nigeria if supported by training, experience, and structured implementation.

The gut-brain axis (GBA) has emerged as a central focus in the study of neurodevelopmental disorders, particularly autism spectrum disorder (ASD). Research suggests that microbial composition and its metabolic byproducts influence neural development, synaptic plasticity, and behavior [1,2,3]. A structured bibliometric analysis of Scopus and Web of Science records was performed using Bibliometrix and VOSviewer to trace trends and thematic evolution of GBA–ASD literature [7,8]. In parallel, a data-driven pathway modeling approach maps microbial metabolites (e.g., short-chain fatty acids, tryptophan catabolites) to host signaling pathways including vagal stimulation, immune cytokine modulation, and blood–brain barrier (BBB) permeability [4,5]. Simulations implemented in Python’s NetworkX illustrate how perturbations in metabolite flux may influence CNS outcomes. The findings reveal growing emphasis on butyrate, serotonin, microglial priming, and maternal immune activation in ASD-related GBA studies, and highlight the need for rigorous empirical validation of computational predictions [9,10,11].

This research is a collaboration involving a university team, a partnering corporation, and a hemodialysis clinic, which is a cross-disciplinary research initiative in the field of Artificial Intelligence of Things (AIoT) within the medical informatics domain. The research has two objectives: (1) The development of an <i>Internet of Things (IoT)</i><i>-based</i><i> Information System</i> customized for the hemodialysis machines at the clinic, including transmission bridges, clinical personnel dedicated web/app, and a backend server. The system has been deployed at the clinic and is now officially operational; (2) The research also utilized de-identified, anonymous data (collected by the officially operational system) to train, evaluate, and compare <i>Deep Learning-based Intradialytic Blood Pressure</i><i> (BP)</i><i>/Pulse </i><i>Rate (PR) </i><i>Predictive Models</i>, with subsequent suggestions provided. Both objectives were executed under the supervision of the Institutional Review Board (IRB) at Mackay Memorial Hospital in Taiwan. The system completed for objective one has introduced three significant services to the clinic, including automated hemodialysis data collection, digitized data storage, and an information-rich human-machine interface as well as graphical data displays, which replaces traditional paper-based clinical administrative operations, thereby enhancing healthcare efficiency. The graphical data presented through web and app interfaces aids in real-time, intuitive comprehension of the patients’ conditions during hemodialysis. Moreover, the data stored in the backend database is available for physicians to conduct relevant analyses, unearth insights into medical practices, and provide precise medical care for individual patients. The training and evaluation of the predictive models for objective two, along with related comparisons, analyses, and recommendations, suggest that in situations with limited computational resources and data, an Artificial Neural Network (ANN) model with six hidden layers, SELU activation function, and a focus on artery-related features can be employed for hourly intradialytic BP/PR prediction tasks. It is believed that this contributes to the collaborating clinic and relevant research communities.

<b>Background:</b><b> </b>High educational attainment is a well-recognized protective factor against health problems such as diabetes. However, the theory of Minorities' Diminished Returns (MDRs) suggests that this protective effect is weaker for ethnic minorities compared to non-Latino Whites. This diminished effect is thought to result from structural inequalities, such as lower-quality education and fewer occupational opportunities, faced by ethnic minorities. <b>Objective:</b><b> </b>This study examined the protective effect of years of schooling—used as a proxy for educational attainment—on diabetes mellitus (DM), overall and by ethnicity. Based on the MDRs framework, we hypothesized that the protective effect of education would be weaker for Latino individuals compared to non-Latinos. <b>Methods:</b><b> </b>Data were drawn from the 2012 wave of the Understanding America Study (UAS), a nationally representative, internet-based panel. The outcome of interest was self-reported doctor diagnosis of DM. Logistic regression models were used to assess the association between educational attainment and DM, with an interaction term to explore differences between Latino and non-Latino individuals. Models were adjusted for age, sex, employment, immigration status, and marital status. Findings were presented as adjusted odds ratios (OR), p-values, and 95% confidence intervals (CIs). <b>Results:</b><b> </b>Higher educational attainment was associated with lower odds of DM in both Latino and non-Latino individuals (p < 0.001). An interaction between education and ethnicity (p < 0.05) indicated that the protective effect of education was weaker for Latino individuals compared to non-Latinos. <b>Conclusion:</b><b> </b>The findings align with the MDRs framework, which suggests that the health benefits of education are not equally distributed across ethnic groups. For Latino individuals, structural barriers such as lower educational quality and labor market discrimination may limit the protective effect of education against DM. While education is a key determinant of health, its unequal returns contribute to ethnic health disparities. Policymakers must address structural inequalities in education and employment that disproportionately affect ethnic minorities. Tackling these disparities through multi-sector policy interventions will require bipartisan political support.

