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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Artificial Neural Network-based State Estimation for Low Observable, Unbalanced Microgrids for Microgrid Building Blocks

The microgrid building blocks (MBB) were proposed as microgrid components with combined sub-components with power conversion, communication, and microgrid control capability, or a subset of such sub-components. This work addresses the microgrid controller, present in an MBB, which requires accurate state estimation to perform its tasks, including for monitoring, power flow (dispatch), fault detection, etc. In this paper, an artificial neural network (ANN)-based framework for state estimation is proposed for an MBB, especially for unbalanced and low observable microgrids. To overcome the challenge of low observability in unbalanced systems, a concept of extended adjacent matrix is introduced to reduce the required number of measurements for state estimation. Addressing the challenges, a feed forward neural network (FNN) is utilized to enhance estimation accuracy and reliability with the reduced number of measurements. The proposed state estimation is validated through extensive simulations on a microgrid, which was achieved from the modified IEEE 34-bus distribution test feeder with multiple distributed energy resources (DERs) and demonstrated superior performance in estimation accuracy and low observability.

Choi, Jongchan↗
DOE OSTI2020

Sensor Drift Estimation for Reactor Systems by Fusing Multiple Sensor Measurements

A nuclear power plant is instrumented with a variety of sensors that monitor its variables to estimate the state and initiate safety actions, if needed. We address the problem of estimating drifts or errors in sensor measurements due to factors such as calibration changes. We propose an information fusion method that uses measurements from other sensors to generate an estimate of a sensor measurement, and its difference from an actual measurement provides an error estimate. We present two fusers based on the ensemble of trees and support vector machine that are trained using sensor measurements collected at an emulated test loop of a pressurized water reactor under no-drift conditions. We present error estimates for a differential pressure sensor of the heat exchanger of the primary coolant system, under twenty controlled scenarios using the test loop. Both positive and negative errors are captured by both methods in scenarios involving calibration drifts, blocking, air gap and electromagnetic interference. The root mean square errors of estimated drifts are typically within 2% percent of the maximum.

Rao, Nageswara↗
DOE OSTI2026

Entanglement Throughput Measurements and Capacity Estimates for Aerial-Inground Fiber

The throughput of entangled qubit pairs per second (eqps) is a basic performance metric of quantum networks that provide the entanglement distribution capability. Over fiber connections, it is measured using specialized instruments, including photonic entanglement sources and single-photon detectors. Extensive theory has been developed to estimate the capacity of a generic quantum channel, which in turn is applied to estimate the maximum achievable eqps over a fiber connection. However, there is a gap in relating these two performance metrics, in part due to their disparate nature, namely, mathematical formulae of the channel capacity and specialized instrumentation for eqps measurements. We describe eqps measurements collected over testbed connections composed of aerial and inground fiber of lengths up to 45 km. We estimate the normalized capacity using the transmissivity parameter derived from single photon detector measurements. These estimates are then converted to capacity bounds on the connection's eqps using the source eqps rate derived from co-located detector measurements. The results indicate a consistency between eqps measurements and their capacity estimates, and provide insights into relating the parameters of analytic capacity estimates to physical measurements.

Rao, Nageswara [ORNL] (ORCID:0000000234085941)↗
DOE OSTI2023

Detection of False Data Injection Attacks in Battery Stacks Using Input Noise-Aware Nonlinear State Estimation and Cumulative Sum Algorithms

Grid-scale battery energy storage systems (BESSs) are vulnerable to false data injection attacks (FDIAs), which could be used to disrupt state of charge (SoC) estimation. Inaccurate SoC estimation has negative impacts on system availability, reliability, safety, and the cost of operation. In this article a combination of a Cumulative Sum (CUSUM) algorithm and an improved input noise-aware extended Kalman filter (INAEKF) is proposed for the detection and identification of FDIAs in the voltage and current sensors of a battery stack. The series-connected stack is represented by equivalent circuit models, the SoC is modeled with a charge reservoir model and the states are estimated using the INAEKF. Further, the root mean squared error of the states’ estimation by the modified INAEKF was found to be superior to the traditional EKF. By employing the INAEKF, this article addresses the research gap that many state estimators make asymmetrical assumptions about the noise corrupting the system. Additionally, the INAEKF estimates the input allowing for the identification of FDIA, which many alternative methods are unable to achieve. The proposed algorithm was able to detect attacks in the voltage and current sensors in 99.16% of test cases, with no false positives. Utilizing the INAEKF compared to the standard EKF allowed for the identification of FDIA in the input of the system in 98.43% of test cases.

