Third annual report, july 1, 1963 - jun. 30, 1964
Neurological functions - pathology, physiology, pharmacology, endocrinology with general clinical and behavioral patterns
NEUROSCIENCE↗
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Results for “behavioral pattern”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Poster Abstract: Leveraging Large Language Models to Reveal Interpretable Cooling Behaviors from Smart Thermostat Data
Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.
Nihar, Kopal↗
Statistical limits on computer-defined eeg patterns related to behavior
Statistical limits on computer-defined EEG PATTERNS related to behavior of monkeys
BEHAVIOR↗
Frosted Tracks
SAND2025-01893O Frosted Tracks is a software tool to group trajectories according to sequences of their behavior. The goal is to start with a very large number of trajectories and identify groups that exhibit similar behavior patterns. The application combines TICC and Metric DBSCAN clustering algorithms for behavioral segmentation and labeling of air/sea trajectory data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Dalbey, Keith↗
System diagnostic builder
The System Diagnostic Builder (SDB) is an automated software verification and validation tool using state-of-the-art Artificial Intelligence (AI) technologies. The SDB is used extensively by project BURKE at NASA-JSC as one component of a software re-engineering toolkit. The SDB is applicable to any government or commercial organization which performs verification and validation tasks. The SDB has an X-window interface, which allows the user to 'train' a set of rules for use in a rule-based evaluator. The interface has a window that allows the user to plot up to five data parameters (attributes) at a time. Using these plots and a mouse, the user can identify and classify a particular behavior of the subject software. Once the user has identified the general behavior patterns of the software, he can train a set of rules to represent his knowledge of that behavior. The training process builds rules and fuzzy sets to use in the evaluator. The fuzzy sets classify those data points not clearly identified as a particular classification. Once an initial set of rules is trained, each additional data set given to the SDB will be used by a machine learning mechanism to refine the rules and fuzzy sets. This is a passive process and, therefore, it does not require any additional operator time. The evaluation component of the SDB can be used to validate a single software system using some number of different data sets, such as a simulator. Moreover, it can be used to validate software systems which have been re-engineered from one language and design methodology to a totally new implementation.
Nieten, Joseph L.↗
Examination of the anomalous behavior of three gravity gradient satellites
The anomalous oscillatory behavior of three satellites was examined. The satellites were all hinged, two-body, gravity gradient configurations passively stabilized about their three principal axes by gravity gradient and damper torques. Two basic behavior patterns were observed. Either the satellites were stable with attitude perturbations less than + or - 5 deg or their behavior tended towards a low frequency rigid body oscillation dominated by large yaw motions and in some cases by yaw inversions. A causal relationship between sun angle and the character of satellite behavior was observed which appears to indicate that thermal distortion is a critical factor in a gravity gradient satellite's dynamic behavior. The behavior appeared to be futher modified by the existence of response frequencies that were higher than anticipated values.
Goldman, R. L.↗
Impatience versus achievement strivings in the Type A pattern: Differential effects on students' health and academic achievement
Psychometric analyses of college students' responses to the Jenkins Activity Survey, a self-report measure of the Type A behavior pattern, revealed the presence of two relatively independent factors. Based on these analyses, two scales, labeled Achievement Strivings (AS) and Impatience and Irritability (II), were developed. In two samples of male and female college students, scores on AS but not on II were found to be significantly correlated with grade point average. Responses to a health survey, on the other hand, indicated that frequency of physical complaints was significantly correlated with II but not with AS. These results suggest that there are two relatively independent factors in the Type A pattern that have differential effects on performance and health. Future research on the personality factors related to coronary heart disease and other disorders might more profitably focus on the syndrome reflected in the II scale than on the Type A pattern.
Spence, Janet T.↗
Performance evaluation of an agent-based occupancy simulation model
Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types were first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Renal electrolyte circadian rhythms - Independence from feeding and activity patterns
Experiments were conducted on six unanesthetized chair-acclimatized adult male squirrel monkeys (Saimiri sciureus) weighing 600-900 g to determine whether internal synchronization is the result of simple passive dependence of renal excretory rhythms on endogenous rhythms of those variable that influence electrolyte excretion such as dietary intake and muscular activity. Independence of the urinary rhythms from diurnal variations in feeding, drinking, and activity was secured by depriving the animals of food, water, and training them to perform a two-hourly schedule of feeding, drinking, and activity throughout day and night. Results indicate that the internal synchronization which is normally observed between the behavioral and urinary rhythms cannot be explained by any direct dependence of renal function on behavioral patterns. The most probable mechanism for circadian internal synchronization is that the various behavioral and renal rhythms are controlled by potentially independent separate oscillators which are normally kept in synchrony with one another.
