International American University

Abstract Selecting an appropriate supportive response for a mental health question post is a key step toward scalable mental health question answering, yet it remains difficult due to semantic mismatch, noisy informal language, and the need to align supportive intent beyond surface lexical overlap. We propose MF-GAT, a novel multi view graph attention matching framework that constructs a Concept Interaction Graph (CIG) to explicitly encode post response concept alignments and their interaction structure. MF-GAT learns three complementary evidence streams, including local interaction features, multi view fusion features, and global context features, and applies view specific graph attention to propagate and reweight informative relational signals over the CIG. A gated fusion module then adaptively integrates the view representations into a unified matching vector for prediction. We evaluate MF-GAT both as a pair classification model and as a retrieval ranking model for selecting the best support from a candidate pool, reporting Accuracy and F1 together with standard ranking metrics including MRR and NDCG. On the MHQA benchmark, MF-GAT achieves 0.95 Accuracy and 0.85 F1, outperforming BERT (0.89, 0.67), CIG-GCN (0.92, 0.79), ARC-II (0.85, 0.60), and MatchPyramid (0.82, 0.59). These results show that novel multi view interaction graph modeling with attention based propagation improves both supportive response classification and practical retrieval quality for mental health support selection.

Climate variability and extreme rainfall events pose significant challenges to infrastructure development in Bangladesh, where standard statistical models often fail to account for nonlinear anomalies and low-frequency extremes. This study employs two machine learning approaches, Prophet and Long Short-Term Memory (LSTM) networks, to predict long-term yearly rainfall using historical data from 1980 to 2024 across two climatically sensitive locations. Prophet, an additive decomposition model, and LSTM, a recurrent neural network with memory-based learning, were benchmarked using Root Mean Squared Error (RMSE) and the Coefficient of Determination (R2). Results demonstrate that LSTM consistently outperformed Prophet in both Rajshahi and Ishwardi, obtaining lower RMSE values (102.4 mm and 118.7 mm, respectively) and higher R2 scores (0.88 and 0.85). While Prophet generated smoother forecasts, it underfitted severe years, whereas LSTM correctly captured interannual volatility and identified a greater number of high-risk years above the 90th percentile threshold. The estimates were transformed into practical adaptation techniques, including elevated foundation design, decentralised rainwater collection, and the use of water-resistant materials, thereby integrating predictive analytics into civil engineering applications. This integration provides a framework for climate-resilient infrastructure planning aligned with national adaptation plans. Future studies should expand to include multivariate forecasting that incorporates exogenous climate drivers, such as ENSO, soil moisture, and vegetation indices, as well as examine the scalable integration of GIS and BIM platforms for real-time urban resilience design.

Breast cancer etiology is multifactorial with African American women experiencing a significant health disparity in clinical presentation and outcomes. The selenium-containing protein SELENOF has been implicated in breast carcinogenesis by cell culture and animal studies. SELENOF translation is highly regulated in part by the RNA helicase eIF4a3, which binds to the key regulatory regions in the SELENOF mRNA and suppress its translation. In addition, SELENOP, the primary selenium transporter, plays a critical role in selenium delivery to tissues and may influence selenoprotein synthesis. This study aimed to examine the levels of SELENOF and eIF4a3, along with SELENOF and SELENOP genotypes, in breast cancer tissues from African American and Caucasian women To study their roles in breast cancer outcome and racial disparity, human tissues were assessed by multiplex immunofluorescence staining with antibodies directed against SELENOF and eIF4a3 and DNA from these tissues were genotyped for previously studied variations in SELENOF and the selenium transporter protein SELENOP Elevated levels of both SELENOF and eIF4a3 were observed in breast cancer tissues. SELENOF expression and genotype varied by HER2 status, while SELENOP genotypes were associated with breast cancer and showed age-related differences. SELENOF and eIF4a3 were also higher in tissues derived from African American women, who also exhibited higher frequency of a SELENOP polymorphism in the non-coding region of the gene These findings suggest that SELENOF, eIF4a3, and SELENOP may contribute to breast cancer progression and racial disparities in outcomes. Their differential expression and genetic variation highlight potential molecular mechanisms underlying these disparities and may inform future therapeutic or diagnostic strategies.

Three-dimensional mapping of retinal microvasculature is essential for monitoring systemic vascular health. Existing methods rely heavily on manual annotation or training-intensive deep learning models that lack generalizability. Here we report RADAR. This is an annotation-free computational framework for the 3D segmentation and quantification of optical coherence tomography angiography data. The pipeline integrates adaptive physics-aware denoising with topology-preserving centerline extraction to reconstruct complex networks without manual labeling. We validated the framework in healthy individuals and patients with early-stage diabetic retinopathy. The method outperformed standard segmentation tools and resolved layer-specific morphological alterations obscured in conventional two-dimensional projections. Quantitative analysis revealed distinct patterns of compensatory remodeling and increased tortuosity in diabetic eyes. RADAR enables precise extraction of volumetric biomarkers including vessel length and branching complexity. It provides a scalable tool for early detection and longitudinal assessment of ocular and systemic vascular diseases.

This review examining the thermo-chemical enhancements of solar thermal systems through hybrid nanofluid and phase change materials (PCMs) which were crucial for efficiently supplying high-temperature heat in solar-driven industrial processes and mitigating solar intermittency. Also the hybrid and single-component nanofluids based on metal oxides, carbonaceous materials, and composite nanoparticle could be enhances interfacial energy transportations, increasing effective thermal conductivity by 10–25% and strengthening micro-convective heat transfer. The latent-heat storage media including paraffin wax, eutectic salts, and encapsulated composite PCMs providing a high storage capacity of 250–442 kJ/kg which be reducing thermal losses. Moreover the integrated nanofluid–PCM systems demonstrating a significant thermo-chemical synergy, achieving high thermal efficiencies of 62–85% and high exergy efficiencies of 20–42% highlighting both effective energy utilization and high-quality heat conversion. Hence the review discusses the mechanistic basis of nanoscale interactions, thermal–chemical coupling, and stability pathways that govern performance. Overall these advanced materials and hybrid strategies provide a sustainable framework for maximizing solar-to-thermal energy conversion and storage, enabling applications in hydrogen production, solar desalination, and high-temperature industrial processes.