NASA

The field of human space travel is in the midst of a dramatic revolution. Upcoming missions are looking to push the boundaries of space travel, with plans to travel for longer distances and durations than ever before. Both the National Aeronautics and Space Administration (NASA) and several commercial space companies (e.g., Blue Origin, SpaceX, Virgin Galactic) have already started the process of preparing for long-distance, long-duration space exploration and currently plan to explore inner solar planets (e.g., Mars) by the 2030s. With the emergence of space tourism, space travel has materialized as a potential new, exciting frontier of business, hospitality, medicine, and technology in the coming years. However, current evidence regarding human health in space is very limited, particularly pertaining to short-term and long-term space travel. This review synthesizes developments across the continuum of space health including prior studies and unpublished data from NASA related to each individual organ system, and medical screening prior to space travel. We categorized the extraterrestrial environment into exogenous (e.g., space radiation and microgravity) and endogenous processes (e.g., alteration of humans’ natural circadian rhythm and mental health due to confinement, isolation, immobilization, and lack of social interaction) and their various effects on human health. The aim of this review is to explore the potential health challenges associated with space travel and how they may be overcome in order to enable new paradigms for space health, as well as the use of emerging Artificial Intelligence based (AI) technology to propel future space health research.

The South China Sea (SCS) is a receptor of pollution sources from various parts of Asia and is heavily impacted by strong meteorological systems, which thus dictate aerosol variability over the region. This study analyzes long-term aerosol optical properties observed at Dongsha Island (a representative site in northern SCS) from 2009 to 2021 and Taiping Island (a representative site in southern SCS) from 2012 to 2021 to better apprehend the temporal evolution of columnar aerosols over the SCS. The noticeable difference in loadings, optical properties, and compositions of aerosols between northern and southern SCS was due to the influence of dissimilar emission sources and transport mechanisms. Column-integrated aerosol optical depth (AOD) over northern SCS (range of monthly mean at 500 nm; 0.12–0.51) was significantly greater than southern SCS (0.09–0.21). The maximum AOD in March (0.51 ± 0.28) at Dongsha was attributed to westerlies coupled with biomass-burning (BB) emissions from peninsular Southeast Asia, whereas the maximum AOD at Taiping in September (0.21 ± 0.25) was owing to various pollution from the Philippines, Malaysia, and Indonesia. Fine-mode aerosol dominated over northern SCS (range of monthly mean Angstrom exponent for 440–870 nm: 0.85–1.36) due to substantial influence from continental sources including anthropogenic and BB emissions while coarse-mode particles dominated over southern SCS (0.54–1.28) due to relatively more influence from marine source. More absorbing columnar aerosols prevailed over northern SCS (range of monthly mean single scattering albedo at 675 nm: 0.92–0.99) compared to southern SCS (0.95–0.98) owing to differences in aerosol composition with respect to sources. Special pollution events showcased possible significant impacts on marine ecosystems and regional climate. This study encourages the establishment of more ground-based aerosol monitoring networks and the inclusion of modeling simulations to comprehend the complex nature of aerosol over this vast marginal sea.

Using type II radio bursts from Wind/WAVES and the associated coronal mass ejections (CMEs) from SOHO/LASCO, Gopalswamy et al. (2005) found a hierarchical relationship between the wavelength range of the type II bursts and CME kinetic energy. Under ‘DH (Decametric-Hectometric) Type II bursts’, they have included m (metric)-DH, pure DH and DH-km bursts. In this work, we consider the pure DH, m-DH and DH-km (kilometric) subsets separately. We find that CMEs associated with DH-km type II bursts have the largest values of average speed, non-halo width, mass, and halo fraction. CMEs associated with m-DH type II bursts have a slightly larger average speed and mass than those causing pure DH type II bursts. CMEs associated with m-DH and pure DH type II bursts have slightly a lower speed and halo fraction compared to those associated with the combined set of DH type II bursts in Gopalswamy et al. (2005), while CMEs causing the DH-km type II bursts have even larger values of CME parameters. DH-km type II burst associated CMEs have the largest solar energetic particles (SEPs) association compared to m-DH and pure DH type II burst associated CMEs. The DH-km type II burst associated CMEs SEP association is slightly smaller than that of Gopalswamy et al. (2005) m-km type II burst associated CMEs. The CMEs associated with major SEP have a larger average speed than the pure DH and m-DH CMEs but smaller than the DH-km CMEs.

HelioSwarm (HS) is a NASA Medium-Class Explorer mission of the Heliophysics Division designed to explore the dynamic three-dimensional mechanisms controlling the physics of plasma turbulence, a ubiquitous process occurring in the heliosphere and in plasmas throughout the universe. This will be accomplished by making simultaneous measurements at nine spacecraft with separations spanning magnetohydrodynamic and sub-ion spatial scales in a variety of near-Earth plasmas. In this paper, we describe the scientific background for the HS investigation, the mission goals and objectives, the observatory reference trajectory and instrumentation implementation before the start of Phase B. Through multipoint, multiscale measurements, HS promises to reveal how energy is transferred across scales and boundaries in plasmas throughout the universe.