25 ENERGY STORAGE↗
DOE OSTI2024

Deep Multitask Learning Models for Radiation Estimation at High Energy Accelerator Facility

Controlling the dose of radiation exposure in potential radioactive facilities is critical for ensuring the safety of staff and the public. Here, in this paper, we developed machine learning models to estimate radiation exposure efficiently at the Thomas Jefferson National Accelerator Facility (JLab), aiming to enhance safety in both accelerator facilities and public areas. Multiple sensors were deployed around the three experimental halls at JLab. Data on single-beam currents, energy levels, and radiation values at the sensor locations were collected during accelerator operation. We proposed a multi-task learning model for radiation estimation, utilizing either one-dimensional convolutional neural networks (1-D CNNs) or long short-term memory networks (LSTMs) as the backbone. The proposed model was trained to simultaneously estimate radiation levels at the sensor locations. Experimental results demonstrated that the proposed model with LSTM backbone achieved the best estimation performance, with an average R 2 score of 0.7557 for estimation within the same year and 0.7157 for estimation across different years. These results significantly surpassed those of competing models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A Rapid Decision Support Tool for Estimating Impacts of a Vadose Zone Volatile Organic Compound Source on Groundwater and Soil Gas

Diminishing rates of subsurface volatile contaminant removal by soil vapor extraction (SVE) oftentimes warrants an in‐depth performance assessment to guide remedy decision‐making processes. Such a performance assessment must include quantitative approaches to better understand the impact of remaining vadose zone contamination on soil gas and groundwater concentrations. The spreadsheet‐based Soil Vapor Extraction Endstate Tool (SVEET) software functionality has recently been expanded to facilitate quantitative performance assessments. The updated version, referred to as SVEET2, includes expansion of the input parameter ranges for describing a site (site geometry, source characteristics, etc.), an expanded list of contaminants, and incorporation of elements of the Vapor Intrusion Estimation Tool for Unsaturated‐zone Sources software to provide soil gas concentration estimates for use in vapor intrusion evaluation. As part of the update, SVEET2 was used to estimate the impact of a tetrachloroethene (PCE) vadose zone source on groundwater concentrations, comparing SVEET2 results to field‐observed values at an undisclosed site where SVE was recently terminated. PCE concentrations from three separate monitoring wells were estimated by SVEET2 to be within the range of 6.0–6.7 μg/L, as compared to actual field concentrations that ranged from 3 to 11 μg/L PCE. These data demonstrate that SVEET2 can rapidly provide representative quantitative estimates of impacts from a vadose zone contaminant source at field sites. Finally, in the context of the SVE performance assessment, such quantitative estimates provide a basis to support remedial and/or regulatory decisions regarding the continued need for vadose zone volatile organic compound remediation or technical justification for SVE termination, which can significantly reduce the cost to complete for a site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Estimating UV-B, UV-Erithemic, and UV-A Irradiances From Global Horizontal Irradiance and MERRA-2 Ozone Column Information

