ONR Award Research Articles

Model-Based Interpretation of Thermal-Gradient Effects in Li-Ion Cells

The present study develops a combined experimental and modeling approach to study temperature gradients in Li-ion batteries. Although through-plane temperature differences may be small (ΔT ≈ 1-10 °C), large interelectrode thermal gradients (∇T ≈ 1,000-10,000 °C/m) can be present and have been found to significantly affect Li-ion battery performance and degradation [1,2]. Figure 1 illustrates a thin pouch cell (order of 300 microns thick) sandwiched between cool and warm plates. The cell uses a graphite anode and a NMC622 cathode. By maintaining the temperature of the cool and warm blocks, a thermal gradient can be enforced across the cell. Depending on how the cell is positioned, the cell’s cathode or anode can be on the cool or warm side. Experiments include overpotential analysis and galvanostatic intermittent titration technique (GITT) at different states of charge. Results show different behaviors as the thermal gradient is applied and the directionality of the thermal gradient changes.This presentation presents a pseudo-two-dimensional (P2D) model [3] that has been modified to represent the experimental conditions, including the thermal gradients. The model considers the temperature dependencies of thermodynamic properties, electrolyte conductivities, electrode diffusion coefficients, intercalation rates, etc. [4]. The model captures some, but not all, of the measured behaviors. Thus, an important objective is to investigate physical and chemical behaviors that are not included in typical P2D models. For example, off-diagonal Onsager contributions could be significant in the presence of high temperature gradients. The results predict how thermal gradients can contribute to Li-ion concentration fluxes (Soret contribution), and thus affect battery performance. The scientific intent is to understand the thermal gradient behaviors and thus assist battery design guidelines and battery-management control considerations. Acknowledgments The authors thank the Office of Naval Research (Dr. Michele Anderson) for financial support (ONR award numbers: N00014-23-1-2694 and N00014-22-1-2411) of this work.

Progress in understanding seismic wind noise

Previous work at NCPA on predicting and measuring wind noise in the ground used a quasistatic calculation employing Shield’s wind noise correlation function. This method predicted the vertical displacements at the surface well, but under predicted the horizontal displacement and predicted a rapid decay of levels with depth that was not observed in the measurements. A new measurement of the wind noise correlation coefficients using flush mount microphones was presented at the Seattle Meeting (2aPa1). In this talk additional measurements and improved methods for evaluating the coefficients are described. An improved calculation of the seismic levels due to wind induced pressures at the ground has been developed based on the wavenumber-frequency analysis developed for studying airframe noise due to turbulence. This calculation correctly predicts the surface vertical component and the near constant seismic level in the first 40cm of the ground. It still under predicts the horizontal component, but the under prediction is smaller than that predicted by the quasistatic calculation. [This research was sponsored by the Department of the Navy, Office of Naval Research under ONR Award No. N00014-18-2489.]

0283 Neurobehavioral Resilience and Vulnerability to Sleep Loss Differs Between Objective and Self-Rated Metrics Regardless of Categorization Method Utilized

Abstract Introduction Trait-like individual differences in neurobehavioral responses to sleep restriction (SR) and total sleep deprivation (TSD) are robust and phenotypic. We investigated whether the concordance between multiple approaches for defining differential vulnerability depends on the methods and metrics utilized for categorization, including comparisons between objective and self-rated metrics. Trait-like individual differences in neurobehavioral responses to sleep restriction (SR) and total sleep deprivation (TSD) are robust and phenotypic. We investigated whether the concordance between multiple approaches for defining differential vulnerability depends on the methods and metrics utilized for categorization, including comparisons between objective and self-rated metrics. Methods Forty-one adults (33.9±8.9y; 18 females) participated in a 13-day experiment (two baseline nights [10h-12h time-in-bed, TIB], 5 SR nights [4h TIB], 4 recovery nights [12h TIB], and 36h TSD). The 10-minute Psychomotor Vigilance Test (PVT), Digit Symbol Substitution Test (DSST), Digit Span Task (DS), Karolinska Sleepiness Scale (KSS), Profile of Mood States Fatigue (POMS-F) and Vigor (POMS-V) were administered every 2h during wakefulness. Three approaches (Raw Score [average SR score], Change from Baseline [average SR minus average baseline score], and Variance [intraindividual SR score variance]), and six thresholds (±1 standard deviation, and the best and worst performing 12.5%, 20%, 25%, 33%, and 50%) categorized Resilient and Vulnerable groups. Kendall’s tau-b correlations assessed the group categorization’s concordance between pairings of PVT lapses (reaction time [RT]>500ms), PVT mean response speed (1/RT), DSST number correct, DS total number correct, KSS score, POMS-F score, and POMS-V score (tau-b=0.0: zero; 0.1: weak; 0.4: moderate; 0.70: strong; 1.0: perfect). Results Generally, tau-b correlations comparing Resilient and Vulnerable categorizations between two objective metrics (i.e., PVT, DSST, DS) revealed weak to moderate significant relationships (tau-b=0.29-0.53, p<0.001-0.049) between at least two of the approaches at most thresholds. However, comparisons between one objective (i.e., PVT, DSST, DS) and one self-rated metric (i.e., KSS, POMS) revealed a general lack of significant relationships (tau-b=-0.25-0.28, p=0.052-1.00), regardless of approach or threshold. Conclusion Comparisons between two objective metrics revealed significantly concordant Resilient and Vulnerable categorizations, whereas categorizations were generally not significantly correlated between one objective and one subjective metric, regardless of the method utilized. Our findings support and extend previous assertions that SR differentially impacts objective and subjective neurobehavioral domains and have important implications when assessing resilience and vulnerability to sleep loss in both laboratory and applied settings. Support (If Any) ONR Award No. N00014-11-1-0361; NIH UL1TR000003; NASA NNX14AN49G and 80NSSC20K0243; NIH R01DK117488

