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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Perturbation-Based Diagnosis of False Data Injection Attack Using Distributed Energy Resources

Modern smart grid relies on various sensor measurements for its operational control. In a successful false data injection attack, the attacker manipulates the measurements from the grid sensors such that undetected errors are introduced into the estimates of the system parameters leading to catastrophic situations. This paper proposes a novel perturbation based false data injection attack detection mechanism that utilizes inverter based distributed energy resources (DERs) to create low magnitude perturbation signal in the distribution system voltage that is inconsequential to the normal grid operation. Two voltage sensitivity analysis based algorithms are designed to identify the optimal set of DERs that can create the voltage perturbation signal of desired magnitude. An analytical method of voltage sensitivity analysis is used to compute the magnitude of voltage perturbation signal at each node in a computationally efficient manner. Then, a detection mechanism is developed that checks for the presence of the perturbation sequence in each sensor measurement. A sensor measurement is deemed authentic if the voltage perturbation signal is present in the data. In case of sensor malfunction or cyber-attack, the perturbation signal will not be present in the measurement data. Performance of the proposed attack detection mechanism is validated via simulation of the IEEE 69 bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

On the Formalization of Development and Assessment Process for Digital Twins in the Nearly Autonomous Management and Control System

In recent years, the autonomous control system has been encouraged in advanced reactors for restoring economic viability, simplifying the operation and maintenance, and enabling remote-site power generations [1]. Since the reactor is expected to be operated for a long period of time with a limited number of individuals onsite, it is recommended that the autonomous control system should have access to very realistic models of the state of processes in the whole lifecycle, together with these process behaviors in interaction with their environment in the real world. As a result, digital twin (DT) technology is suggested in autonomous control systems. DT is defined as a digital representation of a physical object or system, which contains a record for the histories of loads, operation and maintenance status, predictions for the near-term transient of important state variables, and decision-making process [2]. Since machine learning (ML) can recognize patterns within a complex system in real-time applications, it has been used to build DTs in the autonomous control systems for advanced reactors. Meanwhile, due to the rareness of operation data in accident scenarios, the development and assessment of DTs is expected to be mainly driven by simulations. Although the capability and feasibility of ML-based DTs are recognized in improving the safety and efficiency of reactor control, a major concern from the regulatory commission and the nuclear industry is whether the information from a DT is developed and assessed in accordance with expectation and requirements by the target decision. Such concerns not only affect the acceptance criteria for DTs, but also values that can be extracted from DTs and autonomous control system during operations. Inspired by the success of formal methods in improving the reliability and robustness of computer programming and software development, it is suggested that the development and assessment process (DAP) for both separate DTs and integral control system should be formalized in a transparent, consistent, and improvable manner. In this study, a digital-twin development and assessment process (DT-DAP) is proposed by adapting the evaluation model development and assessment process (EMDAP) [3] to requirements by the autonomous control system, ML algorithms, and DT technology. To demonstrate the framework, a baseline nearly autonomous management and control (NAMAC) system with ML-based DTs for diagnosis and prognosis is developed and assessed based on the framework. It is found that with selected testing methods and techniques, the DT-DAP can help identify errors in DTs and NAMAC which would otherwise be left unverified. Meanwhile, it is found that the DT-DAP can improve the DTs and NAMAC by continuously learning and iterating through different elements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results

