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

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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Barriers to the Implementation of Surveillance for Stage I Testicular Seminoma

Purpose: Postorchiectomy adjuvant radiotherapy (RT) for Stage I seminoma can be associated with long-term toxicity, and management strategies with a lower treatment burden achieve the same excellent cure rate. Because studies suggest that radiation oncologists in the United States continue to recommend RT for these patients, we sought to identify factors associated with management recommendations. Methods and Materials: We conducted a one-time internet-based survey among 491 randomly selected American radiation oncologists self-described as specializing in genitourinary oncology. Results: Response rate was 53% (n = 261). Forty-nine percent of respondents worked in university-affiliated practices. Sixty-two percent of respondents always/usually recommended adjuvant RT for patients with Stage I seminoma, whereas 21% always/usually recommended surveillance and 3% always/usually recommended chemotherapy. One third (33%) expressed concerns that patients who experienced relapse during surveillance could not be salvaged. Although 88% of physicians were aware of an increased risk of second malignant neoplasms (SMN) after adjuvant RT, 85% underestimated its magnitude. Compared with physicians not typically recommending RT, physicians who always/usually recommended RT were more likely to believe that patients might not be salvaged at relapse during surveillance (p = 0.008) and were less aware of the association between RT and SMN (p = 0.04). Conclusions: Respondents who always/usually recommend postorchiectomy RT for patients with Stage I seminoma are more likely to underestimate late RT morbidity and to believe that surveillance is associated with increased mortality. Given the equivalent efficacy and reduced morbidity of surveillance compared with RT, our findings underscore the need for ongoing physician education to increase appropriate clinical implementation of surveillance strategies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Patterns of Practice in Palliative Radiotherapy for Painful Bone Metastases: A Survey in Japan

Purpose: To determine the current patterns of practice in Japan and to investigate factors that may make clinicians reluctant to use single-fraction radiotherapy (SF-RT). Methods and Materials: Members of the Japanese Radiation Oncology Study Group (JROSG) completed an Internet-based survey and described the radiotherapy dose fractionation they would recommend for four hypothetical cases describing patients with painful bone metastasis (BM). Case 1 described a patient with an uncomplicated painful BM in a non-weight-bearing site from non-small-cell lung cancer. Case 2 investigated whether management for a case of uncomplicated spinal BM would be different from that in Case 1. Case 3 was identical with Case 2 except for the presence of neuropathic pain. Case 4 investigated the prescription for an uncomplicated painful BM secondary to oligometastatic breast cancer. Radiation oncologists who recommended multifraction radiotherapy (MF-RT) for Case 2 were asked to explain why they considered MF-RT superior to SF-RT. Results: A total of 52 radiation oncologists from 50 institutions (36% of JROSG institutions) responded. In all four cases, the most commonly prescribed regimen was 30 Gy in 10 fractions. SF-RT was recommended by 13% of respondents for Case 1, 6% for Case 2, 0% for Case 3, and 2% for Case 4. For Case 4, 29% of respondents prescribed a high-dose MF-RT regimen (e.g., 50 Gy in 25 fractions). The following factors were most often cited as reasons for preferring MF-RT: 'time until first increase in pain' (85%), 'incidence of spinal cord compression' (50%), and 'incidence of pathologic fractures' (29%). Conclusions: Japanese radiation oncologists prefer a schedule of 30 Gy in 10 fractions and are less likely to recommend SF-RT. Most Japanese radiation oncologists regard MF-RT as superior to SF-RT, based primarily on the time until first increase in pain.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Edge-cloud computing performance benchmarking for IoT based machinery vibration monitoring

Advances in low cost and reliable sensing, connectivity (Internet of Things), computational power, and advanced analytics, are leading to a new wave of innovation in machinery status sensing and condition monitoring. Significant research efforts are directed towards cloud computing architectures. However, given the latency, bandwidth, cost, security, and privacy concerns, further supported by the ever-increasing capabilities of edge computing devices, there is a need to consider both edge and cloud computing together to make informed decisions based upon context and performance. In this work, we present an edge-cloud performance evaluation for IoT based machinery vibration monitoring, to foster deployment for the contexts considered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗
DOE OSTI2019

Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates

We leverage the frontiers of the Internet of Things technology in a recently developed end-to-end wireless sensor network (WSN) system that samples, collects, stores, and displays mountain hydrology measurements in near real-time. At the core of the system lies an ultra-low power, radio channel-hoping, and self-organizing mesh that allows for remote autonomous sampling of snow. Such properties, combined with a rugged weather-sealed design of the devices and multi-level data replication, provides reliable real-time data at spatial and temporal scales previously impractical to achieve in mountain environments. The system was deployed at three 1 km 2 sites across the North Fork of the Feather River basin with a cluster of 12 sensor nodes for each location. Measurements show that existing operational autonomous systems are non-representative spatially, with biases that can reach up to 50%. A comparison between a wet and dry year showed that snow depths exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Temporally dense analysis using elastic net regression shows that dominant features at the sub-km 2 scale are site-dependent and differ from the watershed scale. Newly introduced explanatory variables, based on the nearest neighbor with a Landsat assimilated historical product, consistently explained up to 90% of the variance in the watershed-scale SWE for both years. At two WSN sites, lagged cross-correlation of snowmelt with stream flow measurements showed a significant improvement of up to 100% compared with existing systems, suggesting that WSNs can be instrumental in improving runoff forecasting and water management.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

The development of remote wireless radiation dose monitoring system

Internet of things (IoT) technology has recently shown a large flow of IT trends in human life. In particular, our lives are now becoming integrated with a lot of items around the 'smart-phone' with IoT, including Bluetooth, Near Field Communication (NFC), Beacons, WiFi, and Global Positioning System (GPS). Our project focuses on the interconnection of radiation dosimetry and IoT technology. The radiation workers at a nuclear facility should hold personal dosimeters such as a Thermo-Luminescence Dosimeter (TLD), an Optically Stimulated Luminescence Dosimeter (OSL), pocket ionization chamber dosimeters, an Electronic Personal Dosimeter (EPD), or an alarm dosimeter on their body. Some of them have functions that generate audible or visible alarms to radiation workers in a real working area. However, such devices used in radiation fields these days have no functions for communicating with other areas or the responsible personnel in real time. In particular, when conducting a particular task in a high dose area, or a number of repair works within a radiation field, radiation dose monitoring is important for the health of the workers and the work efficiency. Our project aims at the development of a remote wireless radiation dose monitoring system (RWRD) that can be used to monitor the radiation dose in a nuclear facility for radiation workers and a radiation protection program In this project, a radiation dosimeter is the detection device for personal radiation dose, a smart phone is the mobile wireless communication tool, and, Beacon is the wireless starter for the detection, communication, and position of the worker using BLE (Bluetooth Low Energy). In this report, we report the design of the RWRD and a demonstration case in a real radiation field. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2012

Shopping For Danger: E-commerce techniques applied to collaboration in cyber security

Collaboration among cyber security analysts is essential to a successful protection strategy on the Internet today, but it is uncommonly practiced or encouraged in operating environments. Barriers to productive collaboration often include data sensitivity, time and effort to communicate, institutional policy, and protection of domain knowledge. We propose an ambient collaboration framework, Vulcan, designed to remove the barriers of time and effort and mitigate the others. Vulcan automated data collection, collaborative filtering, and asynchronous dissemination, eliminating the effort implied by explicit collaboration among peers. We instrumented two analytic applications and performed a mock analysis session to build a dataset and test the output of the system.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

A System of Agents for Supporting Optimization and Control of a Connected Community

The residential sector consumes a significant portion of the electricity sold in the United States. Above 60% of the energy used in the sector is used to operate heating, ventilation, and air conditioning (HVAC) systems and water heating (WH) systems. With the increase of intelligence in the grid and the new decision and control options enabled by the Internet of Things; control of these devices can be used to support the grid. Therefore, this article presents a scalable multiagent system for optimizing HVAC and WH systems while maintaining comfort. It allows a utility to orchestrate the shifting of energy from critical periods without direct control, but instead by using a price signal. The architecture, optimization formulation, implementation strategy and results from an implementation project are discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Autonomous Wireless Technology Detection in Seamless IoT Applications