Weathering the storm: Impacts of hurricane-induced noise on the probability of detecting cetaceans

Hurricanes Dorian (Category 5; 2019), Florence (Category 4; 2018), and Humberto (Category 3; 2019) impacted the soundscape as observed at the Atlantic Deepwater Ecosystem Observatory Network (ADEON) locations in the US Mid- and South Atlantic Outer Continental Shelf. Passive acoustic data obtained from bottom-mounted hydrophones at the ADEON locations were examined to assess changes in ambient sound before, during, and afterhurricane presence. Ambient sound level increased up to 25 dB in the 0.1–7 kHz band which could impact the detectability of cetaceans vocalizing at those frequencies. The probability of detection of fin whales, minke whales, and pilot whales was estimated using empirical ambient sound levels, modelled propagation loss, and pre-defined detection parameters. Detection area, as defined by range of minimum detectability of the cetacean from the receiver, was estimated before, during, and after hurricane presence at each ADEON location. Detection area changed considerably during hurricane presence with site-specific impacts for each of the cetaceans, which may affect estimates of their abundance from passive acoustic recordings. [Study concept, oversight, and funding were provided by ONR Award N00014-16-1-2594 and BOEM under Contract No. M16PC00003, in partnership with ONR and NOAA. Funding for ship time was provided under separate contracts by ONR, Code 32.]

127 Cognitive Throughput, Behavioral Attention, and Sleepiness Show Robust Relationships During Sleep Loss but Not During Recovery

Abstract Introduction The Digit Symbol Substitution Task (DSST) is a frequently used measure to determine cognitive throughput responses to sleep loss. However, the specific time course of relationships between cognitive throughput and behavioral attention [using the 10-minute Psychomotor Vigilance Test (PVT10)] and subjective sleepiness [using the Karolinska Sleepiness Scale (KSS)] across sleep loss and recovery remains unknown yet is critical for assessing whether tasks involving learning and those without learning track each other. Repeated measures correlation (rmcorr) examined within-individual associations between measures of these tests throughout a highly controlled sleep deprivation study. Methods Forty-one healthy adults (ages 21-49;mean ± SD, 33.9 ± 8.9y;18 females) participated in a 13-night experiment consisting of two baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the DSST, the KSS, and the PVT10, was administered every 2h during wakefulness. Rmcorr analyses compared DSST [number correct], KSS score, and PVT10 performance [lapses (reaction time [RT] >500ms) and 1/RT (response speed)] by examining correlations by day (e.g., Baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr ranges were as follows: r=0.1: small; r=0.3: moderate; r=0.5: large. Results During SR and TSD, correlations were significant, ranging from moderate to large, with the strongest correlation occurring during TSD. By contrast, baseline and recovery correlations were not significant or were small for DSST relative to PVT10 lapses, PVT10 response speed, or KSS scores. Additionally, all three pairs showed moderate to large correlations by time point across the entire study. Conclusion The various test measure relationships were consistently strong at specific times of day throughout the study. In addition, the associations between cognitive throughput and behavioral attention and sleepiness were strongest during sleep loss, particularly during TSD, suggesting that these measures are most acutely attuned to neurobehavioral changes resulting from sleep loss. The lack of a significant relationship at baseline and at recovery may be due to the learning effect reported for the DSST that is not present for the PVT10 or KSS. Support (if any) ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