The ground ultraviolet (UV) solar radiation is relevant due to its impacts on plastics degradation (mainly UVA) and on human health (UVB and erithemic UV (UVE)). UV ground measurements are not as ubiquitous as the relatively common global horizontal irradiance (GHI) measurements. Three simple models that estimate the UVA, UVB, and UVE components of solar irradiance from GHI and ozone column information are locally adjusted and validated. Five one-minute datasets from three sites in southeastern South America and two in the United States are used for simultaneous solar irradiance and UV data. All sites correspond to temperate mid-latitude regions. Simultaneous atmospheric total ozone column information is obtained from the reanalysis modern-era retrospective analysis for research and applications (MERRA-2) database for each site. Aside from locally adjusted models, average models with a single set of coefficients are also evaluated. For instance, the best average model is able to estimate UVE with a typical uncertainty below 12% and mean biases between +-3%, relative to the average of the measurements. Similar results are reported for the UVB and UVA components. These results, which can be useful in regions with similar climate and geography, provide a simple way to estimate UV irradiance under all-sky conditions with known uncertainty. This is an alternative to global satellite-based UV estimates, which can have high uncertainties at specific locations. Because MERRA-2 information has a global coverage, when coupled with good satellite-based estimates for GHI, UV irradiances can be estimated by this method over a large territory.

environmental UV radiation↗
DOE OSTI2020

Multichannel spectral estimation in acoustics: A state-space approach

Spectral estimation is a necessary methodology to analyze the frequency content of noisy data sets especially in acoustic applications. Many spectral techniques have evolved starting with the classical Fourier transform methods based on the well-known Wiener-Khintchine relationship relating the covariance-to-spectral density as a transform pair culminating with more elegant model-based parametric techniques that apply prior knowledge of the data to produce a high-resolution spectral estimate. Multichannel spectral representations are a class of both nonparametric, as well as parametric, estimators that provide improved spectral estimates. In any case, classical nonparametric multichannel techniques can provide reasonable estimates when coupled with peak-peaking methods as long as the signal levels are reasonably high. Furthermore, parametric multichannel methods can perform quite well in low signal level environments even when applying simple peak-picking techniques. In this paper, the performance of both nonparametric (periodogram) and parametric (state-space) multichannel spectral estimation methods are investigated when applied to both synthesized noisy structural vibration data as well as data obtained from a sounding rocket flight. It is demonstrated that for the multichannel problem, state-space techniques provide improved performance, offering a parametric alternative compared to classical methods.

42 ENGINEERING↗
DOE OSTI2022

Ensemble approximate control variate estimators: Applications to multi-fidelity importance sampling.

The recent growth in multifidelity uncertainty quantification has given rise to a large set of variance reduction techniques that leverage information from model ensembles to provide variance reduction for estimates of the statistics of a high-fidelity model. In this paper we provide two contributions: (1) we utilize an ensemble estimator to account for uncertainties in the optimal weights of approximate control variate (ACV) approaches and derive lower bounds on the number of samples required to guarantee variance reduction; and (2) we extend an existing multifidelity importance sampling (MFIS) scheme to leverage control variates. Our approach directly addresses a limitation of many multifidelity sampling strategies that require the usage of pilot samples to estimate covariances. As such we make significant progress towards both increasing the practicality of approximate control variates—for instance, by accounting for the effect of pilot samples—and using multifidelity approaches more effectively for estimating low-probability events. The numerical results indicate our hybrid MFIS-ACV estimator achieves up to 50% improvement in variance reduction over the existing state-of-the-art MFIS estimator, which had already shown an outstanding convergence rate compared to the Monte Carlo method, on several problems of computational mechanics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Estimating the Contributions to Human Error Probability from the Convolution of the Distribution of Time Available and Time Required

As part of their duties, Human Reliability Analysis must often evaluate if crews in nuclear power plants (NPPs) can complete tasks associated with a human-failure event within time limits. For example, the time required in NPP scenarios is determined by systematic and structured walkthroughs, feasibility studies, recorded times from training exercises, and interviews with experienced operators and experts. Typically, a point estimate is derived for the estimate (mean, maximum, or 95th percentile of time required). Using point-estimate values can mask the risk associated with variability among crews, plant conditions and set-up, environmental conditions, and other impact factors under which these actions are executed. While point estimates for time required and time available have served the industry well, without considering the uncertainty they could lead to biased understanding about the risk. The Integrated Human Event Analysis System - General Methodology (IDHEAS-G) model (developed by the US Nuclear Regulatory Commission, NRC) for human error probability calculates human error probability by summing two probabilities: insufficient time and cognitive error. As such, the model takes a more holistic approach by considering the full distributions for time required and time available to calculate the human error probability because the time available to complete the task is insufficient. In this study, we expand on the work of the NRC and discuss methods for estimating these time considerations. For example, for the time required, the impact of Performance Influencing Factors (PIFs) on the distribution was divided into impacts that are aleatory in nature, such as crew-to-crew variability, and those that are epistemic (i.e., the PIFs). Starting with the factors that introduce aleatory uncertainty, a first-order distribution was developed from a large set of time required (i.e., NPP task completion times) data for the range of operator actions that occur in the NPP control room under simulated accident conditions. The first-order distribution can then be adjusted to account for epistemic uncertainty using research associated with the impact of applicable PIFs on the time required. We also develop guidance for analysts to address the probability distributions for the time available. The guidance we developed on how to estimate time required and time available distributions is based on the identification of pertinent research and data, data analyses, and expert knowledge elicitation.

