Human Assistant Planetary Exploration Robots

NASA's projected return to the lunar surface with the Artemis program opens the possibility for technological developments that maximize human exploration in ways that were unimaginable during the Apollo program. Specifically, the implementation of heads-up displays (HUD) has the potential to greatly increase astronaut performance during extravehicular activities (EVA) by increasing their situational awareness, providing relevant scientific resources, and presenting live personal biometric data that can safeguard mission success and astronaut health. Though they are an excellent mechanism for conveying information without incurring unnecessary physical strain, the implementation of heads-up displays in the vacuum of space presents challenges including: illumination variance; brightness; contrast; interface control mechanisms; and the general volume of data that can be conveyed without inducing cognitive burnout through clutter. To satisfy the desire to improve astronaut performance with HUDs while also accounting for the above design challenges, ALEXEI, short for Augmented Lunar Exploration and Extravehicular Interface, was developed in the Embry-Riddle Aeronautical University (ERAU) Spacesuit Utilization of Innovative Technology Laboratory (S.U.I.T. Lab) to be an augmented reality interface that can enhance an astronauts ability to conduct successful, scientifically relevant EVAs on the lunar surface. The resulting deliverable was created for the 2020 NASA SUITS (Spacesuit User Interface Technologies for Students) Challenge and balances features that past and current astronauts have expressed a functional desire for while also being cautious of human limitations in terms of visualizing and synthesizing information. Those features include a simplistic visual appearance, which displays a breadth of information that monitors mission progress, provides detailed instructions of mission tasks, and displays biometric vital signs, while only requiring a minimalistic level of input control. Future development of this system will include implementing silent speech control (sponsored by the ERAU Office of Undergraduate Research), which reduces the amount of physical strain needed to control the interface, a robust navigational aid, and user testing in a high-fidelity environment to confirm the usability of the interface. Continued refinement of this device will meet 2021 NASA SUITS Challenge objectives and continue the S.U.I.T. Lab's mission of human-centric design to aid in exploration of the cosmos. The following paper will discuss the history of EVA mission architecture, current methodologies used to aid astronauts on EVA, demonstrate the need for AR display technology in an EVA environment, describe the human factors principles that guided the design of ALEXEI, and discuss the plans for future development and testing.

One major factor for operational safety and planning of human extravehicular activities (EVAs) is the topography of the landing region. During planning for EVAs for future crewed missions, digital elevation models (DEMs) and derived slope maps are often used to place constraints on proposed EVA paths to minimize the slopes and vertical meters traveled. However, the specification of these constraints has not yet been studied in detail and has to date relied on generalized assumptions. The Apollo program was the subject of meticulous planning and execution over an extended period. Our study concentrated on the pivotal aspect of mission planning, namely the topographic conditions of all Apollo landing sites. Furthermore, there are numerous conditions of lunar exploration whose "ground truth" can only be fully comprehended by astronauts who have personally visited the Moon. Certainly, there are technical constraints for the use and operation of equipment and tools, such as suits and instrument carts. However, astronaut observational physiological and psychological experiences and constraints are need to be considered for the planning of successful EVAs. In this study, we examined a variety of data sources, including topographic data, Apollo astronaut reports, audio and video recordings, in order to assess their experience and performance in relation to the topography and slopes along each Apollo extra vehicular activity (EVA). The ability of the astronauts to accurately perceive distances, slopes, and to move with confidence is contingent upon topographic and illumination conditions, as well as the specific equipment that they are required to carry. Future mission planning must take realistic traverse speeds into account, as well as the perception of both real and perceived hazards on challenging traverses.

<div class="htmlview paragraph">During the first two weeks of September 2005, the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) Advanced Extravehicular Activity (AEVA) team led the field test portion of the 2005 Research and Technology Studies (RATS). The Desert RATS field test activity is the culmination of the various individual science and advanced engineering discipline areas year-long technology and operations development efforts into a coordinated field test demonstration under representative (analog) planetary surface terrain conditions. The purpose of the RATS is to drive out preliminary exploration concept of operations EVA system requirements by providing hands-on experience with simulated planetary surface exploration extravehicular activity (EVA) hardware and procedures. The RATS activities also are of significant importance in helping to develop the necessary levels of technical skills and experience for the next generation of engineers, scientists, technicians, and astronauts who will be responsible for realizing the goals of the Constellation Program.</div> <div class="htmlview paragraph">The 2005 Desert RATS was the eighth RATS field test and was the most systems-oriented, integrated field test to date with participants from NASA field centers, the United States Geologic Survey (USGS), industry partners, and research institutes. Each week of the test, the 2005 RATS addressed specific sets of objectives. The first week focused on the performance of surface science astro-biological sampling operations, including planetary protection considerations and procedures. The second week supported evaluation of the Science, Crew, Operations, and Utility Testbed (SCOUT) prototype rover and its sub-systems. Throughout the duration of the field test, the Communications, Avionics, and Infomatics pack (CAI-pack) was tested. This year the CAI-pack served to provide information on surface navigation, science sample collection procedures, and EVA timeline awareness. Additionally, 2005 was the first year since the Apollo program that two pressurized suited test subjects have worked together simultaneously. Another first was the demonstration of recharge of cryogenic life support systems while in-use by the suited test subjects. The recharge capability allowed the simulated EVA test duration to be doubled, facilitating SCOUT proto-type rover testing. This paper summarizes Desert RATS 2005 test hardware, detailed test objectives, test operations and test results.</div>