119 Behavioral Attention and Sleepiness Display Robust Stable Relationships Across Sleep Loss but not Across Recovery

Abstract Introduction The 10-minute and 3-minute versions of the Psychomotor Vigilance Test (PVT10 and PVT3) and the Karolinska Sleepiness Scale (KSS) are commonly used to assess objective behavioral attention deficits and subjective sleepiness in response to sleep loss, respectively. However, the precise time course of relationships between behavioral attention and subjective sleepiness across sleep loss and recovery remains unknown but is critical for determining whether objective and subjective measures track each other. Repeated measures correlation (rmcorr) examined within-individual association between these measures throughout a highly controlled sleep deprivation study. Methods Forty-one healthy adults (ages 21-49;mean±SD, 33.9±8.9y;18 females) participated in a 13-night experiment consisting of two baseline nights (10h-12h time-in-bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the PVT10, PVT3, and KSS, was administered every 2h during wakefulness. Rmcorr compared PVT10 [lapses (reaction time [RT] >500ms) and 1/RT (response speed)], PVT3 (lapses [RT>355ms], 1/RT), and KSS scores by examining correlations by day (e.g., Baseline day 2) and time point (e.g., 1000h-2000h). Rmcorr ranges: r=0.1:small; r=0.3:moderate; r=0.5:large. Results Generally, the correlations between the PVT10 and KSS and the PVT3 and KSS showed a similar pattern for lapses and 1/RT. PVT lapses and KSS scores showed small or non-significant correlations during baseline and recovery, whereas SR and TSD showed moderate correlations. PVT 1/RT and KSS scores showed moderate correlations during baseline, moderate to large correlations during SR and TSD, but small correlations during recovery. PVT10 and PVT3 1/RT showed stronger correlations with KSS scores than lapses. Additionally, all relationships showed moderate to large correlations by time point across the study. Conclusion Overall, the relationship between behavioral attention and sleepiness was stronger across sleep loss (SR or TSD) relative to fully rested states while it was consistently relatively strong at specific times of day throughout the study. In contrast to published literature, there is a remarkably stable relationship between an individual’s objective behavioral attention performance and perceptions of sleepiness during sleep loss, which is not evident during recovery or at baseline. Support (if any) ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

121 Behavioral Attention Relationships Vary Between Demographic Groups Across Sleep Loss and Recovery

Abstract Introduction The Psychomotor Vigilance Test (PVT) is a behavioral attention measure widely used to describe sleep loss deficits. Although there are reported differences in PVT performance for various demographic groups, no study has examined the relationship between measures on the 10-minute PVT (PVT10) and the 3-minute PVT (PVT3) within sex, age, and body mass index (BMI) groups throughout a highly controlled sleep deprivation study. Methods Forty-one healthy adults (mean±SD ages, 33.9±8.9y) participated in a 13-night experiment [2 baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR1-5) nights (4h TIB), 4 recovery nights (R1-R4; 12h TIB), and 36h total sleep deprivation (TSD)]. A neurobehavioral test battery, including the PVT10 and PVT3 was completed every 2h during wakefulness. Repeated measures correlation (rmcorr) compared PVT10 and PVT3 lapses (reaction time [RT] >355ms [PVT3] and >500ms [PVT10]) and response speed (1/RT) by examining correlations by day (e.g., baseline day 2) and time point (e.g., 1000h-2000h) within sex groups (18 females), within age groups defined by a median split (median=32, range=21-49y), and within BMI groups defined by a median split (median=25, range=17-31). Results PVT10 and PVT3 1/RT was significantly correlated at all study days and time points excluding at baseline for the younger group and at R2 for the higher BMI group. PVT10 and PVT3 lapses showed overall lower correlations across the study relative to 1/RT. Lapses were not significantly correlated at baseline for any group, for males across recovery (R1-R4), for the high BMI group at R2-R4, for the older group at R2-R3, or for the younger group at SR5 or R3. Conclusion Differentiating participants based on age, sex, or BMI revealed important variation in the relationship between PVT10 and PVT3 measures across the study. Surprisingly, lapses were not significantly correlated at baseline for any demographic group or across recovery for males or the high BMI or older group. Thus, PVT10 and PVT3 lapses may be less comparable in certain populations when well-rested. These findings add to a growing literature suggesting demographic factors may be important factors to consider when evaluating the effects of sleep loss. Support (if any) ONR Award N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243; NIHR01DK117488

122 Different Duration Psychomotor Vigilance Tests Show Robust Stable Relationships Across Sleep Loss That Deteriorate in Recovery

