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Macquarie Island Cloud and Radiation Experiment (MICRE) Ice Nucleating Particle Measurements Field Campaign Report
The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Macquarie Island Cloud and Radiation Experiment (MICRE) Ice Nucleating Particle Measurements campaign was aligned as a supplemental activity with the second year of the overall MICRE campaign. The purpose of the supplemental campaign was to provide a first-ever annual cycle of ice nucleating particle (INP) measurements at a Southern Ocean (SO) site, toward the investigation/characterization of sources of INPs in the marine boundary layer as being from the production or primary sea spray aerosol (SSA) emissions versus other long-range transport influences. Furthermore, measuring the variability in INPs seasonally in this region, as this may relate to meteorology and ocean biological processes, is expected to help constrain (via parameterizations) numerical model simulations of cloud phase and regional climate. In concert with other SO studies during the time frame of the campaign (March 2017 to March 2018), MICRE sought to evaluate the hypotheses that SSA INPs represent the major INP source in the SO and, due to their generally less efficient INP activation properties compared to terrestrial sources, that they may directly influence the persistence of supercooled clouds in a unique manner for this climatic region. These measurements supplemented the original deployment of primarily radiation measurements provided in the ARM MICRE request and the associated Australian Antarctic Division (AAD) Antarctic Clouds and Radiation Experiment (ACRE). Assistance and collaboration was obtained from the AAD, the Commonwealth Scientific and Industrial Research Organization (CSIRO) and the Australian Bureau of Meteorology (BOM). Follow-up plans will include investigations with their additional aerosol, cloud, and radiative transfer measurements. AAD also saw to the transport to Australia and storage of filters at the end of the campaign. The added effort involved an average of twice-weekly collections (a 48-hour and a 72-hour collection) of pre-sterilized, open-faced, polycarbonate filter samples for post-processing of INP number concentrations and tests to analyze the nature of INP sources. Collections were made by AAD technician Emry Thaggard Crocker after online training from our team. Images of the filter sampler and site are shown in Figure 1. Sampling occurred from an approximate elevation of 4m MSL. Filters were returned frozen to Colorado State University (CSU) at the end of the campaign. Initial processing to obtain INP temperature spectra (0 to -28°C) of the concentrations of INPs active via the immersion freezing mechanism were conducted using the CSU ice spectrometer (IS) instrument systems (Hiranuma et al. 2015; McCluskey et al. 2018). In this method, particles are first removed from filters using ultrapure water while under mechanical agitation. Arrays of the particle suspensions from a portion of filter rinses are then cooled to obtain immersion freezing data per volume of water, and finally are related to sampling volumes to obtain numbers per volume of air following the analysis methods of Vali (1971). A total of 103 filters were collected, including blanks at selected intervals throughout the project. For archival and completion of tasks under this ARM proposal, nearly half of the filter collections (46) were processed for basic temperature spectra, and half of these (23) were also tested for microbial/proteinaceous contributions toward INPs via 95°C pre-treatment of portions of the original suspensions.
Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)
In this paper, we present peptide-pair encoding (PPE), a general-purpose probabilistic segmentation of protein sequences into commonly occurring variable-length sub-sequences. The idea of PPE segmentation is inspired by the byte-pair encoding (BPE) text compression algorithm, which has recently gained popularity in subword neural machine translation. We modify this algorithm by adding a sampling framework allowing for multiple ways of segmenting a sequence. PPE segmentation steps can be learned over a large set of protein sequences (Swiss-Prot) or even a domain-specific dataset and then applied to a set of unseen sequences. This representation can be widely used as the input to any downstream machine learning tasks in protein bioinformatics. In particular, here, we introduce this representation through protein motif discovery and protein sequence embedding. (i) DiMotif: we present DiMotif as an alignment-free discriminative motif discovery method and evaluate the method for finding protein motifs in three different settings: (1) comparison of DiMotif with two existing approaches on 20 distinct motif discovery problems which are experimentally verified, (2) classification-based approach for the motifs extracted for integrins, integrin-binding proteins, and biofilm formation, and (3) in sequence pattern searching for nuclear localization signal. The DiMotif, in general, obtained high recall scores, while having a comparable F1 score with other methods in the discovery of experimentally verified motifs. Having high recall suggests that the DiMotif can be used for short-list creation for further experimental investigations on motifs. In the classification-based evaluation, the extracted motifs could reliably detect the integrins, integrin-binding, and biofilm formation-related proteins on a reserved set of sequences with high F1 scores. (ii) ProtVecX: we extend k-mer based protein vector (ProtVec) embedding to variablelength protein embedding using PPE sub-sequences. We show that the new method of embedding can marginally outperform ProtVec in enzyme prediction as well as toxin prediction tasks. In addition, we conclude that the embeddings are beneficial in protein classification tasks when they are combined with raw amino acids k-mer features.