<b>Background: </b>Nicotine pouches, gummies, and candies have emerged as popular alternatives to traditional tobacco products among U.S. adolescents. While parental educational attainment is generally associated with youth substance use, marginalization-related diminished returns (MDRs) suggest that this effect may be weaker in marginalized populations, including non-Latino White communities. In particular, place-based marginalization—such as neighborhood economic disadvantage and school-level poverty—may attenuate the benefits of parental education. This study examines MDRs in the relationship between parental educational attainment and nicotine pouch/gummy/candy use among non-Latino White 12th graders in the 2024 Monitoring the Future (MTF) study. <b>Methods: </b>This study analyzed nationally representative data from the 2024 MTF study, focusing on non-Latino White 12th graders who reported parental education levels and adolescents’ use of nicotine pouch/gummy/candy. Structural equation modeling (SEM) was used to estimate the effects of parental education on adolescents’ use of nicotine pouches, gummies, and candies, while adjusting for demographic covariates. Place-based marginalization was operationalized using rural vs urban /suburban residence. Interaction terms tested whether the effect of parental education varied based on place of residence. <b>Results: </b>Higher parental educational attainment was associated with lower use of nicotine pouches, gummies, and candies. However, this effect was significantly weaker in rural areas. <b>Conclusion: </b>Public health interventions should account for place-based disparities rather than assuming a uniform effect of SES factors. This study highlights the need for policy responses that address structural inequities beyond individual family SES.

<b>Background: </b>Electronic cigarette (e-cigarette) use and vaping tobacco have increased rapidly worldwide, raising concerns about their health effects, social acceptability, and regulatory challenges. In many countries, e-cigarettes are more commonly used by individuals from higher socioeconomic status (SES) backgrounds, who, in theory, should have greater knowledge about e-cigarettes and their associated risks. However, it remains unclear why a group with more knowledge about e-cigarette risks would also hold more positive attitudes toward vaping and exhibit higher usage rates — a phenomenon that may represent a knowledge-behavior paradox. Understanding this paradox, along with the complex relationships between e-cigarette knowledge, attitudes, and behaviors, is critical for informing effective public health interventions, campaigns, social media messaging, and regulatory policies. <b>Objectives: </b>This study aimed to evaluate the complex relationship between SES, e-cigarette knowledge, pro-vaping attitudes, and e-cigarette use. <b>Methods: </b>The SMOKES Study (Study of Measurement of Knowledge and Examination of Support for Tobacco Control Policies) used a multi-center, cross-sectional design, collecting data from 2,403 college and university students across 15 provinces in Iran (covering nearly half of the country's provinces). The survey measured family income, age, sex, ethnicity, e-cigarette use, knowledge, and attitudes. Structural Equation Modeling (SEM) was employed to examine the interrelations between SES, knowledge, attitudes, and behavior, while adjusting for age, sex, and ethnic minority status. <b>Results</b>: SEM analysis confirmed the hypothesized paradox. Although greater knowledge about e-cigarettes was linked to less favorable attitudes toward vaping and lower use, pro-vaping attitudes emerged as the strongest predictor of vaping behavior, while knowledge played a weaker protective role. Notably, individuals with higher SES simultaneously showed higher knowledge and, paradoxically, more pro-e-cigarette attitudes and greater usage. Female students and ethnic minority students reported higher correct knowledge and lower pro-vaping attitudes and use. Although age and higher family income were associated with more favorable attitudes, they did not directly predict vaping behavior. These results suggest that for higher SES individuals, poor knowledge is not the main driver of e-cigarette use; rather, their pro-e-cigarette attitudes, which seem to outweigh the influence of knowledge, play a key role. <b>Conclusions: </b>Although individuals from higher SES backgrounds report greater correct knowledge about e-cigarettes, this knowledge does not necessarily translate into reduced positive attitudes or lower usage. This study highlights the complexity of these paradoxical effects and suggests that public health strategies need to go beyond simple education and knowledge-based interventions. Targeted approaches should address industry messaging, challenge misconceptions, and strengthen regulatory efforts to reduce e-cigarette use among young adults, including those from higher SES backgrounds.

