Search Results (193)

Load-redistribution false-data-injection (LR-FDI) attacks can degrade power-system operation by reshaping the perceived nodal demand pattern, thereby inducing congestion-aware redispatch and economic inefficiency while preserving the net system load. Prior LR-FDI studies commonly adopt bilevel/Stackelberg formulations with a continuous attack vector and an embedded operator response; however, these formulations often (i) do not represent explicit compromised-load selection, (ii) become computationally restrictive when combinatorial target sets are considered, and (iii) offer limited transparency for structured, stage-wise attack planning. This paper proposes a sequential two-stage attacker–operator framework for LR-FDI vulnerability assessment that integrates sparse load compromise decisions with screening-regularized attack synthesis and post-attack operational evaluation. In Stage-1, a mixed-integer nonlinear program identifies economically influential load buses via binary selection and determines admissible perturbation magnitudes under total-load conservation and proportional shift bounds. To confine the attacker-side search region and avoid economically exaggerated solutions, a screening-derived conservative operating-cost ceiling is first estimated through a parametric load-sensitivity analysis and then used to regularize the attack-synthesis step. In Stage-2, the system operator’s corrective redispatch is evaluated by solving an active-power-oriented economic dispatch model with nonlinear network-consistent assessment of operational outcomes. Using the IEEE 24-bus RTS, results show that the hourly operating-cost deviation reaches ≈0.2% in the most adverse feasible cases, and the cumulative daily impact approaches ≈5% only under selectively realizable compromised-load patterns, accompanied by a nearly 80% increase in total active-power transmission losses relative to the base case. Overall, the framework yields a practically grounded quantification of conditionally severe economic and network stress under coordinated LR-FDI scenarios and provides actionable insight for prioritizing vulnerable load locations for protection and monitoring. Full article

This study examines how pre-service teachers construct pedagogical knowledge to promote equity in school settings through reflection and research from an intersectional gender perspective. Situated within current debates on gender, interculturality, and social justice in teacher education, the study explores how pre-service teachers develop critical awareness of inequality and envision transformative practices. Using a qualitative design, three reflective workshops were conducted with students from Early Childhood and Elementary Education programs in Chilean universities. Thematic analysis identified nine principal codes, which were later organized into four analytical domains: knowledge construction, interculturality and inclusion, gender practices, and intersectional meanings. Results show that participants conceive teaching as a political and ethical practice linked to community engagement, democratic coexistence, and affective responsibility. They also challenge traditional gender roles by proposing co-care and collective well-being as foundations for equitable education. Furthermore, intercultural and situated pedagogies emerge as key strategies for connecting theory with practice and validating diversity within the classroom. Participants demonstrate emerging forms of intersectional and gender awareness, questioning the feminization of teaching and proposing notions of co-care and collective well-being that transcend binary gender norms. They also value intercultural and contextual pedagogies, emphasizing empathy, recognition of diversity, and the validation of students’ origins and trajectories. Full article

Background: Systemic arterial hypertension (SAH) shows a high prevalence among postmenopausal women and represents an important public health concern. Objective: To evaluate factors associated with SAH in postmenopausal women participating in a resistance training program. Methods: This observational, cross-sectional study included 55 postmenopausal women (66.0 ± 4.9 years) recruited from the “More Active Women” research project, an umbrella experimental and longitudinal study involving resistance training interventions. Cross-sectional data were collected during the baseline assessment (April–May 2025). Sociodemographic variables, nutritional status (body mass index and waist circumference), and behavioral and health-related variables obtained through structured interviews and anthropometric assessments were analyzed. Associations were tested using Pearson’s chi-square test or Fisher’s exact test, with effect size estimated by Phi or Cramer’s V when appropriate, and binary logistic regression was performed for adjusted analyses. Results: Significant associations were observed between SAH and elevated BMI (p = 0.03; φ = 0.30), waist circumference > 88 cm (p = 0.006; φ = 0.40), and lower educational level (p = 0.003; V = 0.47). In the adjusted analysis, waist circumference ≤ 88 cm was associated with a lower likelihood of SAH (OR = 5.54; 95% CI: 0.965–31.872; p = 0.007), whereas lower educational level was associated with a higher likelihood of hypertension (OR = 13.98; 95% CI: 1.505–129.833; p = 0.004). Conclusion: Excess central adiposity and lower educational level are associated with SAH in postmenopausal women, highlighting the importance of integrated health promotion strategies that address both cardiometabolic risk factors and social determinants of health during aging. Full article