Abstract Introduction The Psychomotor Vigilance Test (PVT), a behavioral attention measure widely used to capture sleep loss deficits, is available in 10-minute (PVT10) and 3-minute (PVT3) versions. The PVT3 is a briefer and presumably comparable assessment to the more commonly used PVT10 yet the relationship between the measures from the two versions across specific time points and in recovery after sleep loss has not been investigated. Repeated measures correlation (rmcorr) evaluated within-individual associations between measures on the PVT10 and PVT3 throughout a highly controlled sleep deprivation study. Methods Forty-one healthy adults (ages 21-49; mean±SD, 33.9±8.9y; 18 females) participated in a 13-night experiment consisting of 2 baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR1-5) nights (4h TIB), 4 recovery nights (R1-R4; 12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the PVT10 and PVT3 was completed every 2h during wakefulness. Rmcorr compared PVT10 and PVT3 lapses (reaction time [RT] >355ms [PVT3] or >500ms [PVT10]) and response speed (1/RT) by examining correlations by day (e.g., baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr ranges were as follows: 0.1-0.3, small; 0.3-0.5, moderate; 0.5-0.7, large; 0.7-0.9, very large. Results All time point correlations (1000h-2000h) were significant (moderate to large for lapses; large to very large for 1/RT). Lapses demonstrated large correlations during R1, moderate correlations during SR1-SR5 and TSD, and small correlations during R2 and R4, and showed no significant correlations during baseline or R3. 1/RT correlations were large for SR1-SR4 and TSD, moderate for SR5 and R1-R4, and small for baseline. Conclusion The various PVT relationships were consistently strong at specific times of day throughout the study. In addition, higher correlations observed for 1/RT relative to lapses and during SR and TSD relative to baseline and recovery suggest that the PVT10 and PVT3 are most similar and best follow performance when most individuals are experiencing behavioral attention deficits during sleep loss. Both measures track SR and TSD performance well, with 1/RT presenting as more comparable between the PVT10 and PVT3. Support (if any) ONR Award N00014-11-1-0361; NIH UL1TR000003; NASA NNX14AN49G and 80NSSC20K0243; NIHR01DK117488

117 Comparison of Various Methods to Differentiate Resilience and Vulnerability to Sleep Loss Using Self-Rated Measures

Abstract Introduction There are robust, trait-like individual differences in subjective perceptions in response to sleep restriction (SR) and total sleep deprivation (TSD). How to best define neurobehavioral resilience and vulnerability to sleep loss remains an open question. We compared multiple approaches and cutoff thresholds for defining resilience and vulnerability using scores on the Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue and Vigor (POMS-F and POMS-V) subscales. Methods Forty-one adults (33.9±8.9y;18 females) participated in a 13-night experiment (two baseline nights [10h-12h time in bed, TIB], 5 SR nights [4h TIB], 4 recovery nights [12h TIB], and 36h TSD). The KSS, POMS-F, and POMS-V were administered every 2h during wakefulness. Resilience and vulnerability were defined by the following: average score during SR1-5, average change from baseline to SR1-5, and variance during SR1-5. Resilient and vulnerable groups were defined by the following cutoffs: the top and bottom 12.5%, 20%, 25%, 33%, 50%, and +/-1 standard deviation. Bias-corrected and accelerated bootstrapped t-tests compared the scores of resilient and vulnerable groups during baseline and across SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group (tau=0.4:moderate,0.7:strong). Results Resilient and vulnerable groups for POMS-F, as defined by all three approaches, significantly differed in their scores at all cutoffs during SR. However, only raw score and change from baseline approaches defined significantly different resilient and vulnerable groups during SR for KSS, and only raw score and variance approaches defined significantly different groups during SR for POMS-V. Notably, raw scores at baseline significantly differed between resilient and vulnerable groups for all measures. Correlations revealed moderate to strong associations between all three approaches at all cutoffs for POMS-F, between raw score and change from baseline approaches for KSS, and between raw score and variance approaches for POMS-V. Conclusion Defining resilience and vulnerability on self-rated measures by change from baseline was comparable to using raw score for KSS and POMS-F, whereas defining these groups by variance was comparable for POMS-F and POMS-V. Differences across methods may be due to the differential impact of SR on these various distinct subjective states. Support (if any) ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

128 Raw Scores Best Differentiate Resilience and Vulnerability to Sleep Loss for Cognitive Throughput and Working Memory

Abstract Introduction Substantial individual differences exist in cognitive deficits due to sleep restriction (SR) and total sleep deprivation (TSD), but the best approach to define such resilience and vulnerability remains a critical question. We compared multiple approaches and cutoff thresholds to define resilience and vulnerability using the Digit Symbol Substitution Task (DSST) and the Digit Span Task (DST). Methods Forty-one healthy adults (mean±SD ages,33.9±8.9y) participated in a 13-night experiment [two baseline nights (10h-12h time-in-bed, TIB), 5 SR nights (4h TIB), 4 recovery nights (12h TIB), and 36h TSD]. The DSST [measuring cognitive throughput] and DST [measuring working memory] were administered every 2h during wakefulness. Resilient/vulnerable groups were defined by average performance (DSST: number correct; DST: total correct from forward and backward versions) during SR1-5, average performance change from baseline during SR1-5, and variance in performance during SR1-5. Within each approach, groups were defined by +/-1 standard deviation (SD) and the top and bottom 12.5%, 20%, 25%, 33%, 50%. Bias-corrected and accelerated bootstrapped t-tests compared performance between resilient and vulnerable groups during baseline and SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group. Results T-tests showed significant differences between resilient/vulnerable groups at all raw score cutoffs for DSST and DST performance during SR and at baseline. Change from baseline t-tests showed significant differences during SR between the DSST groups only at 12.5%, 20%, and SD whereas DST t-tests showed significant differences at all cutoffs. Variance t-tests revealed a significant difference between the DSST groups only at 25% during SR. For the DSST, the variance vs. change from baseline comparison at all cutoffs and between raw score vs. change from baseline for the SD cutoff showed moderate correlations, and for the DST, the raw score vs. change from baseline correlation was moderate for 25% and 33%. Conclusion The resilient/vulnerable groups defined by raw score were more consistent than those defined by change from baseline or variance, and raw score did not track these approaches well. As such, raw score is the optimal approach to define cognitive throughput and working memory performance resiliency/vulnerability during sleep loss. Support (if any) ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

