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At least 73 records · Page 4

Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Barnett, T. L.↗

Evaluation of the CEAS model for barley yields in North Dakota and Minnesota

The CEAS yield model is based upon multiple regression analysis at the CRD and state levels. For the historical time series, yield is regressed on a set of variables derived from monthly mean temperature and monthly precipitation. Technological trend is represented by piecewise linear and/or quadriatic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-79) demonstrated that biases are small and performance as indicated by the root mean square errors are acceptable for intended application, however, model response for individual years particularly unusual years, is not very reliable and shows some large errors. The model is objective, adequate, timely, simple and not costly. It considers scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Barnett, T. L.↗

Comparison of CEAS and Williams-type models for spring wheat yields in North Dakota and Minnesota

The CEAS and Williams-type yield models are both based on multiple regression analysis of historical time series data at CRD level. The CEAS model develops a separate relation for each CRD; the Williams-type model pools CRD data to regional level (groups of similar CRDs). Basic variables considered in the analyses are USDA yield, monthly mean temperature, monthly precipitation, and variables derived from these. The Williams-type model also used soil texture and topographic information. Technological trend is represented in both by piecewise linear functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test of each model (1970-1979) demonstrate that the models are very similar in performance in all respects. Both models are about equally objective, adequate, timely, simple, and inexpensive. Both consider scientific knowledge on a broad scale but not in detail. Neither provides a good current measure of modeled yield reliability. The CEAS model is considered very slightly preferable for AgRISTARS applications.

Barnett, T. L.↗

Space Station needs, attributes and architectural options. Volume 2, book 1, part 3: Manned Space Station relevance to commercial telecommunications satellites

A document containing a forecast of satellite traffic and revelant technology trends to the year 2000 was prepared which includes those space station capabilities and characteristics that should be provided to make the station useful to commercial satellite owners. The document was circulated to key representative organizations within the commercial telecommunications satellite and related communities of interest, including spacecraft manufacturers, commercial satellite owners, communications carriers, networks and risk insurers. The prospectus document is presented as well as the transmittal letter and the mailing list of the people and companies that were asked to review it. Key commercial telecommunications comments are summarized the actual response letters from the industry are included.

Source record↗

Brazil wheat yield covariance model

A model based on multiple regression was developed to estimate wheat yields for the wheat growing states of Rio Grande do Sul, Parana, and Santa Catarina in Brazil. The meteorological data of these three states were pooled and the years 1972 to 1979 were used to develop the model since there was no technological trend in the yields during these years. Predictor variables were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature.

Callis, S. L.↗

Brazil soybean yield covariance model

A model based on multiple regression was developed to estimate soybean yields for the seven soybean-growing states of Brazil. The meteorological data of these seven states were pooled and the years 1975 to 1980 were used to model since there was no technological trend in the yields during these years. Predictor variables were derived from monthly total precipitation and monthly average temperature.

Callis, S. L.↗

Role of research aircraft in technology development

The United States's aeronautical research program has been rich in the use of research aircraft to explore new flight regimes, develop individual aeronautical concepts, and investigate new vehicle classes and configurations. This paper reviews the NASA supercritical wing, digital fly-by-wire, HiMAT, and AD-1 oblique-wing flight research programs, and draws from these examples general conclusions regarding the role and impact of research aircraft in technology development. The impact of a flight program on spinoff technology is also addressed. The secondary, serendipitous results are often highly significant. Finally, future research aircraft programs are examined for technology trends and expected results.

Szalai, K. J.↗

Power requirements for commercial communications spacecraft

Historical data on commercial spacecraft power systems are presented and their power requirements to the growth of satellite communications channel usage are related. Some approaches for estimating future power requirements of this class of spacecraft through the year 2000 are proposed. The key technology drivers in satellite power systems are addressed. Several technological trends in such systems are described, focusing on the most useful areas for research and development of major subsystems, including solar arrays, energy storage, and power electronics equipment.

Billerbeck, W. J.↗

Advanced laptop and small personal computer technology

Advanced laptop and small personal computer technology is presented in the form of the viewgraphs. The following areas of hand carried computers and mobile workstation technology are covered: background, applications, high end products, technology trends, requirements for the Control Center application, and recommendations for the future.