Background: The association between physical activity and vitamin D status is not yet fully understood. This study aims to investigate the prevalence of physical inactivity and its associated factors, including serum 25-hydroxyvitamin D (25[OH]D) concentration, among adolescents in Northern Sudan. Methods: A school-based cross-sectional study was conducted in Almatamah, River Nile State, Sudan, and a questionnaire was used to collect sociodemographic data. Standardized methods were used to measure physical activity and serum 25(OH)D levels. Physical activity was expressed as metabolic equivalent minutes per week (MET-min/week). A multivariate binary regression was performed. Results: Three hundred and thirteen adolescents [159 (50.8%) males and 154 (49.2%) females] were enrolled in the study. The median (interquartile, IQR) values for age, 25(OH)D, and physical activity were 15.1 (14.0–16.2) years, 20.2 (9.6–31.2) ng/mL, and 1080 (495–3360) MET-min/week, respectively. The median (IQR) physical activity score was higher in males than in females [3287.5 (1680.0–4659.0) MET-min/week vs. 495.0 (314.3–990.0) MET-min/week]. Of the enrolled adolescents, 220 (70.3%) had inadequate physical activity levels (<3000 MET-min/week). Serum 25(OH)D level was significantly lower in adolescents with inadequate physical activity than in those with adequate physical activity levels [17.7 (7.8–28.0) ng/mL vs. 26.4 (17.3–36.8) ng/mL]. In the multivariable binary analysis, female sex (adjusted odds ratio [AOR]: 35.0; 95% CI: 13.89–88.08), a lower paternal education level (AOR: 2.812; 95% CI: 1.39–5.70), and having a skilled father (AOR: 2.08; 95% CI: 1.05–4.12) were factors associated with inadequate physical activity among adolescents, whereas 25(OH)D levels were inversely associated with insufficient physical activity (AOR: 0.97; 95% CI: 0.95–0.99). Conclusions: Interventions are needed to address the high level of physical inactivity among adolescents in Northern Sudan, particularly among girls. Programs that promote physical activity both at home and school help ensure that children and adolescents maintain adequate physical activity and 25(OH)D levels. Full article

Background: Advanced liver fibrosis (LF) is a major determinant of prognosis across chronic liver diseases. Current biomarkers are often etiology-specific and lack cross-cohort robustness. Shared molecular drivers across etiologies remain incompletely defined, and effective anti-fibrotic therapies are limited. Methods: We developed a multi-algorithm consensus machine-learning framework to derive a robust LF progression signature. In the training non-alcoholic fatty liver disease (NAFLD) cohort GSE213621 (n = 368), samples were formulated as a binary classification task (mild fibrosis, F0–F2; advanced fibrosis, F3–F4). Candidate genes were screened in parallel using Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme Gradient Boosting (XGBoost). Genes selected by at least two algorithms were defined as a high-consensus pool, and genes consistently selected by all four algorithms were prioritized to construct a core signature. Model performance was evaluated by stratified cross-validation in the training cohort and externally validated in four independent cohorts of different etiologies (GSE49541, GSE84044, GSE130970, and GSE276114). Cellular sources of signature genes were characterized using single-cell RNA sequencing (scRNA-seq) datasets GSE136103 (human) and GSE172492 (mouse). For therapeutic discovery, the high-consensus expression profile was queried against the Connectivity Map (CMap) to prioritize compounds predicted to reverse the fibrotic transcriptional program. Withaferin A (WFA) was selected for experimental validation in a carbon tetrachloride (CCl₄)-induced mouse LF model and in the transforming growth factor-β1 (TGF-β1)-stimulated human hepatic stellate cell line LX-2. Bulk liver RNA-seq profiling was performed to interrogate WFA-associated molecular changes in vivo. Results: We identified a six-gene signature (CLEC4M, COL25A1, ITGBL1, NALCN, PAPPA, and PEG3) that discriminated advanced from mild fibrosis, achieving a mean AUC of 0.890 in internal cross-validation and an average AUC of 0.864 across external validation cohorts. scRNA-seq analysis revealed cell-type-specific expression with prominent enrichment in fibroblast populations. In vivo, WFA markedly attenuated CCl₄-induced fibrosis (p < 0.05) and reversed 1314 fibrosis-associated differentially expressed genes (adjusted p < 0.05), which were enriched in fatty acid metabolism and PPAR signaling, as well as extracellular matrix (ECM)–receptor interaction and focal adhesion (adjusted p < 0.05). In vitro, WFA suppressed TGF-β1-induced LX-2 activation, reducing α-SMA and Fibronectin expression (p < 0.05). Conclusions: We report a six-gene signature that robustly predicts advanced LF across etiologies, define its cellular context using single-cell atlases, and validate the anti-fibrotic activity of WFA in both in vivo and in vitro models. Bulk liver RNA-seq and cellular evidence further suggest that WFA-associated effects are linked to lipid metabolic programs, ECM remodeling, and attenuation of hepatic stellate cell activation. Full article

