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NASA NTRS2015

Science Training History of the Apollo Astronauts

Following President Kennedy’s initiation of Project Apollo, NASA underwent substantial changes in personnel, organization, and programs and faced a major question: what to do on the Moon after landing. Once a decision that science activities, particularly geoscience, should be pursued, considerable debate ensued over how to accomplish this. Questions arose over instruments and tools required, samples and photos to be returned, landing site selection, and crew composition. Answers to these questions required major efforts for planning traverses on the Moon and training the astronauts in the extensive procedures necessary in low gravity to use tools, set up instruments, take adequate photos, collect and document samples, and provide proper descriptions. In addition to astronauts on the surface, an astronaut in lunar orbit managed additional instruments, photography and verbal descriptions. Training for these activities averaged nearly one hundred hours per month for over a year for each crew. There were many problems as the training progressed: adjusting groups and backgrounds of the training personnel for the best combination of personalities and skills, overcoming logistical troubles, revising awkward procedures, determining optimum means of communications between all involved groups, and devising contingency procedures for real-time problems. By the last mission these problems were overcome.

Phinney, William C.↗

NASA NTRS2018

Pre-Flight Training of Autonomic Responses for Mitigating the Effects of Spatial Disorientation During Spaceflight

The National Aeronautics and Space Administration (NASA) has identified a potential risk of spatial disorientation, motion sickness, and degraded performance to astronauts during re-entry and landing of the proposed Orion crew vehicle. The purpose of this study was to determine if a physiological training procedure, Autogenic-Feedback Training Exercise (AFTE), can mitigate these adverse effects. Fourteen men and six women were assigned to two groups (AFTE, no-treatment Control) matched for motion sickness susceptibility and gender. All subjects received a standard rotating chair test to determine motion sickness susceptibility; three training sessions on a manual performance task; and four exposures in the rotating chair (Orion tests) simulating angular accelerations of the crew vehicle during re-entry. AFTE subjects received 2 hours of training before Orion tests 2, 3, and 4. Motion sickness symptoms, task performance, and physiological measures were recorded on all subjects. Results showed that the AFTE group had significantly lower symptom scores when compared to Controls on test 2 (p=.05), test 3 (p=.03), and test 4 (p=.02). Although there were no significant group differences on task performance, trends showed that AFTE subjects were less impaired than Controls. Heart rate change scores (20 revolutions per minute minus baseline) of AFTE subjects indicated significantly less reactivity on Test 4 compared to Test 1 (10.09 versus 16.59, p=.02), while Controls did not change significantly across tests. Results of this study indicate that AFTE may be an effective countermeasure for mitigating spatial dis-orientation and motion sickness in astronauts.

Orion↗

NASA NTRS2018

A 3D Citizen Science Video Game for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network aimed at accurately assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. We present here the active learning component of the project, which consists of an interactive video game prototype for tablet and mobile devices where players are able to intuitively label morphology classifications over mm-scale 3D coral reef imagery. Active learning applications present a novel methodology for engaging the public while efficiently providing large-scale training and test data for increasingly complex and data-intensive machine learning algorithms. NeMO-Net trains players on domain-specific knowledge through interactive tutorials and periodically checks players' input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide personalized classification training. Players can rate the classifications of other players, unlock rewards and join a global community as they explore and classify coral reefs and other shallow marine environments.

Citizen Science↗

NASA NTRS2020

NASA's Geoscience Training Program for Astronauts

Geoscientists have been training and preparing astronauts to observe the Earth from space and explore other planetary surfaces with a legacy that reaches back to the early days of the space program. Continuing this legacy and critical function, a core NASA team has been closely coordinating with the Johnson Space Center (JSC) Flight Operations Directorate (FOD) as well as academic, institute, and other governmental partners to conduct a comprehensive geoscience training program that ranges from initial astronaut candidate training to preparing crew for Artemis missions. Described below are the three program training phases, along with recent program highlights and forward planning.

