Towards Adaptive Decision Support: A Perspective from Intelligent and Annotated Visual Analytics for Exploring Big Urban Mobility Data
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Xu, Haowen↗
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Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)
This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Situational awareness-enhancing community-level load mapping with opportunistic machine learning
Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.
14 SOLAR ENERGY↗
SANS coupled with fluid invasion approaches for characterization of overall nanopore structure and mesopore connectivity of organic-rich marine shales in China
The pore structure of shales, including pore morphology, connectivity, pore volume, specific surface area (SSA), and pore size distribution (PSD), is a significant factor in controlling gas storage and transport and the migration mechanisms of hydrocarbons. However, the lack of comprehensive characterization for both accessible and inaccessible pore structure increases the difficulty of gas-in-place estimation and gas exploration. In order to investigate the nanoscale pore system, integration of high-pressure mercury intrusion porosimetry (MIP), low-pressure N 2 /CO 2 adsorption (LNA/LCA), and small-angle neutron scattering (SANS) were employed to obtain a multi-scale quantitative characterization of the nanopore structure of organic-rich marine shale samples from the Longmaxi and Niutitang Formations in China. In this work, PSDs obtained from the combined techniques appropriately cover an overall nanopore size range of shale (0.35–15,000 nm) and overcome the limits of the individual method. Uni-, bi, and multi-modal PSDs were observed, but the sizes of a significant portion of the nanopores observed in these shales range from 0.35 to 100 nm. Pore volumes and surface areas of micropores (<2 nm), mesopores (2–50 nm), and macropores (>50 nm) were characterized based on the best performance window of each technique: LCA for micropores; LNA and SANS for mesopores; and MIP for macropores. It was found that micropores are the major contributor to the total SSA for both the Longmaxi and Niutitang shales. With respect to pore volume, however, the contribution to the total pore volume has a trend of micropore < mesopore < macropore for the Longmaxi shale samples, but micro-/mesopore volumes are greater than macropore volumes for samples of the Niutitang shale. Strong correlations were also observed between total organic carbon (TOC) content and micropore volume and surface area, which implies that organic matter is a controlling factor in the micropore system of organic-rich shales. In addition, strong correlations between methane adsorption capacity and both micro-/mesopore volumes and SSAs indicate that micro-/mesopores are governing factors for methane storage. Furthermore, the fractions of accessible mesopore volume and surface area were quantitatively estimated by SANS and LNA. Correlation analyses suggest that the accessibility of the mesopore surface area could be an indicator for gas transport and storage in mesopores in organic matter. Thus, a shale with higher connectivity could have higher gas diffusion capability but lower gas adsorption capacity, and vice versa.
58 GEOSCIENCES↗
Influence of processing on secondary phase formation and microstructural evolution at U-10Mo alloy and Zr interlayer interfaces
Thermo-mechanical processing of uranium-10 wt. % molybdenum (U-10Mo) fuel plates leads to microstructure changes at the U-10Mo/Zr interfaces. Secondary phases formed at this interface are particularly important to interfacial bond strength, process optimization, and maintaining structural integrity of the U-10Mo fuel plates during irradiation. In this work, we determined the phases and phase transformation products occurring at the interface of the U-10Mo fuel and Zr interlayer when the fuel plate is subjected to short and long hot isostatic pressure times. Interfacial morphology, structure and composition of phases formed, and relative hardness across the U-10Mo/Zr interfaces were studied using a multi-length scale, multi-modal characterization approach involving electron microscopy, atom probe tomography, and atomic force microscopy. Here, results highlight that the extent of phase transformations, secondary phase formation, and hardness variability across interfaces can be controlled by modifying processing parameters. Phase diagram construction and thermodynamic calculations were performed using the Thermocalc software to identify expected phases formed at interfaces during the maximum hold temperature of 560 °C experienced during HIP.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Face mask integrated with flexible and wearable manganite oxide respiration sensor
Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La 0.7 Sr 0.3 MnO 3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices.
36 MATERIALS SCIENCE↗
Data-driven model for divertor plasma detachment prediction
We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks: an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. The benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable of providing accurate detachment prediction (usually within a few per cent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing with the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilising this knowledge could open a new path for plasma control in magnetic fusion energy research.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification
Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.
36 MATERIALS SCIENCE↗
Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion
Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. In this work, we build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.
