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DOE OSTI2021

Power Over Ethernet Investigation Report

This report provides insight into collaboration between Savannah River National Laboratory’s (SRNL) Global Security Directorate, Augusta University (AU) and the Georgia Cyber Center (GCC) team with regards to device analysis, investigation, and R&D efforts. Most recently, the GCC/AU team approached SRNL to receive cybersecurity expertise in a joint effort to identify differences between a potentially questionable PoE injector recovered by Columbia County and an identical one purchased by the GCC. The questionable device was installed at a fire station operated in Columbia County, shown in the figure 1-1 below.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

Deuterium Concentration Effects on Cell Cycle Progression

Deuterium (D), which is found in natural water at ~ 150 ppm, seems to play an important role in biology. For example, D concentrations above 150 ppm are known to produce toxic effects in many organisms. There is also evidence to suggest D levels significantly less than 150 ppm can cause delays in progression through the normal mitotic cell cycle. Some have even theorized that the D:H ratio in cells may impact an organism’s radiation resistance. Therefore, evaluating the role of D and the D:H ratio in eukaryotic and prokaryotic cells should lead to a better understanding of cell cycle progression and radiation resistance in these organisms. Research in this field has likely been stalled by the limited availability of D 2 O with varying D concentrations needed to accurately study the deuterium effects. However, SRNL can currently manufacture D 2 O in varying concentrations, and we have assembled a unique team of radiation biologists, microbiologists, radiochemists, and health physics to form an interdisciplinary research group to study the cell cycle as a function of D concentration in order to address several fundamental science questions. Proposed work in FY20 was a collaborative effort with Augusta University to utilized BSL-2 mammalian cell lines. Lab work was halted due to the COVID-19 pandemic. An intensive literature review was performed and identified pertinent knowledge gaps that could be filled in future research efforts.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2024

Ultrasound Interim cyber-physical research evaluation

Recent events have presented Medical Practitioners with concerns about safety and privacy implications associated with GE’s VScan MIot Ultrasound Device. Concerns relate to the devices potential risk to broadcast location data that can pose serious risks to device users and patients. This report was commission in collaboration with Augusta University to determine if device concerns pose real threat to operators and patients. In effort to ensure all potential threat vectors are targeted, the initial step was to analyze the supply chain. Here the devices are CT scanned to look for any hardware anomalies that could present threat vectors to users of these devices. (See SRNL-STI-2024-00225 for detailed analysis). No anomalies were found associated with the hardware utilized within the GE VScan Air.

42 ENGINEERING↗

DOE OSTI2025

Adaptive Dynamic Digital Twin for Test Scenario Generation

Vehicle testing has been an important part in the development of both highly automated vehicles (HAV) and advanced driving assistant systems (ADAS). Obtaining a good representation of the Vehicle Under Test (VUT) is crucial for test scenario library generation (TSLG). Current vehicle testing methods often involve calibrating car-following models using vehicle trajectory data to create static representations that cannot be dynamically updated. For instance, when multiple vehicle trajectories are collected, it is difficult to automatically determine whether a new trajectory improves the model's representativeness or degrades its accuracy. In this paper, we introduce a dynamically updated digital twin modeling framework featuring an adaptive mechanism that evaluates new trajectory data. This mechanism can decide whether to incorporate newly collected data into the current model or create a separate digital twin model when the trajectory significantly differs from prior data. Vehicle location, speed, and acceleration extracted from the newly collected trajectory data are used to support the dynamic update decision. By integrating this digital twin model into the test library generation process, we demonstrate its ability to assist in generating test libraries while effectively handling newly collected data.

Chen, Hanlin [ORNL] (ORCID:0000000165087715)↗

DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