Graff, T. G.↗

NASA NTRS2020

Flight Simulation Scenarios for Commercial Pilot Training and Crew State Monitoring

NASA Langley researchers addressed the Commercial Aviation Safety Team Safety Enhancement 211 through a series of studies to address "Attention-related Human Performance Limiting States" which include channelized attention, diverted attention, startle/surprise, and confirmation bias. The present report focuses on the development of improved training scenarios for operationally realistic Line-Oriented Flight Training scenarios. Areas addressed in the report include: (1) Highlights of events in the LOFT scenario used; (2) Interesting findings with implications for simulator motion; (3) Eye-tracking heat maps in proximity to failure events; (4) Researcher observations of crews as test subjects versus a pilot and a research team co-pilot; and (5) The results of a follow-up questionnaire completed by pilot participants regarding their usual training as well as the scenarios employed in the SHARP studies. These pilot ratings and comments are of value to simulation training developers.

James R. Comstock, Jr.↗

NASA NTRS1993

Toward an Embedded Training Tool for Deep Space Network Operations

There are three issues to consider when building an embedded training system for a task domain involving the operation of complex equipment: (1) how skill is acquired in the task domain; (2) how the training system should be designed to assist in the acquisition of the skill, and more specifically, how an intelligent tutor could aid in learning; and (3) whether it is feasible to incorporate the resulting training system into the operational environment. This paper describes how these issues have been addressed in a prototype training system that was developed for operations in NASA's Deep Space Network (DSN). The first two issues were addressed by building an executable cognitive model of problem solving and skill acquisition of the task domain and then using the model to design an intelligent tutor.

Johnson, W. Lewis↗

NASA NTRSDate not supplied

The Instrumented Walking and Turning Test to Evaluate Suited Gait Dynamics and Performance in Extravehicular Activity Training Environments

Background and aims: Walking will be required for many exploration tasks on the Moon during the Artemis program. Walking in a straight line on the confined floorspace of a testing area, and repetitive treadmill walking that requires no change in direction may not adequately reflect the balance and coordination required during ambulation. Also, performance of turning maneuvers may be affected differently in different extravehicular activity (EVA) training facilities that simulate partial gravity. For example, the Neutral Buoyancy Lab (NBL) simulates lunar gravity by adding weight to underwater subjects to alter buoyancy and achieve the equivalent ground reaction force of 1/6 of Earth’s gravity (1/6G), whereas the Active Response Gravity Offload System (ARGOS) uses a computer controlled overhead suspension system programmed to continuously offload a percentage of a subject’s weight to simulate 1/6G. The degree to which dynamic movements such as turning are comparable across these EVA training facilities has not yet been evaluated. The instrumented gait test helps NASA scientists and engineers evaluate gait dynamics and performance in suited conditions, and this test demonstrates the unique characteristics and limitations of EVA training facilities. We developed an instrumented walking and turning test using inertial measurement units (IMUs) and conducted the test at NASA’s EVA training facilities. Results were used to compare suited walking and turning characteristics in the ARGOS and the NBL. Methods: Subjects donned the Mark III space suit during offloading with the ARGOS spreader bar gimbal and donned the Z2.5 space suit while underwater in the NBL with weights and floatation added to achieve realistic suit center of gravity. The test team securely attached three Opal (APDM, OR, USA) wireless IMUs on the space suit for each test run: one on the middle of the hard upper torso, and one on the left and on the right ankle bearings. During the NBL tests, the IMUs were encased in a waterproof housing (GoPro) with foam added to create a tighter fit. At both testing facilities, 6.3 m x 1.0 m (LxW) walking lines were marked, and a cone for turning or walking around was located at the end of the walking path with another line on the other side of the cone to indicate the stopping point after walking around the cone. Under simulated 1/6G, subjects began by standing at the marked line with their arms folded across the chest, they then walked at a preferred speed along the straight walking path until they reached the end, turned 180 degrees around the cone, and finally stopped at the marked stopping point. All IMU data recorded during testing were automatically saved to the internal memory. Then, raw IMU signals were processed using custom MATLAB (Mathworks, MA, USA) code to compare gait parameters during both the walking and the turning components of the task. These parameters included time (s), speed (m/s for walking and rad/s for turning), step number (n) and walk:turn time ratio (% time spent straight walking versus turning). Results: Less time, faster gait, fewer steps, and higher walk:turn ratio during both walking and turning components were exhibited during tests performed at the ARGOS versus those performed at the NBL. During the NBL tests, the slower walking speed continued at the same rate throughout a U-shape turn. During the ARGOS tests, the subjects performed shorter and tighter turns at 4 times the speed of the NBL turns because they walked 30% faster and the vertical offloading system gave them more support. Conclusion: Our data show that the differences in walking and turning parameters during the NBL tests may be due to the high viscosity in the water environment where the motion of the lower limbs was slow and did not reach full flexion and extension. These tests improve the current knowledge of testing environments in preparation for EVAs on the lunar surface.