47 OTHER INSTRUMENTATION↗
Scalable Multi-Facility Workflows for Artificial Intelligence Applications in Climate Research
Earth observation satellites and earth system models are sources of vast, multi-modal datasets that are invaluable for advancing climate and environmental research. However, their scale and complexity pose significant challenges for processing and analysis. In this paper we discuss our experiences in developing and using a scientific research application using an automated multi-facility workflow that orchestrates data collection, preprocessing, artificial intelligence (AI) inferencing, and data movement across diverse computational resources, leveraging the Advanced Computing Ecosystem Testbed at the Oak Ridge Leadership Computing Facility (OLCF). We demonstrate that our workflow can be seamlessly integrated and orchestrated across research facilities managed by different federal agencies, thus allowing users to extract new scientific insights from climate datasets. The experimental results indicate that the multi-facility workflow significantly reduces processing time, enhances scalability, and maintains high efficiency across varying workloads. Notably, our workflow processes 12,000 high-resolution satellite images in just 44 seconds using 80 workers distributed across 10 nodes on the OLCF systems. Such high throughput is essential for dynamic tokenization and sharding of petascale satellite data for distributed AI model training and inferencing at scale across thousands of GPUs.
Kurihana, Takuya [ORNL] (ORCID:0000000156698565)↗
Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10)
This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, DMREF, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR).
36 MATERIALS SCIENCE↗
Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)
This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, GammaPrint-700, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR). To download the dataset: (1) Create a Globus account. (2) Create a Globus Endpoint on your computer. (3) Transfer the dataset from the OLCF DOI-DOWNLOADS Collection to your Collection. Common troubleshooting steps: (a) Confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. (b) Create an exception for Globus in your antivirus software so that it can create an Endpoint. (c) Manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab.
36 MATERIALS SCIENCE↗
Advanced Materials and Manufacturing Technologies Nondestructive Examination Efforts at Idaho National Laboratory: Report of FY-24 Efforts
This report details FY-24 nondestructive examination (NDE) efforts at Idaho National Laboratory (INL) in support of the Advanced Materials and Manufacturing Technologies (AMMT) program. While the goal of this endeavor is to develop a multi-modal, multi-length scale workflow for nondestructive characterization of advanced manufactured (AM) nuclear reactor components, substantial development remains until this is a reality. In support of this effort X-ray computed tomography (XCT), X-ray diffraction (XRD), neutron computed tomography (nCT), neutron diffraction, lock-in thermography (LIT), multi-point lock-in thermography (MLIT), and positron annihilation spectroscopy (PAS) were all used on AM specimens to examine defects such as voids, porosity, and residual stress. In addition to summarizing the results of these NDE applications, recommendations for integrating these into a more comprehensive undertaking to promote NDE of engineering-scale components are also included.
36 MATERIALS SCIENCE↗
Scaling Subspace-Driven Approaches Using Information Fusion
In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Networks (CNNs) formalism. Upon unfolding the set of subspaces constituting each data modality, and learning their corresponding encoders, an optimized integration of the generated inherent information is carried out to yield a characterization of various classes. Referred to as deep Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is compared against the independently developed state-of-the-art approach named Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal datasets show that our approach is competitive and more robust in the presence of noise.
Ghanem, Sally↗
A layered abduction model of perception: Integrating bottom-up and top-down processing in a multi-sense agent
A layered-abduction model of perception is presented which unifies bottom-up and top-down processing in a single logical and information-processing framework. The process of interpreting the input from each sense is broken down into discrete layers of interpretation, where at each layer a best explanation hypothesis is formed of the data presented by the layer or layers below, with the help of information available laterally and from above. The formation of this hypothesis is treated as a problem of abductive inference, similar to diagnosis and theory formation. Thus this model brings a knowledge-based problem-solving approach to the analysis of perception, treating perception as a kind of compiled cognition. The bottom-up passing of information from layer to layer defines channels of information flow, which separate and converge in a specific way for any specific sense modality. Multi-modal perception occurs where channels converge from more than one sense. This model has not yet been implemented, though it is based on systems which have been successful in medical and mechanical diagnosis and medical test interpretation.
Josephson, John R.↗
Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks
Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.
