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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Outcome of the First wwPDB/CCDC/D3R Ligand Validation Workshop

Crystallographic studies of ligands bound to biological macromolecules (proteins and nucleic acids) represent an important source of information concerning drug-target interactions, providing atomic level insights into the physical chemistry of complex formation between macromolecules and ligands. Of the more than 115,000 entries extant in the Protein Data Bank archive, ~75% include at least one non-polymeric ligand. Ligand geometrical and stereochemical quality, the suitability of ligand models for in silico drug discovery/design, and the goodness-of-fit of ligand models to electron density maps vary widely across the archive. We describe the proceedings and conclusions from the first Worldwide Protein Data Bank/Cambridge Crystallographic Data Centre/Drug Design Data Resource (wwPDB/CCDC/D3R) Ligand Validation Workshop held at the Research Collaboratory for Structural Bioinformatics at Rutgers University on July 30-31, 2015. Experts in protein crystallography from academe and industry came together with non-profit and for-profit software providers for crystallography and with experts in computational chemistry and data archiving to discuss and make recommendations on best practices, as framed by a series of questions central to structural studies of macromolecule-ligand complexes. What data concerning bound ligands should be archived in the Protein Data Bank? How should the ligands be best represented? How should structural models of macromolecule-ligand complexes be validated? What supplementary information should accompany publications of structural studies of biological macromolecules? Consensus recommendations on best practices developed in response to each of these questions are provided, together with some details regarding implementation. Important issues addressed but not resolved at the workshop are also enumerated.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Application-Driven Creation of Building Metadata Models with Semantic Sufficiency

Semantic metadata models such as Brick, RealEstateCore, Project Haystack, and BOT promise to simplify and lower the cost of developing software for smart buildings, enabling the widespread deployment of energy efficiency applications. However, creating these models remains a challenge. Despite recent advances in creating models from existing digital representations like point labels and architectural models, there is still no feedback mechanism to ensure that the human input to these methods results in a model that can actually support the desired software. In this paper, we introduce the notion of semantic sufficiency, a practical principle for semantic metadata model creation that asserts that a model is "finished" when it contains the metadata necessary to support a given set of applications. To support semantic sufficiency, we design a standard representation for capturing application metadata requirements and a templating system for generating common metadata model components with limited user input. We then construct an iterative model creation workflow that integrates metadata requirements to direct the model creation effort, and present several novel optimizations that increase the model utility while minimizing the effort by a human operator. These new abstractions for model creation and validation lower model development costs and ensure the utility of the resulting model, thus facilitating the adoption of intelligent building applications.

applications↗
DOE OSTI2021

Modeling Data Flows with Network Calculus in Cyber-Physical Systems: Enabling Feature Analysis for Anomaly Detection Applications

The electric grid is becoming increasingly cyber-physical with the addition of smart technologies, new communication interfaces, and automated grid-support functions. Because of this, it is no longer sufficient to only study the physical system dynamics, but the cyber system must also be monitored as well to examine cyber-physical interactions and effects on the overall system. To address this gap for both operational and security needs, cyber-physical situational awareness is needed to monitor the system to detect any faults or malicious activity. Techniques and models to understand the physical system (the power system operation) exist, but methods to study the cyber system are needed, which can assist in understanding how the network traffic and changes to network conditions affect applications such as data analysis, intrusion detection systems (IDS), and anomaly detection. In this paper, we examine and develop models of data flows in communication networks of cyber-physical systems (CPSs) and explore how network calculus can be utilized to develop those models for CPSs, with a focus on anomaly and intrusion detection. This provides a foundation for methods to examine how changes to behavior in the CPS can be modeled and for investigating cyber effects in CPSs in anomaly detection applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics

Mesoscopic simulations of hydrocarbon flow in source shales are concerning, in part due to the heterogeneous shale pores with sizes ranging from a few nanometers to a few micrometers. Additionally, the sub-continuum fluid–fluid and fluid–solid interactions in nano- to micro-scale shale pores, which are physically and chemically sophisticated, must be captured. To address those challenges, we present a GPU-accelerated package for simulation of flow in nano- to micro-pore networks with a many-body dissipative particle dynamics (mDPD) mesoscale model. Based on a fully distributed parallel paradigm, the code offloads all intensive workloads on GPUs. Other advancements, such as smart particle packing and no-slip boundary condition in complex pore geometries, are also implemented for the construction and the simulation of the realistic shale pores from 3D nanometer-resolution stack images. Our code is validated for accuracy and compared against the CPU counterpart for speedup. In our benchmark tests, the code delivers nearly perfect strong scaling and weak scaling (with up to 512 million particles) on up to 512 K20X GPUs on Oak Ridge National Laboratory’s (ORNL) Titan supercomputer. Moreover, a single-GPU benchmark on ORNL’s SummitDev and IBM’s AC922 suggests that the host-to-device NVLink can boost performance over PCIe by a remarkable 40%. Lastly, we demonstrate, through a flow simulation in realistic shale pores, that the CPU counterpart requires 840 Power9 cores to rival the performance delivered by our package with four V100 GPUs on ORNL’s Summit architecture. This simulation package enables quick-turnaround and high-throughput mesoscopic numerical simulations for exploring complex flow phenomena in nano- to micro-porous rocks with realistic pore geometries.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