Moore-Ede, M. C.↗
Subjective workload and individual differences in information processing abilities
This paper describes several experiments examining the source of individual differences in the experience of mental workload. Three sources of such differences were examined: information processing abilities, timesharing abilities, and personality traits/behavior patterns. On the whole, there was little evidence that individual differences in information processing abilities or timesharing abilities are related to perceived differences in mental workload. However, individuals with strong Type A coronary prone behavior patterns differed in both single- and multiple-task performance from individuals who showed little evidence of such a pattern. Additionally, individuals with a strong Type A pattern showed some dissociation between objective performance and the experience of mental workload.
Damos, D. L.↗
Linking human-building interactions in shared offices with personality traits
Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five-Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share control, knowledge of control, group decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. Finally, the study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Pattern-based fault diagnosis using neural networks
An architecture for a real-time pattern-based diagnostic expert system capable of accommodating noisy, incomplete, and possibly erroneous input data is outlined. Results from prototype systems applied to jet and rocket engine fault diagnosis are presented. The ability of a neural network-based system to be trained via the presentation of behavioral patterns associated with fault conditions is demonstrated.
Dietz, W. E.↗
Data mining of space heating system performance in affordable housing
The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems in terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. In conclusion, from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Electrophysiological patterns and cerebral impedance characteristics in orienting and discriminative behavior.
Electrophysiological patterns and cerebral impedance characteristics in orienting and discriminative behavior
ELECTRIC IMPEDANCE↗
Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch
For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methods to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Pilot-Induced Oscillations and Human Dynamic Behavior
This is an in-depth survey and study of pilot-induced oscillations (PIO's) as interactions between human pilot and vehicle dynamics; it includes a broad and comprehensive theory of PIO's. A historical perspective provides examples of the diversity of PIO's in terms of control axes and oscillation frequencies. The constituents involved in PIO phenomena, including effective aircraft dynamics, human pilot dynamic behavior patterns, and triggering precursor events, are examined in detail as the structural elements interacting to produce severe pilot-induced oscillations. The great diversity of human pilot response patterns, excessive lags and/or inappropriate gain in effective aircraft dynamics, and transitions in either the human or effective aircraft dynamics are among the key sources implicated as factors in severe PIO's. The great variety of interactions which may result in severe PIO's is illustrated by examples drawn from famous PIO's. These are generalized under a pilot-behavior-theory-based set of categories proposed as a classification scheme pertinent to a theory of PIO's. Finally, a series of interim prescriptions to avoid PIO is provided.
McRuer, Duane T.↗
2013 New Mexico Mid-Region Travel Survey
The 2013 New Mexico Mid-Region Travel Survey assessed travel behavior patterns to update a travel demand model for the Albuquerque Metropolitan Planning Area, which consists of Bernalillo County, Valencia County, and southern Sandoval County. It includes the cities of Albuquerque, Rio Rancho, Los Lunas, and Belen as well as some tribal lands. The Mid-Region Council of Governments contracted with Westat to conduct the survey, which included the collection of socio-demographic data and a one-day (24-hour) period of household travel behavior collected during weekdays (Monday through Friday). The survey also included a random selection of a 20% subsample of households (1,023 participants) to take part in a wearable global positioning system, technology-based component of the study, which was used to assess the level of trip under-reporting from the self-reported component of the survey.
1Hz data↗
WholeTraveler Anonymized Data Phase 1
Phase 1 of the WholeTraveler Study data collection consisted of an online-only survey. This survey captured data on three categories of observable variation in the population relevant to transportation decisions. First, the survey collected traditional demographic data such as age, gender, income, and education level. Second, it collected data across personality, psychological, and preference categories. This included: 1. The "Big Five" inventory personality traits: openness to new experience, conscientiousness, extroversion, agreeableness, and neuroticism; 2. Risk and time preferences; and 3. Environmental preferences. Third, the survey collected data on historical behavior patterns including: 1. Adoption of (as well as interest in) new technologies or innovations (e.g., smartphones, PEVs, solar panels, adaptive cruise control [ACC]); 2. Car ownership history and current car ownership status; 3. Recent mode use across different time scales (e.g., previous week, previous month, previous year); and 4. Timing of major life events such as starting a family as well as overall lifecycle trajectory patterns. Data from Phase 1 and Phase 2 are linked by a unique respondent identifier. Anonymized versions of the Phase 1 and Phase 2 data are both available on Livewire.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