115 Age and Sex Differences in Behavioral Attention Across Baseline, Sleep Loss, and Recovery

Abstract Introduction There are established individual differences in performance resulting from sleep loss. However, differences in behavioral attention performance between demographic subgroups remain unclear, especially during recovery after sleep loss. Thus, we examined demographic subgroup performance differences during baseline, sleep loss (sleep restriction [SR] and total sleep deprivation [TSD]), and recovery (R). Methods Forty-one healthy adults participated in a 13-night experiment (2 baseline nights [10h-12h time-in-bed, TIB], 5 SR nights [4h TIB], 4 recovery nights [12h TIB], and 36h TSD). The 10-minute Psychomotor Vigilance Test (PVT), measuring behavioral attention, was administered every 2h during wakefulness. PVT lapses (reaction time [RT]>500ms) and 1/RT (response speed) were measured. PVT performance differences were investigated by sex (18 females) and by median split on age (range: 21-49y; median: 32y). Repeated measures ANOVAs on each study day examined PVT performance with demographic groups as the between-subject factor. Results SR1-2 and R1-2 showed significant between-subject effects by age: the older group had faster mean 1/RT than the younger group. SR2 showed a significant time*age group interaction: the older group had faster 1/RT from 0800h-1400h. B2, SR1, and R1 showed significant between-subject effects by sex: males had faster mean 1/RT than females. SR3 showed a significant time*sex interaction: males had faster 1/RT at 0800h and 1200h. PVT lapses (log transformed) analyses by age and by sex revealed significant between-subject effects at SR1 and R1. The direction of effects for lapses paralleled those for 1/RT: the younger group and females had more lapses than the older group and males, respectively. No other study days showed significant between-subjects or interaction effects. Conclusion For both age and sex, significant between-subject effects and/or interactions were revealed only in the beginning half of SR or recovery and not during TSD. These findings suggest that group differences exist when the effects of sleep loss are mild (i.e., SR1-3) or when the post-effects of sleep loss have diminished (i.e., R3-4); however, when the effects of sleep loss become more severe (i.e., SR4-5 or after a night of TSD), the well-established individual differences in response to sleep loss may overwhelm group differences. Support (if any) ONR Award No.N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

118 Relationships Between Perceptions of Subjective States Differ by Sleep Loss and During Recovery in Healthy Adults

Abstract Introduction The Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue and Vigor subscales (POMS-F and POMS-V) are commonly used to assess subjective sleepiness, fatigue and vigor in response to sleep loss. However, the detailed time course of relationships between these measures across sleep loss and recovery remains unknown yet is critical for assessing varying changes in perception of subjective states. Repeated measures correlation (rmcorr) examined within-individual association between the measures throughout a highly controlled sleep deprivation study. Methods Forty-one healthy adults (ages 21-49; mean±SD, 33.9±8.9y; 18 females) participated in a 13-night experiment consisting of two baseline nights (10h-12h time-in-bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the KSS, POMS-F, and POMS-V, was administered every 2h during wakefulness. Rmcorr compared KSS, POMS-F, and POMS-V scores by examining correlations by study day (e.g., Baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr cutoffs were as follows: r=0.1:small, 0.3:moderate, 0.5:large. Results KSS and POMS-F maintained positive correlations throughout the study, whereas POMS-F and POMS-V and KSS and POMS-V were inversely correlated. All correlations were significant except those for POMS-F and POMS-V across recovery day 1 and KSS and POMS-F across recovery day 4. All measure pairs showed moderate to large correlations across baseline and SR1-5, but only small to moderate correlations across recovery. KSS and POMS-F and KSS and POMS-V showed moderate to large correlations across TSD; however, POMS-F and POMS-V only showed a small correlation. All three pairs showed consistent moderate (POMS-F and POMS-V) or large (KSS and POMS-F, KSS and POMS-V [moderate at 2000h]) correlations when analyzed by time point across the study. Conclusion Overall, the strength of relationships between KSS, POMS-F, and POMS-V scores varied as a function of type of sleep loss (SR or TSD) and by fully rested states, but not by time of day. This demonstrates the importance of determining perceptions of sleepiness, fatigue, and vigor in relation to each other, especially during recovery for all three constructs. Support (if any) ONR Award No. N00014-11-1-0361; NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

