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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

PINGU: A vision for neutrino and particle physics at the South Pole

The Precision IceCube Next Generation Upgrade (PINGU) is a proposed low-energy in-fill extension to the IceCube Neutrino Observatory. With detection technology modeled closely on the successful IceCube example, PINGU will provide a 6 Mton effective mass for neutrino detection with an energy threshold of a few GeV. Also, with an unprecedented sample of over 60 000 atmospheric neutrinos per year in this energy range, PINGU will make highly competitive measurements of neutrino oscillation parameters in an energy range over an order of magnitude higher than long-baseline neutrino beam experiments. PINGU will measure the mixing parameters Θ 23 and Δm $2\atop{32}$ , including the octant of Θ 23 for a wide range of values, and determine the neutrino mass ordering at 3σ median significance within five years of operation. PINGU's high precision measurement of the rate of v T appearance will provide essential tests of the unitarity of the 3 ×3 PMNS neutrino mixing matrix. PINGU will also improve the sensitivity of searches for low mass dark matter in the Sun, use neutrino tomography to directly probe the composition of the Earth's core, and improve IceCube's sensitivity to neutrinos from Galactic supernovae. Finally, reoptimization of the PINGU design has permitted substantial reduction in both cost and logistical requirements while delivering performance nearly identical to configurations previously studied.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

A complex carotenoid palette tunes avian color vision.

The brilliantly coloured cone oil droplets of the avian retina function as long-pass cut-off filters that tune the spectral sensitivity of the photoreceptors and are hypothesized to enhance colour discrimination and improve colour constancy. Although it has long been known that these droplets are pigmented with carotenoids, their precise composition has remained uncertain owing to the technical challenges of measuring these very small, dense and highly refractile optical organelles. In this study, we integrated results from high-performance liquid chromatography, hyperspectral microscopy and microspectrophotometry to obtain a comprehensive understanding of oil droplet carotenoid pigmentation in the chicken ( Gallus gallus ). We find that each of the four carotenoid-containing droplet types consists of a complex mixture of carotenoids, with a single predominant carotenoid determining the wavelength of the spectral filtering cut-off. Consistent with previous reports, we find that the predominant carotenoid type in the oil droplets of long-wavelength-sensitive, medium-wavelength-sensitive and short-wavelength-sensitive type 2 cones are astaxanthin, zeaxanthin and galloxanthin, respectively. In addition, the oil droplet of the principal member of the double cone contains a mixture of galloxanthin and two hydroxycarotenoids (lutein and zeaxanthin). Short-wavelength-absorbing apocarotenoids are present in all of the droplet types, providing filtering of light in a region of the spectrum where filtering by hydroxy- and ketocarotenoids may be incomplete. Furthermore, birds rely on a complex palette of carotenoid pigments within their cone oil droplets to achieve finely tuned spectral filtering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Driver Distraction Behavior Detection using a Vision Transformer Model based on Transfer Learning Strategy

Driver distraction behavior is one of the critical factors in traffic accidents. Thus, advanced driver state detection system has become the focus in the field of intelligent vehicle. However, in practical applications, insufficient samples of driving distraction behaviors bring great challenges to training a personalized behavior distraction detection model for a specific driver. To this end, a novel transformer model based on a transfer learning strategy is proposed in this paper to accurately recognize driver distraction behavior. Inspired by the effect of the transformer network in visual recognition, we firstly present a transformer behavior distraction detection system to identify the behavior categories that cause driver distraction. Then, for the specific driving dataset in practical application scenarios, the transfer learning strategy is introduced into the driver distraction detection model to further train the general transformer network. The effectiveness of the transformer based on the transfer learning strategy is validated compared with other traditional deep learning methods. The results show that the proposed detection method has better generalization ability and higher accuracy.

Fang, Zhenwu↗
DOE OSTI2020

Advanced Synchrophasor-based Application for Potential Distributed Energy Resources Management: Key Technology, Challenge and Vision

With an increasing number of distributed energy resources (DERs) integrating into the power system, the power grid is becoming more complex and uncertain. Owing to most of DERs are through grid-connected converters to integrate into the power system, the intermittent nature and resource-depended characteristic of the DERs are challenging their monitoring and control. Conventional synchronized measurement devices (SMDs) may be difficult to provide qualified data for DERs management for its accuracy and reporting-rate limitation. The monitoring systems developed basing on SMDs may be insufficient for depicting detailed dynamic manifestations of power systems with a high proportion integration of DERs. This paper shares some advanced synchrophasor-based applications developed in FNET/GridEye, which have a broad prospect for assisting system operator on the control and integration of the DERs.

Wang, Weikang↗
DOE OSTI2020

Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images

Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.

14 SOLAR ENERGY↗
DOE OSTI2022

Unleash Values From Grid-Edge Flexibility: An Overview, Experience, and Vision for Leveraging Grid-Edge Distributed Energy Resources to Improve Grid Operations

The power system landscape is presently undergoing a dramatic change with the rapid integration of numerous distributed energy resources (DERs) at the grid edge. Broadly, we consider grid-edge DERs as any electricity source, storage, and demand response program connected to the medium-to-low-voltage distribution system.

distributed energy resource↗
DOE OSTI2025

Vision Foundation Models in Remote Sensing: A survey

Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.

data models↗
DOE OSTI2014

Vision 20/20: The role of Raman spectroscopy in early stage cancer detection and feasibility for application in radiation therapy response assessment

