Interpretable Recurrent Convolutional Neural Networks for Cyber Alert Triaging.
Abstract not provided.
Salaudeen, Olawale↗
SEARCH · Engineering Papers
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An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies
Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. However, without proper image collection and interpretation, triage decisions can be hard to make. While automation approaches can enhance these triage applications, tissue phantoms must be developed to train and mature these novel technologies. Here, we have developed a tissue phantom modeling the ultrasound views imaged during the enhanced focused assessment with sonography in trauma exam (eFAST). The tissue phantom utilized synthetic clear ballistic gel with carveouts in the abdomen and rib cage corresponding to the various eFAST scan points. Various approaches were taken to simulate proper physiology without injuries present or to mimic pneumothorax, hemothorax, or abdominal hemorrhage at multiple locations in the torso. Multiple ultrasound imaging systems were used to acquire ultrasound scans with or without injury present and were used to train deep learning image classification predictive models. Performance of the artificial intelligent (AI) models trained in this study achieved over 97% accuracy for each eFAST scan site. We used a previously trained AI model for pneumothorax which achieved 74% accuracy in blind predictions for images collected with the novel eFAST tissue phantom. Grad-CAM heat map overlays for the predictions identified that the AI models were tracking the area of interest for each scan point in the tissue phantom. Overall, the eFAST tissue phantom ultrasound scans resembled human images and were successful in training AI models. Tissue phantoms are critical first steps in troubleshooting and developing medical imaging automation technologies for this application that can accelerate the widespread use of ultrasound imaging for emergency triage.
60 APPLIED LIFE SCIENCES↗
Providing Housing, Food and Medical Support for 25,000 Katrina Evacuees with 12 Hours Notice: The Harris County Medical Support of the Superdome Evacuees
Hurricane Katrina was responsible for trapping 25,000 people in the New Orleans Superdome and isolating many others throughout Louisiana and Mississippi. The transport of these evacuees to the Reliant Park (Houston, Texas) used 500 buses each containing about 55 people. Processing the arriving evacuees included addressing their health status and medical needs as follows: an initial triage at disembarkation, a secondary triage in the Reliant Astrodome and Center, and definitive clinical care in the Reliant Arena "Katrina" Clinic. Baylor College of Medicine (BCM) physicians boarded buses and identified the sickest for emergency transport to Harris County Hospital District (HCHD) hospitals. BCM departments represented included pediatrics, family and community medicine, internal medicine, radiology, obstetrics and gynecology, orthopedics, surgery, and psychiatry. Astrodome and Center triage was managed by BCM physicians and staffed by HCHD Nurses and volunteers from Texas and beyond. The Reliant Astrodome, Center and Arena reached peak headcounts of 15,000,4500, and 2500, respectively Most evacuees visiting the triage sites in the Astrodome and Center were treated using "over-the-counter" medications with the remaining being transported to the "Katrina" clinic. The clinic was equipped with a lab, pharmacy, digital X-ray, and ultrasound machines in addition to electronic patient records created using 80 computer terminals. The Katrina clinic saw more than 15,000 patients during 15 days of operations (2,000 on the first full day), administered 10,000 tetanus shots, and filled thousands of prescriptions. At the peak of operations, the clinic saw 150 patients/hour with 25 physicians scheduled for each 12-hour shift. Approximately 900 people were transported to hospital emergency rooms. Within 3 weeks of arriving at the Reliant Park facilities, more than 90% of the families found permanent housing, enrolled children in schools, and found work. Using data obtained from manual and electronic medical records, this presentation will document the major milestones and lessons learned from this extraordinary project to help the Katrina evacuees.
Hamilton, Douglas↗
Fluorescence in situ hybridization in 1 mL of selective urine for the detection of upper tract urothelial carcinoma: a feasibility study
Kidney-sparing surgery of upper tract urothelial carcinoma (UTUC) requires a stringent follow-up with frequent ureteroscopies. Triage testing could reduce the number of follow-up ureteroscopies and hence minimize the invasiveness of follow-up. The use of urine-based markers for triage seems appealing but should be feasible with selective urine from outpatient cystoscopy to maximize the reduction of invasiveness. In this study, the feasibility of UroVysion{sup ®} fluorescence in situ hybridization (FISH) for the detection of UTUC in 1 mL of selective urine is investigated. Ten consecutive patients with biopsy-proven UTUC and five patients with negative diagnostic ureteroscopy findings were included in this case-control study. During ureteroscopy, 1 mL of selective urine was collected passively with a ureteral splint for Urovysion® FISH. The FISH rater was blinded to any clinical information. The results of FISH were compared to the findings of concomitantly collected selective urine cytology and the patients’ UTUC status. FISH was feasible in all samples with a sensitivity of 90% and a specificity of 80% for UTUC. In comparison, selective cytology resulted in a diagnostic yield of 87% with a sensitivity of 80% and a specificity of 67%. In conclusion, UTUC detection is feasible with FISH in 1 mL of passively collected selective urine. Thus from a technical point of view, FISH could be used as an outpatient triage test to decide if follow-up ureteroscopy is necessary after kidney-sparing surgery of UTUC. Evaluation of the diagnostic accuracy of FISH for the suggested pathway deserves further attention.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability
Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient’s status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.