Rai, Man Mohan↗
Miniature Exercise Device-2 (MED-2): Preliminary ISS Evaluation Results for a Compact Motorized Resistive and Aerobic Rowing Exercise Device
Future human missions beyond Low Earth Orbit (LEO) will require onboard equipment to provide exercise capabilities for the crew to counter the adverse physiological effects of long-duration microgravity. To accomplish this within the physical constraints of a space vehicle or transit module, a single miniature device that provides both resistive and aerobic exercise modalities is required. To meet this need, Johnson Space Center’s (JSC) Software, Robotics, and Simulation Division (ER) developed the Miniature Exercise Device-2 (MED-2). MED-2 integrates a torque-controlled servomotor and a series-elastic actuator to provide highly-controllable load profiles and a large magnitude output performance in a very small package. This innovative technology is derived from years of JSC/ER design, development and operational experience with cutting-edge robotics, motor controllers, software and actuator/sensor miniaturization, including Robonaut 2 and MED-1. MED-2 was presented at the 2016 ISS R&D Conference. This is an update now that the last of six crewmembers will have completed planned MED-2 sessions on the International Space Station (ISS) in May 2018.Current state-of-the-art ISS exercise equipment consists of two treadmills, a resistive exercise device and two cycle ergometers with a total mass of several thousand pounds and a total volume of several cubic yards. This equipment has proven vital to mitigate the musculoskeletal and cardiovascular degradation effects of microgravity. However, due to the large operational volume and mass of these ISS devices, tailoring them for smaller vehicles, such as Orion, is not possible. In addition, each of the current ISS devices targets a single specific modality. Compared to the existing spaceflight (and even terrestrial) exercise equipment, MED-2 is a new archetype altogether. The combined features of compact size, multi-modality and high-performance is attributable to its innovative series elastic actuator and motor controller. Following its arrival on ISS in 2016, MED-2 was evaluated in two parts. The first and shorter evaluation was an engineering functional checkout of the hardware. As this was a novel exercise device previously never used on ISS, the initial checkout assessed the operation of the hardware and ensured the motion and dynamic range of the crew did not present any collision or other hazards. The second portion of the study collected the heart rates, kinematics and utilized operational volumes of six astronauts to determine the quality of both the resistive and aerobic exercise modalities as delivered by MED-2. Investigators from JSC Biomedical Research and Environmental Science Division (SK) and Glenn Research Center are currently evaluating the data and preparing preliminary results. For the resistive exercise modality, MED-2 demonstrated a range of constant resistive loads from 10-150 lbf. With a displacement range of 84 inches, the MED-2 accommodates users from 5th percentile Japanese female through 95th percentile American male for all of its certified exercises. The displacement measurement accuracy has also been verified within 2.5 percent full range. The crew was able to successfully perform all prescribed resistive exercises, except Goblet Squats which were not feasible with a constant load profile. For the aerobic exercise modality, MED-2 simulated a rowing motion with prescribed and user-selected resistance levels. It has demonstrated rates up to 60 strokes per minute on the ground. MED-2 loads and displacements performance are the same as those cited for the resistive modality. Although each of the crew was able to perform the prescribed aerobic rowing sets, there was considerable variability in the rowing motion among different crewmembers. Also, as expected, the crew was unable to get the full benefits of a typical terrestrial rowing stroke because the current configuration does not allow the user to reach past their feet. These observations have already informed the requirements for other microgravity rowing devices currently in development. One of the unique features of the MED-2 device is the intuitive touch-screen control system. This One Portal graphical user interface (GUI) was developed based on JSC/ER’s heritage knowledge and experience of developing and sustaining the current ISS exercise equipment. Through this interface, the crew easily performed prepared prescriptions as well as had the ability to adjust exercise modality, load and other exercise details such as number of repetitions and number of sets. This touch-screen and GUI fulfilled the MED-2 project goal to simplify the interaction between the user and the device. Furthermore, the extent to which MED-2 utilizes a touchscreen and GUI to control exercise equipment is unmatched among the existing ISS exercise devices. As a motorized device, MED-2 technology can provide a customizable force profile that can be varied as a function of strap displacement, strap velocity or a combination of these and other variables. During 2017, JSC/ER developed and flight-certified a resistive exercise algorithm that mimics the 1-G inertial effects of free-weights and enables adjustable eccentric-to-concentric loading ratios. Subsequent development will explore varying the load profiles and incorporating additional exercises beyond the current list of certified movements.
Exercise↗
COVID-19 Joint Pandemic Modeling and Analysis Platform
The non-pharmaceutical intervention to reduce the impact and spread of COVID-19 requires the development of policies and guidance through a collaborative effort among government, academia, medicine, and citizens. To operationalize this effort, we have developed an all-encompassing situational awareness platform that can process multi-modal and multi-source data allowing informed decision making. Besides, showing the current spread of infection, the platform also captures the impact of human dynamics on the infection spread, location, and availability of critical infrastructure, prediction, and high-performance computing driven simulation. The platform is extensible, allowing third-party integration and services to consume the curated data and analytics in near real-time. We believe the platform will augment critical decision making for reducing the impact and spread of the pandemic.
Thakur, Gautam↗