Mohammad Elahi

Hina Raja, Asim Munawar, Mohammad Delsoz et al.

Purpose: In this paper, we present an automated method for article classification, leveraging the power of Large Language Models (LLM). The primary focus is on the field of ophthalmology, but the model is extendable to other fields. Methods: We have developed a model based on Natural Language Processing (NLP) techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we have employed zero-shot learning (ZSL) LLM models and compared against Bidirectional and Auto-Regressive Transformers (BART) and its variants, and Bidirectional Encoder Representations from Transformers (BERT), and its variant such as distilBERT, SciBERT, PubmedBERT, BioBERT. Results: The classification results demonstrate the effectiveness of LLMs in categorizing large number of ophthalmology papers without human intervention. Results: To evalute the LLMs, we compiled a dataset (RenD) of 1000 ocular disease-related articles, which were expertly annotated by a panel of six specialists into 15 distinct categories. The model achieved mean accuracy of 0.86 and mean F1 of 0.85 based on the RenD dataset. Conclusion: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval in other domains too. We performed trend analysis that enables the researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.

Mohammad Elahi, Max O. Bloomfield, Theodorian Borca-Tasciuc et al.

Modern package designs make use of technologies such as backside power delivery (BSPD) and 3D stacked chiplets that require accounting for the heterogeneity in back end of the line (BEOL) structures in hot-spot prediction. Multiscale homogenization strategies have been demonstrated to be effective for steady-state simulations, however accurate 3D transient simulations that include BEOL structures remain an open challenge. In this work, we demonstrate a transient thermal workflow that accounts for the 3D heterogeneous structures in the BEOL for problems with strong- and weak- temporal scale separation under the assumption of temperature independent constitutive properties. Our workflow, based on Bloomfield et. al. 2025, automatically extracts, meshes, and homogenizes thermal properties from GDSII and OASIS files to construct thermal property maps. Property maps (heat capacity and conductivity) have been generated for a 1 mm by 1 mm SoC-style model die that was constructed with LibreLane for 100 by 100 grids with 5 micron by 5 micron representative volume elements (RVEs), and 50 by 50 grids with 10 micron by 10 micron RVEs. The expressions for a transient effective conductivity are provided and a demonstration of the impact of the transient effects are provided for a single RVE. Finally, transient conductivity maps have been provided for a time integration timestep of dt=0.001.