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DOE OSTI2025

E-scooter safety: How attitudinal factors influence risky behavior among shared e-scooter riders

In recent years, e-scooter usage for short-distance trips has grown rapidly. This surge in e-scooter use, combined with the high exposure of e-scooter riders to accident risk, has sparked concerns regarding e-scooter safety. Despite some studies focusing on e-scooter safety, little is known about how attitudinal factors lead e-scooter riders to engage in risky riding behaviors. In this paper, we developed a survey-based empirical model to identify the attitudinal factors influencing engagement in risky behaviors among e-scooter users. We used survey data collected from 420 shared e-scooter users in Chicago in 2022. The survey showed that 47.7% of respondents had experienced at least one collision or fall-off while riding e-scooters. We employed the Partial Least Squares Structural Equation Model (PLS-SEM) to examine the relationships between latent attitudinal factors and risky behavior engagement. Moreover, we conducted Permutation Multi-group Analysis (PMGA) to assess the moderating effect of socio-demographic factors within the estimated model. The findings suggest that riders’ unsafe riding attitude and riding confidence are the most influential factors shaping their risky behavior engagement. In addition, accident experience, infrastructure suitability, perceived enjoyment, traffic risk perception, and operational risk perception are among the other significant predictors. Among socio-demographic factors, gender, age, education, and car use frequency significantly influence riders’ engagement in risky behaviors. The results highlight the importance of infrastructure suitability and accident experience in analyzing e-scooter users’ riding behavior. The developed model advances our understanding of factors contributing to e-scooter riders’ risky behavior engagement. The findings offer valuable insights for policymakers and e-scooter vendors aiming to mitigate e-scooter users’ accident risk. Specifically, we recommend three safety countermeasures: (1) safety training programs to encourage a safer attitude, (2) practice-based initiatives to enhance riding confidence, and (3) infrastructure improvements, especially the expansion of bike lanes.

E-scooter↗

DOE OSTI2026

2005 Washoe County Travel Characteristics Study

The primary objective of the 2005 Washoe County Travel Characteristics Study was to document travel behavior data characteristics of regional households in Washoe County, Nevada, in order to update the regional transportation model. The household travel survey, which was conducted from October through November, was one of several surveys conducted in the fall of 2005 for the Regional Transportation Commission (RTC) of Washoe County, with the other surveys focusing on documenting transit usage, visitor travel behavior, and external travel through the region. The data collected were drawn from 1,174 households within the RTC planning area and contain information about 2,679 household members, 2,138 vehicles, and 11,077 unlinked trips. The household travel survey entailed the collection of activity and travel information for all household members regardless of age during a specific 24-hour period. The survey relied on the willingness of regional households to 1) provide demographic information about their household, its members, and its vehicles and 2) have all household members record all travel and activity for a specific 24-hour period, including address information for all locations visited, trip purpose, mode, and travel times.

1Hz data↗

DOE OSTI2021

Quantifying Turbine-Level Risk to Golden Eagles Using a High-Fidelity Updraft Model and a Stochastic Behavioral Model