Kyoung Jae Kim↗

NASA NTRS2016

The Man-machine Integration Design and Analysis System (MIDAS) Software Training Documentation

This document is a training manual for MIDAS v5 that takes an user through the hardware and software requirements for the MIDAS V5 software, the steps required to download the software, and the steps that a user needs to take to create a MIDAS simulation. The training guide also illustrates how the models interact to generate MIDAS predictions of operator performance along task, workload, and situation awareness timelines and provides the test routines that were conducted to verify the operation of the integrated MIDAS models. The training guide shows the user how the MIDAS task model interacts with and controls an anthropometric model through its use of behavioral primitives. The training documentation also illustrates one approach that has been used to filter and analyze MIDAS output.

Brian F. Gore↗

NASA NTRSDate not supplied

Development of the Suited Injury Modes and Effects Analysis for Identification of Top Injury Risks in Lunar Missions and Training

A new Exploration Extravehicular Activity Services (xEVAS) suit is being designed to replace the current Extravehicular Mobility Unit (EMU) for the National Aeronautics and Space Administration’s (NASA’s) Artemis program to return astronauts to the lunar surface. This new suit will allow for increased range of motion compared to the current EMU and Apollo era suits and additional features will enhance the health and safety of exploration. With the design of lunar missions and the xEVAS suit progressing, it is important to consider possible injuries and injury mechanisms that could occur in the suit. To address these concerns, the suited Injury Modes and Effects Analysis (IMEA) was developed to outline suited injury scenarios and rank them based on risk score. The IMEA documents possible scenarios and underlying mechanisms of injury. History has shown that more suit injuries occur during training than in flight; therefore, currently planned training events to prepare for lunar missions and tasks during lunar surface EVAs were considered. Each scenario is ranked with likelihood and consequence scorings based on our current understanding of suit and application of Artemis design reference missions. The scoring allowed identification of the high-risk cases that will drive further work in suited injury. Mechanisms of injury, injury outcomes, and mitigation strategies are evaluated within each scenario. The Suited Injury Summit was held on January 5, 2022, to vet the IMEA with external experts. This was an all-day virtual meeting with the suited injury team; ergonomists; suit engineers; safety engineers; the flight operations directorate; flight doctors; astronauts; astronaut strength, conditioning, and rehabilitation specialists (ASCRS); and external subject matter experts (SMEs). External SMEs consisted of surgeons with varying specialties. The intent of this meeting was to walk through the top injury risks identified in the analysis, identify any gaps that were not captured, and discuss mitigations. With participation from all groups, countless lessons-learned came from the Summit meeting. Using these, the top 10 risks have been identified: neutral buoyancy laboratory training, hand/glove injuries, poor suit fit, field training, specific EVA tasks/design of task, boots/ankle injuries, falls from heights, background radiation, repetitive contact, and ambulation/longdistance ambulation. Mitigation steps have also been determined for each of the top risks. The IMEA and documentation of top risks is a living document. Yearly meetings are planned to update the analysis and reevaluate top risks and mitigations. The IMEA is being used to drive work in suited injury, and this work will continue to evolve with IMEA and lunar mission updates.