<b>Background:</b> As a central component of the brain's reward system, nucleus accumbens (NAcc) plays a crucial role in reward salience and substance use behaviors. Changes in the NAcc are also relevant to higher rates of substance use of youth and adults from low-income backgrounds. Although resting-state functional connectivity (rsFC) of the NAcc provides valuable insights into the neural mechanisms underlying reward processing and the propensity for self-reported reward salience and substance use, research exploring the association between NAcc rsFC and brain networks beyond the default mode network (DMN) and prefrontal cortex (PFC) is limited. <b>Objective:</b> To investigate the role of the resting-state functional connectivity of the NAcc with the cingulo-opercular network, sensorimotor mouth network, and sensorimotor hand network in the association between socioeconomic status, self-reported reward salience, and future substance use. <b>Methods:</b> Data were obtained from the Adolescent Brain Cognitive Development (ABCD) study. NAcc rsFC with the cingulo-opercular network, sensorimotor mouth network, and sensorimotor hand network was assessed at baseline. Socioeconomic status was measured using family income. Self-reported reward salience was assessed using validated psychometric scales. Substance use outcomes were tracked longitudinally over the study period. Structural Equation Modeling was employed to examine the covariances between family income, NAcc rsFC, reward salience, and subsequent substance use. <b>Results:</b> Higher baseline family income was positively associated with baseline NAcc rsFC (B = 0.092, p < 0.001) and negatively associated with baseline reward salience (B = -0.040, p = 0.036) and future substance use (B = -0.081, p < 0.001). Baseline NAcc rsFC was strongly and positively associated with reward salience (B = 0.734, p < 0.001) and future substance use up to age 13 (B = 0.124, p < 0.001). Additionally, baseline reward salience was positively associated with future substance use (Covariance = 0.176, p < 0.001). <b>Conclusion:</b> The findings suggest that NAcc rsFC with brain networks beyond the DMN or PFC may contribute to the links between low parental socioeconomic status, reward salience, and substance use risk. Expanding the understanding of NAcc rsFC provides new insights into the neural mechanisms underlying these associations. These results have important implications for developing targeted interventions aimed at preventing substance use, particularly among low-income youth with heightened reward salience. Further research is needed to explore causal pathways and moderating factors influencing these relationships.

<b>Background: </b>Educational attainment is often regarded as a pathway to economic stability and social mobility. However, the Minorities’ Diminished Returns (MDRs) framework has demonstrated that the effects of educational attainment on various economic, behavioral, and health outcomes are weaker for marginalized populations, including racial/ethnic minorities, immigrants, LGBTQ+ individuals, and those living in disadvantaged areas. While MDRs have been documented for various marginalized demographic groups, the role of trauma in moderating socioeconomic outcomes remains underexplored. <b>Objective:</b> This study examines whether lifetime trauma exposure diminishes the positive association between educational attainment and poverty-to-income ratio (PIR), a key indicator of economic well-being. <b>Methods:</b> Using data from the National Survey of American Life (NSAL), we analyzed a nationally representative sample of 6,008 adults, including Black, White, Latino, and Other racial/ethnic groups. We employed linear regression models to evaluate the association between the independent variable educational attainment and the outcome PIR. We then tested lifetime trauma as a moderator of this association. Models controlled for age, gender, employment, and race/ethnicity. <b>Results:</b> Educational attainment was positively associated with PIR across all groups, but the strength of this association was significantly attenuated for individuals with a history of lifetime trauma. These effects were independent of covariates. <b>Conclusions:</b> These findings extend the MDRs framework by highlighting trauma as a potential contributor to diminished returns of education on socioeconomic wellbeing. Structural inequities that increase trauma exposure in minoritized populations may also limit the economic benefits of education, particularly for groups with multiple trauma exposures. Policies aimed at addressing economic inequality must integrate social policies that reduce trauma and stress.