116 Behavioral Attention Raw Scores Best Differentiate Cognitive Resilience and Vulnerability to Sleep Loss

Abstract Introduction There are substantial, stable individual differences in cognitive performance resulting from sleep restriction (SR) and total sleep deprivation (TSD). The best method for defining cognitive resilience and vulnerability to sleep loss remains an unanswered, yet important question. To investigate this, we compared multiple approaches and cutoff thresholds to define resilience and vulnerability using the 10-minute Psychomotor Vigilance Test (PVT). Methods Forty-one healthy adults (ages 21-49; mean±SD, 33.9±8.9y; 18 females) participated in a 13-night experiment [2 baseline nights (10h-12h time-in-bed, TIB), 5 SR nights (4h TIB), 4 recovery nights (12h TIB), and 36h TSD]. The PVT was administered every 2h during wakefulness. PVT lapses (reaction time [RT]>500 ms) and 1/RT (response speed) were measured. Resilient and vulnerable groups were defined by three approaches: average performance during SR1-5, average performance change from baseline to SR1-5, and variance in performance during SR1-5. Within each approach, resilient/vulnerable groups were defined by +/- 1 standard deviation and by the top and bottom 12.5%, 20%, 25%, 33%, 50%. Bias-corrected and accelerated bootstrapped t-tests compared PVT performance between the resilient and vulnerable groups during baseline and SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group. Results T-tests revealed that the resilient and vulnerable PVT lapses groups, defined by all three approaches, had significantly different mean PVT lapses at all cutoffs. Resilient and vulnerable PVT 1/RT groups, defined by raw scores and by change from baseline, had significantly different mean PVT 1/RT at all cutoffs. However, resilient/vulnerable PVT 1/RT groups defined by variance only differed at the 33% and 50% cutoffs. Notably, raw scores at baseline significantly differed between resilient/vulnerable groups for both PVT measures. Variance vs. raw scores and variance vs. change from baseline had the lowest correlation coefficients for both PVT measures. Conclusion Defining resilient and vulnerable groups by raw scores during SR1-5 produced the clearest differentiation between resilient and vulnerable groups at every cutoff threshold for PVT lapses and response speed. As such, we propose that using PVT raw score is the optimal approach to define resilient and vulnerable groups for behavioral attention performance during sleep loss. Support (if any) ONR Award No.N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

An Empirical Exploration of Resilience in Human-Autonomy Teams Operating Remotely Piloted Aircraft Systems

Project overview Team resilience is an interactive and dynamic process that develops over time while a team maintains performance. This study aims to empirically investigate systems-level resilience in a Remotely Piloted Aircraft (RPA) System simulated task environment by examining team interaction during novel events. The approach used in the current study to measure systems-level resilience was developed by Hoffman & Hancock (2017). In their conceptual study, resilience was considered a key feature of success in emerging complex sociotechnical systems; in our case, that is applied to Human-Autonomy Teams (HATs). Hoffman and Hancock conceptualized a resilience measure dynamically by means of several components, such as the time it took the system to recognize and characterize anomalies, and the time taken to specify and achieve new goals. In their framework, there were two main sub-events which expressed resilience via time-based measures, and upon which we designed ours in this study: (1) time taken to design a new process and (2) time required to implement it (Hoffman & Hancock, 2017). Design In this current research, there were three heterogeneous team members who used a text-based system to communicate and successfully photograph target waypoints: (1) navigator – provided information regarding a flight plan with speed and altitude restrictions of each waypoint; (2) pilot – controlled the RPA by adjusting its altitude and airspeed through negotiating with the photographer in order to take a good photo of the target waypoints; and (3) photographer – screened camera settings and sent feedback to the other team members regarding the status of target’s photograph. This study followed the Wizard of Oz paradigm wherein the navigator and photographer were seated together in one room and were told that the pilot was a synthetic agent. In actuality, the pilot was a well-trained experimenter who was working from a separate room. This ‘synthetic’ pilot used restricted vocabulary to simulate that of a computer. The main manipulations in this study consisted of three degraded conditions: (1) automation failure - role-level display failures while processing specific targets, (2) autonomy failure - autonomous agent behaved abnormally while processing specific targets (i.e., it provided misinformation to other team members or demonstrated incorrect actions), and (3) malicious cyber-attacks - the hijacking of the synthetic agent, which led to the synthetic agent providing false, detrimental information to the team about the RPA destination. Because the malicious cyber-attack only occurred once (during the final mission), we will focus on the automation and autonomy failures for this study. Each failure was imposed at a selected target waypoint and the teams had to find a solution in a limited amount of time. The time limit for each failure was related to the difficulty of the failure. Each failure was introduced at a pre-selected target waypoint for each team. Method In this experiment, there were 22 teams, with only two participants randomly assigned to the navigator and photographer roles for each team, because the pilot was a highly-trained experimenter. The current task was comprised of ten 40-minute missions in which teams needed to take as many “good” photos as possible of ground targets while avoiding alarms and rule violations. For this study, using the RPAS paradigm, we calculated two team resilience scores (1) time taken to design a new process and (2) time required to implement it (Hoffman & Hancock, 2017). For the calculations, we used the message sent time (in seconds) for each role to express resilience in terms of the proportion of total task time (2400 seconds). As an outcome measure, we used target processing efficiency as a coordination and time-based performance score, which was based on how quickly teams were able to take a good photo of each target. Results and discussion We found that teams were more resilient during automation failures and progressed toward targets more successfully than during autonomy failures. We see three possible explanations for this: (1) automation failures were more explicit than autonomy failures, since at least one team member interacted with other teammates; (2) autonomy failures took more time for human teammates to identify the failure, because the autonomous agent’s abnormal behavior was not as straight forward; and 3) human teammates overtrusted to the autonomous agent and lack confidence in themselves and let the failure go on. Acknowledgements This research is supported by ONR Award N000141712382 (Program Managers: Marc Steinberg, Micah Clark). We also acknowledge the assistance of Steven M. Shope of Sandia Research Corporation, who integrated the synthetic agent and the testbed.