Abstract. This paper provides an overview of the United States (US) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2 (E3SMv2) fully coupled regionally refined model (RRM) and documents the overall atmosphere, land, and river results from the Coupled Model Intercomparison Project 6 (CMIP6) DECK (Diagnosis, Evaluation, and Characterization of Klima) and historical simulations – a first-of-its-kind set of climate production simulations using RRM. The North American (NA) RRM (NARRM) is developed as the high-resolution configuration of E3SMv2 with the primary goal of more explicitly addressing DOE's mission needs regarding impacts to the US energy sector facing Earth system changes. The NARRM features finer horizontal resolution grids centered over NA, consisting of 25→100 km atmosphere and land, a 0.125∘ river-routing model, and 14→60 km ocean and sea ice. By design, the computational cost of NARRM is ∼3× of the uniform low-resolution (LR) model at 100 km but only ∼ 10 %–20 % of a globally uniform high-resolution model at 25 km. A novel hybrid time step strategy for the atmosphere is key for NARRM to achieve improved climate simulation fidelity within the high-resolution patch without sacrificing the overall global performance. The global climate, including climatology, time series, sensitivity, and feedback, is confirmed to be largely identical between NARRM and LR as quantified with typical climate metrics. Over the refined NA area, NARRM is generally superior to LR, including for precipitation and clouds over the contiguous US (CONUS), summertime marine stratocumulus clouds off the coast of California, liquid and ice phase clouds near the North Pole region, extratropical cyclones, and spatial variability in land hydrological processes. The improvements over land are related to the better-resolved topography in NARRM, whereas those over ocean are attributable to the improved air–sea interactions with finer grids for both atmosphere and ocean and sea ice. Some features appear insensitive to the resolution change analyzed here, for instance the diurnal propagation of organized mesoscale convective systems over CONUS and the warm-season land–atmosphere coupling at the southern Great Plains. In summary, our study presents a realistically efficient approach to leverage the fully coupled RRM framework for a standard Earth system model release and high-resolution climate production simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Non-topological logarithmic corrections in minimal gauged supergravity

We compute the logarithmic correction to the entropy of asymptotically AdS4 black holes in minimal N = 2 gauged supergravity. We show that for extremal black holes the logarithmic correction computed in the near horizon geometry agrees with the result in the full geometry up to zero mode contributions, thus clarifying where the quantum degrees of freedom lie in AdS spacetimes. In contrast to flat space, we observe that the logarithmic correction for supersymmetric black holes can be non-topological in AdS as it is controlled by additional four-derivative terms other than the Euler density. The available microscopic data and results in 11d supergravity indicate that the full logarithmic correction is topological, which suggests that the topological nature of logarithmic corrections could serve as a diagnosis of whether a low-energy gravity theory admits an ultraviolet completion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources

Abstract Motivation Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis, and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous, and potentially high-dimensional data sources, such as multi-omics and clinical data. In the literature, several approaches have been proposed to manage missing data, but are usually limited to the recovery of subsets of features for a subset of patients. A largely overlooked problem is the integration of multiple sources of data when one or more of them are completely missing for a subset of patients, a relatively common condition in clinical practice. Results We propose miss-Similarity Network Fusion (miss-SNF), a novel general-purpose data integration approach designed to manage completely missing data in the context of patient similarity networks. miss-SNF integrates incomplete unimodal patient similarity networks by leveraging a non-linear message-passing strategy borrowed from the SNF algorithm. miss-SNF is able to recover missing patient similarities and is “task agnostic”, in the sense that can integrate partial data for both unsupervised and supervised prediction tasks. Experimental analyses on nine cancer datasets from The Cancer Genome Atlas (TCGA) demonstrate that miss-SNF achieves state-of-the-art results in recovering similarities and in identifying patients subgroups enriched in clinically relevant variables and having differential survival. Moreover, amputation experiments show that miss-SNF supervised prediction of cancer clinical outcomes and Alzheimer’s disease diagnosis with completely missing data achieves results comparable to those obtained when all the data are available. Availability and implementation miss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF.

Biochemistry & Molecular Biology↗
DOE OSTI2025

WiDS Livermore Datathon 2025

The WiDS Datathon 2025 requires participants to build a model to predict both an individual’s sex and their ADHD diagnosis using functional brain imaging data of children and adolescents and their socio-demographic, emotions, and parenting information. The task is to create a multi-outcome model to predict two target variables: 1) ADHD (1=yes or 0=no) and 2) female (1=yes or 0=no).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A hardware-in-the-loop (HIL) testbed for cyber-physical energy systems in smart commercial buildings

In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. Here, this study further demonstrates the HIL testbed’s capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

System for controller area network payload decoding

A system for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This system can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.

