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NASA NTRS2018

Quantifying Long-Term Changes in Carbon Stocks and Forest Structure from Amazon Forest Degradation

Despite sustained declines in Amazon deforestation, forest degradation from logging and firecontinues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arcof deforestation. Limited data on the magnitude of carbon losses and rates of carbon recoveryfollowing forest degradation have hindered carbon accounting efforts and contributed to incompletenational reporting to reduce emissions from deforestation and forest degradation (REDD+). Wecombined annual time series of Landsat imagery and high-density airborne lidar data to characterizethe variability, magnitude, and persistence of Amazon forest degradation impacts on abovegroundcarbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of thecarbon stocks in intact forests, and differences persisted even after 15 years of regrowth. Incomparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD.Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a historyof one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD,respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires,selective logging before burning did not explain additional variability in modeled ACD loss andrecovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure thatcan aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recoveredfaster than attributes of canopy structure that are critical for biodiversity in tropical forests, includingthe abundance of tall trees. We provide the first comprehensive look-up table of emissions factors forspecific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon lossand recovery trajectories provide an important foundation for assessing the long-term contributionsfrom forest degradation to regional carbon cycling and advance our understanding of the currentstate of frontier forests.

Rappaport, Danielle I.↗

NASA NTRS2024

Fault Diagnosis of Power Components with Reliability Assessment in Extraterrestrial Microgrids

This research investigates the possible failures caused by aging and other environmental and external factors that could significantly impact the performance of extraterrestrial power systems. Additionally, it presents a reliability assessment model for the space microgrid based on fault tree analysis (FTA). The reliability assessment model developed in this paper represents a tool that can be used by engineers to harden the system design for operational and economic benefits. To improve the reliability of the system, this work provides a broad review of the different fault detection and diagnosis (FDD) algorithms used for power microgrids and space applications. Using data sets from the Habitat Simulator developed through the NASA-funded Resilient Extraterrestrial Habitat Institute, this paper compares the applicability and accuracy of the different FDD methods. The primary FDD approach proposed and assessed in this work is based on the Markov reliability model. It predicts and detects future faults in the space microgrids by using past data samples and categorizing them into different classes. Data-driven-based models such as artificial neural networks are also investigated, tested, and evaluated using simulation data sets. According to the simulation results and the broad FDD algorithm comparison, this study provides the crew or maintenance engineers with a clear methodology to detect and localize power system failures.

Leila Chebbo↗

NASA NTRS1994

Spacecraft On-Board Information Extraction Computer (SOBIEC)

The Jet Propulsion Laboratory is the Technical Monitor on an SBIR Program issued for Irvine Sensors Corporation to develop a highly compact, dual use massively parallel processing node known as SOBIEC. SOBIEC couples 3D memory stacking technology provided by nCUBE. The node contains sufficient network Input/Output to implement up to an order-13 binary hypercube. The benefit of this network, is that it scales linearly as more processors are added, and it is a superset of other commonly used interconnect topologies such as: meshes, rings, toroids, and trees. In this manner, a distributed processing network can be easily devised and supported. The SOBIEC node has sufficient memory for most multi-computer applications, and also supports external memory expansion and DMA interfaces. The SOBIEC node is supported by a mature set of software development tools from nCUBE. The nCUBE operating system (OS) provides configuration and operational support for up to 8000 SOBIEC processors in an order-13 binary hypercube or any subset or partition(s) thereof. The OS is UNIX (USL SVR4) compatible, with C, C++, and FORTRAN compilers readily available. A stand-alone development system is also available to support SOBIEC test and integration.