60 APPLIED LIFE SCIENCES↗
Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions
From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program. During this research experience, I was not only able to work on the above titled research project, but also gain an immense exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans. To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs) on the International Space Station will be different than the capabilities available and needed during deep space exploration missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the project was to research what AI tools currently exist or are in development, and to see how they might be implemented onboard during exploration class spaceflights of the future. The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC), and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound. The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated bibliography was subsequently used to create a review outline that would become the basis for drafting of a future publication. For the review outline, we took those medical conditions researched within the annotated bibliography (condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory, dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions. With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.
Ryan A. Lacinski↗
Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions
BACKGROUND The medical capabilities necessary for long-duration exploration missions (LDEMs) will differ tremendously from those currently available to crew medical officers (CMOs) on the International Space Station (ISS). Ground support will be more challenging due to distance-related communication delays and data transmission, and resource utilization must be optimized given limited ability for resupply. Clinical decision support systems (CDSSs) can help mitigate these limitations. The recent launch of generative artificial intelligence (AI) tools based upon large language models (LLM) support the creation of a smart assistant for onboard triage, diagnosis, and guided treatment of medical conditions during these missions. The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool can help predict which clinical problems and outcomes are likely to occur for a design reference mission (DRM) and assist Medical Operations and systems engineering teams in creating a medical system that may optimally mitigate the predicted risks. The purpose of this study was to identify AI tools currently available or in development for the assistive diagnosis and care of medical conditions predicted for an extended duration Lunar mission. METHODS The 119 medical conditions currently built into the IMPACT suite were categorized into systems, and these diagnoses were used as keywords for our literature search. Using PubMed and Google Scholar, we performed a literature survey of AI tools applicable to these conditions. Article inclusion criteria included publication between the years 2017-2023, as the sentinel paper discussing the “selective attention” driving ChatGPT and other generative transformer models was published in June 2017. Where applicable, we reviewed only the top 1000 research articles (based on relevance) for each of the keywords/phrases. AI tools whose training sets were exclusive to a pediatric patient population were excluded. We also excluded any medical diagnostic tools (such as CT, MRI, mass spectrometry) or procedures (such as endoscopy, surgery) that are unlikely to be available during LDEMs due to mass and volume constraints, CMO knowledge, skills, and abilities, and/or inherent procedural risks. RESULTS Our survey highlighted several AI-driven tools for the triage, diagnosis, and management of those medical conditions highlighted by IMPACT. Selected publications for each medical condition were then screened for inclusion within ten systems-based categories including: general diagnostic tools (25), tools to diagnose and manage respiratory (40), dermatologic (34), neurologic (28), auditory and vestibular (30), ophthalmic (34), musculoskeletal (104), infection-associated (92), and gynecologic (19) conditions, as well as tools that could be deployed in the setting of trauma and emergency (34). CONCLUSIONS Numerous AI-driven tools were highlighted within this literature survey, ranging from chatbot assistants that triage knee pain to vision transformer models for diagnosis of ophthalmic conditions using ocular surface images captured with a mobile phone. Remaining challenges include optimizing connectivity and integration of existing and developing systems into the vehicles or habitats. Notably, findings from this survey could help guide the initial design of an all-encompassing, onboard medical AI assistant for use during future LDEMs.