Verma, Kiren E.↗
DOE OSTI2024

Toward an integrated multi-gigahertz ionizing particle diagnostic

Needs arising at both current and future accelerator facilities call for the development of radiation-hardened position-sensing diagnostics that can operate with multi-GHz repetition rates. Such instruments are likely to also have applications in the diagnosis of rapid plasma behavior. Here, building on the recent work of our Advanced Accelerator Diagnostics Collaboration, we are exploring the development of integrated multi-GHz ionizing particle detection systems based on chemical-vapor deposition diamond sensors, with the initial goal of producing a quadrant detector that can determine the intensity and centroid position of a particle beam at a repetition rate between 5 and 10 GHz. Results from our initial high-speed characterization work are presented, including those from a single-channel sensor with a GHz response. Approaches to achieving multi-GHz (5–10 GHz) rate capability, including the design of a dedicated Application Specific Integrated Circuit and the use of 3D RF-solver computer aided design software, are presented and discussed in more detail. 3D RF simulations suggest clean pulses of duration less than 250 ps (FWHM less than 125 ps) can be achieved with the approaches developed by this work.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Controller area network decoder (CAN-D)

A system and method for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This technology can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system and method can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.

Verma, Kiren E.↗
DOE OSTI2023

A use-case-driven approach for demonstrating the added value of digitalisation in wind energy

Digitalisation is one of the key drivers for reducing the costs and risks of wind energy. When considering whether to embark on a digitalisation initiative, two key questions arise. The first is what business or operational opportunities might feasibly be addressed and the second is which of the many potential aspects of digitalisation are relevant to those opportunities. In this work, we show how these questions can be answered with a use-case-driven approach, based around a survey aiming to collect and collate the main "pain points" (or everyday challenges) of people in the wind energy sector. Although the relatively low number of participants of the survey (46) means that the results should only be used indicatively, it is still possible to make some general recommendations for priorities for digitalisation efforts in the wind energy sector. Firstly, digitalisation efforts should focus both on supporting people carrying out cross-lifecycle tasks, in particular sharing data, managing data, undertaking general data analyses and accessing data. Tools to do this should deal with varying data formats and naming conventions, make metadata more accessible, define data and metadata standards, make more data publicly available and improve the quality of data. Secondly, efforts should also focus on supporting people in the wind farm operational phase, in particular with failure detection, fault diagnosis, failure rate modelling and predictive maintenance. Solutions to do this should focus on accessible and validated tools for fault detection, cloud or other data pipeline solutions for SCADA data and tools for exhaustive data documentation. Finally, digitalisation efforts should focus on better communicating and helping people become aware of existing solutions and tools, as well as on helping people to exert a stronger influence on possible solutions.

17 WIND ENERGY↗
DOE OSTI2024

Ultrasonic Nondestructive Diagnosis of Cylindrical Batteries Under Various Charging Rates

Lithium-ion batteries have been used increasingly as electrochemical energy storage systems for electronic devices and vehicles. It is important to accurately estimate the state of charge (SoC) of a battery management system to control the battery operation to optimize performance, lifetime, and safety. The current work experimentally leverages ultrasonic diagnostic technology to investigate the SoC of lithium-ion batteries during the charge/discharge processes. A cylindrical-type nickel-cobalt-aluminum (NCA)–based 2500mAh 20A (INR18650-25R) battery was used for ultrasonic measurements with various charge/discharge rates of C/10.4, C/5.2, and C/1.3 at constant currents. The ultrasonic signals were analyzed for extracting wave velocity and wave attenuation. For all the testing rates, wave velocity increased in the charge process and decreased in the discharge process. Further, velocity profiles corresponding to lower rates of C/10.4 and C/5.2 exhibited primary peaks at the maximum SoCs, whereas the absolute wave velocity of C/1.3 rate showed primary peaks that occurred slightly after the SoC peak, indicating a delayed maximum Young's modulus. The wave attenuation computed for the C/10.4 rate had local maxima in the charge and discharge processes and depicted negative correlations with SoC, ranging from 0% to 18%, and positive correlations with SoC from 18% to 85%. On the other hand, the wave attenuation curves of the C/1.3 rate showed no local peaks and had negative correlations with SoC, ranging from 0% to 28%, and positive correlations with SoC ranging from 28% to 53%.