Graph neural network for neutrino physics event reconstruction
Liquid argon time projection chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction techniques. Here, this article describes NUGRAPH 2, a graph neural network for low-level reconstruction of simulated neutrino interactions in a LArTPC detector. Simulated neutrino interactions in the MicroBooNE detector geometry are described as heterogeneous graphs, with energy depositions on each detector plane forming nodes on planar subgraphs. The network utilizes a multihead attention message-passing mechanism to perform background filtering and semantic labeling on these graph nodes, identifying those associated with the primary physics interaction with 98.0% efficiency and labeling them according to particle type with 94.9% efficiency. The network operates directly on detector observables across multiple two-dimensional representations but utilizes a three-dimensional-context-aware mechanism to encourage consistency between these representations. Model inference takes 0.12 s / event on a CPU and 0.005 s / event batched on a GPU. This architecture is designed to be a general-purpose solution for particle reconstruction in neutrino physics, with the potential for deployment across a broad range of detector technologies, and offers a core convolution engine that can be leveraged for a variety of tasks beyond the two described in this paper.
Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis
Accurate cloud type identification and coverage analysis are crucial in understanding the Earth’s radiative budget. Traditional computer vision methods rely on low-level visual features of clouds for estimating cloud coverage or sky conditions. Several handcrafted approaches have been proposed; however, scope for improvement still exists. Newer deep neural networks (DNNs) have demonstrated superior performance for cloud segmentation and categorization. These methods, however, need expert engineering intervention in the preprocessing steps—in the traditional methods—or human assistance in assigning cloud or clear sky labels to a pixel for training DNNs. Such human mediation imposes considerable time and labor costs. We present the application of a new self-supervised learning approach to autonomously extract relevant features from sky images captured by ground-based cameras, for the classification and segmentation of clouds. We evaluate a joint embedding architecture that uses self-knowledge distillation plus regularization. We use two datasets to demonstrate the network’s ability to classify and segment sky images—one with ~85,000 images collected from our ground-based camera and another with 400 labeled images from the WSISEG database. We find that this approach can discriminate full-sky images based on cloud coverage, diurnal variation, and cloud base height. Additionally, it semantically segments the cloud areas without labels. The approach shows competitive performance in all tested tasks, suggesting a new alternative for cloud characterization.
ANL Design Team Analysis: Transportable Modular Reactor by Balance of Plant Elimination
HolosGen LLC is developing “Holos-Quad” a transportable gas-cooled reactor, with integral load following power conversion systems equipping subcritical power modules (SPMs), fully comprised within standard transport containers. The design eliminates the Balance of Plant (BoP) and greatly simplifies the design layout. A closed-loop full Brayton gas thermodynamic cycle converts the fuel cartridges thermal energy into electricity. This ARPA-E project utilizes advanced reactor modeling tools and manufacturing-testing of a scaled SPM equipped with a surrogate fuel cartridge to eliminate the most significant technology gaps to accelerate commercialization. The Argonne Design Team contributes to the research by demonstrating feasibility of various aspects of the Holos-Quad design by performing high-fidelity neutronic modeling, and investigating various reactivity control systems. This work consists of two main tasks: evaluation of HolosGen’s design features and alternative options study for reactivity control.
Isokinetic exercise: A review of the literature
Isokinetic muscle training has all the advantages of isometrics and isotonics while minimizing their deficiencies. By holding the speed of movement constant throughout the full range of motion, isokinetic training devices respond with increased resistance rather than acceleration when the power output of the muscle is increased. Isokinetic training is superior to isometric and isotonic training with respect to increases in strength, specificity of training, desirable changes in motor performance tasks, lack of muscle soreness, and decreases in relative body fat.
Effects of noise frequency on performance and annoyance for women and men
Effects of noise frequencies on both performance on a complex psychomotor task and annoyance were investigated for men (n = 30) and women (n = 30). Each subject performed a complex psychomotor task for 50 min in the presence of low-frequency noise, high-frequency noise, or ambient noise. Women and men learned the task at different rates. Little effect of noise was shown. Annoyance ratings were subsequently obtained from each subject for noises of various frequencies by the method of magnitude estimation. High-frequency noises were more annoying than low-frequency noises regardless of sex and immediate prior exposure to noise. Sex differences in annoyance did not occur. No direct relationship between learning to perform a complex task while exposed to noise and annoyance by that noise was demonstrated.