In the current investigation, the solar photocatalytic degradation of two cationic model dyes (methyl green (MG) and crystal violet (CV)) was studied using α-Fe₂O₃/ZnFe₂O₄ nanocomposite. The fine powder of nanoparticles was obtained by co-precipitation method at pH = 10 and characterized by X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM) and UV-vis spectroscopy. The surface properties were further examined through temperature-programmed desorption (TPD) and point of zero charge (PZC) measurements to assess the acid–base characteristics and surface charge behavior of the material. Adsorption and photocatalytic performance were systematically evaluated in both single and binary systems. Dark adsorption experiments showed a better affinity of the α-Fe₂O₃/ZnFe₂O₄ heterosystem towards MG dye in both cases. Under natural sunlight irradiation in the individual system, the photocatalytic activity of the nanoparticles was significantly higher for MG (81.67% removal) compared to CV (41.70%). Kinetics analysis revealed that the photodegradation of both dyes followed a pseudo-first-order model. In binary systems, competitive adsorption effects strongly influenced the degradation behavior, with MG showing preferential adsorption and higher degradation rates. Moreover, the MG discoloration kinetics followed a second-order model, while CV kinetics transitioned from second- to zero-order with increased initial concentration. Full article

This paper addresses the Pickup and Delivery Problem with Time Windows (PDPTW), an NP-hard combinatorial optimization problem with major practical relevance in logistics and transportation. The study focuses on a quadratic unconstrained binary optimization (QUBO) formulation for quantum annealing and benchmarks it against two classical optimization paradigms. A modular Python framework is developed that encodes PDPTW in three ways: a mixed-integer linear programming (MILP) model that serves as an exact reference, a genetic algorithm (GA) metaheuristic, and a QUBO model that is compatible with quantum annealers. The framework supports test scenarios with increasing structural complexity, with both feasible and intentionally infeasible instances. An additional contribution is the conceptual design and preliminary analysis of an automatic-penalty weight-tuning scheme for the QUBO model. Experimental results show that the proposed QUBO formulation can produce high-quality solutions for simpler PDPTW instances, but its performance strongly depends on the careful calibration of penalty weights. MILP provides optimal baselines on small instances but becomes intractable as problem size grows. The GA scales to the largest scenario and finds feasible solutions of reasonable quality, but they are not necessarily optimal. The evaluation also includes a large number of problem instances and runs on IBM Quantum hardware using the Quantum Approximate Optimization Algorithm (QAOA). Full article

Background: Tobacco smoke exposure in the home remains common among U.S. families and has been increasingly associated with adverse mental health outcomes, including anxiety and depression, among children and adolescents. Rising rates of youth anxiety and depression, coupled with evidence that secondhand smoke and related psychosocial stressors may disrupt emotional development, underscore the importance of examining household smoking exposures as a modifiable risk factor for youth mental health. This study examines associations between exposure to smoke in households and the likelihood of caregiver-reported anxiety and depression in US children and adolescents aged 6–17 years, using data from the 2022–2023 National Survey of Children’s Health (NSCH). Methods: A retrospective analysis of NSCH data for two age cohorts, children (6–11 years) and adolescents (12–17 years), for the years 2022–2023 was conducted. Descriptive statistics were generated for the selected sample by frequencies and counts for each of the dependent and independent variables, followed by binary logistic regressions for each measured mental health variable based on current diagnosis, severity levels (not severe, mild, moderate, severe) and household tobacco use. Results: This study found significant associations between parental smoking and increased odds of caregiver-reported anxiety and depression in both children and adolescents. Specifically, children living with parents who smoke had 1.55 times the odds of severe anxiety, while adolescents had 1.38 times the odds of currently experiencing anxiety and 1.31 times the odds of currently experiencing depression. Smoking inside the household was not significantly associated with caregiver-reported anxiety or depression. These findings suggest that parental smoking serves as a marker for broader psychosocial and environmental stressors that contribute to youth mental health outcomes. Conclusions: Parental smoking is a significant, modifiable risk factor for anxiety and depression among US children and adolescents. These results emphasize the need for targeted, evidence-based interventions to reduce parental smoking, improve awareness of associated mental health risks, and address social determinants of health. Policies promoting smoke-free households, integrated cessation support, and culturally tailored education programs are essential to mitigate the impact of parental smoking on child and adolescent mental health. Full article

