958 papers
This paper presents a transparent, retrieval-assisted framework that grounds natural-language numeric evidence into structured optimization schemas, demonstrating that while schema retrieval is highly effective, the primary bottleneck for automating optimization problem instantiation lies in the downstream task of accurately matching extracted numbers to their corresponding slots.
This study demonstrates that integrating conversational AI into Medicare Annual Wellness Visits enables scalable, multi-domain functional fall-risk screening that effectively identifies a substantial "hidden burden" of high fall risk among older adults who do not self-report a history of falls.
This study analyzes 2022 Ghana Demographic and Health Survey data to reveal that zero-dose and under-vaccinated children are disproportionately concentrated in Northern regions and among rural, less-educated populations with limited maternal healthcare engagement, highlighting the need for geographically targeted interventions and integrated antenatal counseling.
This study analyzes five grass OsNAC25-like proteins to demonstrate that while sequence identity and structure-prediction confidence consistently show higher conservation in the N-terminal DNA-binding domain compared to the C-terminal region, protein-language-model embeddings are significantly influenced by specific candidate identity, highlighting the finger-millet sequence as a priority for further phylogenetic investigation.
This systematic literature review synthesizes 78 studies to reveal that children with disabilities in remote archipelagic regions remain disproportionately vulnerable due to exclusion from Disaster Risk Reduction planning, leading to the proposal of the IDRR-CDAR Framework to guide inclusive policy and practice.
The paper introduces RareLens, an open-access quantitative framework that reveals significant disparities between NIH research funding and patient burden for rare and autoimmune diseases, demonstrating that over half of the analyzed conditions are underfunded relative to their impact, with Postural Orthostatic Tachycardia Syndrome (POTS) exhibiting the most severe funding gap.
This study presents the development and validation of a rapid, sensitive, and environmentally sustainable RP-HPLC-UV method for quantifying atorvastatin in pharmaceutical formulations, utilizing an ethanol-based mobile phase to achieve superior chromatographic efficiency and lower detection limits compared to conventional methods.
This paper proposes a curvature-guided composite kernel framework that leverages quantum mechanics principles to project RNA-seq data into a Hilbert space, utilizing a gravitational search mechanism to identify differentially expressed genes by minimizing representational loss on the latent manifold.
This study utilized computational molecular docking to identify Minocycline, Rosiglitazone, and Sertraline among six FDA-approved drugs as promising candidates for repurposing as BACE1 inhibitors to treat Alzheimer's disease.
This study introduces BoneNet, a multimodal deep learning framework that integrates a YOLOv8m backbone with dilated residual attention blocks and tabular data fusion to achieve high-accuracy automated detection, severity grading, and explainable depth estimation of peri-implant bone loss while transparently identifying performance limitations in severe cases.