Training high performance skills using above real-time training
The Above Real-Time Training (ARTT) concept is a unique approach to training high performance skills. ARTT refers to a training paradigm that places the operator in a simulated environment that functions at faster than normal time. Such a training paradigm represents a departure from the intuitive, but not often supported, feeling that the best practice is determined by the training environment with the highest fidelity. This approach is hypothesized to provide greater 'transfer value' per simulation trial, by incorporating training techniques and instructional features into the simulator. These techniques allow individuals to acquire these critical skills faster and with greater retention. ARTT also allows an individual trained in 'fast time' to operate at what appears to be a more confident state, when the same task is performed in a real-time environment. Two related experiments are discussed. The findings appear to be consistent with previous findings that show positive effects of task variation during training. Moreover, ARTT has merit in improving or maintaining transfer with sharp reductions in training time. There are indications that the effectiveness of ARTT varies as a function of task content and possibly task difficulty. Other implications for ARTT are discussed along with future research directions.
International Space Station ECLSS Technical Task Agreement Summary Report
This Technical Memorandum provides a summary of current work accomplished under Technical Task Agreement (TTA) by the Marshall Space Flight Center (MSFC) regarding the International Space Station (ISS) Environmental Control and Life Support System (ECLSS). Current activities include ECLSS component design and development, computer model development, subsystem/integrated system testing, life testing, and general test support provided to the ISS program. Under ECLSS design, MSFC was responsible for the six major ECLSS functions, specifications and standard, component design and development, and was the architectural control agent for the ISS ECLSS. MSFC was responsible for ECLSS analytical model development. In-house subsystem and system level analysis and testing were conducted in support of the design process, including testing air revitalization, water reclamation and management hardware, and certain nonregenerative systems. The activities described herein were approved in task agreements between MSFC and NASA Headquarters Space Station Program Management Office and their prime contractor for the ISS, Boeing. These MSFC activities are in line to the designing, development, testing, and flight of ECLSS equipment planned by Boeing. MSFC's unique capabilities for performing integrated systems testing and analyses, and its ability to perform some tasks cheaper and faster to support ISS program needs, are the basis for the TTA activities.
Operational Methodology for the International Space Station (ISS) High Rate Communications Outage Recorder (HCOR)
The HCOR will be used onboard the ISS to record digital data during Ku-band Loss of Signal (LOS) periods. This recorded data will be played back to the ground during Ku-band Acquisition of Signal (AOS) periods. The Data Management (DM) Team at the Payload Operations and Integration Center is the primary operator of this complex recorder. The record and playback capabilities - along with the memory management functions - are presented in this paper. To illustrate how the DM Team plans to manage the record, playback and memory management tasks of operating the HCOR, an operational scenario for a ninety-minute orbit is presented.
Prototype Videodisk-Based Part-Task Thermal Imaging Trainer
Thermal images, or infrared images, are representations of the world based on heat, instead of visible light. Research has shown that the resulting thermal image results in perceptual differences leading to difficulties in interpretation (e.g., the determination of slope angle, concavity/convexity), or increased identification latencies. A joint research project between the United States (NASA and U.S. Army) and Israel (Ministry of Defense and Israel Air Force) has resulted in the development of a prototype part-task trainer for the acquisition of perceptual skills associated with thermal imaging usage. This prototype system is videodisk-based under computer control, using recordings of thermal images. A lesson section introduces declarative knowledge, in which the basic physics and heuristics of thermal imagery are taught. An exercise section teaches procedural knowledge, with the user viewing dynamic, actual imagery, with an interactive detection/location determination task. The general philosophy and design of the trainer will be demonstrated.
Effects of Single-Task Interference on Dual-Task Interference
Van Sergi, Ruthruff and Johnston found that dual-task practice can markedly reduce interference in the Psychological Refractory Period paradigm. Their results are consistent with a bottleneck model in which practice serves primarily to reduce stage durations. This model predicts that single-task practice on Task 1 alone should produce marked reductions in interference, whereas single-task practice on Task 2 should not. New experiments test these predictions. Alternative theoretical accounts are also considered.