To minimize the effects of wind farms on Golden Eagle (Aquila chrysaetos) populations while enabling sustainable development of renewable energy resources, it is important to understand how eagles interact with atmospheric flows, terrain features, and anthropogenic structures. Models that predict migratory flight paths provide one tool that helps us grasp how the location of wind farms may influence interactions and impacts on migrating Golden Eagles. The current state-of-the-art in predicting migratory flight paths uses a deterministic fluid-flow analogy to predict eagle trajectory using only an orographic updraft potential computed from topographical features. This model does not take into account variables, such as thermal updrafts and time varying atmospheric conditions that are known to influence migratory behavior. In this work, we improve on the model with the objective of developing tools that advance our understanding of how atmospheric flows and terrain features affect migratory eagle behavior and their interactions with wind farms. Specifically, we 1) incorporate both orographic and thermal updraft information in simulating eagle flight paths; 2) incorporate stochasticity into eagle travel patterns to better capture the influence of exogenous factors on, and the inherent stochasticity of eagle behavior; 3) consider spatio-temporal atmospheric data at wind-farm-scale when computing updraft potential; and 4) account for how atmospheric conditions and the direction of migration change seasonally and how these changes affect eagle migratory flight behavior. We tested the model using a 50km by 50km region with 50 m resolution in the western United States. We simulated 900 independent, probabilistic eagle tracks during southerly and northerly migration, assuming eagles solely rely on orographic updrafts. The preliminary results indicate that the inclusion of finer resolution atmospheric data allows for the inclusion of realistic conditions that an eagle experiences. The stochasticity in eagle tracks provides a platform to include uncertainty in eagle decision making and help produce robust eagle presence maps. We will deploy updraft and downdraft velocities computed using a high-fidelity, wind farm scale, computational fluid dynamics solver under development at National Renewable Energy Laboratory. This work is a first step in the development of a predictive and generalizable eagle behavior model at the wind farm scale that does not rely on empirical data collection. Although the current model is intended for migratory eagles, we will extend and refine this model to inform the development of additional behavioral modes, including resident eagle behavior. This modeling approach improves our ability to understand eagle use of the landscape at a fine scale, and it is our hope that this work will ultimately help advance strategies that minimize the impact of wind development on Golden Eagle populations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2021

The CanBikeCO Mini Pilot: Preliminary Results and Lessons Learned

In fall 2020, the Colorado Energy Office, as part of the State of Colorado’s “Can Do Colorado” initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, starting in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The nonmotorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO 2 . Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

The CanBikeCO Mini Pilot: Procedure and Preliminary Results

In fall 2020, the Colorado Energy Office, as part of the State of Colorado's "Can Do Colorado" initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, start in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The non-motorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO2. Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

GraphCH: A Deep Framework for Assessing Cyber-Human Aspects in Insider Threat Detection

Insider threat is one of the most damaging cyber attacks that could cause the loss of intellectual property and enterprise data security breaches. Action sequence data such as host logs are used to investigate such threats and develop anomaly-based AI detectors. However, insider threat actions are similar to legitimate user activities, causing AI detectors to fail and suffer from high false alarm rates. Therefore, user cyber activity logs are inadequate to fully unfold insider threats. In this study, we adopt human psychological principles of risk-taking and impulsiveness along with host data to assess the influence and usefulness of human behavioral aspects in insider threat detection. Here, we hypothesize that individuals' impulsive and risk-taking behavior correlates with cyberspace activities. To validate our hypothesis, we conducted an IRB-approved study recruiting 35 participants who work in a large U.S. university and collected their cyber and psychological data for 90 days. Host and human-behavioral data analysis and mapping indicate that impulsive and risk-taking users trigger more system errors causing (un)intentional insider threats and are susceptible to attackers' social engineering and cognitive hacking. Utilizing cyber-human aspects, we introduce a Cyber-Human Graph Neural Network (GNN) based framework GraphCH to identify abnormal user behaviors and detect insider threats.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions

This paper proposes an approach to select a maintenance strategy from a predefined set of choices considering the decision maker’s behavioral risk profile. It is assumed that the damage state is characterized by a continuous state parameter probabilistically inferred from observable sensor data. This work applies an engineering application of consequence-based decision-making incorporating the acceptable risk intensity of the decision-maker, i.e., the decision-maker’s (individual or an organization) valuation of the outcome of a decision, using a risk profile model. The utility of a decision-maker is subjective, and this paper considers the fact that different decision-makers mentally assign a different importance factor (the utility) to the seriousness or urgency to take necessary actions with the increasing intensity of structural damage. The approach herein incorporates a layer of human psychology on selecting appropriate maintenance strategies that not only depend on the posterior distribution of unmeasurable damage state but also consider the behavioral risk profile of the decision-maker. Further, the collective decision-making of an organization consisting of many individuals is also investigated. The approach is exemplified in a case study involving life cycle monitoring of a miter gate, part of a lock system enabling navigation of inland waterways.