<b>Background: </b>Emotional problems have been associated with substance use, yet longitudinal research examining this relationship during childhood and adolescence in large, diverse, community-based samples remains limited.<b> </b><b>Aims: </b>This study investigates the prospective associations between three emotional problems—hopelessness, depression, and suicide attempts—before ages 9–10 and the subsequent initiation of tobacco and marijuana use before ages 14–15, using data from the Adolescent Brain Cognitive Development (ABCD) study.<b> </b><b>Methods: </b>Data from the ABCD study were analyzed. Baseline emotional problems, including hopelessness, depression, and suicide attempts, were assessed at ages 9–10 through structured parent interviews. Substance use outcomes (tobacco and marijuana initiation) were tracked from baseline to follow-up at ages 14–15 using structured self-report measures. Structural Equation Modeling (SEM) was employed to assess the predictive roles of these early-life emotional problems, controlling for potential confounders such as demographic factors and family and neighborhood socioeconomic status.<b> </b><b>Results: </b>Baseline hopelessness, depression, and suicide attempts at ages 9–10 were significant predictors of tobacco and marijuana use initiation at ages 14–15. These associations remained robust after adjusting for confounders, indicating the independent effects of early emotional problems on adolescent substance use initiation.<b> </b><b>Conclusions: </b>Emotional problems in early childhood, including hopelessness, depression, and suicidal behavior, are critical predictors of substance use initiation during adolescence. These findings underscore the importance of early identification and targeted mental health interventions to reduce the risk of substance use among vulnerable youth.

<b>Objective: </b>This study investigates the pathways linking socioeconomic status (SES), trauma, early puberty, and tobacco use, with a focus on how these relationships differ by sex. Using data from the Adolescent Brain Cognitive Development (ABCD) study, we explore how SES and race contribute to trauma exposure, which in turn may influence early puberty and tobacco use. The study also examines potential mediating effects of trauma and early puberty on the association between SES and tobacco use, while comparing these pathways for males and females.<b> </b><b>Methods: </b>Data were drawn from the ABCD study, and structural equation modeling (SEM) was employed to test direct and indirect pathways between SES, trauma, early puberty, and tobacco use. The sample was stratified by sex to assess differences in these relationships for males and females. Key predictors included SES, race, and age, while outcomes were trauma, early puberty, and tobacco use. The model assessed mediating effects of trauma and early puberty on tobacco use.<b> </b><b>Results: </b>Trauma was a significant predictor of early puberty for females (B = 0.032, SE = 0.015, p = 0.039) but not males. Early puberty was significantly linked to tobacco use for females (B = 0.048, SE = 0.015, p = 0.001) but not for males. Additionally, trauma had an effect on tobacco use among females (B = 0.048, SE = 0.014, p < 0.001) but not males.<b> </b>Lower SES was significantly associated with higher trauma exposure for both males (B = -0.109, SE = 0.014, p < 0.001) and females (B = -0.110, SE = 0.015, p < 0.001). <b>Conclusions: </b>The findings suggest that trauma and early puberty play more significant roles in the pathways from SES to tobacco use for females than for males. While trauma and early puberty are crucial mediators for females, these factors are less predictive for males. These results highlight the importance of sex-specific interventions targeting trauma and early puberty as pathways to early tobacco use.

<b>Background: </b>Research on Minorities’ Diminished Returns (MDRs) consistently reveals that social determinants of health, especially educational attainment, do not yield equal health benefits across racial and ethnic groups in the United States. MDRs suggest that social stratification, segregation, lower education quality, and labor market discrimination contribute to diminished health returns of education among minoritized groups. However, few studies have tested the relevance of MDRs in American Indian and Alaska Native (AIAN) populations compared to non-Hispanic White adults. <b>Objectives:</b> This study aimed to examine the strength of the inverse association between educational attainment and hypertension prevalence, hypothesizing that the protective effect of education on hypertension risk is reduced among AIAN adults relative to non-Hispanic Whites. <b>Methods:</b> Using data from the 2023 National Health Interview Survey (NHIS), we analyzed a nationally representative sample of adults aged 18 and older. Logistic regression models examined the association between educational attainment and self-reported hypertension diagnosis, stratified by racial/ethnic group (AIAN vs. non-Hispanic White). Models were adjusted for key covariates, including age, gender, income, and insurance status. <b>Results:</b> Higher educational attainment was associated with a lower prevalence of hypertension in the combined sample of AIAN and non-Hispanic White adults. However, this protective association was significantly weaker among AIAN adults compared to non-Hispanic White adults, as evidenced by a significant interaction between race and education. <b>Conclusion:</b> AIAN adults exhibit a higher prevalence of hypertension even at higher levels of educational attainment compared to non-Hispanic White adults, supporting the relevance of MDRs for AIAN populations. This finding underscores the need for public health interventions that address structural barriers and contextual factors unique to AIAN populations. Policies focused solely on educational access may be insufficient to reduce hypertension risk among AIAN adults without addressing broader social and structural inequities.

Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that matter, classify a text based on its topic like whether it is about a particular subject like physics or chemistry as is useful in search engines. The model leverages a sequential architecture, transforms words into dense vectors using an Embedding layer, and captures intricate sequential patterns with two Long Short-Term Memory (LSTM) layers. This model aims to effectively classify sentiments in text data using a 50-dimensional embedding dimension and 20 % dropout layers. The use of rectified linear unit (ReLU) activations enhances non-linearity, while the SoftMax activation in the output layer aligns with the multi-class nature of sentiment analysis. Both training and test accuracy were well over 80%.

The most prevalent technique behind security data breaches exists through SQL Injection Attacks. Organizations and individuals suffer from sensitive information exposure and unauthorized entry when attackers take advantage of SQL injection (SQLi) attack vulnerability’s severe risks. Static and heuristic defense methods remain conventional detection tools for previous SQL injection attacks study's foundation is a detection system developed using the Gated Recurrent Unit (GRU) network, which attempts to efficiently identify SQL Injection attacks (SQLIAs). The suggested Gated Recurrent Unit model was trained using an 80:20 train-test split, and the results showed that SQL injection attacks could be accurately identified with a precision rate of 97%, an accuracy rate of 96.65%, a recall rate of 92.5%, and an F1-score of 94%. The experimental results, together with their corresponding confusion matrix analysis and learning curves, demonstrate resilience and outstanding generalization ability. The GRU model outperforms conventional machine learning (ML) models, including K-Nearest Neighbor’s (KNN), and Support Vector Machine (SVM), in terms of identifying sequential patterns in SQL query data. Recurrent neural architecture proves effective in the detection of SQLi attacks through its ability to provide secure protection for contemporary web applications.

Over the past few decades, cardiovascular disease and related complications have surpassed all others as the important causes of death on a universal scale. At the moment, they are the important cause of mortality universal, including in India. It is important to know how to find cardiovascular problems early so that patients get better care and prices go down. This project utilizes the UCI Heart Disease Dataset to develop ML and DL models capable of detecting cardiac diseases. Heart illness was categorized using Convolutional Neural Network (CNN) models, which are able to detect intricate patterns in supplied data. A confusion matrix rating, an F1-score, a ROC curve, accuracy, precision, and recall were some of the measures used to grade the model. It did much better than the Neural Network, Deep Neural Network (DNN), and Gradient Boosted Trees (GBT) models, with 91.71% accuracy, 88.88% precision, 82.75% memory, and 85.70% F1-score. Comparative study showed that CNN was the most accurate model. Other models had different balances between accuracy and recall. The experiment results show that the optional CNN model is a decent way to identify cardiovascular disease. This means that it could be used in healthcare systems to find diseases earlier and treat patients better.

<b>Background:</b> Previous research shows that socioeconomic status (SES) positively impacts children's development, yet the benefits are not equally distributed across racial groups. According to the Minorities’ Diminished Returns (MDRs) framework, Black children tend to experience smaller gains from parental education compared to White children. <b>Objective:</b> Building on the MDRs framework, this study examines whether high financial strain contributes to the diminished returns of parental education for Black children, using data from the Adolescent Brain Cognitive Development (ABCD) Study. We hypothesized that: (1) there would be a positive effect of parental education on total cortical volume, (2) this effect would be weaker for Black than White children, and (3) higher household financial strain in Black families would mediate the diminished returns of parental education on total cortical volume for Black children. <b>Methods:</b> Data were drawn from the baseline ABCD Study, focusing on 7,936 9- and 10-year-old children identified as either Black (n = 1,775) or White (n = 6,161). Parental education was the key independent variable, covariates included age, sex, household income, and marital status, race was the moderator, financial strain was the mediator, and total cortical volume was the outcome. Structural Equation Models (SEMs) were employed to examine the associations between parental education and cortical volume, with financial strain as a mediator and race as a moderator. <b>Results:</b> Higher parental education was associated with greater cortical volume in the pooled sample. However, this effect was significantly weaker for Black children. Financial strain partially mediated the observed diminished returns of parental education. <b>Conclusion:</b> High financial strain experienced by middle-class Black families partially explains why the association between parental education and child development is weaker in Black than White families. Interventions aimed at enhancing educational quality, increasing employability, expanding access to higher-paying jobs, and reducing labor market discrimination against Black individuals may help address racial inequities in child development in the U.S. Efforts to reduce financial strain should extend beyond low-income populations to also support higher-educated minority families.