Teresa Reiber↗

NASA NTRSDate not supplied

Helmet-Mounted Display Technology for Eva Training in NASA's Neutral Buoyancy Lab

Currently, during extravehicular activity (EVA) on the International Space Station (ISS), astronauts are in constant communication with mission control; however, this paradigm will significantly change during future NASA exploration missions due to limited and time-delayed communications. As exploration missions will thereby require an increase in crew autonomy, it is hypothesized that heads-up display (HUD) technology can supplement communications with mission control. Before introducing novel display technology to EVA operations, it must first be demonstrated and evaluated in an EVA training environment. The UC Davis Center for Spaceflight Research, in collaboration with NASA Johnson Space Center’s Human Physiology, Performance, Protection, and Operations (H-3PO) Lab has developed a low-cost and rapid development helmet-mounted display (HMD) for EVA training at NASA’s Neutral Buoyancy Lab (NBL). The first two phases of HMD testing (Phase I and II) at the NBL focused on technology and capability development and demonstration. Crew member feedback was obtained that evaluated aspects of HMD including its user satisfaction, visibility, readability, and usefulness. Overall, feedback indicated HMD was readable, un-obtrusive, and the available display modes had potential to be useful and enhance EVA training. HMD display modes currently available include: real-time biofeedback (metabolic rate), phase elapsed time (PET), ability to set timers, task procedural aids (e.g. Pistol Grip Tool settings) and custom messages. The goal for Phase III, which is currently underway, is to evaluate the effects that access to bioinformatics through HMD has on astronaut training and performance at the NBL. For this phase of testing, crewmembers will be asked to perform a go/no-go task while wearing HMD throughout the NBL run to characterize crew member state of situational awareness with respect to HMD. This information could be used to inform how best to display relatively important or supplemental, but not critical, data in the future.

Heads up display↗

NASA NTRSDate not supplied

Development of the Suited Injury Modes and Effects Analysis for Identification of Top Injury Risks in Lunar Missions and Training

A new Exploration Extravehicular Activity Suit (xEVAS) is being designed to replace the current Extravehicular Mobility Unit (EMU) for the National Aeronautics and Space Administration’s (NASA’s) Artemis program to return astronauts to the lunar surface. This new suit will allow for increased range of motion compared the current EMU and Apollo era suits and will have additional features that will enhance the health and safety of exploration. With the design of lunar missions and the xEVAS progressing, it is important to consider possible injuries and injury mechanisms that could occur in the suit. To address these concerns, the suited Injury Modes and Effects Analysis (IMEA) was developed to outline suited injury scenarios and rank them based on risk score. The IMEA documents possible scenarios and underlying mechanisms of injury while wearing an extravehicular activity (EVA) suit. Tasks during lunar surface EVA as well as training events to prepare for lunar missions were considered as history has shown that more suit injuries occur during training than in flight. Each scenario is ranked with a consequence and likelihood scoring based on our current understanding of the suit and Artemis design reference missions to identify high-risk cases that will drive further work in suited injury. Injuries, mechanisms of injury, and mitigation strategies are evaluated within each scenario. The Suited Injury Summit was held on January 5, 2022, to vet the IMEA with external experts. This was an all-day virtual meeting with the suited injury team, ergonomists, suit engineers, safety engineers, the flight operations directorate, flight doctors, astronauts, astronaut strength, conditioning, and rehabilitation specialists (ASCRS), and external subject matter experts (SMEs). External SMEs consisted of surgeons with varying specialties. The intent of this meeting was to walk through the top injury risks identified in the analysis, identify any gaps that were not captured, and discuss mitigations. With participation from all groups, countless lessons-learned came from the Summit meeting. Using the lessons-learned and discussion from the Summit, the top 10 risks have been identified: neutral buoyancy laboratory training, hand/glove injuries, poor suit fit, field training, specific EVA tasks/design of task, boots/ankle injuries, falls from heights, background radiation, repetitive contact, and ambulation/long-distance ambulation. Mitigation steps have also been determined for each of the top risks. The IMEA and documentation of top risks is a living document. Yearly meetings are planned to update the analysis and reevaluate top risks and mitigations. The IMEA is being used to drive work in suited injury, and this work will continue to evolve with IMEA and lunar mission updates.