Johnson, Roger L.↗

High performance network and channel-based storage

In the traditional mainframe-centered view of a computer system, storage devices are coupled to the system through complex hardware subsystems called input/output (I/O) channels. With the dramatic shift towards workstation-based computing, and its associated client/server model of computation, storage facilities are now found attached to file servers and distributed throughout the network. We discuss the underlying technology trends that are leading to high performance network-based storage, namely advances in networks, storage devices, and I/O controller and server architectures. We review several commercial systems and research prototypes that are leading to a new approach to high performance computing based on network-attached storage.

Katz, Randy H.↗

High efficiency low cost GaAs/Ge cell technology

Viewgraphs on high efficiency low cost GaAs/Ge cell technology are presented. Topics covered include: high efficiency, low cost GaAs/Ge solar cells; advantages of Ge; comparison of typical production cells for space applications; panel level comparisons; and solar cell technology trends.

Ho, Frank↗

High-performance network and channel based storage

In the traditional mainframe-centered view of a computer system, storage devices are coupled to the system through complex hardware subsystems called I/O channels. With the dramatic shift toward workstation-based computing, and its associated client/server model of computation, storage facilities are now found attached to file servers and distributed throughout the network. In this paper, we discuss the underlying technology trends that are leading to high-performance network-based storage, namely advances in networks, storage devices, and I/O controller and server architectures. We review several commercial systems and research prototypes that are leading to a new approach to high-performance computing based on network-attached storage.

Katz, Randy H.↗

Automatic Inspection of Aircraft Using Open-Architecture Robotic Platforms

Aging aircraft structures and composite materials are requiring rapid inspection of large areas. Removal from the aircraft for NDE at an inspection facility is not economical as compared to field inspection. Detection and characterization of defects are labor intensive, time consuming and when the process is manual the results are subjected to human error. These limitations of NDE created a need for portable, user friendly inspection systems that can rapidly scan large areas of complex structures and locate all the detrimental material conditions. Addressing this need has been an evolutionary process that followed the technology trend, and unique devices were developed to allow rapid field inspection. This development involves multi-disciplinary approaches to integrate NDE, telerobotics, neural networks, advanced materials science, imbedded computing and automated control. These efforts have led to the development of various portable inspection systems and the current trend is towards fully automatic systems that will operate autonomously.

Bar-Cohen, Yoseph↗

Reliability Considerations for Ultra- Low Power Space Applications

NASA, the aerospace community, and other high reliability (hi-rel) users of advanced microelectronic products face many challenges as technology continues to scale into the deep sub- micron region and ULP devices are sought after. Technology trends, ULP microelectronics, scaling and performance tradeoffs, reliability considerations, and spacecraft environments will be presented from a ULP perspective for space applications.

reliability↗

Airspace Operations: Vision for 2045 and Beyond

We are seeing interesting changes in airspace operations. We are experiencing growth in global aviation for passenger and cargo travel. At the same time, drones of all sizes, urban air mobility, electric aircraft, commercial space transportation, supersonics, hypersonics, and increasingly autonomous vehicles will continue to mature. These operations along with current aviation will require access to airspace operations. Such access and scalability needs will only continue to increase in the future. Given that systems and procedures that will enable and support the future density and diversity takes a considerable amount of time to build and harmonize across the globe, it is appropriate that research efforts to enable 2045 operations begin now. A perfect storm is brewing as a number of factors are coming together, including: anticipated growth in diversity and density; limitations of our current system to support the growth and diversity; lack of utilization of latest technologies in an increasingly digitized world to support air traffic management; and a long lead time to conduct research, develop requirements, and built and deploy air traffic management systems. All these factors indicate that now is the time to start thinking about the needs of 2045 and beyond. In a limited manner, Unmanned Aircraft System Traffic Management (UTM) has shown that new thinking and implementation paths for airspace operations is possible. The current system as it exists is based on many assumptions and limitations of technologies (e.g., radar, human-centered voice communications) which may not be true moving forward given the technologies around us are changing. The panel will discuss the following and related topics: 1. Expected growth in density, diversity, and needed scalability, 2. Likely requirements of air traffic management system to enable and support 2045 and beyond operations, 3. Assumptions related to air traffic management and operations that need to reevaluated based on technology trends, 4. Identification of research priorities and harmonization of research across the globe, and 5. Transition approaches from current air traffic operations to new vision 2045. The panel discussion will be useful for global air traffic management researchers, managers, strategists, airspace users, air traffic management system developers and integrators, and academic researchers.