42 ENGINEERING↗

DOE OSTI2025

XPS post-mortem analysis of plasma-facing units extracted from WEST after the C3 (2018) and C4 (2019) campaigns

Four monoblocks coming from one ITER-like plasma-facing unit from the Q3B sector of the lower divertor, named as monoblock (MB)3, MB9, MB20, and MB30, were exposed to the deuterium and helium plasma mixture during the C3 (2018) and C4 (2019) campaigns of the Tungsten Environment in Steady-state Tokamak (WEST), followed by a detailed ex-situ X-ray photoelectron spectroscopy investigation. The surface and in-depth chemistry of the tungsten monoblocks indicated the formation of a re-deposited mixture in the deposition-dominated area of the divertor, thicker than 218 and 172 nm for MB3 and MB9, respectively. The redeposition layer was dominated by a mixture of boron carbides accompanied by tungsten carbides in MB3, while in MB9, the redeposition layer was dominated by tungsten borides. The remaining two monoblocks, MB20 and MB30, were collected from the erosion region and showed similar chemical behavior with a blended mixture of oxidized and metallic tungsten followed by boron carbides within a 50 nm depth range. Boron fixation in the layers is an expected consequence of the boronizations used during the operation, but the chemical status of redeposited elements was characterized for the first time in this work.

Marin, Alexandru [Pennsylvania State University]↗

DOE OSTI2024

Liquid state theory of the structure of model polymerized ionic liquids

We employ polymer integral equation theory to study a simplified model of semiflexible polymerized ionic liquids (PolyILs) that interact via hard core repulsions and short range screened Coulomb interactions. The multi-scale structure in real and Fourier space of PolyILs (ions chosen to mimic Li, Na, K, Br, PF 6 , and TFSI) are determined as a function of melt density, Coulomb interaction strength, and ion size. Comparisons with a homopolymer melt, a neutral polymer–solvent-like athermal mixture, and an atomic ionic liquid are carried out to elucidate the distinct manner that ions mediate changes of polymer packing, the role of excluded volume effects, and the influence of chain connectivity, respectively. The effect of Coulomb strength depends in a rich manner on ion size and density, reflecting the interplay of steric packing, ion adsorption, and charge layering. Ion-mediated bridging of monomers is found, which intensifies for larger ions. Intermediate range charge layering correlations are characterized by a many-body screening length that grows with PolyIL density, cooling, and Coulomb strength, in disagreement with Debye–Hückel theory, but in accord with experiments. Qualitative differences in the collective structure, including an ion-size-dependent bifurcation of the polymer structure factor peak and pair correlation function, are predicted. The monomer cage order parameter increases significantly, but its collective ion counterpart decreases, as ions become smaller. Such behaviors allow one to categorize PolyILs into two broad classes of small and large ions. Furthermore, dynamical implications of the predicted structural results are qualitatively discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Impact of external screening on the valence and core level photoelectron spectra of monolayer W⁢S 2

Transition metal dichalcogenides (TMDs) represent an emerging class of layered materials with applications in microelectronics, optoelectronics, photonics, and catalysis. The confinement of charge carriers within the highly anisotropic, quasi-two-dimensional geometry of one-layer (1L) TMDs leads to reduced, variable dielectric screening, giving rise to a quasiparticle band gap that is highly susceptible to the surrounding dielectric environment. Here, exploiting the contrasting external dielectric environments of gold-supported and suspended 1L W⁢S 2 , we show how the electronic states of W⁢S 2 under the effective and ineffective screening environments align at a junction made within the same sheet of material. Photoelectron spectra point to the close alignment of the charge neutrality levels of W⁢S 2 in both environments, and the breakdown of rigid shifts between the valence states and core levels with the core levels shifting more than twice as much as the valence states. Furthermore, the effectively screened W⁢S 2 exhibits a valence state with the photoemission linewidth twice as large as the ineffectively screened suspended W⁢S 2 , presumably originated from the locally varying W⁢S 2 -Au distance and substrate disorders. Collectively, these findings provide key insights into the electronic behavior of W⁢S 2 and its photoemission process with the electronic states renormalized according to the external screening environments.