A Real-Time Greedy-Index Dispatching Policy for using PEVs to Provide Frequency Regulation Service

This article presents a real-time greedy-index dispatching policy (GIDP) for using plug-in electric vehicles (PEVs) to provide frequency regulation services. A new service cost allocation mechanism is proposed to award PEVs based on the amount of service they provided, while considering compensations for delayed-charging and reduction of battery lifetime due to participation of the service. The GIDP transforms the optimal dispatch problem from a high-dimensional space into a one-dimensional space while preserving the solution optimality. When solving the transformed problem in real-time, the global optimality of the GIDP solution can be guaranteed by mathematically proved “indexability”. Because the GIDP index can be calculated upon the PEV’s arrival and used for the entire decision making process till its departure, the computational burden is minimized and the complexity of the aggregator dispatch process is significantly reduced. Finally, simulation results are used to evaluate the proposed GIDP, and to demonstrate the potential profitability from providing frequency regulation service by using PEVs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control

Occupants are the core of the built environment. Traditional Heating, Ventilation, and Air-Conditioning (HVAC) systems operate with predefined schedules and maximum occupancy assumptions with no consideration of specific occupant information. These generalized assumptions usually do not align with the actual demand and result in over-conditioning and occupant discomfort. In recent years, with the aid of Information & Communication Technology (ICT) and Computer Science (CS), it is possible to acquire real-time and accurate occupant information to satisfy the exact thermal requirement through specific HVAC control in one particular built environment. This mechanism is called HVAC “Occupant-centric Control (OCC).” HVAC OCC strategy starts with collecting the occupant’s information (e.g., presence/absence) and then applies it to meet the occupant’s requirement (e.g., thermal comfort). However, even though some research studies and field pilot demonstrations have been devoted to the field of OCC, there is a lack of systematic knowledge about occupant data, which is the principal component of OCC for HVAC researchers and practitioners. To fill this gap, this review paper discusses OCC with a particular emphasis on occupant information and investigates how this information can assist HVAC operation in providing an acceptable built environment in required spaces during the required time. Finally, we provide a fine-grained, comprehensive picture of occupant information, discuss its features, the modalities of information feed-in into the HVAC control, and the application of commonly utilized occupant information for OCC.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

SU-E-T-372: Evaluation and Comparison of Second-Check Monitor Unit Calculation Software with Pinnacle Treatment Planning System

Purpose: The purpose of this study was to evaluate and compare the accuracy of dose calculation algorithms in the second check software programs Radcalc, Diamond, IMSure, and MUcheck, against the Pinnacle3 treatment planning system (TPS). Methods: Baseline accuracy of the second check software was established by comparison against Pinnacle TPS data using open square fields of 5, 10, 20, 30 and 40cm in a SAD setup. 18 previously treated patients’ files were exported from the Pinnacle3 TPS to each of the four second check softwares, consisting of 146 step and shoot intensity modulated radiotherapy (IMRT) beams and 60 Smart Arcs. Monitor unit (MU) calculated in each of the software were compared with the TPS and the values were represented as a percent difference. Open fields were calculated as a baseline for each software’s accuracy using 5×5, 10×10, 20×20, 30×30, and 40×40 fields. Box plots, Pearson correlation, and Bland-Altman analysis were used for comparison of the results. Results: The baseline accuracy was established to within 0.6%, −1.4%, −0.2%, and −1.0% for Diamond, IMSure,MUcheck, and Radcalc, respectively. In the clinical data, the dose difference represented as mean ± 1 standard deviation were 0.7%±0.1%, −0.3%±0.1%, −1.5%±0.1%, and 0.4%±0.0% for Diamond, IMSure, MUcheck, and Radcalc, respectively Conclusion: The implementation of Clarkson algorithm for the dose calculation between each of the software in question can vary considerably. The currently used second check software, Radcalc has shown the best agreement on average, variance, and smallest percent range from Pinnacle3 TPS values. The closest in average percent difference from the TPS data was the IMSure software, but has significantly larger variance and percent range. The mean percent differences in Diamond and MUcheck were significantly larger than Radcalc and IMSure.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Probabilistic Look-ahead Contingency Analysis Integrated with GE EMS Tool