Teresa Reiber↗

NASA NTRSDate not supplied

Feasibility Study of a Multi Tilt-rotor Aircraft as the Artemis Lunar Training Vehicle

The Lunar Landing Research Vehicles (LLRVs) and the Lunar Landing Training Vehicles (LLTVs) provided astronaut candidates for the Apollo program with essential experience and confidence required to complete the missions, and contributed to six successful manned landings on the moon. The primary challenge in terrestrial training was being able to replicate the ratio of bank angle to linear acceleration that a pilot would experience in lunar gravity. Presently, as the Artemis program seeks to return humans to the Moon by 2025, engineers are evaluating suitable platforms to serve as an In-Flight Trainer (IFT) or Artemis Lunar Training Vehicle (ALTV) for astronauts training in the task of manual landing. The program is investigating the viability of current technology in the field of electric vertical takeoff and landing (eVTOL) vehicles and is evaluating using a multi tilt-rotor aircraft platform as a candidate platform for a preliminary ALTV. The tilt-rotor capability enables the vehicle attitude to be decoupled from its flight path, which is a crucial requirement in realistically simulating lunar gravity on Earth. Other key considerations include compensating for a lack of aerodynamic forces while flying through the atmosphere of Earth, as well as the ability to simulate the dynamics of multiple different lander designs for the Human Landing System (HLS) program. This paper details the feasibility study and presents a preliminary flight control architecture for an IFT based on a notional multi tilt-rotor platform. The modeling-following control law, based on nonlinear dynamic inversion (NDI), removes the need for gain scheduling because the vehicle operates across a wide range of flight conditions. The inner-loop dynamic control allocation strategy consists of a static portion that is optimized offline for trim while compensating for the difference in gravity and a dynamic portion that is computed in real time. The reference model consists of the full closed-loop dynamics of a generic HLS design. The modularity of the flight control architecture enables evaluation of multiple HLS concepts with minimal modifications to the control law. Simulation results of the multi tilt-rotor configuration following the final portion of the Apollo 11 descent trajectory are shown.

Jing Pei↗

NASA NTRSDate not supplied

Feasibility Study of a Multi-Tilt-Rotor Aircraft as the Artemis Lunar Training Vehicle

The Lunar Landing Research Vehicles (LLRVs) and the Lunar Landing Training Vehicles (LLTVs) provided astronauts of the Apollo program with essential experience and confidence required to complete the missions, and contributed to six successful manned landings on the moon. The primary challenge in terrestrial training was being able to replicate the ratio of tilt angle to linear acceleration that a pilot would experience in lunar gravity. Presently, as the Artemis program seeks to return humans to the Moon by 2025, engineers are evaluating suitable platforms to serve as an In-Flight Trainer (IFT) or Artemis Lunar Training Vehicle (ALTV) for astronauts training in the task of manual landing. The program is investigating the viability of current technology in the field of electric vertical takeoff and landing (eVTOL) vehicles and is evaluating using a multi-tilt-rotor aircraft platform as a candidate for a preliminary ALTV. The tilt-rotor capability enables the vehicle attitude to be decoupled from its flight path, which is a crucial requirement in realistically simulating lunar gravity on Earth. Other key considerations include compensating for a lack of aerodynamic forces while flying through the atmosphere of Earth, as well as the ability to simulate the dynamics of multiple different lander designs for the Human Landing System (HLS) program. This paper details the feasibility study and presents a preliminary flight control architecture for an IFT based on a notional multi-tilt-rotor platform. The model-following control law, based on nonlinear dynamic inversion (NDI), removes the need for gain scheduling. The inner-loop dynamic control allocation strategy consists of a static portion that is optimized offline for trim while compensating for the difference in gravity and a dynamic portion that is computed in real time. The reference model consists of the full closed-loop dynamics of a generic HLS design. The modularity of the flight control architecture enables evaluation of multiple HLS concepts with minimal modifications to the control law. Simulation results of the multi-tilt-rotor configuration following the final portion of the Apollo 11 descent trajectory are shown.