Kopardekar, Parimal H.↗

An Approach for Identifying IASMS Services, Functions, and Capabilities From Data Sources

Assuring safety in the NAS with the inclusion of new entrants that are part of Advanced Air Mobility (AAM) will require overcoming unique safety challenges that result from combining innovative technologies with novel airspace concepts for moving people and cargo using semi-autonomous/autonomous vehicles. Overcoming these AAM safety assurance challenges is the focus of the In-time Aviation Safety Management System (IASMS). The IASMS Concept of Operations (ConOps) describes an interconnected set of services, functions, and capabilities (SFCs)designed to manage operational risks, identify unknown risks, and inform system design to mitigate risk. This paper describes a broad approach for identifying SFCs involving technology trends in research, assessment of known and unknown risks in safety reports, and causal and contributing factors in aviation accidents and incidents. This approach leverages these sources to identify potential SFCs that enable the Monitor, Assess, and Mitigate (M-A-M)functionality that represents the enabling framework of the IASMS.

Kyle Ellis↗

Turning Limitations into Opportunities in the Development of the Orion Medical System: Incorporation of mHealth Devices

INTRODUCTION: Selection of medical devices for the Orion medical system is a complex task which is dependent on the current state of terrestrial technology. In this work we review the interplay between NASA requirements, constraints, and opportunities for synergy during the development of the Orion medical system. METHODS: The “plan to treat” list of conditions, and subsequently the medical resource list for the Orion Artemis II lunar flyby mission was completed using semi-quantitative methods. During this process, some medical resources were excluded to comply with mass and volume limits, while other resources applicable to multiple medical requirements were prioritized for inclusion. Choices were also examined considering NASA level of care standards for the Artemis design reference missions. As a means to optimize capabilities in spite of mass and volume constraints, ideas for integration of multiple devices into a single platform were explored, including a ‘hackathon’ challenge which was conducted to generate and evaluate concepts for combining medical devices into a single app-based mobile device (ultrasound, ECG, etc.). RESULTS: The Orion team identified 128 medical conditions that we “plan to treat”, with 139 unique medical resources to address these conditions, including 5 digital diagnostic devices (ECG, otoscope, stethoscope, pulse oximeter, and blood pressure monitor). During selection of these digital devices the team noted that miniaturized medical devices often use mobile device platforms (mHealth), some displaying and transmitting data exclusively via mobile device. Of the 5 initially selected devices, all are capable of mobile connection, one which cannot function without a mobile device (ECG). Furthermore, only one of these devices connects natively to a Windows platform, which is currently the only operating system available on Orion. DISCUSSION: Significant savings in mass and volume as well as training and usability improvements can be achieved by leveraging mHealth technologies. However, the platform presents significant challenges for deployment within the Artemis Program including lack of internet connectivity, incompatible operating systems, and radiation concerns. Each of these drawbacks are surmountable with investment in the supporting vehicle architecture, which may become necessary to support the technology trends seen in commercially available medical devices today. Objective: Learn about the opportunities and challenges of leveraging mHealth devices in the development of medical capabilities for austere environments. Learn about the constraints of the NASA spaceflight environment with regards to digital medical device integration.

Douglas Ebert↗

Modern Scientific Data Governance Framework

Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies and stewardship best practices tend to be disconnected from operational data management practices and enforcement and mainly exist in well-meaning documents or reports. These governance policies are, at best, partially implemented and rarely monitored or audited. In addition, existing governance policies keep adding additional data management steps that require a human, ‘a data steward’, in the loop, and the cost of data management can no longer scale proportionately with the current and future increased data volume and complexity. The goal for developing an updated data governance framework is to modernize scientific data governance to the reality of Big data and align it with the current technology trends such as cloud computing and AI. The goals of this framework are two folds. One is to ensure thoroughness that the governance adequately covers the entire data life cycle. Two, provide a practical approach that offers a consistent and repeatable process for different projects. Three core principles ground this framework. First, focus on just enough governance and prevent data governance from becoming a roadblock toward the scientific process. Remove any unnecessary processes and steps. Second, automate data management steps where possible. Actively remove steps that require ‘human in the loop’ within the management process to be efficient and scale with increasing data. Third, all the processes should continually be optimized using quantified metrics to streamline the monitoring and auditing workflows.

Rahul Ramachandran↗