Eisenman, David↗

NASA NTRS1990

Natural hydrocarbons, urbanization, and urban ozone

The combined effects of emission control and urbanization, with its concomitant intensification of the urban heat island, on urban ozone concentrations are studied. The effect of temperature on ozone is considered, and attention is given to the temperature effect on ozone photochemistry. Model calculations suggest that ozone concentration enhancements are caused by the effect of temperature on the atmospheric chemistry of peroxyacetyl nitrate, as well as the temperature dependence of natural and anthropogenic hydrocarbon emissions. It is pointed out that, because of the sensitivity of urban ozone to local climatic conditions and the ability of trees to moderate summertime temperatures, the inadvertent removal of trees from urbanization can have an adverse effect on urban ozone concentration, while a temperature increase in the urban heat island caused by urbanization can essentially cancel out the ozone-reducing benefits obtained from a 50-percent reduction in anthropogenic hydrocarbon emissions.

Cardelino, C. A.↗

NASA NTRS1993

Using machine learning techniques to automate sky survey catalog generation

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.

Fayyad, Usama M.↗

NASA NTRS2019

The Value of Long-Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation-Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications

Observation‐based long‐term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long‐term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow‐2 SWE, and University of Arizona [UA] SWE) we reevaluated by seasonal snow cover and land cover classifications over the conterminous United States from1982 to 2017, using the historical airborne gamma radiation SWE observations (20,738 measurements).We found that there are similar patterns in SSMI/S and GlobSnow‐2 SWE when compared against the gamma SWE. However, GlobSnow‐2 SWE had better agreement with gamma SWE than SSMI/S SWE in some forested‐type classes and maritime and prairie snow classes. As compared to SSMI/S and GlobSnow‐2SWE, UA SWE has much better agreement with gamma SWE in all land cover types and snow classes. Tree cover and topographic heterogeneity affect the agreement between the gamma and gridded SWE and accuracy of gamma SWE itself with the largest differences typically occurring when the percent tree cover was 80% or higher, the terrain slope was steeper than 2.5°, and the elevation range exceeded 100 m. The results demonstrate the reliability of the UA SWE products and the benefits of the gamma radiation approach to measure SWE, especially in forested regions.

Eunsang Cho↗

NASA NTRS1982

Aerial Infrared Photos for Citrus Growers

Handbook advises on benefits and methods of aerial photography with color infrared film. Interpretation of photographs is discussed in detail. Necessary equipment for interpretation is described--light table, magnifying lenses, and microfiche viewers, for example. Advice is given on rating tree condition; identifying effects of diseases, insects, and nematodes; and evaluating effects of soil, water, and weather.

Blazquez, C. H.↗

NASA NTRS2014

Characterization of Plastic Degrading Bacteria from Environmental Samples by Genetic and Biochemical Analysis

Plastic is the major waste-product during NASA space missions, recycling this waste-stream to produce other beneficial materials would decrease upmass. Bacterial called plastisomes have been demonstrated to metabolize non-biodegradable plastics such as polyethylene and polystyrene. Characterization and engineering of these bacteria, and their eventual incorporation as life support systems would enable space flight beyond lower earth orbit. We will utilize molecular techniques to identify and isolate the most productive plastisome. Environmental samples obtained from locations known to be rich in plastic will be cultured in a laboratory defined-media supplemented with plastic as the sole carbon source. Cultures will be monitored for growth over time. Ribosomal DNA will be amplified from cultures that exhibit growth using PCR. These amplified fragments will be sequenced to determine the identity of the consortia in the cultures. We will then perform bioinformatics analysis on the data to identify the plastisomes and generate phylogenetic trees. Morphological and physiological profile of the plastisomes will also be conducted by microscopy and biochemical tests. Our results would reveal a bacterial strain that can break down plastics efficiently. The implication for this project would not only benefit space exploration but also make a major impact towards sustainability development on Earth.