human error probability, human performance, time e↗
DOE OSTI2023

Data associated with “Different methods of estimating riverbed sediment grain size diverge at the basin scale ” (v2)

This data package is associated with the publication “Different methods of estimating riverbed sediment grain size diverge at the basin scale” published in Frontiers in Earth Science (Regier et al., 2025). The distribution of sediment grain size in streams and rivers is often quantified by the median grain size (d50), a key metric for understanding and predicting hydrologic and biogeochemical function of streams and rivers. Manual methods to measure d50 are time-consuming and ignore larger grains, while model-based methods to estimate d50 often over-generalize basin characteristics, and therefore cannot accurately represent site-scale heterogeneity. Here, we apply a machine learning-enabled photogrammetry methodology (You Only Look Once, or YOLO) for estimating d50 for grains > 2 mm based on images collected from streams and rivers throughout the Yakima River Basin (YRB). To understand how such methods may help bridge the gaps in resolution and accuracy between manual and catchment characteristics model-based d50 estimates, we compared YOLO d50 values to manual and model-based estimates across the YRB. We found distinct differences among methods for d50 averages and variability, and relationships between d50 estimates and basin characteristics. Source images can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052. This data package was originally published in May 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. In addition to the readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) and subfolders containing data, figures, and scripts. The data folder contains datasets used for the analyses in the manuscript in image, text-delimited or geospatially-referenced formats. The figures folder contains the figures from the manuscript in different formats. The scripts folder contains all of the scripts used to complete the analyses in the manuscript. All files are .csv, .rds, .dbf, .prj, .shp, .shx, .jpg, .png, .R, .Rproj, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Exploring Uncertainty in Moment Estimation for Small Earthquakes in Southern Nevada Using the Coda Envelope Method

Compiling source parameter estimates for small earthquakes is important both for our understanding of earthquake physics and for accurately assessing earthquake hazard. Reliable source parameter estimates are difficult to achieve for small earthquakes, in part due to our inability to accurately model the relevant physical processes at high frequencies. The coda envelope methodology developed by Mayeda and Walter (1996) and Mayeda et al. (2003) can mitigate this concern and estimate the moment of small earthquakes by determining the parameters that control the shape of the S-wave coda envelope while eliminating path effects by minimizing the scatter between seismic stations. Here, we use an open-source implementation of this technique called the Coda Calibration Tool (CCT; Barno, 2017) to calculate CCT-based moment magnitude estimates of small earthquakes (M L 0–3) in the Rock Valley, Nevada, region within the Nevada National Security Site. The Rock Valley data set is of particular interest because it allows us to explore the changes in uncertainties of the coda calibration method with earthquake size and depth. We found that a consistent linear relationship exists between the local magnitude M L and our coda-derived M w estimates for earthquakes as small as M L 0–3, but that current CCT workflows do not accurately characterize very shallow events. We also demonstrate that the epistemic uncertainty in the apparent stress value assumed by the CCT algorithm can influence magnitude estimates of small earthquakes. In conclusion, these results provide valuable insight into the seismicity of this region, and inform future analysis and modeling efforts for nuclear monitoring and seismic hazard.