Dielectric properties↗

DOE OSTI2025

Size-dependent attraction of Cu solutes to clusters formed at Ag grain boundaries

We report a size-dependent solute clustering mechanism at grain boundaries in a sputtered ultrafine-grained Ag-Cu alloy, where large Cu clusters form despite weak individual solute-solute interactions. X-ray diffraction confirms limited Cu solubility in the Ag matrix, while scanning transmission electron microscopy reveals Cu clustering at both ordinary GBs and GB junctions. Density functional theory calculations show that 12-atom Cu clusters are energetically preferred, while smaller three-atom clusters are significantly less stable. Additional calculations demonstrate a marked increase in solute-cluster attraction energy with cluster size. As a result, these findings point to a previously unrecognized pathway for grain-boundary solute clustering in immiscible systems, driven by collective solute-cluster interactions, with implications for segregation behavior and stability in nanocrystalline and ultrafine-grained alloys.

Alloys↗

DOE OSTI2025

Dynamics of hot QCD matter 2024 — Bulk properties

The second Hot QCD Matter 2024 conference at IIT Mandi focused on various ongoing topics in high-energy heavy-ion collisions, encompassing theoretical and experimental perspectives. This proceedings volume includes 19 contributions that collectively explore diverse aspects of the bulk properties of hot QCD matter. The topics encompass the dynamics of electromagnetic fields, transport properties, hadronic matter, spin hydrodynamics, and the role of conserved charges in high-energy environments. These studies significantly enhance our understanding of the complex dynamics of hot QCD matter, the quark–gluon plasma (QGP) formed in high-energy nuclear collisions. Advances in theoretical frameworks, including hydrodynamics, spin dynamics and fluctuation studies, aim to improve theoretical calculations and refine our knowledge of the thermodynamic properties of strongly interacting matter. Experimental efforts, such as those conducted by the ALICE and STAR collaborations, play a vital role in validating these theoretical predictions and deepening our insight into the QCD phase diagram, collectivity in small systems, and the early-stage behavior of strongly interacting matter. In conclusion, combining theoretical models with experimental observations offers a comprehensive understanding of the extreme conditions encountered in relativistic heavy-ion and proton-proton collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

How to describe collective decay of uncoupled modes in the input–output formalism

We extend the input–output formalism to study the behavior of uncoupled discrete modes (bosonic cavity modes and fermionic qubits) when they decay to the same Markovian continuum. When the continuum interacts with only a single mode, this decay is irreversible. However, when multiple modes decay to the same Markovian continuum they develop correlations and decay collectively. In the input–output formalism these correlations manifest in additional terms in the quantum Langevin equation. For two modes, this collective decay can dramatically extend the lifetimes of both modes (Dicke subradiance) and, within the single-mode subsystem, induces non-Markovian memory effects including energy backflow.

Propp, Tzula B. (ORCID:0000000277481434)↗

DOE OSTI2023

Mesoscopic two-point collective dynamics of glass-forming liquids

The collective density–density and hydrostatic pressure–pressure correlations of glass-forming liquids are spatiotemporally mapped out using molecular dynamics simulations. It is shown that the sharp rise of structural relaxation time below the Arrhenius temperature coincides with the emergence of slow, nonhydrodynamic collective dynamics on mesoscopic scales. The observed long-range, nonhydrodynamic mode is independent of wave numbers and closely coupled to the local structural dynamics. Below the Arrhenius temperature, it dominates the slow collective dynamics on length scales immediately beyond the first structural peak in contrast to the well-known behavior at high temperatures. Furthermore, these results highlight a key connection between the qualitative change in mesoscopic two-point collective dynamics and the dynamic crossover phenomenon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Ramification of complex magnetism in Nd 2 Ir 2 O 7 observed by Raman scattering spectroscopy