Background/Objectives: This study examined the reasons and sociodemographic drivers behind delays in seeking medical care among participants in the All of Us Research Program. Methods: A cross-sectional study was conducted using data collected between 2018 and 2024. The primary outcome was the prevalence of reasons for delayed medical care (DMC). Descriptive statistics were used to calculate the prevalence of the various reported reasons for delayed medical care. Binary logistic regression was applied to examine the association between sociodemographic characteristics and each reported reason for delayed medical care. Results: Out of a total of 633,000 All of Us participants, 300,820 participants had complete data on the healthcare utilization and access survey and were eligible for final analysis. The most common reported reasons for DMC were out-of-pocket expenses (16.68%), nervousness about seeing a provider (14.18%), and inability to get time off work (11.04%). Females had significantly higher odds of DMC due to out-of-pocket costs (OR = 1.31, 95% CI: 1.28–1.33). Black (OR = 0.81, 95% CI: 0.78–0.84) and Asian (OR = 0.94, 95% CI: 0.89–0.99) individuals had lower odds of DMC due to out-of-pocket costs. Married individuals had more than twice the odds of DMC due to childcare responsibilities (OR = 2.45, 95% CI: 2.33–2.56). Conclusions: A significant proportion of participants reported DMC due to various reasons, with financial, medical visit anxiety, and work-related reasons being the most common. These findings highlight actionable intervention targets, including nurse-led cost navigation and financial counseling, flexible scheduling/telehealth to reduce work-related delays, and patient-centered communication and outreach strategies to reduce visit-related anxiety and support caregiving and transportation needs. Full article

This study employs global optimization techniques to examine optimal vaccine pricing strategies that consider quantity discounts and vaccine distribution equity, under centralized procurement by group purchasing organizations. Based on the economic characteristics of the vaccine market, a mathematical programming model incorporates the payment capacities and willingness to pay of different member countries, minimizing the maximum adjusted price disparities across pricing tiers and thereby enhancing the overall fairness of vaccine distribution. To further reduce computational complexity and enhance practical applicability, this study improves the model by reducing the number of binary variables. Experimental analysis is conducted using real-world data from the Vaccine Alliance (Gavi) and the Pan American Health Organization (PAHO). The results show that the improved model reduces computation time by over 30% on average and demonstrates effective control over price differentiation across various pricing tiers and parameter settings. Full article

Background/Objectives: Lesbian, gay, bisexual, and transgender (LGBT) cancer survivors face disproportionately high structural and psychosocial barriers to post-diagnosis care. However, heterogeneity within this population remains understudied. This study aimed to characterize healthcare utilization (HCU) barriers among LGBT cancer survivors, assess psychosocial vulnerabilities (discrimination, stress, and social support), and identify survivor subgroups at greatest risk for care disengagement. Methods: Data were drawn from the All of Us Research Program. A sample of 3502 LGBT cancer survivors was analyzed, including lesbian (n = 730), gay (n = 1285), bisexual (n = 1296), and transgender/gender expansive (TGE) (n = 209) individuals. HCU barriers were assessed using 21 binary indicators. Psychosocial measures included the Everyday Discrimination Scale, Perceived Stress Scale, and MOS Social Support Survey. Agglomerative hierarchical cluster analysis identified latent HCU barrier profiles. Differences across clusters and identity groups were assessed using ANOVA and chi-square tests, and multinomial logistic regression examined demographics, socioeconomic, and psychosocial predictors of cluster membership. Results: Three distinct HCU barrier clusters were identified: low (59.7%), moderate (27.8%), and high (12.5%). Bisexual and TGE survivors were disproportionately represented in the high-barrier cluster, which was characterized by widespread cost-related nonadherence, structural delays in care, and higher levels of perceived discrimination and stress. In adjusted models, bisexual identity, lower income, female sex assigned at birth, and higher discrimination and perceived stress were independently associated with increased odds of high-barrier cluster membership. Conclusions: Substantial heterogeneity exists in HCU barriers among LGBT cancer survivors. Bisexual and TGE survivors experience a concentrated burden of structural and psychosocial barriers to survivorship care, highlighting the relevance of targeted, data-driven approaches to reduce access inequities within this population. Full article