R A Lacinski↗
Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics
Point-of-care ultrasound imaging is a critical tool for patient triage during trauma for diagnosing injuries and prioritizing limited medical evacuation resources. Specifically, an eFAST exam evaluates if there are free fluids in the chest or abdomen but this is only possible if ultrasound scans can be accurately interpreted, a challenge in the pre-hospital setting. In this effort, we evaluated the use of artificial intelligent eFAST image interpretation models. Widely used deep learning model architectures were evaluated as well as Bayesian models optimized for six different diagnostic models: pneumothorax (i) B- or (ii) M-mode, hemothorax (iii) B- or (iv) M-mode, (v) pelvic or bladder abdominal hemorrhage and (vi) right upper quadrant abdominal hemorrhage. Models were trained using images captured in 27 swine. Using a leave-one-subject-out training approach, the MobileNetV2 and DarkNet53 models surpassed 85% accuracy for each M-mode scan site. The different B-mode models performed worse with accuracies between 68% and 74% except for the pelvic hemorrhage model, which only reached 62% accuracy for all model architectures. These results highlight which eFAST scan sites can be easily automated with image interpretation models, while other scan sites, such as the bladder hemorrhage model, will require more robust model development or data augmentation to improve performance. With these additional improvements, the skill threshold for ultrasound-based triage can be reduced, thus expanding its utility in the pre-hospital setting.
47 OTHER INSTRUMENTATION↗
Deep Interactive Learning with Sharkzor
Sharkzor is a web application for machine-learning assisted image sort and summary. Deep learning algorithms are leveraged to infer, augment, and automate the user’s mental model. Initially, images uploaded by the user are spread out on a canvas. The user then interacts with the images to impute their mental model into the applications algorithmic underpinnings. Methods of interaction within Sharkzor’s user interface and user experience support three primary user tasks: triage, organize and automate. The user triages the large pile of overlapping images by moving images of interest into proximity. The user then organizes said images into meaningful groups. After interacting with the images and groups, deep learning helps to automate the user’s interactions. The loop of interaction, automation, and response by the user allows the system to quickly make sense of large amounts of data.
97 MATHEMATICS AND COMPUTING↗
Accurate Gene Expression-Based Biodosimetry Using a Minimal Set of Human Gene Transcripts
Purpose: Rapid and reliable methods for conducting biological dosimetry are a necessity in the event of a large-scale nuclear event. Conventional biodosimetry methods lack the speed, portability, ease of use, and low cost required for triaging numerous victims. Here we address this need by showing that polymerase chain reaction (PCR) on a small number of gene transcripts can provide accurate and rapid dosimetry. The low cost and relative ease of PCR compared with existing dosimetry methods suggest that this approach may be useful in mass-casualty triage situations. Methods and Materials: Human peripheral blood from 60 adult donors was acutely exposed to cobalt-60 gamma rays at doses of 0 (control) to 10 Gy. mRNA expression levels of 121 selected genes were obtained 0.5, 1, and 2 days after exposure by reverse-transcriptase real-time PCR. Optimal dosimetry at each time point was obtained by stepwise regression of dose received against individual gene transcript expression levels. Results: Only 3 to 4 different gene transcripts, ASTN2, CDKN1A, GDF15, and ATM, are needed to explain ≥0.87 of the variance (R{sup 2}). Receiver-operator characteristics, a measure of sensitivity and specificity, of 0.98 for these statistical models were achieved at each time point. Conclusions: The actual and predicted radiation doses agree very closely up to 6 Gy. Dosimetry at 8 and 10 Gy shows some effect of saturation, thereby slightly diminishing the ability to quantify higher exposures. Analyses of these gene transcripts may be advantageous for use in a field-portable device designed to assess exposures in mass casualty situations or in clinical radiation emergencies.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Roles of Intramolecular and Intermolecular Interactions in Functional Regulation of the Hsp70 J-protein Co-Chaperone Sis1
Unlike other Hsp70 molecular chaperones, those of the eukaryotic cytosol have four residues, EEVD, at heir C-termini. EEVD(Hsp70) binds adaptor proteins of the Hsp90 chaperone system and mitochondrial membrane preprotein receptors, thereby facilitating processing of Hsp70-bound clients through protein folding and translocation pathways. Among J-protein co-chaperones functioning in these pathways, Sis1 is unique, as it also binds the EEVD(Hsp70) motif. However, little is known about the role of the Sis1:EEVD(Hsp70) interaction. We found that deletion of EEVD(Hsp70) abolished the ability of Sis1, but not the ubiquitous J-protein Ydj1, to partner with Hsp70 in in vitro protein refolding. Sis1 co-chaperone activity with Hsp70ΔEEVD was restored upon substitution of a glutamic acid of the J-domain. Structural analysis revealed that this key glutamic acid, which is not present in Ydj1, forms a salt bridge with an arginine of the immediately adjacent glycine-rich region. Thus, restoration of Sis1 in vitro activity suggests that intramolecular interactions between the J-domain and glycine-rich region control co-chaperone activity, which is optimal only when Sis1 interacts with the EEVD(Hsp70) motif. However, we found that disruption of the Sis1:EEVD(Hsp70) interaction enhances the ability of Sis1 to substitute for Ydj1 in vivo. Our results are consistent with the idea that interaction of Sis1 with EEVD(Hsp70) minimizes transfer of Sis1-bound clients to Hsp70s that are primed for client transfer to folding and translocation pathways by their preassociation with EEVD binding adaptor proteins. These interactions may be one means by which cells triage Ydj1- and Sis1-bound clients to productive and quality control pathways, respectively.