plastic conversion↗

NASA NTRSDate not supplied

MEDPRAT Treatment Clusters: Improving Representation of Mission Medical Risk

INTRODUCTION The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) implements a computational model that aims to quantify spaceflight medical risk by utilizing probabilistic techniques to simulate critical event incidence and outcomes over thousands of simulated mission trials. The goal of MEDPRAT is to characterize mission medical risk and provide insight into medical resource utilization. In order to analyze the medical resource space, treatment must be mapped from each simulated condition, and resources consumed as a result of this treatment must be tracked throughout the course of the mission. A new MEDPRAT feature, ‘treatment clusters’, provide a more sophisticated method of defining the structure and interaction between resources, more closely mimicking the way treatment is carried out clinically. METHODS Treatment clusters expand on the two existing treatment groupings (combination and alternate) adding a new grouping: bundled treatment. Treatment clusters may be combined to any depth, giving users the ability to specify complex treatment trees whose behavior is governed by several user-specified parameters. This approach emphasizes reusability, as treatment clusters, once defined, can be used to create more complex treatment trees or applied to many conditions. By configuring parameters for contribution, efficacy, necessity, primacy, and equivalence, resource relationships and dependencies can be more accurately represented, thereby allowing users to build capabilities with desired treatment properties, for example an intravenous capability for conditions such as anaphylaxis, acute radiation syndrome, etc. MEDPRAT v1.0 remains backward compatible with existing treatment structures, giving users the ability to define new treatment clusters as evidence becomes available, without having to recode their existing treatment databases. In addition to facilitating the representation of more complex treatment options, by pairing treatment clusters with the internal optimization routine, the MEDPRAT set selector, medical resources can be identified as organized in bundles, where appropriate, so that optimized resource sets include groups of highly-dependent resources only when all resources of the group are together. For example, it would be wasteful to include ultrasound gel but not an ultrasound machine, since the gel provides no benefit as a treatment without the ultrasound machine. With treatment clusters, the user may require that both resources are available to provide any benefit as treatment, so that if one resource is optimized out of the set, the other resource will be optimized out as well. RESULTS AND CONCLUSIONS We will report on MEDPRAT treatment clusters used in a bundling study under the IMPACT project of the ExMC element. We will discuss an example of a complex treatment tree. Through the implementation of this feature MEDPRAT enables treatment to be defined and applied in a way that is more representative of the real world, providing more accurate insight into mission medical risk and the medical resource space.

Lawrence Leinweber↗

NASA NTRS2016

Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity

This paper introduces a data driven model for predicting airport arrival capacity with a look-ahead time 2-8 hour forecast. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport's arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system. Serious imbalances between demand and capacity occur most frequently when the airport capacity is severely degraded due to either bad airport terminal surface weather or inclement convective weather around airport arrival fixes. A model that predicts the weather-impacted airport arrival meter fix throughput may help ATM personnel to plan GDP operations more efficiently. This paper identifies the characteristics of air traffic flow across arrival meter fixes at Newark Liberty International Airport (EWR). The proposed approach, based on machine-learning methods, is developed to predict the weather impacted EWR arrival Meter Fix (MF) throughput. Sector forecast coverage is used to envision the weather impact on airport arrival MF flow, and the validation is accomplished by using Convective Weather Avoidance Model (CWAM) 0.5 to 2-hour and Collaborative Convective Forecast Product (CCFP) 4 to 8-hour look-ahead forecast data for the period of April-September in 2014. Furthermore, the regression tree ensemble learning of random forests approach for translating a sector forecast coverage model to an EWR arrival meter fix throughput model is examined. The results suggest that ATM decision makers in charge of MF flow control and GDP planning may benefit from adopting the airport arrival meter capacity prediction models to estimate the inclement weather impacts.

Wang, Yao X.↗

NASA NTRS2019

Model Based Engineering for Software Assurance

NASA's successful development of next generation space vehicles, habitats, and robotic systems will require reliable hardware and software systems. The aim of this initiative is to develop modeling methodology and tools to support Model-Based Systems Engineering (MBSE) for software assurance and reliability analysis. This effort expands the Unified Modeling Language (UML) software design models to include fault data for the extraction of Failure Modes and Effects Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) for software. We explored different modeling approaches to integrate the UML software design models with the Systems Modeling Language (SysML) system models to generate an integrated model and reliability tools that take into account software and hardware interfaces.The benefits of this concept directly affect the safety community with quick turnarounds to produce software assurance and reliability analysis artifacts and the ability to visualize failure effects, both hardware and software. The result is enhanced system design integrity and early identification of system risks. This initiative will enable software assurance activities early in the system design lifecycle, facilitating the discovery of design weaknesses and enhancing the capability to produce safe, hazard-free systems