Background/Objectives. Chronic benzodiazepine (BDZ) use is frequently maintained beyond recommended durations due to neuroadaptation, psychological dependence, and withdrawal-related issues. Passiflora incarnata L., herba (P. incarnata) has shown anxiolytic and GABAergic activity that may mitigate withdrawal symptoms, while cognitive-behavioural therapy (CBT) targets maladaptive beliefs and behaviours sustaining BDZ misuse. This study investigates the independent and interactive effects of P. incarnata and CBT on BDZ dose reduction during a three-month tapering program. Methods. This retrospective observational study included 186 outpatients with anxiety or depressive disorders in clinical remission undergoing BDZ tapering, of whom 93 received a dry extract of P. incarnata as adjunctive treatment and 93, matched for diagnosis, age and sex, followed a standard tapering protocol. BDZ doses were assessed at baseline and three months. CBT was recorded as a binary variable based on the information documented in the medical records. An ANCOVA was performed to assess the impact of CBT and P. incarnata on BDZ reduction (change in mg diazepam equivalents), adjusting for sex, age, education, baseline anxiety and depression scores, initial BDZ and antidepressant dosage. A subgroup analysis was conducted to investigate the role of P. incarnata dosage in BDZ reduction. Results. Both CBT and P. incarnata were associated with significantly greater reductions in BDZ dosage at three months (CBT: p = 0.005, effect size: 0.032; P. incarnata: p < 0.001, effect size: 0.128). A significant interaction between CBT and P. incarnata was also observed (p = 0.037, effect size: 0.018), indicating a synergistic effect when both interventions were combined. Baseline sociodemographic characteristics, BDZ and antidepressant dosage and symptom severity did not differ significantly between groups. Patients taking 400–600 mg of P. incarnata dry extract showed a higher BDZ reduction compared to those taking 200 mg. Conclusions. These findings suggest that P. incarnata and CBT exert independent yet complementary effects in supporting BDZ tapering. Their combination appears to enhance dose reduction beyond either intervention alone, supporting a multimodal approach that addresses both neurobiological and psychological components of BDZ addiction. Prospective controlled studies are needed to confirm these results and to clarify their impact on long-term discontinuation outcomes. Full article

The proliferation of binary vulnerabilities in the software supply chain has become a critical security challenge. Existing vulnerability detection approaches—including dynamic analysis, static analysis, and decompilation-assisted analysis—all suffer from limitations such as insufficient coverage, high false-positive and false-negative rates, or poor compatibility. Although decompilation technology can serve as a bridge connecting binary-code and source-code vulnerability detection tools, current schemes suffer from inadequate semantic restoration quality and lack of tool compatibility. To address these issues, this paper proposes LLMVulDecompiler, a binary decompilation model based on fine-tuned large language models designed to generate high-precision decompiled code that integrates directly with source-code static analysis tools. We construct a dedicated training and evaluation dataset that covers multiple compiler optimization levels (e.g., O0–O3) and a diverse set of program functionalities. We adopt a two-stage fine-tuning strategy that involves first building foundational decompilation capabilities, then enhancing vulnerability-specific features. Additionally, we design a low-cost inference pipeline and establish multi-dimensional evaluation criteria, including restoration similarity, compilation success rate, and functional correctness. Experimental results show that the model significantly outperforms baseline models in terms of average edit distance, compilation success rate, and black-box test pass rate on the HumanEval-C benchmark. In tests on 12 real-world CVE (Common Vulnerabilities and Exposures) instances, the approach achieved a detection accuracy of 91.7%, with substantially reduced false-positive and false-negative rates. This study demonstrates the effectiveness of specialized fine-tuning of large language models for binary decompilation and vulnerability detection, offering a new pathway for binary security analysis. Full article