Assessing the Effects of Momentary Priming on Memory Retention During an Interference Task
A memory aid, that used brief (33ms) presentations of previously learned information (target words), was assessed on its ability to reinforce memory for target words while the subject was performing an interference task. The interference task required subjects to learn new words and thus interfered with their memory of the target words. The brief presentation (momentary memory priming) was hypothesized to refresh the subjects memory of the target words. 143 subjects, in a within subject design, were given a 33ms presentation of the target memory words during the interference task in a treatment condition and a blank 33ms presentation in the control condition. The primary dependent measure, memory loss over the interference trial, was not significantly different between the two conditions. The memory prime did not appear to hinder the subjects performance on the interference task. This paper describes the experiment and the results along with suggestions for future research.
Out-of-plane properties
This paper summarizes three areas of research which were performed to characterize out-of-plane properties of composite materials. In the first investigation, a series of tests was run to characterize the through-the-thickness tensile strength for a variety of composites that included 2D braids, 2D and 3D weaves, and prepreg tapes. A new test method based on a curved beam was evaluated. Failures were significantly different between the 2D materials and the 3D weaves. The 2D materials delaminated between layers due to out-of-plane tensile stresses while the 3D weaves failed due to the formation of radial cracks between the surface plies caused by high circumferential stresses along the inner radius. The strength of the 2D textile composites did not increase relative to the tapes. Final failure in the 3D weaves was caused by a circumferential crack similar to the 2D materials and occurred at a lower bending moment than in other materials. The early failures in the 3D weaves were caused by radial crack formation rather than a low through-the-thickness strength. The second investigation focused on the development of a standard impact test method to measure impact damage resistance. The only impact tests that currently exist are compression after impact (CAI) tests which incorporate elements of both damage resistance and damage tolerance. A new impact test method is under development which uses a quasi-static indentation (QSI) test to directly measure damage resistance. Damage resistance is quantified in terms of the contact force to produce a unit of damage where a metric for damage may be area in C-scan, depth of residual dent , penetration, damage growth, etc. A final draft of an impact standard that uses a QSI test method will be presented to the ASTM Impact Task Group on impact. In the third investigation, the impact damage resistance behavior of a variety of textile materials was studied using the QSI test method. In this study, the force where large damage initiates was measured and the delamination size as a function of force was determined. The force to initiate large damage was significantly lower in braids and weaves. The delamination diameter - impact forace relationship was quanitfied using a damage resistance parameter, Q(*), which related delamination diameter to imapct force over a range of delamination sizes. Using this Q(*) parameter to rate the materials, the stitched uniweaves, toughened epoxy tapes, and through-the-thickness orthogonal interlock weave were the most damage resistant.
Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques
Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.
Local search to improve coordinate-based task mapping
We present a local search strategy to improve the coordinate-based mapping of a parallel job’s tasks to the MPI ranks of its parallel allocation in order to reduce network congestion and the job’s communication time. The goal is to reduce the number of network hops between communicating pairs of ranks. Our target is applications with a nearest-neighbor stencil communication pattern running on mesh systems with non-contiguous processor allocation, such as Cray XE and XK Systems. Utilizing the miniGhost mini-app, which models the shock physics application CTH, we demonstrate that our strategy reduces application running time while also reducing the runtime variability. Furthermore, we further show that mapping quality can vary based on the selected allocation algorithm, even between allocation algorithms of similar apparent quality.
Staging For Wakefield Acceleration Experiment Hardware Development: Fast Kicker and High Power RF Delay Section
This project pursues demonstration of the complete staging of high gradient wakefield acceleration at Argonne Wakefield Accelerator (AWA) facility, which is well aligned with the core mission of AWA and the roadmap laid out for the dielectric wakefield accelerator technology toward future linear colliders. During the project, we have successfully demonstrated the feasibility of the staged short pulse TBA (C. Jing, et al, Nucl. Instrum. Meth. A., 898, 72-76, 2018). We have successfully performed the first over 100MW dielectric power extractor. As the last task, one of the key elements for the drive beam distribution system, the fast kicker, has been built and successfully demonstrated this year too. The staging technology demonstrated in this project is crucial for the 500MeV Demonstrator that is planned at AWA in the next five years. The plan is to use two staged meter long dielectric disk accelerators to achieve 500MeV energy gain within ~10 meter long experimental space. While this SBIR project is closed, we plan to continue to investigate the dielectric accelerator technologies under Euclid's general scientific and commercial interests.