25 ENERGY STORAGE↗
DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗
DOE OSTI2025

Idiomatic Correctness-Checking via Julienne in Fortran 2023

This paper presents a unified approach to unit testing and runtime assertion checking using Fortran 2023. The paper describes the support for our approach in the Julienne framework. Julienne leverages recent Fortran standards to implement object-oriented design patterns, support testing parallel programs, and implement functional programming patterns in order to craft idioms inspired by natural-language expressions. The presented idioms employ novel operators to write expressions that evaluate to a test-diagnosis object encapsulating two components: (1) the test outcome or assertion outcome and (2) an automatically generated diagnostic string. Two other novel aspects of the approach include (1) the ability to enforce assertions inside pure procedures and (2) the ability to output rich diagnostic information inside pure procedures during error termination when assertions fail. The latter capability mitigates against a reason that Fortran programmers commonly cite for not writing pure procedures: difficulty obtaining useful program output inside pure procedures when debugging code. This paper demonstrates how the adoption of the proposed idioms leads naturally to a unifying theme across two otherwise disparate technologies: unit testing and runtime assertion checking. Finally, this paper describes the usage of the Julienne testing framework for writing unit tests and assertions in the Matcha high-performance computing application and the Fiats deep learning library.

Rouson, Damian↗
DOE OSTI2024

Hydra: Computer Vision for Data Quality Monitoring

Hydra: Computer Vision for Data Quality Monitoring Mar 13, 2024, 4:15 PM 30m Charles B. Wang Center, Stony Brook University Poster Track 1: Computing Technology for Physics Research Poster session with coffee break Speaker Ayan Roy Description Hydra is an extensible framework for training, managing, and deploying machine learning models for near real-time data quality monitoring. It is designed to take some of the burden off of shift crews by providing ‘round-the-clock’ monitoring of plots representing the data being collected. The Hydra system is backed by a database which is leveraged for near push button training and is primarily controlled and viewed through a set of web interfaces. This web interface contains a simple-to-use web based GUI for labeling of the datasets used in training; making it possible to label thousands of images quickly and efficiently. To aid in the analysis of Hydra inferences gradCAM, a method of interpretability, is performed and overlaid on the target image, highlighting regions of interest, making diagnosis of problems much faster. Development began in 2019 for the GlueX Experiment in Hall-D, the Hydra system has grown to encompass all of the experimental halls at Jefferson Laboratory. This talk will focus on the features of Hydra as well as provide details of the challenges present with deploying to disparate experimental halls. With a roadmap of development established, Hydra aims to continue to grow in richness of feature set and expand to encompass other monitoring tasks in differing communities.

Roy, Ayan↗
DOE OSTI2024

Decomposition characteristics of C4F7N-based SF6-alternative gas mixtures

C4F7N [2,3,3,3-tetrafluoro-2-(trifluoromethyl)propanenitrile]/CO2 gas mixtures are being developed as an eco-friendly electrical insulator to replace SF6, the most potent greenhouse industrial gaseous dielectric. However, recent studies have reported complicated and often conflicting decomposition pathways for C4F7N/CO2 gas mixtures, which has raised concerns. In this work, the decomposition characteristics of C4F7N/CO2 gas mixtures were studied comprehensively by both designed computations and experiments. Computations were performed starting from fundamental propositions of C4F7N/CO2 decompositions, which were further experimentally verified by pyrolysis, long-term thermal aging with/without catalytic materials (industrial-grade molecular sieves 4A), and electrical decomposition by spark discharge. The results of both computations and experiments suggest that in an ideal thermal decomposition, C4F7N is likely to decompose into C2F6 and small fluoronitriles first at high temperatures. The generation of C3F6 and C2N2 from C4F7N thermal decomposition at lower temperatures appears because of the catalytic effect of incompatible materials, for example, the industrial-grade molecular sieves 4A that we tested. The electron impact dissociation of C4F7N plays an important role in C4F7N electrical decomposition, leading to additional formation of distinctive small molecules of CF4 and C2N2 of low concentrations. It was pointed out based on a real arcing test in a load disconnector that the decomposition of C4F7N gas mixtures in real applications will be at a much moderate and manageable rate than what was obtained from the highly accelerated laboratory tests presented in this work. The signatures of decomposition products extracted in this study provide invaluable guidance for developing decomposition-based diagnosis and fixation of decomposition byproducts toward SF6-free power grids.