Background/Objectives: Cervical cancer is one of the most common cancers among women of reproductive age, with 80% of the cases occurring in developing countries. Cervical cancer is largely preventable by effective screening programs. This study assessed the knowledge, attitudes, cultural beliefs, and screening practices related to cervical cancer among women in the rural community of Lutubeni, Eastern Cape Province. Methods: A descriptive cross-sectional study was conducted among 95 women aged 25 years or older attending Lutubeni Clinic. Data was collected using a structured, validated questionnaire covering demographics, reproductive health, knowledge of cervical cancer, attitudes, cultural perceptions, and screening practices. Statistical analysis involved descriptive summaries, chi-square tests, and binary logistic regression. Results: Most participants exhibited poor knowledge of cervical cancer symptoms (47.4%) and risk factors (61.1%), with only 3.2% demonstrating good overall knowledge. Vaginal bleeding (60.0%) and foul-smelling discharge (50.5%) were the most recognized symptoms. Only 40.0% were aware of human papillomavirus (HPV) vaccination. While 87.4% knew about cervical cancer screening, only 55.8% had ever been screened. Of these, 43.2% had screened only once, primarily at the clinic (33.7%), mostly initiated by health professionals (41.1%). Positive attitudes toward screening were observed in 52.6%, while 88.4% held cultural beliefs that hindered open discussion about sexual health. Statistically significant factors associated with screening uptake included educational level (p = 0.047), knowledge of symptoms (p = 0.04), risk factors (p < 0.0001), prevention (p < 0.0001), treatment (p = 0.001), and attitudes (p < 0.0001). Independent predictors of poor screening practice were holding an associate degree (OR = 0.04, p = 0.042), having good preventive knowledge (OR = 0.02, p = 0.012), and having negative attitudes (OR = 36.22, p = 0.005). Conclusions: High awareness alone does not guarantee participation in cervical cancer screening in rural South Africa. Interventions must address cultural barriers, stigma, and negative perceptions while strengthening health education that links HPV vaccination with screening awareness. The unexpected association between associate degree attainment and poor screening underscores the complexity of behavioral determinants and warrants further investigation in larger cohorts. Full article

This study examines the underrepresentation of women in STEMM (Science, Technology, Engineering, Mathematics, and Medicine) within Peruvian public universities and identifies factors associated with women’s program choice. A cross-sectional survey was administered to first-term students across three public institutions spanning Peru’s Highlands, Coast, and Amazon regions. Data from 1142 students (145 women) were used for descriptive analysis of segregation, while an inferential sample (N = 152; 76 STEMM, 76 non-STEMM) was used for modeling. The instrument was an adapted “University Students’ Questionnaire on STEM Studies in Higher Education (QSTEMHE)” (Cronbach’s α = 0.89). Descriptive statistics and a penalized (Firth) binary logistic regression were used to evaluate sociodemographic, contextual/experiential, and motivational predictors of enrolling in a STEMM major. The cross-sectional design limits causal inference, and perception data are subject to self-report biases. Women accounted for 12.7% of STEMM enrolment overall, with pronounced horizontal segregation: engineering programs frequently recorded critically low female participation (≈3–5% in Civil, Mechanical, and Computer Engineering), whereas Medicine and Sanitary Engineering showed comparatively higher representation (27–38%). Perception data indicated that STEMM students more strongly rejected gender–ability stereotypes than non-STEMM peers, although a substantial proportion still reported constraining gender expectations and rigid household roles. In the penalized regression, Prior Interest in STEM (OR = 7.76; p = 0.018) and Motivation: Opportunities (OR = 2.24; p = 0.0001) significantly increased the probability of choosing STEMM. Crucially, Ethnicity emerged as a significant barrier: identifying as ‘Quechua’ (OR = 0.19; p = 0.0004) or ‘Other(s)’ (OR = 0.16; p = 0.011) significantly decreased this likelihood. Age, area of residence, and Motivation: Altruism was not significant. Findings support early, gender-responsive career guidance, mentoring, addressing intersectional ethnic barriers, and targeted financial aid to strengthen women’s participation and retention in STEMM. Full article