Layered Dynamics and System Effectiveness of Human-Autonomy Teams Under Degraded Conditions

Project overview Teamwork can be defined as dynamic team interaction between two or more interdependent members to achieve a shared goal. Many studies have examined how coordination dynamics are associated with team effectiveness in the context of all-human teams (Gorman, Amazeen, & Cooke, 2010), and later, in human-autonomy teams (HAT)s (Demir, Likens, Cooke, Amazeen, & McNeese, 2018). HATs must have autonomous agents that act as effective teammates and help enable HATs to function as collaborative systems. Synergistic relationships among a system’s human and technological components provide the basis for emergent systems-level outcomes. Layered dynamics, a recent empirical modelling technique aimed at achieving this objective (Gorman et al., 2019), considers reorganization of the sociotechnical system across individual components and the overall system. The current study examined layered dynamics of HATs during automation and autonomy failures and addresses how members of HATs interact with each other and technological aspects of the system. Design and Method We utilized a simulated Remotely Piloted Aircrtaft System (RPAS) Synthetic Task Environment with three heteregeneous and interdepedent roles: (1) a navigator, who created a dynamic flight plan and provided waypoint related information; (2) a pilot, who used this information to monitor and adjust settings. The pilot also communicated with the photographer to negotiate settings and enable proper conditions to obtain a good photograph; and (3) a photographer, who monitored and adjusted the camera to take good target photos, and provided feedback to the team. This study utilized a Wizard of Oz paradigm, in which the navigator and photographer were instructed that the pilot was a synthetic agent. However, the pilot was a highly-trained experimenter, in a separate room, who simulated an autonomous agent using limited vocabulary. There were 22 teams, and two participants were randomly assigned to the navigator and photographer roles. This task was comprised of ten 40-minute missions, and teams needed to take as many good photos as possible while avoiding alarms and rule violations. The primary manipulation was the application of three degraded conditions: (1) automation failure - role-level display failures, (2) autonomy failure - autonomous agent’s abnormal behavior, and (3) malicious cyber-attacks - the hijaking of the RPAS, with the synthetic agent providing false, detrimental information. We symbolically represented RPAS using layered dynamics, and calculated entropy measures for each (Gorman et al., 2019): (1) communications: team members interacting within the chat system; (2) vehicle: states of the RPA, including airspeed/altitude, turns, fuel, battery, remaining film, and termperature level; and (3) controls: the interface controls between the RPA and the team members. To measure team performance, we used a time and coordination based metric for each target in each mission. Results and Discussion Our main findings were: 1) vehicle and communication entropy were higher than control entropy and were associated with better adaptation to both failures, and 2) control entropy had a negative association with initial status on team performance, while vehicle entropy had a positive association. These findings describe the tendency of low performing teams to anticipate targets poorly. This was due to a failure to interact with the technology in a timely manner. This lagged effect can be attributed to teams taking too long to interact with the technology. These findings shed light on how the layered dynamics approach can help understand team behavior under degraded conditions. Acknowledgements This research is supported by ONR Award N000141712382 (Program Managers: Marc Steinberg, Micah Clark). We also acknowledge the assistance of Steven M. Shope, Sandia Research Corporation who integrated the synthetic agent and the testbed.