Jing Pei↗

NASA NTRSDate not supplied

Development of the Suited Injury Modes and Effects Analysis for Identification of Top Injury Risks in Lunar Missions and Training

Tessa Reiber↗

NASA NTRSDate not supplied

Artificial Neural Network (ANN) Surface Longwave and Shortwave Fluxes Trained on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) project provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed to provide key data for the applied sciences and educational users within a week of observation. FLASHFlux achieves this by using simplified calibration, an operational meteorological product from Global Modeling and Assimilation Office (GMAO), and its own surface parameterizations model. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Terra and NOAA-20 observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1o x 1o gridded data that combines Terra and NOAA-20 observations. Currently, FLASHFlux uses the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) to derive its surface fluxes (Kratz et al., 2010; Gupta et al, 2001). A new Machine Learning (ML) based approach using Artificial Neural Networks to derive Surface Longwave (LW) & Shortwave (SW) fluxes based on training data from the CERES Clouds Radiative Swath (CRS) product is being investigated to replace LPSA and LPLA in the SSF surface flux products. One of the biggest hurdles in training ML model is model fitting. To overcome the problem of overfitting we use feature engineering that helps in finding the important feature and remove features that are irrelevant to the model. In our training we employed the Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training. We intercompare ANN fluxes against surface fluxes produced from the Fu-Liou model in CRS and the LPSA/LPLA in FLASHFlux SSF. Furthermore, we validated ANN derived fluxes to the Baseline Surface Radiation Network (BSRN).

P C Sawaengphokhai↗

DOE OSTI2015

Method for training honeybees to respond to olfactory stimuli and enhancement of memory retention therein

A specialized conditioning protocol for honeybees that is designed for use within a complex agricultural ecosystem. This method ensures that the conditioned bees will be less likely to exhibit a conditioned response to uninfected plants, a false positive response that would render such a biological sensor unreliable for agricultural decision support. Also described is a superboosting training regime that allows training without the aid of expensive equipment and protocols for training in out in the field. Also described is a memory enhancing cocktail that aids in long term memory retention of a vapor signature. This allows the bees to be used in the field for longer durations and with fewer bees trained overall.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Industrial Energy Training and Certification

Compressed air systems, ammonia refrigeration systems, chilled water systems, steam systems, process heating systems, combined heat and power systems, pump systems and fan systems are major industrial energy systems commonly found in manufacturing facilities. Efficiency of these systems contributes significantly to whole facilities' energy performance. On the national, even international level, well-structured training and highly recognized certification programs help develop a highly-skilled and qualified workforce to maintain and improve facilities' energy performance, particularly as technologies within these systems become more advanced. The purpose of this paper is to review currently available training and certification programs focusing on these systems and to identify the gap between market's needs and currently available programs. Three major conclusions are: first, most training programs focus on operations, maintenance, safety and design although some briefly touch the energy performance aspect; second, except CRES by RETA and PSA Certificate and PSAP Master Certification by HI, no other certifications had been found emphasizing on knowledge and skills for improving and maintaining these systems' energy performance; third, developing energy efficiency focused training and ANSI accredited certification programs on these energy systems will fill the gap between market's needs and currently available programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Industrial Energy Training and Certification

We present compressed air systems, ammonia refrigeration systems, chilled water systems, steam systems, process heating systems, combined heat and power systems, pump systems and fan systems are major industrial energy systems commonly found in manufacturing facilities. Efficiency of these systems contributes significantly to whole facilities' energy performance. On the national, even international level, well-structured training and highly recognized certification programs help develop a highly-skilled and qualified workforce to maintain and improve facilities' energy performance, particularly as technologies within these systems become more advanced. The purpose of this paper is to review currently available training and certification programs focusing on these systems and to identify the gap between market's needs and currently available programs. Three major conclusions are: first, most training programs focus on operations, maintenance, safety and design although some briefly touch the energy performance aspect; second, except CRES by RETA and PSA Certificate and PSAP Master Certification by HI, no other certifications had been found emphasizing on knowledge and skills for improving and maintaining these systems' energy performance; third, developing energy efficiency focused training and ANSI accredited certification programs on these energy systems will fill the gap between market's needs and currently available programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