Raman spectroscopy is an optical technique capable of identifying chemical constituents of a sample by their unique set of molecular vibrations. Research on the applicability of Raman spectroscopy in the differentiation of cancerous versus normal tissues has been ongoing for many years, and has yielded successful results in the context of prostate, breast, brain, skin, and head and neck cancers as well as pediatric tumors. Recently, much effort has been invested on developing noninvasive “Raman” probes to provide real-time diagnosis of potentially cancerous tumors. In this regard, it is feasible that the Raman technique might one day be used to provide rapid, minimally invasive real-time diagnosis of tumors in patients. Raman spectroscopy is relatively new to the field of radiation therapy. Recent work involving cell lines has shown that the Raman technique is able to identify proteins and other markers affected by radiation therapy. Although this work is preliminary, one could ask whether or not the Raman technique might be used to identify molecular markers that predict radiation response. This paper provides a brief review of Raman spectroscopic investigations in cancer detection, benefits and limitations of this method, advances in instrument development, and also preliminary studies related to the application of this technology in radiation therapy response assessment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Vision 20/20: Perspectives on automated image segmentation for radiotherapy

Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods’ strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Vision 20/20: Simultaneous CT-MRI — Next chapter of multimodality imaging

Multimodality imaging systems such as positron emission tomography-computed tomography (PET-CT) and MRI-PET are widely available, but a simultaneous CT-MRI instrument has not been developed. Synergies between independent modalities, e.g., CT, MRI, and PET/SPECT can be realized with image registration, but such postprocessing suffers from registration errors that can be avoided with synchronized data acquisition. The clinical potential of simultaneous CT-MRI is significant, especially in cardiovascular and oncologic applications where studies of the vulnerable plaque, response to cancer therapy, and kinetic and dynamic mechanisms of targeted agents are limited by current imaging technologies. The rationale, feasibility, and realization of simultaneous CT-MRI are described in this perspective paper. The enabling technologies include interior tomography, unique gantry designs, open magnet and RF sequences, and source and detector adaptation. Based on the experience with PET-CT, PET-MRI, and MRI-LINAC instrumentation where hardware innovation and performance optimization were instrumental to construct commercial systems, the authors provide top-level concepts for simultaneous CT-MRI to meet clinical requirements and new challenges. Simultaneous CT-MRI fills a major gap of modality coupling and represents a key step toward the so-called “omnitomography” defined as the integration of all relevant imaging modalities for systems biology and precision medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Vision 20/20: Mammographic breast density and its clinical applications

Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are “dense” and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Vision 20/20: Positron emission tomography in radiation therapy planning, delivery, and monitoring

Positron emission tomography (PET) is increasingly considered as an effective imaging method to support several stages of radiation therapy. The combined usage of functional and morphological imaging in state-of-the-art PET/CT scanners is rapidly emerging to support the treatment planning process in terms of improved tumor delineation, and to assess the tumor response in follow-up investigations after or even during the course of fractionated therapy. Moreover, active research is being pursued on new tracers capable of providing different insights into tumor function, in order to identify areas of the planning volume which may require additional dosage for improved probability of tumor control. In this respect, major progresses in the next years will likely concern the development and clinical investigation of novel tracers and image processing techniques for reliable thresholding and segmentation, of treatment planning and beam delivery approaches integrating the PET imaging information, as well as improved multimodal clinical instrumentation such as PET/MR. But especially in the rapidly emerging case of ion beam therapy, the usage of PET is not only limited to the imaging of external tracers injected to the patient. In fact, a minor amount of positron emitters is formed in nuclear fragmentation reactions between the impinging ions and the tissue, bearing useful information for confirmation of the delivered treatment during or after therapeutic irradiation. Different implementations of unconventional PET imaging for therapy monitoring are currently being investigated clinically, and major ongoing research aims at new dedicated detector technologies and at challenging applications such as real-time imaging and time-resolved in vivo verification of motion compensated beam delivery. This paper provides an overview of the different areas of application of PET in radiation oncology and discusses the most promising perspectives in the years to come for radiation therapy planning, delivery, and monitoring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities

Attenuation correction is an essential component of the long chain of data correction techniques required to achieve the full potential of quantitative positron emission tomography (PET) imaging. The development of combined PET/magnetic resonance imaging (MRI) systems mandated the widespread interest in developing novel strategies for deriving accurate attenuation maps with the aim to improve the quantitative accuracy of these emerging hybrid imaging systems. The attenuation map in PET/MRI should ideally be derived from anatomical MR images; however, MRI intensities reflect proton density and relaxation time properties of biological tissues rather than their electron density and photon attenuation properties. Therefore, in contrast to PET/computed tomography, there is a lack of standardized global mapping between the intensities of MRI signal and linear attenuation coefficients at 511 keV. Moreover, in standard MRI sequences, bones and lung tissues do not produce measurable signals owing to their low proton density and short transverse relaxation times. MR images are also inevitably subject to artifacts that degrade their quality, thus compromising their applicability for the task of attenuation correction in PET/MRI. MRI-guided attenuation correction strategies can be classified in three broad categories: (i) segmentation-based approaches, (ii) atlas-registration and machine learning methods, and (iii) emission/transmission-based approaches. This paper summarizes past and current state-of-the-art developments and latest advances in PET/MRI attenuation correction. The advantages and drawbacks of each approach for addressing the challenges of MR-based attenuation correction are comprehensively described. The opportunities brought by both MRI and PET imaging modalities for deriving accurate attenuation maps and improving PET quantification will be elaborated. Future prospects and potential clinical applications of these techniques and their integration in commercial systems will also be discussed.

61 RADIATION PROTECTION AND DOSIMETRY↗