This report documents implementation of the smart sampling-based probabilistic look-ahead contingency analysis algorithm, the integration with General Electric (GE) Grid Solutions’ commercial energy management system (EMS) tool, and the case study results using real world data from the Bonneville Power Administration (BPA).The probabilistic look-ahead contingency analysis algorithm incorporates forecast errors of renewable energy and load into contingency analysis functions to enable a predictive capability with a more complete picture, revealing the potential violations that are not normally detected by traditional deterministic approaches. To test its performance under practical environments, the probabilistic look-ahead contingency analysis algorithm has been integrated with the dominant GE EMS tool as a proof-of-concept for a seamless integration. To meet the output format of GE EMS tool, an extreme value distribution (EVD) algorithm is newly adapted to analyze the GE EMS’s violation-only output. Real world data from BPA are used to test its performance, and the results clearly show the effectiveness of the developed algorithm and improved situational awareness. This effort is an example of successful technology transition and cooperation under a real-world environment because it is a joint effort from department of energy, a national laboratory, a commercial vendor, and a large utility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Computer Science↗
DOE OSTI2014

SU-E-I-97: Smart Auto-Planning Framework in An EMR Environment (SAFEE)

Purpose: Our Radiation Oncology Department uses clinical practice guidelines for patient treatment, including normal tissue sparing and other dosimetric constraints. These practice guidelines were adapted from national guidelines, clinical trials, literature reviews, and practitioner's own experience. Modern treatment planning systems (TPS) have the capability of incorporating these practice guidelines to automatically create radiation therapy treatment plans with little human intervention. We are developing a software infrastructure to integrate clinical practice guidelines and radiation oncology electronic medical record (EMR) system into radiation therapy treatment planning system (TPS) for auto planning. Methods: Our Smart Auto-Planning Framework in an EMR environment (SAFEE) uses a software pipeline framework to integrate practice guidelines,EMR, and TPS together. The SAFEE system starts with retrieving diagnosis information and physician's prescription from the EMR system. After approval of contouring, SAFEE will automatically create plans according to our guidelines. Based on clinical objectives, SAFEE will automatically select treatment delivery techniques (such as, 3DRT/IMRT/VMAT) and optimize plans. When necessary, SAFEE will create multiple treatment plans with different combinations of parameters. SAFEE's pipeline structure makes it very flexible to integrate various techniques, such as, Model-Base Segmentation (MBS) and plan optimization algorithms, e.g., Multi-Criteria Optimization (MCO). In addition, SAFEE uses machine learning, data mining techniques, and an integrated database to create clinical knowledgebase and then answer clinical questions, such as, how to score plan quality or how volume overlap affects physicians' decision in beam and treatment technique selection. Results: In our institution, we use Varian Aria EMR system and RayStation TPS from RaySearch, whose ScriptService API allows control by external programs. These applications are the building blocks of our SAFEE system. Conclusion: SAFEE is a feasible method of integrating clinical information to develop an auto-planning paradigm to improve clinical workflow in cancer patient care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

WISP: Watching grid Infrastructure Stealthily through Proxies (Final Technical Report)

The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Precursor Analysis Report: Conti Ransomware Attack on the Health Service Executive of Ireland 2021

The Conti Ransomware Attack on the Health Service Executive (HSE) of Ireland 2021 Precursor Analysis Report leverages publicly available information about the attack and catalogs anomalous observables for each technique employed by the adversary. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. The HSE provides public healthcare corporate services and operational services throughout Ireland, with critical functions including the acute national ambulance service, acute hospital service, and community healthcare service. On 14 May 2021, Conti ransomware encrypted 80 percent of the HSE’s Information Technology (IT) infrastructure across corporate, hospital, community, and electronic health record services. Conti is a ransomware-as-a-service operation that encrypts local files, uses double extortion against victims, and is facilitated by many intrusion tools. The attack forced the HSE to shut down its entire IT infrastructure to contain the ransomware, forcing employees to revert to pen and paper recordkeeping and leading to the cancellation of many appointments and procedures. The adversary also exfiltrated 700 GB of data, compromising the confidentiality of patients’ protected health information. Had the adversary targeted the COVID-19 cloud systems or operational technology assets, such as Internet of Medical Things medical devices or smart building management systems, the impact of the attack would almost certainly have been far more severe. Researchers and analysts identified 21 unique techniques (used in a sequence of 23 steps) likely utilized during the attack with a total of 1,185 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Twenty-one of the identified techniques used during the attack on the HSE were precursors to the triggering event. Analysis identified 1,086 observables associated with these precursor techniques, 850 of which were assessed to have an increased likelihood of being perceived in the 57 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2017

