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University of Central Missouri

Academic institutionnorthamerica · us
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Research library5linked papers
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Selected work

Representative Papers

A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architectures

Jul 19, 2026

This study addresses engineering constraints in large-scale AI data center deployments, such as prolonged grid interconnection approval timelines and equipment delivery delays. To overcome these challenges, the authors propose a phased energy deployment architecture that integrates modular construction with a hybrid on-site energy system combining natural gas generation and grid-forming energy storage. This system enables islanded operation prior to full grid connection and facilitates seamless transition to grid-tied mode through a hybrid control strategy blending grid-forming and grid-following inverters. Electromagnetic transient simulations and modular design validation demonstrate that the hybrid system reliably supports high-power loads from early to mid-deployment stages and effectively manages islanding, reconnection, and recovery under grid disturbances. The approach significantly shortens construction timelines while enhancing power supply reliability and sustainability.

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AutoACSL: Synthesizing ACSL Specifications by Integrating LLMs with CPG-Based Static Analysis

Jun 18, 2026

Automatically generating ACSL formal specifications for C programs is often hindered by insufficient semantic precision, heavy reliance on expert knowledge, and verification challenges. This work proposes a novel approach that integrates static analysis via Code Property Graphs (CPGs) with large language models (LLMs). By leveraging CPGs to extract key semantic features—such as arithmetic operations and loop structures—the method constructs structured prompts that deeply embed static analysis into LLM prompt engineering, enabling the generation of verifiable specifications enriched with runtime error prevention constraints. A closed-loop feedback mechanism with the Frama-C/WP verifier iteratively refines specification quality. Experiments on 604 C programs demonstrate a 98% specification generation success rate and a 96% complete proof rate, representing a 24.7%–51.7% improvement in complete proof rates over a pure code-prompting baseline across four mainstream LLMs.

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Mamba-SSM with LLM Reasoning for Biomarker Discovery: Causal Feature Refinement via Chain-of-Thought Gene Evaluation

Apr 15, 2026

This study addresses the susceptibility of gene biomarkers extracted by deep sequential models to confounding effects from tissue composition, which degrades classification performance. For the first time, it integrates chain-of-thought reasoning from large language models (LLMs) into gene selection, leveraging the Mamba state space model to process TCGA-BRCA RNA-seq data and applying causal feature refinement to gradient-based salient genes for deconfounding. The work introduces the concept of “selective credibility,” demonstrating that precise deconfounding substantially enhances performance—even without full recall of known biomarkers. The LLM-filtered 17-gene signature achieves an AUC of 0.927, significantly outperforming both the original 50-gene set (AUC: 0.832) and a 5,000-gene variance-based baseline (AUC: 0.903), while reducing feature dimensionality by 294-fold.

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Recent publications

Latest Papers

A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architectures

Jul 19, 2026

This study addresses engineering constraints in large-scale AI data center deployments, such as prolonged grid interconnection approval timelines and equipment delivery delays. To overcome these challenges, the authors propose a phased energy deployment architecture that integrates modular construction with a hybrid on-site energy system combining natural gas generation and grid-forming energy storage. This system enables islanded operation prior to full grid connection and facilitates seamless transition to grid-tied mode through a hybrid control strategy blending grid-forming and grid-following inverters. Electromagnetic transient simulations and modular design validation demonstrate that the hybrid system reliably supports high-power loads from early to mid-deployment stages and effectively manages islanding, reconnection, and recovery under grid disturbances. The approach significantly shortens construction timelines while enhancing power supply reliability and sustainability.

0 citationsRead paper

AutoACSL: Synthesizing ACSL Specifications by Integrating LLMs with CPG-Based Static Analysis

Jun 18, 2026

Automatically generating ACSL formal specifications for C programs is often hindered by insufficient semantic precision, heavy reliance on expert knowledge, and verification challenges. This work proposes a novel approach that integrates static analysis via Code Property Graphs (CPGs) with large language models (LLMs). By leveraging CPGs to extract key semantic features—such as arithmetic operations and loop structures—the method constructs structured prompts that deeply embed static analysis into LLM prompt engineering, enabling the generation of verifiable specifications enriched with runtime error prevention constraints. A closed-loop feedback mechanism with the Frama-C/WP verifier iteratively refines specification quality. Experiments on 604 C programs demonstrate a 98% specification generation success rate and a 96% complete proof rate, representing a 24.7%–51.7% improvement in complete proof rates over a pure code-prompting baseline across four mainstream LLMs.

0 citationsRead paper

Mamba-SSM with LLM Reasoning for Biomarker Discovery: Causal Feature Refinement via Chain-of-Thought Gene Evaluation

Apr 15, 2026

This study addresses the susceptibility of gene biomarkers extracted by deep sequential models to confounding effects from tissue composition, which degrades classification performance. For the first time, it integrates chain-of-thought reasoning from large language models (LLMs) into gene selection, leveraging the Mamba state space model to process TCGA-BRCA RNA-seq data and applying causal feature refinement to gradient-based salient genes for deconfounding. The work introduces the concept of “selective credibility,” demonstrating that precise deconfounding substantially enhances performance—even without full recall of known biomarkers. The LLM-filtered 17-gene signature achieves an AUC of 0.927, significantly outperforming both the original 50-gene set (AUC: 0.832) and a 5,000-gene variance-based baseline (AUC: 0.903), while reducing feature dimensionality by 294-fold.

0 citationsRead paper