58 GEOSCIENCES↗
DOE OSTI2020

Calculating Average Hot Water Mixes of Residential Plumbing Fittings Using the ANSI 301-2019 Hot Water Draw Model and National Residential Data to Estimate Hot Water Use in Showerheads and Lavatory Faucets

Available studies of hot water use percentages may not necessarily be generalized to a national level given regional differences, varying methodological approaches, and limited sample sizes. While some publicly available usage data, reports, and surveys estimate actual household usage, a lack of water use data specific to end uses make more precise savings calculations difficult. Additionally, most hot water draw models tend to focus on overall household use and may not readily estimate lavatory fixture use. However, reviewing more recent studies and standards enables the U.S. Environmental Protection Agency’s WaterSense program to update its estimates for hot water use and consumers’ corresponding energy and monetary savings to better reflect realworld conditions. This report specifically focuses on improving hot water use and savings estimates of lavatory faucets and showerheads. To estimate the hot water use of lavatory faucets and showerheads, we employed the 2015 Residential Energy Consumption Surveys (RECS) microdata alongside the ANSI 301-2019 Hot Water Draw Model. These estimates account for regional differences in hot water use, including regional cold water inlet temperatures. As a result, the refinements presented in this report are more robust, more recent, and better describe the geographic variation than previous inputs used by the WaterSense program. In addition, the approach described in this paper can be updated over time or tailored to regionally specific needs given available inputs. We conclude that hot water percentages for showers and faucets calculated using publicly available national data are close to the percentages found by regional studies and are consistent with household-level models of water use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A semi-supervised learning method to produce explainable radioisotope proportion estimates for NaI-based synthetic and measured gamma spectra

Quantifying the radioactive sources present in gamma spectra is an ever-present and growing national security mission and a time-consuming process for human analysts. While machine learning models exist that are trained to estimate radioisotope proportions in gamma spectra, few address the eventual need to provide explanatory outputs beyond the estimation task. In this work, we develop two machine learning models for a NaI detector measurements: one to perform the estimation task, and the other to characterize the first model’s ability to provide reasonable estimates. To ensure the first model exhibits a behavior that can be characterized by the second model, the first model is trained using a custom, semi-supervised loss function which constrains proportion estimates to be explainable in terms of a spectral reconstruction. The second auxiliary model is an out-of-distribution detection function (a type of meta-model) leveraging the proportion estimates of the first model to identify when a spectrum is sufficiently unique from the training domain and thus is out-of-scope for the model. In demonstrating the efficacy of this approach, we encourage the use of meta-models to better explain ML outputs used in radiation detection and increase trust.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Reactor Power Level Estimation by Fusing Multi-Modal Sensor Measurements

Estimates of the power level of a nuclear reactor based on measurements from an independent monitoring sensor system can help in the compliance verification of its declared operations. We present a three-level fusion method to estimate the power level of a nuclear reactor using features derived from infrared, electromagnetic, and acoustic sensor measurements collected in proximity to the reactor. Based on a simplified analytical model of the secondary coolant system of the reactor, we identify partial regression functions of the power level in terms of the temperature difference between inlet and outlet of coolant pipes, and the activity levels of four fans and four pumps, which are estimated as features from the sensor measurements. The power level estimator employs a combination of aggregate and complementary fusion steps at three levels to incorporate the multi-modal features in a structure that reflects the secondary cooling system and its partial regression functions. Using the measurements from a test campaign at an operational reactor, we show that this estimator achieves 3.47% or lower root mean square error under 5-fold cross validation. More generally, these results illustrate a progressive reduction in estimation error as additional modalities are appropriately incorporated, and that the fuser outperforms single modality features and their sub-combinations.

