Namal College

Objectives This study aimed to evaluate temporal trends in adult sickle cell disease (SCD) mortality in the United States from 1999 to 2020, stratified by age, sex, race, and geography, to identify disparities and guide targeted interventions. Methods A retrospective observational study was conducted using national mortality data from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiological Research (CDC WONDER) database. The sample included adults aged ≥15 years who died from SCD between 1999 and 2020 (n = 17,443). Age-adjusted mortality rates (AAMRs) were calculated and stratified by demographic and geographic variables. Temporal trends were assessed using Mann-Kendall trend tests, and t-tests were applied to compare continuous variables across subgroups. Statistical significance was defined as P < 0.05. Results The AAMR for adult SCD increased by 132% over the study period (P = 0.014). The greatest rise in mortality was observed among adults aged ≥65 years (P = 0.008) and women (P = 0.015). Black individuals accounted for 97.5% of SCD-related deaths, underscoring severe racial disparities. Geographically, the Southern region exhibited the highest AAMR and was the only region with a statistically significant increase in mortality over time (P = 0.001). Conclusions Adult SCD mortality in the United States has risen significantly from 1999 to 2020, with disproportionate increases among older adults, women, and individuals in the Southern region. The findings highlight urgent needs for targeted national interventions, development of age-specific care models, and implementation of equity-focused health policies to address persistent racial and regional disparities in SCD outcomes.

Cadmium-free buffer layers are pivotal for the sustainable development of thin-film photovoltaics. This work numerically investigates SnS2 as a high-performance, environmentally benign alternative to CdS for antimony selenosulfide (Sb2(S,Se)3) solar cells using AFORS-HET software. The SnS2/Sb2(S,Se)3 heterojunction exhibits a significantly lower conduction band offset (CBO ≈ 0.23 eV) than its CdS counterpart (CBO ≈ 0.49 eV), which is identified as the primary factor for suppressed interface recombination and enhanced electron injection efficiency. A comprehensive optimization strategy is presented: tuning the S content in Sb2(S,Se)3 to 40% optimizes the trade-off between band gap widening and hole transport barrier at the ETL/absorber interface; adjusting the absorber thickness to 340 nm balances light absorption and carrier collection efficiency; and elevating the SnS2 carrier concentration to 1021 cm−3 strengthens the built-in potential and induces a beneficial hole-blocking “spike” at the front contact. The synergistically optimized device achieves a power conversion efficiency (PCE) of 10.39%, a substantial improvement over the 7.56% efficiency of the CdS-based reference cell in our simulation framework.

Abstract This study presents a finite propagation speed model for stagnation point flow of hybrid nanofluid with applications of nonlinear radiated effects. A synthesized hybrid nanofluid is subject to the utilization of Al 2 O 3 and γ‐Al 2 O 3 nanoparticles dispersed in ethylene glycol (EG) and engine oil. Heat transfer impact is further assessed with an external heat source and quadratic thermal constraints. This combination is strategically chosen due to its enhanced thermal conductivity and industrial relevance in lubrication, heat exchangers, and automotive cooling systems. The governing equations, formulated based on boundary layer approximations, are transformed into a system of nonlinear ordinary expressions. For the solution approach, the shooting technique is implemented. It is claimed that the hybrid nanofluid exhibits superior heat transfer performance compared to conventional nanofluids, making it a promising candidate for energy‐efficient thermal management applications. The findings of this study contribute to the optimization of nanofluid‐based thermal systems in engineering applications such as aerospace, manufacturing, and electronic cooling technologies.

Background: The expanding integration of Artificial Intelligence (AI) in education has revolutionized academic practices, improving efficiency in research and writing but simultaneously raising concerns about academic integrity and reduced self-efficacy. In developing contexts such as South Asia, where digital literacy and ethical frameworks are still evolving, understanding these dynamics is crucial to ensure that AI complements rather than compromises educational integrity. Objective: This study aimed to examine the relationship between AI dependency, academic integrity, and self-efficacy among research students, while identifying gender-based and demographic variations influencing these variables. Methods: A quantitative cross-sectional study was conducted among 302 actively enrolled research students aged 18–50 years, selected through purposive sampling. Data were collected via three standardized instruments: the Students’ AI Dependency Questionnaire (α = .89–.91), McCabe/ICAI Academic Misconduct Inventory (α = .90), and the Academic Self-Efficacy Scale (α = .87). Descriptive statistics, Pearson correlation, linear regression, and independent t-tests were applied using SPSS v27 to assess associations and group differences at a significance level of p &lt; 0.05. Results: The mean AI dependency score was 91.24 (SD = 16.4), academic integrity 33.66 (SD = 13.82), and self-efficacy 40.67 (SD = 8.63). AI dependency positively predicted academic misconduct (β = .14, p = .019, R² = .42). Academic integrity was negatively correlated with self-efficacy (r = –.20, p &lt; .01) but positively correlated with AI dependency (r = .14, p &lt; .05). Male students showed higher AI dependency (M = 94.68 ± 19.22) than females (M = 89.70 ± 14.22, p = .032). Conclusion: Findings indicate that increased reliance on AI tools may enhance confidence yet elevate risks of academic misconduct, highlighting a paradox between technological competence and ethical vulnerability. Strengthened AI literacy and institutional ethics policies are vital to promote balanced, responsible AI use and preserve academic integrity.