Yu, Hyun Young↗
Design and testing of ultrasound probe adapters for a robotic imaging platform
Medical imaging-based triage is a critical tool for emergency medicine in both civilian and military settings. Ultrasound imaging can be used to rapidly identify free fluid in abdominal and thoracic cavities which could necessitate immediate surgical intervention. However, proper ultrasound image capture requires a skilled ultrasonography technician who is likely unavailable at the point of injury where resources are limited. Instead, robotics and computer vision technology can simplify image acquisition. As a first step towards this larger goal, here, we focus on the development of prototypes for ultrasound probe securement using a robotics platform. The ability of four probe adapter technologies to precisely capture images at anatomical locations, repeatedly, and with different ultrasound transducer types were evaluated across more than five scoring criteria. Testing demonstrated two of the adapters outperformed the traditional robot gripper and manual image capture, with a compact, rotating design compatible with wireless imaging technology being most suitable for use at the point of injury. Next steps will integrate the robotic platform with computer vision and deep learning image interpretation models to automate image capture and diagnosis. This will lower the skill threshold needed for medical imaging-based triage, enabling this procedure to be available at or near the point of injury.
47 OTHER INSTRUMENTATION↗
SU-C-BRD-05: Non-Invasive in Vivo Biodosimetry in Radiotherapy Patients Using Electron Paramagnetic Resonance (EPR) Spectroscopy
Purpose: Medical intervention following a major, unplanned radiation event can elevate the human whole body exposure LD50 from 3 to 7 Gy. On a large scale, intervention cannot be achieved effectively without accurate and efficient triage. Current methods of retrospective biodosimetry are restricted in capability and applicability; published human data is limited. We aim to further develop, validate, and optimize an automated field-deployable in vivo electron paramagnetic resonance (EPR) instrument that can fill this need. Methods: Ionizing radiation creates highly-stable, carbonate-based free radicals within tooth enamel. Using a process similar to nuclear magnetic resonance, EPR directly measures the presence of radiation-induced free radicals. We performed baseline EPR measurements on one of the upper central incisors of total body irradiation (TBI) and head and neck (H&N) radiotherapy patients before their first treatment. Additional measurements were performed between subsequent fractions to examine the EPR response with increasing radiation dose. Independent dosimetry measurements were performed with optically-stimulated luminescent dosimeters (OSLDs) and diodes to more accurately establish the relationship between EPR signal and delivered radiation dose. Results: 36 EPR measurements were performed over the course of four months on two TBI and four H & N radiotherapy patients. We observe a linear increase in EPR signal with increasing dose across the entirety of the tested range. A linear least squares-weighted fit of delivered dose versus measured signal amplitude yields an adjusted R-square of 0.966. The standard error of inverse prediction (SEIP) is 1.77 Gy. For doses up to 7 Gy, the range most relevant to triage, we calculate an SEIP of 1.29 Gy. Conclusion: EPR spectroscopy provides a promising method of retrospective, non-invasive, in vivo biodosimetry. Our preliminary data show an excellent correlation between predicted signal amplitude and delivered dose. With further development, a robust means of predicting delivered radiation dose from EPR measurements is expected. This project was funded by the Biomedical Advanced Research and Development Authority (BARDA) within the U.S. Department of Health and Human Services subcontracted through the Geisel School of Medicine at Dartmouth and by the Dartmouth Physically-Based Biodosimetry Center for Medical Countermeasures Against Radiation (Dart-Dose CMCR) Pilot Program.