Rao, Nageswara↗
DOE OSTI2021

Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks

Subsurface permeability is a key parameter in watershed models that controls the contribution from the subsurface flow to stream flows. Since the permeability is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed watershed models, estimation through inverse modeling has had a long history in subsurface hydrology. The wide availability of stream surface flow data, compared to groundwater monitoring data, provides a new data source to infer soil and geologic properties using integrated surface and subsurface hydrologic models. As most of the existing methods have shown difficulty in dealing with highly nonlinear inverse problems, we explore the use of deep neural networks for inversion owing to their successes in mapping complex, highly nonlinear relationships. We train various deep neural network (DNN) models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Process-based modeling of soil nitrous oxide emissions from United States corn fields under different management and climate scenarios coupled with evaluation using regional estimates

Direct emissions of soil nitrous oxide during a growing season (N 2 O gs ) can be quantified with process-based models considering interactions between management, climate, and soil moisture when key data are available. We used an adapted “parameterized CENTURY/DAYCENT-model” ( p CENTURY) calibrated with crop growth and soil organic matter decay coefficients at the county-level for the estimation of N 2 O gs in the United States Corn Belt. Model estimated N 2 O-emissions from corn-based biofuels scenarios considering crop rotation, fertilizer inputs, tillage, and weather were compared against meta-summary of field observations from 55 studies. Both model and meta-summary ranked N 2 O gs -emissions to be corn > wheat > soybean phase while model likely underestimated cover crop N 2 O gs -emissions. The N 2 O gs -emissions and the associated emission factors (EFs) were modeled and summarized to be greater after anhydrous ammonia than urea application and from conventional tilled than non-tilled fields. Modeled and observed N 2 O gs -emissions after organic and inorganic fertilizer amendment did not differ due to high variability associated with the treatments. However, the organic fertilizer associated EFs were greater according to meta-summary data because of N input rates. Regionalized weather scenarios indicate hotspots for N 2 O gs -emissions can occur where crop N uptake is limited during dry years and in eastern states also during normal or wet seasons. The p CENTURY-derived N 2 O gs EFs (0.91 ± 0.19%) for counties investigated were only slightly lower than literature (1.07 ± 0.57%) or Tier-1 (1%) values. Our preliminary evaluation of regional soil moisture estimates showed reasonable agreement between monthly soil moisture estimates and the North American Soil Moisture Dataset during the growing season, but overestimation of soil moisture in winter-spring can influence the estimates of annual N 2 O emissions so future work is needed to calibrate soil moisture-associated model parameters. Our work provided scenario-based estimates of climate and management impacts on soil N 2 O gs -emissions together with valuable spatial insights into EFs that will be improved by more accurate information of fertilizer inputs and more temporally refined model evaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microbial communities in the liver and brain are informative for postmortem submersion interval estimation in the late phase of decomposition: A study in mouse cadavers recovered from freshwater

Introduction Bodies recovered from water, especially in the late phase of decomposition, pose difficulties to the investigating authorities. Various methods have been proposed for postmortem submersion interval (PMSI) estimation and drowning identification, but some limitations remain. Many recent studies have proved the value of microbiota succession in viscera for postmortem interval estimation. Nevertheless, the visceral microbiota succession and its application for PMSI estimation and drowning identification require further investigation. Methods In the current study, mouse drowning and CO 2 asphyxia models were developed, and cadavers were immersed in freshwater for 0 to 14 days. Microbial communities in the liver and brain were characterized via 16S rDNA high-throughput sequencing. Results Only livers and brains collected from 5 to 14 days postmortem were qualified for sequencing. There was significant variation between microbiota from liver and brain. Differences in microbiota between the cadavers of mice that had drowned and those only subjected to postmortem submersion decreased over the PMSI. Significant successions in microbial communities were observed among the different subgroups within the late phase of the PMSI in livers and brains. Eighteen taxa in the liver which were mainly related to Clostridium_sensu_stricto and Aeromonas , and 26 taxa in the brain which were mainly belonged to Clostridium_sensu_stricto , Acetobacteroides , and Limnochorda , were selected as potential biomarkers for PMSI estimation based on a random forest algorithm. The PMSI estimation models established yielded accurate prediction results with mean absolute errors ± the standard error of 1.282 ± 0.189 d for the liver and 0.989 ± 0.237 d for the brain. Conclusions The present study provides novel information on visceral postmortem microbiota succession in corpses submerged in freshwater which sheds new light on PMSI estimation based on the liver and brain in forensic practice.

Wang, Linlin↗