The ever-increasing use of Internet of Things (IoT) devices results in the implementation of multiple wireless technologies that would not only cater their data rate requirements but also support various applications. To optimize the energy efficiency and security of the wireless transmission, it is imperative to identify the wireless technologies in various IoT implementations. Many of the existing approaches are based on measuring only the receiving signal strength indicator (RSSI). However, such approaches may not work well because of transmit power control and complex channel variations among different wireless technologies. In this article, we propose an autonomous wireless detection scheme that considers multiple distinguishable physical (PHY)-layer settings for real-time identification of wireless technologies for real-time applications. Specifically, the proposed scheme relies on the PHY-layer measurements of the targeted spectrum. Transmission settings, such as bandwidth, carrier frequency, and RSSI are estimated from the raw in-phase and quadrature-phase (I/Q) measurements. In addition, a symbol-level extraction scheme is implemented to extract unique features of modulation settings. These aforementioned features are applied to a machine learning process to identify the received wireless technologies. Compared with raw I/Q measurements, the extracted features are much simplified and, thus, the machine learning classifier can be designed with a simple structure for fast processing on IoT nodes. Finally, the proposed schemes are primarily evaluated theoretically, followed by implementing them on a USRP software-defined radio (SDR)-based hardware testbed. The evaluation results demonstrate high accuracy in the real-time detection of different wireless technologies for seamless IoT applications.

42 ENGINEERING↗
DOE OSTI2025

Model Residuals as Shields: A Two-Level Formulation to Defend Smart Grids From Poisoning Attacks

The advancement of smart grids presents both vast opportunities and heightened cybersecurity risks. Data-driven defense mechanisms, though designed as a shield against these threats, can fall prey to poisoning attacks. We delve into regression settings, underscoring the imperative to fortify defenses against a spectrum of poison ratios, notably those above 0.5—an issue scarcely addressed in prior studies. Recognizing the susceptibilities of smart grids and their manipulable sensors, we exploit the very intent of poisoning attacks, compromising model accuracy, as our defense mechanism. Our proposed two-level optimization framework discerns between poisoned and authentic data based on model residuals, outperforming or matching existing methods in 72% to 77% of precision and 75% to 80% of recalls across various poisoning attacks, poison ratios, and datasets. Once the authentic data are identified, the trained model is adaptable for a variety of applications. Comprehensive evaluations on different smart grid datasets, pitted against myriad poisoning schemes, validate our methodology’s edge over existing methods. Here, we also shed light on the implications of model misspecification originating from temporal auto-correlation, a common feature in Internet of Things and smart grid data.

Adversarial machine learning (ML)↗
DOE OSTI2019

A Cooperative Multi-Agent Deep Reinforcement Learning Framework for Real-Time Residential Load Scheduling

Internet-of-Things (IoT) enabled monitoring and control capabilities are enabling increasing numbers of household users with controllable loads to actively participate in smart grid energy management. Realizing an efficient real-time energy management system that takes advantage of these developments requires novel techniques for managing the increased complexity of the control action space in resolving multiple challenges such as the uncertainty in energy prices and renewable energy output along with the need to satisy physical grid constraints such as transformer capacity. Addressing these challenges, we develop a multi-household energy management framework for residential units connected to the same transformer and containing DERs such as PV, ESS and controllable loads. The goal of our framework is to schedule controllable household appliances and ESS such that the cost of procuring electricity from the utility over a horizon is minimized while physical grid constraints are satisfied at each scheduling step. Traditional energy management frameworks either perform global optimization to satisfy grid constraints but suffer from high computational complexity (for example Integer Program, Mixed Integer Programming frameworks and centralized reinforcement learning) or perform decentralized real-time energy management without satisfying global grid constraints (for example multi-agent reinforcement learning with no cooperation). In contrast, we propose a cooperative multiagent reinforcement learning (MARL) framework that i) operates in real-time, and ii) performs explicit collaboration to satisfy global grid constraints. The novelty in our framework is two fold. Firstly, our framework trains multiple independent learners (IL) for each household in parallel using historical data and performs real-time inferencing of control actions using the most recent system state. Secondly, our framework contains a low complexity knapsack based cooperation agent which combines the outputs of ILs to minimize cost while satisfying grid constraints. Simulation results show that our cooperative MARL approach achieves significant cost improvement over centralized reinforcement learning and day-ahead planning baselines. Moreover, our approach strictly satisfies physical constraints with no apriori knowledge of system dynamics while the baseline approaches have occasional violations. We also measure the training and inference time by ranging the number of households from 1 to 25. Results show that our cooperative MARL approach scales best among various approaches.