Physics↗
DOE OSTI2020

Persistent minimal sequences of SARS-CoV-2

Abstract Motivation Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 14 million cases and more than half million deaths. Given the absence of implemented therapies, new analysis, diagnosis and therapeutics are of great importance. Results Analysis of SARS-CoV-2 genomes from the current outbreak reveals the presence of short persistent DNA/RNA sequences that are absent from the human genome and transcriptome (PmRAWs). For the PmRAWs with length 12, only four exist at the same location in all SARS-CoV-2. At the gene level, we found one PmRAW of size 13 at the Spike glycoprotein coding sequence. This protein is fundamental for binding in human ACE2 and further use as an entry receptor to invade target cells. Applying protein structural prediction, we localized this PmRAW at the surface of the Spike protein, providing a potential targeted vector for diagnostics and therapeutics. In addition, we show a new pattern of relative absent words (RAWs), characterized by the progressive increase of GC content (Guanine and Cytosine) according to the decrease of RAWs length, contrarily to the virus and host genome distributions. New analysis shows the same property during the Ebola virus outbreak. At a computational level, we improved the alignment-free method to identify pathogen-specific signatures in balance with GC measures and removed previous size limitations. Availability and implementation https://github.com/cobilab/eagle. Supplementary information Supplementary data are available at Bioinformatics online.

Pratas, Diogo↗
DOE OSTI2023

Polychlorinated biphenyls, polychlorinated dibenzo- p -dioxins, polychlorinated dibenzofurans, pesticides, and diabetes in the Anniston Community Health Survey follow-up (ACHS II): single exposure and mixture analysis approaches

Dioxins and dioxin-like compounds measurements were added to polychlorinated biphenyls (PCBs) and organochlorine pesticides to expand the exposure profile in a follow-up to the Anniston Community Health Survey (ACHS II, 2014) and to study diabetes associations. Participants of ACHS I (2005–2007) still living within the study area were eligible to participate in ACHS II. Diabetes status (type-2) was determined by a doctor's diagnosis, fasting glucose ≥125 mg/dL, or being on any glycemic control medication. Incident diabetes cases were identified in ACHS II among those who did not have diabetes in ACHS I, using the same criteria. Thirty-five ortho-substituted PCBs, 6 pesticides, 7 polychlorinated dibenzo-p-dioxins (PCDD), 10 furans (PCDF), and 3 non-ortho PCBs were measured in 338 ACHS II participants. Dioxin toxic equivalents (TEQs) were calculated for all dioxin-like compounds. Main analyses used logistic regression models to calculate odds ratios (OR) and 95 % confidence intervals (CI). In models adjusted for age, race, sex, BMI, total lipids, family history of diabetes, and taking lipid lowering medication, the highest ORs for diabetes were observed for PCDD TEQ: 3.61 (95 % CI: 1.04, 12.46), dichloro-diphenyl dichloroethylene (p,p’-DDE): 2.07 (95 % CI 1.08, 3.97), and trans-Nonachlor: 2.55 (95 % CI 0.93, 7.02). The OR for sum 35 PCBs was 1.22 (95 % CI: 0.58–2.57). To complement the main analyses, we used BKMR and g-computation models to evaluate 12 mixture components including 4 TEQs, 2 PCB subsets and 6 pesticides; suggestive positive associations for the joint effect of the mixture analyses resulted in ORs of 1.40 (95% CI: -1.13, 3.93) for BKMR and 1.32 (95% CI: -1.12, 3.76) for g-computation. Furthermore, the mixture analyses provide further support to previously observed associations of trans-Nonachlor, p,p’- DDE, PCDD TEQ and some PCB groups with diabetes.

60 APPLIED LIFE SCIENCES↗