Wang, Lui↗

NASA NTRS2019

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗

NASA NTRS1992

Automating sky object classification in astronomical survey images

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomer Observatory Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 10(exp 7) galaxies and 10(exp 8) stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. The size of this data set precludes manual analysis. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3* and O-BTree, two inductive learning techniques, learn classification decision trees from examples. These classifiers will be used to process the rest of the data. This paper gives an overview of the machine learning techniques used, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our approach is well-suited to the problem. The primary benefits of the approach are increased data reduction throughput and consistency of classification. The classification rules which are the product of the inductive learning techniques will form an object, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically cataloged data.

Fayyad, Usama M.↗

NASA NTRS1993

An adaptive mesh-moving and refinement procedure for one-dimensional conservation laws

We examine the performance of an adaptive mesh-moving and /or local mesh refinement procedure for the finite difference solution of one-dimensional hyperbolic systems of conservation laws. Adaptive motion of a base mesh is designed to isolate spatially distinct phenomena, and recursive local refinement of the time step and cells of the stationary or moving base mesh is performed in regions where a refinement indicator exceeds a prescribed tolerance. These adaptive procedures are incorporated into a computer code that includes a MacCormack finite difference scheme wih Davis' artificial viscosity model and a discretization error estimate based on Richardson's extrapolation. Experiments are conducted on three problems in order to qualify the advantages of adaptive techniques relative to uniform mesh computations and the relative benefits of mesh moving and refinement. Key results indicate that local mesh refinement, with and without mesh moving, can provide reliable solutions at much lower computational cost than possible on uniform meshes; that mesh motion can be used to improve the results of uniform mesh solutions for a modest computational effort; that the cost of managing the tree data structure associated with refinement is small; and that a combination of mesh motion and refinement reliably produces solutions for the least cost per unit accuracy.

Biswas, Rupak↗

NASA NTRS2004

Communicating Medical Needs to Non-Medical Managers

Differences in communication styles and languages between groups often lead to miscommunication, confusion, and/or frustration. Engineers, computer specialists, clinicians, and managers often utilize the English language in very different ways, with different groups using the same words to represent different concepts ("complaint" is a typical example). In addition, medical issues are often perceived as "off-nominal" and not "primary mission tasks" by managers, which can cause them to assign lower priorities to medical training time and resources. Knowledge bases differ due to variations in training and skill sets, and the goals (both immediate and long-term) of the communicators may also vary, with managers being primarily concerned with overall mission objectives, while clinicians focus on individual or group health issues. Furthermore, true communication is only possible when clinicians possess a deep understanding of mission requirements as well as the ability to communicate medical requirements on a priority basis using risk assessment, added value, and cost benefit analysis. These understandable differences may contribute to difficulties in expressing concerns and ideas in an efficient manner, particularly in projects, such as the space program or many military operations, where these varied groups must collaborate, and where the final decisions must be made by fully informed mission commanders. Methods: Three scenario-based approaches were developed utilizing decision trees and problem based learning, to help define and integrate these concepts. Results: Use of these techniques by NASA and military personnel will be presented. Discussion: To enhance communication, particularly of medical needs, one must identify the concerns and motivating factors for the other groups; for example, members of management may focus on financial concerns, a desire for risk mitigation, public perceptions, mission objectives, etc. Training clinicians to frame issues in these terms may lead to better understanding of the medical concerns by other groups.