14 SOLAR ENERGY↗
DOE OSTI2014

Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

GTRI Remote Monitoring System: Training and Operational Needs Assessment Analysis Report

The mission of the United States Department of Energy (DOE) National Nuclear Security Administrations (NNSA's) Global Threat Reduction Initiative (GTRI) is to identify, secure, recover and facilitate the disposition of vulnerable nuclear and high-risk radioactive materials around the world that pose a threat to the United States and the international community. The GTRI's unique mission to reduce and protect vulnerable nuclear and radiological materials located at civilian sites worldwide directly addresses recommendations of the 9/11 Commission1, and is a vital part of the President's National Security Strategy and the Global Initiative. The GTRI Remote Monitoring System (RMS) is a standalone security system that includes radiation and tamper alarms, and CCTV; which can be transmitted securely over the Internet to multiple on-site and off-site locations. Through our experiences during installation of the system at 162 sites, plus feedback received from Alarm Response Training course participants, site input to project teams and analysis of trouble calls; indications were that current system training was lacking and inconsistent. A survey was undertaken to gather information from RMS users across the nation, to evaluate the current level of training and determine what if any improvements needed to be made. Additional questions were focused on the operation of the RMS software. The training survey was initially sent electronically to 245 users at the RMS sites and achieved a 37.6% return rate. Analysis of the resulting data revealed that 34.6% of the respondents had not received training or were unsure if they had, despite the fact that vendor engineers provide training at installation of the system. Any training received was referred to as minimal, and brief, not documented, and nothing in writing. 63.7% of respondents said they were either not at all prepared or only somewhat prepared to use the RMS software required to effectively operate the system. As a result of this analysis, a formal training curriculum will be designed and implemented to include several blended learning delivery options. This training will be piloted at RMS sites; initial training will become a required element of RMS installation and refresher training will be considered for sustainability of operations.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2012

Transportation Energy Data Book: Edition 31

The Transportation Energy Data Book: Edition 31 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Program. Designed for use as a desk-top reference, the Data Book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. The latest edition of the Data Book is available to a larger audience via the Internet (cta.ornl.gov/data). This edition of the Data Book has 12 chapters which focus on various aspects of the transportation industry. Chapter 1 focuses on petroleum; Chapter 2 energy; Chapter 3 highway vehicles; Chapter 4 light vehicles; Chapter 5 heavy vehicles; Chapter 6 alternative fuel vehicles; Chapter 7 fleet vehicles; Chapter 8 household vehicles; Chapter 9 nonhighway modes; Chapter 10 transportation and the economy; Chapter 11 greenhouse gas emissions; and Chapter 12 criteria pollutant emissions. The sources used represent the latest available data. There are also three appendices which include detailed source information for some tables, measures of conversion, and the definition of Census divisions and regions. A glossary of terms and a title index are also included for the reader s convenience.

02 PETROLEUM↗
DOE OSTI2024

Advanced Data Science Model for Detecting Intelligent Malware

This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.

42 ENGINEERING↗
DOE OSTI2026

Cyber Labeling for Energy Industrial IoT

The U.S. Department of Energy’s (DOE) Office of Cybersecurity, Energy Security and Emergency Response (CESER), at the request of the Deputy National Security Advisor for Cyber and Emerging Technologies, Anne Neuberger, initiated research in 2023 to develop a cybersecurity labeling proof-of-concept for energy products to expand on the Federal Communications Commission’s (FCC) proposed U.S. Cyber Trust Mark program. DOE mobilized researchers from six National Laboratories to develop and gather feedback on a proof-of concept label for solar inverters and smart meters, which serve as representative products for market-facing energy sector Industrial Internet of Things (IIoT). This report details the research team’s process across two phases and the resulting findings, which include challenges facing cyber labeling programs and recommendations to implement an expanded IIoT cyber labeling program in the U.S.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2020

Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset

Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics. This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions. We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. Additionally, we tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data.

60 APPLIED LIFE SCIENCES↗