Using Raman scattering spectroscopy, we uncover a complex magnetic behavior of Nd2Ir2O7, which stands out among magnetic pyrochlores by the lowest temperature of the all-in-all-out (AIAO) Ir moments ordering (${T}_{{\rm{Ir}}}^{{\rm{N}}} = 33$ K) and the highest temperature at which AIAO order of rare-earth Nd ions is detected (${T}_{{\rm{Nd}}}^{{\rm{* }}}$ = 15 K). Detected magnetic Raman scattering and calculations of expected response allow us to demonstrate that the ordering of Ir magnetic moments is accompanied by an appearance of one-magnon Raman modes at 26.3 and 29.6 meV compatible with the AIAO order and allowing to estimate the energies of Ir-Ir interactions. An additional two-magnon excitation of the AIAO Nd order at around 33 meV appears in the spectra below the ordering temperature of Nd moments ${T}_{{\rm{Nd}}}^{{\rm{* }}}$ = 15 K. In the temperature range between 15 K and 33 K we observe a broad mode, which demonstrates strong temperature dependence and shifts on cooling below 20 K from 14 meV to higher frequencies, and disappears at 5 K, when two-magnon excitation of Nd moments becomes prominent. We suggest an interpretation of this excitation in terms of continuum arising from collective fluctuations of Nd moments above the transition. This complex behavior emerges from the interplay of strong spin-orbit coupling, electronic correlations, and geometric frustration on two magnetic pyrochlore sublattices of Nd and Ir ions.

magnetic properties and materials↗

DOE OSTI2022

3DBFSVBF (3D BatFinder Smart Video BioFilter and Multi-class BatFinder Smart Video BioFilter) [SWR-22-88]

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.

Yarbrough, John↗

DOE OSTI2022

Sink or source? Insights into the behavior of copper and zinc in the sediment porewater of a constructed wetland by peepers

The H-02 free water surface constructed wetland has been applied to remove heavy metals, mainly copper (Cu) and zinc (Zn), from wastewater on the Savannah River Site (Aiken, SC, USA). More and more studies focus on the metal behavior between the sediment and the overlying water, which directly reflects the stability of metals after sedimentation in constructed wetlands. This study focused on the biogeochemical pathways in metal bioavailability and remobilization in the sediment after metals were removed from the overlying water. The dialysis sampling devices (peepers) were used to collect porewater samples from eight depths in the sediment for the measurement of Cu, Zn, dissolved organic carbon (DOC), and major anions (sulfate and chloride). Surface water samples were also collected for the measurement of Cu, Zn, DOC, and anions. Different temporal trends were observed for dissolved Cu between the surface and bottom waters, but not for dissolved Zn. There were no obvious changes in porewater metal concentrations with increasing depths in the sediment. Sediment served as a sink for Cu as only 3% of porewater samples showed higher labile Cu concentrations than the surface water during the entire year. Furthermore, sediment served as a source for Zn in summer and winter as 32% of porewater samples showed higher labile Zn concentrations than the surface water, and these samples were collected at all sediment depths. We think the seasonal changes in the behaviors of Cu and Zn were primarily controlled by the sulfur dynamics and the metal removal processes in the constructed wetland, as well as the different complexing chemistry between Cu and Zn. Also, our study supports that peeper is a powerful tool for studying the biogeochemistry of metals in the sediment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

BFSVBF (BatFinder Smart Video BioFilter) [SWR-22-87] and Multi-class BatFinder Smart Video BioFilter Keras

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The code has gone through five rounds of development with images used to train the models. This advancement allows for automated real-time data collection, identification, and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. We will discuss the two open source and publicly available machine learning models developed within this scope of this work: 1) a binary model with a 97.5% accuracy in identifying the difference between an object and an empty scene, including wind turbine and clouds; and 2) a multiple classification model with the capability of identifying the type of object detected: bats (90% accuracy), birds (83% accuracy), insects (69% accuracy) and non-biological (99% accuracy).

Yarbrough, John↗