A high resolution agent-based model to support walk-bicycle infrastructure investment decisions: A case study with New York City

Active transportation modes--walk and bicycle--are central for low carbon transport, healthy living, and complete streets initiative. Building a community with amenable walk and bicycle facilities asks for smart planning and investments. It is critical to investigate the impact of infrastructure building or expansion on the overall walk and bicycle mode usage prior to making investment choices utilizing public tax money. This research developed an agent-based model to support investment decisions that allows to assess the impact of changes in walk-bike infrastructures at a high spatial resolution (e.g., block group level). The agent-based model (ABM) utilizes data from a synthetic population simulator generating agents with corresponding socio-demographic characteristics, and integrates facility attributes regarding walking and bicycling (e.g., sidewalk width, bike lane length) into the mode choice decision making process. Moreover, the ABM accounts for the effect of social interactions among agents who live and work at the same geographic locations. Finally, GIS-based maps are developed at block group resolution that allows exploring the effect of walk-bike infrastructure related investments. The results from New York City case study indicate that infrastructure investments such as widening sidewalk and increasing bike lane network can positively influence the active transportation mode choices. In addition, the level of impact varies with geographic locations--different boroughs of New York City will have different impacts. Lastly, social promotions resulting in higher social interaction among agents can reinforce the impacts of infrastructure changes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Detecting Peer-to-Peer Botnets in SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems monitor and control critical infrastructure such as the smart grid. As SCADA systems become increasingly interconnected and adopt more and more cyber-enabled components, the risks of cyber attacks become a major concern. Due to their decentralized organization, peer-to-peer (P2P) botnets are resilient to many existing takedown measures and can be exploited as an effective way to launch cyber attacks on SCADA systems. However, little work has been done to detect P2P botnets in SCADA systems, which carry traffic flows with characteristics significantly different from the Internet. In this paper, we design a P2P-botnet detection method for SCADA systems, leveraging built-in traffic monitoring capabilities of SCADA networking devices. The proposed method consists of two stages. In the first stage, we design a simple feature test to filter out non-P2P hosts, which significantly reduces the data volume for P2P-botnet identification. In the second stage, we jointly consider flow-based and connectivity-based features that effectively set apart bots from benign hosts. We propose to use unsupervised learning for P2P-botnet identification, which not only identifies known P2P botnets but also captures newly emerged ones. Our simulation results show that the proposed system achieves high detection rates with very few false positives. Furthermore, our evaluation shows that the proposed method can detect hosts running P2P SCADA applications that are infected by P2P bots.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting

This study discuss, intelligent buildings are quickly becoming cohesive and integral entities of cyber-physical ecosystems. Modern buildings adapt to internal and external elements and thrive on ever increasing data sources such as ubiquitous smart devices and sensors while mimicking various approaches previously known in software, hardware, and bio inspired systems. This article provides an overview of intelligent buildings of the future from a range of perspectives. First, a brief overview of prospects of U.S. and world energy consumption, concepts of smart buildings as integral entities of smart grids is presented. This is followed by a discussion of the role of connectivity and interoperability as well as current issues and solutions in the areas of security, resilience, and humans in realm of intelligent buildings. The article dives into detail about the intelligence aspect of intelligent buildings of the future. The learning and predictive Artificial Intelligence (AI) techniques behind many of the concepts of intelligent buildings are reviewed, including the latest techniques such as deep learning. As a concrete application of AI in the realm of intelligent buildings, prediction of energy consumption/load/demand, which is a much researched application of AI in the realm of intelligent buildings, is elaborated. The discussion is focused on most recent advancements of AI based demand prediction and the overview consists of existing research and a case study conducted by the authors for deep learning based demand prediction. Then, the article discusses the role of humans in intelligent buildings. Different types of human-building interactions are identified. Further, the article presents how each type of human-building interaction can benefit the overall control of intelligent buildings. The section is concluded by elaborating a case study conducted by the authors to achieve effective and efficient human-building interaction. The article is concluded with compilation of future insights based on latest technological advancements in U.S. industry and government.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

SU-F-I-31: Reproducibility of An Automatic Exposure Control Technique in the Low-Dose CT Scan of Cardiac PET/CT Exams