60 APPLIED LIFE SCIENCES↗
Portable Microfluidic Raman System for Rapid, Label-free Early Disease Signature Detection
In the early stages of infection, patients develop non-specific or no symptoms at all. While waiting for identification of the infectious agent, precious window of opportunity for early intervention is lost. The standard diagnostics require affinity reagents and sufficient pathogen titers to reach the limit of detection. In the event of a disease outbreak, triaging the at-risk population rapidly and reliably for quarantine and countermeasure is more important than the identification of the pathogen by name. To expand Sandia's portfolio of Biological threat management capabilities, we will utilize Raman spectrometry to analyze immune subsets in whole blood to rapidly distinguish infected from non-infected, and bacterial from viral infection, for the purpose of triage during an emergency outbreak. The goal of this one year LDRD is to determine whether Raman spectroscopy can provide label-free detection of early disease signatures, and define a miniaturized Raman detection system meeting requirements for low- resource settings.
59 BASIC BIOLOGICAL SCIENCES↗
Evaluation of the Hanford 200 West Groundwater Treatment System: Fluidized Bed Bioreactor
A fluidized bed reactor (FBR) in the 200W water treatment facility at Hanford is removing nitrate from groundwater as part of the overall pump-treat-reinject process. Control of the FBR bed solids has proven challenging, impacting equipment, increasing operations and maintenance (O&M), and limiting the throughput of the facility. In response to the operational challenges, the Department of Energy Richland Office (DOE-RL) commissioned a technical assistance team to facilitate a system engineering evaluation and provide focused support recommendations to the Hanford Team. The DOE Environmental Management (EM) technical assistance process is structured to identify and triage technologies and strategies that address the target problem(s). The process encourages brainstorming and dialog and allows rapid identification and prioritization of possible options. Recognizing that continuous operation of a large-scale FBR is complex, requiring careful attention to system monitoring data and changing conditions, the technical assistance process focused on explicit identification of the available control parameters (“knobs”), how these parameters interact and impact the FBR system, and how these can be adjusted under different scenarios to achieve operational goals. The technical assistance triage process was performed in collaboration with the Hanford team.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Vulcan-Forge: Architecture and Design of a Multi-Modal Forensic Analysis Plugin for CALDERA
Forge and VULCAN together describe an open-architecture cybersecurity analysis ecosystem that unifies forensic artifact processing, detection engineering, and vulnerability intelligence within integrated platforms. Forge operates as a plugin for MITRE CALDERA, ingesting diverse evidence formats—including EVTX, PCAP/PCAPNG, CSV, JSON, YAML, XML, binaries, and archives—to construct a unified artifact graph enriched with severity scoring, TLP classification, and audit trails. It provides subsystems for artifact parsing, streaming structured-data visualization, NetworkMiner-based packet inspection, PE/.NET binary analysis, and LLM-assisted triage and rule generation, with outputs validated against CCCS-YARA and pySigma schemas. VULCAN complements this by serving as a cybersecurity analyst platform that integrates a Neo4j knowledge graph, Qdrant vector retrieval, SSVC-based triage, and a local LLM to deliver CVE intelligence and forensic analysis through a multi-source ingest pipeline drawing from NVD, CISA KEV, EPSS, MITRE ATT&CK, and CAPEC. Together, they bridge structured threat intelligence with automated forensic analysis and detection workflows.
97 MATHEMATICS AND COMPUTING↗
2013 Caliban and Prospero experiments for the study of the behavior of radiation protection instrumentations in case of a criticality accident - 14346
Experiments using the CALIBAN and PROSPERO reactors were performed in 2013 in the framework of a collaboration between IRSN and CEA. The objective was to provide data about the possibility to use information given by radiation protection instrumentations (RPI) in the case of a criticality accident. In that purpose, the behavior of RPI was studied for various kinetics of the reactors which simulated different starts of a criticality accident. The article presents the safety issues associated with these experiments, i.e. the possible use of information given by the RPI for the emergency response of a criticality accident (detection of the accident, triage of the personnel, follow-up of the accident and reentry). Then, one paragraph is dedicated to the presentation of the tested material. In addition, the CALIBAN and PROSPERO reactors are briefly described, followed by the performed experiments. The RPI are neither designed nor dedicated to be used in case of a criticality accident. However, the preliminary conclusion of the experiments performed in 2013 is that the tested RPI have a correct behavior for slow kinetic experiments performed with the PROSPERO reactor, but might give less reliable and accurate information for pulse experiments performed with the CALIBAN reactor. In the last case, they can nevertheless provide information that can be helpful for the emergency response and the limitation of the consequences of the criticality accident. However, if the RPI are included in the emergency response of a criticality accident (detection, triage, follow-up, reentry), emergency procedures should be adapted. Other experiments might be performed in order to confirm results and to extend the number of tested materials, in particular portable survey meters or contamination area monitors. (authors)
42 ENGINEERING↗