Multi-channel cross-correlation used to estimate time delays

Many marine mammal localization techniques rely on estimating the time of arrival delays of a call between the hydrophones of an array. This is commonly achieved by cross-correlating (some version of) received signals between hydrophone pairs. This process is complicated in cases with noise, multiple animals, and multipath arrivals. In this talk, I explore and demonstrate the potential of a multi-channel cross-correlation technique to help establish time delay estimates. Possible extensions to signal enhancement for detection and classification purposes are also discussed. [This work was supported by ONR Award No. N00014-16-1-2598.]

Tactical Gear does not Affect the Sensory Organization Test of Special Warfare Combatant-craft Crewman

Naval Special Warfare’s Special Warfare Combatant-craft Crewman (SWCC) function primarily on small, high speed boats on oceans or river systems. Gear carried for protection and tactical operation may impact balance by moving the center of mass, altering sensory input, hindering response time, influencing mobility, and increasing risk of musculoskeletal injury. Research on how tactical gear affects the performance of sensory systems (somatosensory, visual, and vestibular) responsible for balance is limited. Dynamic posturography systems assess function and coordination of these systems with the Sensory Organization Test (SOT). PURPOSE: The purpose of this study was to determine if tactical gear affects performance of SWCC Operators on the SOT. METHODS: Eight SWCC Operators cleared for full active duty participated in the study (27.1 ± 6.9 yr, 180.8 ± 6.5 cm, 90.7 ± 5.7 kg). All subjects provided informed consent prior to testing. Subjects tested with tactical gear (TG) and no tactical gear (NTG) in a randomized order. Subjects wore standard operational gear for TG tests that included body armor, blue weapons, magazines, helmet and night optic devices (mean mass = 21.7 ± 1.1 kg). Each subject completed three trials of six conditions twice (TG and NTG). Balance was scored for overall performance (SOTcomp); each condition (C1-C6) which varies the challenge to each sensory system; and sensory system analysis score for the somatosensory (SOM), visual (VIS), vestibular (VEST) and preference (PREF). Data were not normally distributed therefore Wilcoxon Signed Rank tests were used to compare each variable (alpha = 0.05). RESULTS: No significant differences were found between SOT NTG and TG tests (median score NTG vs TG, p-value): SOTcomp 77.5 vs 74.5, p=0.932; C1 93.8 vs 93.3, p=1.0; C2 90.3 vs 87.7, p=0.161; C3 90.8 vs 87.8, p=0.123; C4 81.0 vs 84.2, p=0.161; C5 66.5 vs 63.5, p=0.779; C6 60.7 vs 59.0, p=0.889; SOM 96.0 vs 95.5, p=0.778; VIS 88.0 vs 91.0, p=0.068; VEST 70.5 vs 71.0, p=0.527; PREF 96.5 vs 97.0, p=1.0. CONCLUSION: SWCC Operators demonstrate no effects of wearing tactical gear during the SOT, indicating the basic function of sensory systems is not changed in static conditions. Further evaluation under more challenging and dynamic conditions is appropriate. Supported by ONR Award N00014-10-1-0912/N00014-11-1-0929.

Standard‐target calibration of sonars for marine ecosystem assessment.

Remote acoustic observation and sampling of multiple trophic levels in the marine ecosystem generally require s ubstantial bandwidth, if not the coordinated use of two or more sonars. Calibration is essential. The standard‐target method has a history of application, spanning the total frequency range 1–2500 kHz, with precision solid elastic spheres used as standard targets. The theory of measurement of volume backscattering and form functions of arbitrary targets is elaborated, also revealing the basis for the standard‐target method. [Work supported by ONR Award No. N000140910482.]

Performance of tourmaline hydrophones at low- and midfrequencies.

Tourmaline hydrophones are well suited for measurement of underwater blasts and other impulsive type sounds having extremely fast rise times and high peak pressures. The physical characteristics of these small, lightweight sensors, however, make them an attractive choice for other applications. To determine their performance at low- and midfrequencies, substitution calibrations of two tourmaline sensors with a calibrated laboratory hydrophone were completed using a J-ll underwater sound transducer in a 100-gal tank. The calibration signals consisted of tone bursts containing 5–20 cycles at frequencies ranging from 250 to 10 000 Hz and having sound pressure levels from 110 to 180 dB re 1 μa. Results indicate that a tourmaline hydrophone coupled to an in-line charge amplifier and preamplifier to provide fixed voltage gains ranging from 100× to 10 000× has excellent performance over this range of frequencies. [Work supported by the National Oceanographic Partnership Program (NOPP) through ONR Award No. N000140710992.]

Biocatalytic Paradigms for Unconventional Information Processing

Authors would like to thank Guinevere Strack for critical discussion of the present work. M. Pita wishes to thank the funding support provided by Spanish MICINN Ramon y Cajal 2009 Research Funding Programme. This work was supported by the National Science Foundation (grants CCF-0726698, DMR-0706209) and by the Office of Naval Research (ONR Award #N00014-08-1-1202).