Camera Holding Robotic Devices in Urology

The purpose of this study was to assess the forage productivity and nutritive value of kenaf at different fertilizer application amounts and various stages of maturity. The experiment was conducted from May to September 2020, the amount of 80 kg of kenaf seed/ha was supplied with different types and amounts of nitrogen fertilizer and the plants were harvested at 10-day intervals from different harvesting dates (24th August and 3rd, 13th, 23rd September). According to the different fertilizer types and application amounts, the highest kenaf height was recorded in the inorganic fertilizer amounts of 200 and 250 kg N/ha and the fresh and DM yield were significantly improved in the inorganic nitrogen amount of 250 kg N/ha. The highest CP and TDN content in the leaf was achieved in the inorganic fertilizer amounts of 150 and 200 kg N/ha, respectively; and the highest TDN content in the stem was also found in the inorganic fertilizer amount of 200 kg N/ha. According to the different harvesting dates, the highest DM ratio was found in the harvesting date of 13th September, the leaf ratio increased with advanced maturity, whereas the stem ratio decreased significantly and the highest DM yield of kenaf was recorded in the harvesting dates of 13th and 23rd September. Besides, the highest CP, CF, CA, ADF, NDF and TDN content in the leaf as influenced by different harvesting dates was 15.4, 31.8, 10.2, 22.1, 34.7 and 76.5%, respectively, and the CP, CA, ADF and TDN in stem decreased significantly with advanced maturity of kenaf. In conclusion, the optimal fertilizer amounts and the appropriate harvesting dates for a high forage yield and high-quality kenaf as livestock feed were the inorganic fertilizer application amounts of 200-250 kg N/ha and from 13th and 23rd September, respectively.

Protecting Lunar Colonies From Space Radiation

Enabling efficient extravehicular activity (EVA) is a key requirement for future human planetary exploration. Relative to unsuited human movement, space suits can impose substantial bioenergetic costs through their direct impact on load carried, as well as joint torques and inertial effects. This work focuses on the former, as represented by the weight of the space suit, taken to include suit, life support system, and other gear carried, which must be borne by astronauts during EVA. Gas pressure space suit legs can be treated as human-stabilized columns supported by air pressure, constraining the available forces for suit self-support. Here I model how suit pressure, gravitational acceleration, and gait characteristics interact to determine whether pressure suits are self-supporting or whether part of their weight must be carried by a suited astronaut. For the purposes of this model, optimal suit mass is defined as the maximum allowable mass at which the space suit is self-supporting across the range of human gait. The model suggests that complete suit self-support was achieved during Apollo lunar surface exploration despite 81 kg to 96 kg EVA system mass. However, on Mars, such a suit would not achieve self-support. Achieving Moon-like levels of self-support is estimated to require a 45 kg Mars suit. NASA EVA prototype suits for planetary exploration significantly exceed this mass but can compensate by operating at higher suit pressures. However, if low pressure (3.8 to 4.3 psi) operation is required to enable astronaut mobility, significant suit mass reductions will be required for astronaut explorers of Mars to avoid direct load carrying bioenergetics penalties and their associated impact on portable life support system sizing or performance margins.

Future lunar missions will require innovative habitat systems to support crewed operations on the Moon’s surface for extended durations. To address these needs, a reconfigurable lunar habitat was designed, intended to facilitate extended human exploration beyond low-Earth orbit. This design incorporates state-of-the-art technologies, combining highly demonstrated solutions with emerging advancements to address the Moon’s unique environmental and operational challenges. The proposed habitat is designed to support critical functions such as life support, power generation, communication, extravehicular activities, and crew health maintenance - each necessary for enabling longer-duration missions that contribute to NASA’s vision for future planetary exploration. This design approach emphasizes reconfigurability, crew well-being, and operational efficiency. With a fully integrated design and a layout that adapts to multiple operational scenarios, the habitat minimizes reliance on additional infrastructure and launches. The reconfigurable interior layout maximizes usable volume, enhancing habitability within the constraints of a lunar habitat and supporting evolving mission goals and payload requirements. Upcoming developments include the design and construction of a full-scale, medium-fidelity mockup of the habitat concept, incorporating a fully reconfigurable interior to simulate various mission activities. Comprehensive “day-in-the-life” human factors testing will evaluate the habitat’s design, assessing its ability to support diverse lunar operations and enhance crew habitability. These efforts lay a foundation for future human exploration of the Moon and beyond.