<b>Background:</b> Educational attainment is a well-established predictor of physical health outcomes, including body mass index (BMI). However, according to the theory of Minorities' Diminished Returns (MDRs), the health benefits of education tend to be weaker for ethnic minorities compared to non-Latino Whites, due to structural inequalities and social disadvantages. <b>Objective:</b><b> </b>This study examines whether the association between educational attainment and BMI is weaker among Latino individuals compared to non-Latino individuals, in line with the MDRs framework. <b>Methods:</b><b> </b>Data were drawn from the 2014 wave of the Understanding America Study (UAS), a nationally representative internet-based panel. Body mass index (BMI) was the outcome of interest. Linear regression models were used to analyze the association between educational attainment and BMI, with an interaction term for ethnicity to explore differences in the relationship between Latino and non-Latino people. Models were adjusted for age, sex, marital status, and labor market participation and results were presented as beta coefficients, p-values, and 95% confidence intervals (CIs). <b>Results:</b><b> </b>Higher educational attainment was associated with lower BMI for both Latino and non-Latino participants (p < 0.001). However, the interaction between educational attainment and ethnicity was significant (p < 0.05), indicating that Latino individuals experienced smaller reductions in BMI because of higher education compared to non-Latino people. <b>Conclusion:</b><b> </b>This study provides evidence of diminished returns from educational attainment on BMI among Latino individuals. These findings support the MDRs framework, suggesting that structural barriers may limit the health benefits of education for Latino populations. While education is a key determinant of physical and mental health, its benefits are not equitably distributed across ethnic groups. Structural inequalities, chronic stress, poor neighborhood environments, and adverse educational and occupational conditions likely contribute to this disparity. Addressing these underlying factors through targeted policy interventions is necessary to promote health equity for Latino populations.

<b>Background:</b> Retirement is a universal life stage, marking the culmination of an individual's working years. However, many people face financial challenges during retirement due to insufficient financial planning. Retirement preparedness is essential for ensuring economic security and maintaining a high quality of life in later years. Education is often viewed as a key driver of retirement preparedness, as it is linked to higher earnings, better financial literacy, and improved decision-making. However, the Minorities' Diminished Returns (MDRs) theory suggests that the economic, cognitive, and behavioral benefits of education are weaker for racial and ethnic minorities compared to non-Latino Whites. <b>Objective:</b><b> </b>This study aims to examine the relationship between educational attainment and retirement preparedness, focusing on whether this association differs among Black, Latino, and non-Latino White individuals, using data from the Understanding America Study (UAS). <b>Methods:</b><b> </b>Data were drawn from the UAS, a nationally representative internet-based panel survey. The sample included participants from diverse racial and ethnic backgrounds. Linear regression models were used to evaluate the association between educational attainment, measured in years of schooling, and retirement preparedness. Interaction terms were included to test whether the association varied by race and ethnicity. Models were adjusted for potential confounders, including age, sex, marital status, employment status, and immigration. <b>Results:</b><b> </b>In the overall sample, higher educational attainment was significantly and positively associated with better retirement preparedness (p < 0.001). However, consistent with the MDRs framework, the strength of this association was significantly weaker for Black and Latino participants compared to non-Latino White participants (p < 0.05). Non-Latino Whites with higher education levels reported substantially better retirement preparedness, while the same level of education yielded smaller gains in retirement preparedness for Black and Latino individuals. <b>Conclusion:</b><b> </b>The findings support the Minorities' Diminished Returns theory, showing that although educational attainment enhances retirement preparedness for all groups, Black and Latino individuals derive fewer benefits compared to their non-Latino White counterparts. These disparities point to persistent structural inequalities and systemic barriers within the education system and labor market, as well as the effects of segregation and discrimination, which undermine the economic benefits of education for marginalized populations. Addressing these disparities requires targeted policy interventions aimed at eliminating racial and ethnic inequalities in retirement outcomes and ensuring equitable benefits from educational attainment for all groups.