Purpose: Two separate low-dose CT scans are usually performed for attenuation correction of rest and stress N-13 ammonia PET/CT myocardial perfusion imaging (PET/CT). We utilize an automatic exposure control (AEC) technique to reduce CT radiation dose while maintaining perfusion image quality. Our goal is to assess the reproducibility of displayed CT dose index (CTDI) on same-day repeat CT scans (CT1 and CT2). Methods: Retrospectively, we reviewed CT images of PET/CT studies performed on the same day. Low-dose CT utilized AEC technique based on tube current modulation called Smart-mA. The scan parameters were 64 × 0.625mm collimation, 5mm slice thickness, 0.984 pitch, 1-sec rotation time, 120 kVp, and noise index 50 with a range of 10–200 mA. The scan length matched with PET field of view (FOV) with the heart near the middle of axial FOV. We identified the reference slice number (RS) for an anatomical landmark (carina) and used it to estimate axial shift between two CTs. For patient size, we measured an effective diameter on the reference slice. The effect of patient positioning to CTDI was evaluated using the table height. We calculated the absolute percent difference of the CTDI (%diff) for estimation of the reproducibility. Results: The study included 168 adults with an average body-mass index of 31.72 ± 9.10 (kg/m{sup 2}) and effective diameter was 32.72 ± 4.60 cm. The average CTDI was 1.95 ± 1.40 mGy for CT1 and 1.97 ± 1.42mGy for CT2. The mean %diff was 7.8 ± 6.8%. Linear regression analysis showed a significant correlation between the table height and %diff CTDI. (r=0.82, p<0.001) Conclusion: We have shown for the first time in human subjects, using two same-day CT images, that the AEC technique in low-dose CT is reproducible within 10% and significantly depends on the patient centering.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

INTERSECT Architecture Specification: System-of-systems Architecture (Version 0.5)

Oak Ridge National Laboratory (ORNL)’s Self-driven Experiments for Science / Interconnected ScienceEcosystem (INTERSECT) architecture project, titled “An Open Federated Architecture for the Laboratory of the Future”, creates an open federated hardware/software architecture for the laboratory of the future using a novel system of systems (SoS) and microservice architecture approach, connecting scientific instruments, robot-controlled laboratories and edge/center computing/data resources to enable autonomous experiments, “self-driving” laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The architecture project is divided into three focus areas: design patterns; SoS architecture; and microservices architecture. The design patterns area focuses on describing science use cases as design patterns that identify and abstract the involved hardware/software components and their interactions interms of control, work and data flow. The SoS architecture area focuses on an open architecture specification for the federated ecosystem that clarifies terms, architectural elements, the interactions between them and compliance. The microservices architecture describes blueprints for loosely coupled microservices, standardized interfaces, and multi-programming language support. This document is the SoS Architecture specification only, and captures the system of systems architecture design for the INTERSECT Initiative and its components. It is intended to provide a deep analysis and specification of how the INTERSECT platform will be designed, and to link the scientific needs identified across disciplines with the technical needs involved in the support, development, and evolution of a science ecosystem. PLEASE NOTE: This is a working document and reflects current discussions and design activity among the authors. There may be inconsistencies within the document as different parts evolve at a different pace. We invite comments and thoughts from the public on this and following working drafts. The first finished version of this document is scheduled for release in September 2023.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Impact of cyberattacks on safety and stability of connected and automated vehicle platoons under lane changes

Connected and automated vehicles (CAVs) offer a huge potential to improve the operations and safety of transportation systems. However, the use of smart devices and communications in CAVs introduce new risks. CAVs would leverage vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, thus providing additional system access points compared to traditional systems. Automation makes these systems more vulnerable and increases the consequences of cyberattacks. This study utilizes an infrastructure-based communication platform consisting of cooperative adaptive cruise control and lane control advisories developed by the authors to perform cyber risk assessment of CAVs. The study emulates three types of cyberattacks (message falsification, dedicated denial of service, and spoofing attacks) in a representative traffic environment consisting of multiple CAV platoons and lane change events to analyze the safety and stability impacts of the cyberattacks. Simulation experiments using VISSIM reveals that traffic stream and CAV string is unstable under all three types of cyberattacks. The worst case is represented by the message falsification attack. Increases in volatility are observed over a no attack case, with variations increasing by an average of 43%–51% along with an increase of over 3000 crash conflicts. Similarly, lane change crash conflicts are observed to be more severe compared to rear end crash conflicts, showing a higher probability of severe injuries. Further, the case of slight cyberattack on a single CAV also creates significant disruption in the traffic stream. Analysis of variance (ANOVA) reveals the statistical significance of the results. Furthermore, these results pave the way for future design of secure systems from a monitoring perspective.

97 MATHEMATICS AND COMPUTING↗