Bacal, Kira↗

NASA NTRS1998

A Formal Approach for Designing and Evaluating Procedures

Operator interaction with modern control systems is a topic of great concern in high-risk industries such as nuclear power and commercial aviation. The issues associated with such systems focus on the ability of the operators (e.g., pilots) to achieve mission goals safely while containing failures. Operators must be able to interact safely and reliably with highly automatic and complex systems across the full spectrum of possible operating conditions, including normal, abnormal, and emergency situations. In environments such as commercial aviation, operator interaction with the machine is specified through a set of standard operating procedures (SOP). A procedure represents a collective agreement on the 'best' way to perform a given task. The intent of this paper is to suggest a formal methodology, for designing and evaluating procedures, that is both reliable and systematic. Our approach involves two major elements: a model of the machine and a list of the operator's task specifications (goals). We use formal modeling paradigms for describing the system and super-imposing on it the operator's tasks. Such paradigms, based on recent frameworks such as Statecharts and Hierarchical Hybrid Machines appear to be adequate methods for analyzing operator interaction with modern control systems. To illustrate this methodology, we model and analyze the sequence of actions for an emergency procedure. The procedure, Irregular Engine Start, for a medium-range aircraft, specifies the sequence of immediate actions that must be performed by the crew to avoid an uncontrolled rise in engine temperature during start-up. A model of engine behavior during a hot start is constructed. It also describes the various actions that can be taken by the crew and the resulting outcomes. The model is then opened up as a tree of all possible action sequences. This action tree allows us to trace the correct sequences necessary to achieve the desired end-goal (secure and shut down of the engine). In conclusion, we argue that the current process of designing and evaluating procedures can be improved. We discuss the implications of this approach for designing and evaluating this and other types of procedures. We conclude with insights about the benefits and limitation of this methodology, and offer suggestions for future research.

Degani, Asaf↗

NASA NTRS2002

A Framework for Creating a Function-based Design Tool for Failure Mode Identification

Knowledge of potential failure modes during design is critical for prevention of failures. Currently industries use procedures such as Failure Modes and Effects Analysis (FMEA), Fault Tree analysis, or Failure Modes, Effects and Criticality analysis (FMECA), as well as knowledge and experience, to determine potential failure modes. When new products are being developed there is often a lack of sufficient knowledge of potential failure mode and/or a lack of sufficient experience to identify all failure modes. This gives rise to a situation in which engineers are unable to extract maximum benefits from the above procedures. This work describes a function-based failure identification methodology, which would act as a storehouse of information and experience, providing useful information about the potential failure modes for the design under consideration, as well as enhancing the usefulness of procedures like FMEA. As an example, the method is applied to fifteen products and the benefits are illustrated.

Arunajadai, Srikesh G.↗

NASA NTRS1998

The Laser Vegetation Imaging Sensor (LVIS): An Airborne Laser Altimeter for Mapping Vegetation and Topography

The Laser Vegetation Imaging Sensor (LVIS) is an airborne laser altimeter designed to quickly and extensively map surface topography as well as the relative heights of other reflecting surfaces within the laser footprint. Since 1997, this instrument has primarily been used as the airborne simulator for the Vegetation Canopy Lidar (VCL) mission, a spaceborne mission designed to measure tree height, vertical structure and ground topography (including sub-canopy topography). LVIS is capable of operating from 500 m to 10 km above ground level with footprint sizes from 1 to 60 m. Laser footprints can be randomly spaced within the 7 degree telescope field-of-view, constrained only by the operating frequency of the ND:YAG Q-switched laser (500 Hz). A significant innovation of the LVIS altimeter is that all ranging, waveform recording, and range gating are performed using a single digitizer, clock base, and detector. A portion of the outgoing laser pulse is fiber-optically fed into the detector used to collect the return signal and this entire time history of the outgoing and return pulses is digitized at 500 Msamp/sec. The ground return is then located using software digital signal processing, even in the presence of visibly opaque clouds. The surface height distribution of all reflecting surfaces within the laser footprint can be determined, for example, tree height and ground elevation. To date, the LVIS system has been used to monitor topographic change at Long Valley caldera, CA, as part of NASA's Topography and Surface Change program, and to map tree structure and sub-canopy topography at the La Selva Biological Research Station in Costa Rica, as part of the pre-launch calibration activities for the VCL mission. We present results that show the laser altimeter consistently and accurately maps surface topography, including sub-canopy topography, and vegetation height and structure. These results confirm the measurement concept of VCL and highlight the benefits of airborne prototypes of spaceborne instruments.

Bryan, J.↗