The increase in investment dollars and human capital dedicated to digital health companies continues to accelerate in early 2018. Different sources estimate the amount of investment into digital health in the first half of 2018 to be between $1.5 and $3.5 billion (https://rockhealth.com/reports/2018-midyear-funding-review-digital-health-deja-vu-in-yet-another-record-breaking-half/). However, there remains significant challenges to achieving success and scale across the healthcare industry for startups and other companies at various stages of maturity. While technology has rapidly transformed most of the major industries that account for national GDP in the leading economies of the world, healthcare has remained somewhat resistant to disruption, but the momentum has recently shifted to greater adoption of the unknown through entrepreneurial achievements. The increasing number of digital health companies and the gradual acceptance of various forms of technology to help identify efficiencies for patients and providers indicates that progress is advancing despite the challenges and barriers that exist. This chapter will discuss the impact of the electronic medical record, big data and value-based care on the growth of digital health in the United States.

The objective of this work was to explore the relative value of near- and mid-infrared diffused reflectance spectroscopy in determining the composition of forages and by-products. Sixty-seven samples consisting of 15 alfalfa, 16 tall fescue and 15 orchard grass hays, 10 corn stovers and 11 wheat straws at various stages of maturity were examined by diffuse reflectance using a scanning monochromator (1100–2500 nm), a Fourier near infrared spectrometer (10,000–4000 cm−1, 4 and 16 cm−1 resolution, neat and 5% sample in KBr) and a Fourier mid-infrared spectrometer (4000–400 cm−1, 4 and 16 cm−1 resolution, neat and 5% sample in KBr). Samples were analysed chemically and spectroscopically for fibres, in vitro digestibility, crude protein, nitrobenzene oxidation products and various measures of lignin content. The results showed that diffuse mid-infrared reflectance spectroscopy can perform as well as, and sometimes better than, diffuse near infrared spectroscopy in determining the composition of forages and by-products. In addition, Fourier near infrared spectroscopy did not perform as well as either near infrared using a scanning monochromator or the Fourier mid-infrared spectrometer. Finally, diluting samples with KBr was not beneficial for either Fourier based determinations. Additional work with more diverse data sets and various Fourier instrument configurations will be needed to further define the limits and usefulness of Fourier transform near- and mid-infrared spectroscopy in the determination of forage and by-product composition.

Purpose: Human Immunodeficiency Virus (HIV) continues to be a disease that kills thousands of individuals each year. The HIV infection is incurable. However, HIV infection has turned into a treatable chronic health condition because of improved access to efficient HIV prevention, diagnosis, treatment, and care. Transmission Electron Microscopy’s (TEM) ability to directly visualize virus particles and distinguish ultrastructure morphology at the nanometer scale, makes it useful in HIV-1 research where it is used for assessing the actions of inhibitors that obstruct the maturation and morphogenesis phases of the virus lifecycle. Hence with its use, the disease's serious stage can be avoided by receiving an early diagnosis.
 Materials and Methods: Through the dedicated use of computer vision frameworks and machine learning techniques, we have developed an optimized low-computational-cost 8-layer Convolutional Neural Network (CNN) backbone capable of classifying HIV-1 virions at various stages of maturity and morphogenesis. The dataset including TEM images of HIV-1 viral life cycle phases is analysed and augmented through various techniques to make the framework robust in real-time. The CNN layers then extract pertinent disease traits from TEM images and utilise them to provide diagnostic predictions.
 Results: It was discovered that the framework performed with an accuracy of 99.76% on the training set, 85.83% on the validation set, and 91.33% on the test set, after being trained on a wide range of micrographs which comprised of different experimental samples and magnifications.
 Conclusion: The suggested network's performance was compared to that of other state-of-the-art networks, and it was discovered that the proposed model was undisputed for classifying TEM images of unseen HIV-1 virion and required less time to train and tweak its weights. The framework can operate more effectively than machine learning algorithms that consume a lot of resources and can be deployed with limited computation and memory resource requirements.

Studies on the biosynthesis of starch granules : Part III. The properties of the components of starches from the growing potato tuber

The chemical composition of silage from whole crop barley {Hordeum vulgare L.) harvested at head emergence, milk or dough stage of maturity was estimated and palatability and digestibility trials on sheep were conducted.

Silicified nutlets of a Lithospermum from the Late Miocene Ash Hollow Formation of Bennett County, South Dakota, are described. Lithospermum dakotense sp. nov. shows similarities in size, shape, attachment, and epidermal cell patterns to extant Lithospermum species. Fossil nutlets were preserved in various stages of maturity. This is the first description of definite Lithospermum nutlets from Tertiary strata of North America.

Identification techniques that determine the ripeness of fruits already have several approaches, but most of the methods use instrumental methods that require sophisticated equipment operated by trained technicians. Electronic nose systems have already been used to identify the freshness and ripeness of other fruits, including tomatoes. This is due to the own volatile organic compound profiles of fruits in various stages of maturity. In this study, an electronic nose system is made using a gas sensor array, Raspberry Pi 3B, and a deep learning model. The model is trained using 150 samples wherein data augmentation is implemented to produce 750 grayscale image patterns. The tomatoes are classified into three: ripe, not ripe, and unknown. During training, the model that undergoes data augmentation and uses 24×8 input data has the highest mean score, 0.96. This model is used in the electronic nose system, and 43 out of 50 samples are correctly identified, which is an 86% accuracy.