Many of the larger employers in this country and abroad have benefited from industrial-organizational (I/O) psychologists’ evidence-based practice. However, charitable and not-for-profit organizations have not always been aware of our services or able to afford them when cognizant of them. Volunteering professional services to charitable organizations provides an opportunity to extend these benefits. In addition, volunteers reap the intrinsic rewards of service, acquire opportunities to hone their skills, and learn from others, and pro bono work has the potential of informing our understanding of the science and practice of I/O psychology. This paper provides five case studies from five I/O psychologists who share their volunteer experiences in their own words. Each case study describes what the I/O psychologist did for the organization, how he or she became involved, and what he or she got out of the experience and learned. The paper offers ways SIOP and the SIOP Foundation might facilitate volunteer activities and concludes by inviting readers to share their own volunteer experiences and suggestions for encouraging volunteer work.

Rapidly warming temperatures in the Arctic are driving increasing tundra vegetation productivity, evidenced in both the satellite derived Normalized Difference Vegetation Index (NDVI) imagery and field studies. These trends, however, are not uniformly positive across the circumpolar Arctic. One notable region of negative linear NDVI trends that have persisted over the last 15 years is southwest Alaska’s Yukon-Kuskokwim Delta (YKD). Negative NDVI trends in the YKD region appear inconsistent with our understanding since tundra vegetation is temperature-limited and air temperatures have increased on the YKD. Analysis over a 40-year record from 1982-2021 reveals distinct decadal variability in the NDVI time series, which continues to produce negative linear trends. Similar decadal variability is also evident in summer warmth and 100-km coastal zone spring sea-ice concentrations. This suggests that decadal climate variations can dominate the trends of NDVI through their influence on the drivers of tundra vegetation, namely coastal sea-ice concentrations and summer warmth. The relationships among sea-ice, summer warmth, and NDVI have changed over the 40-year record. Seasonality analysis since 1982 shows declining sea-ice concentration in spring is followed by trends of increasing temperatures, but weakly declining NDVI during the growing season. An additional key finding is that since early 2010s, the relationships between sea-ice concentration and summer warmth, and sea-ice concentration and NDVI have strengthened, while the relationship between NDVI and summer warmth has weakened, indicating that temperature may no longer be the primary limiting factor for Arctic tundra vegetation on the YKD.

The historical global temperature record is an essential data product for quantifying the variability and change of the Earth system. In recent years, better characterization of observational uncertainty in global and hemispheric trends has become available, but the methodologies are not necessarily applicable to analyses at smaller regional areas, or monthly or seasonal means, where station sparsity and other systematic issues contribute to greater uncertainty. This study presents a gridded uncertainty ensemble of historical surface temperature anomalies from the Goddard Institute for Space Studies (GISS) Surface Temperature (GISTEMP) product. This ensemble characterizes the complex spatial and temporal correlation structure of uncertainty, enabling better uncertainty propagation for climate and applied science at regional and sub-annual scales. This work details the methodology for generating the uncertainty ensemble, presents key statistics of the uncertainty evolution over space and time, and provides best practices for using the uncertainty ensemble in future studies. Summary statistics from the uncertainty ensemble agree with the previous GISTEMP global uncertainty assessment, providing confidence in both.

Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: i) the Demer catchment dominated by agriculture, and ii) the Ourthe catchment dominated by mixed forests. We present results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and Leaf Area Index (LAI). The DA experiments covered the period January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture-runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments.

In recent years, evidence has started piling up that some high-energy cosmic neutrinos can be associated with blazars in flaring states. On 2022 February 26, a new blazar-neutrino coincidence was reported: the track-like neutrino event IC220225A detected by IceCube is spatially coincident with the flat-spectrum radio quasar PKS 0215+015. Like previous associations, this source was found to be in a high optical and γ -ray state. Moreover, the source showed a bright radio outburst, which substantially increases the probability of a true physical association. We have performed six observations with the VLBA shortly after the neutrino event with a monthly cadence and are monitoring the source with the Effelsberg 100m-Telescope, and with the Australia Compact Telescope Array. Here, we present first results on the contemporary parsec-scale jet structure of PKS 0215+015 in total intensity and polarization to constrain possible physical processes leading to neutrino emission in blazars.

Relativistic jets from supermassive black holes or stellar mass black holes are among the most powerful astrophysical phenomena. Magnetic field plays an important role in the jet launching and propagation, as well as particle acceleration and radiation. Polarimetry is the only way to observe the magnetic field evolution. The recent launch of the Imaging X-ray Polarimetry Explorer (IXPE) has opened up the X-ray polarization window, which has revealed very interesting phenomena for relativistic jets. However, the field of MeV gamma-ray polarimetry remains largely unexplored. This paper aims to summarize key scientific potentials for MeV polarimetry for blazars and gamma-ray bursts (GRBs) from recent theoretical modeling. These predictions, which are closely related to the cosmic ray acceleration, neutrino production, radiation mechanism, and the jet evolution, can be examined by future MeV polarimeters, such as the Compton Spectrometer and Imager (COSI), the LargE Area burst Polarimeter (LEAP), and the All-sky Medium-Energy Gamma-ray Observatory eXplorer (AMEGO-X).

This paper presents a comprehensive evaluation of various YOLO architectures for smoke and wildfire detection, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, and YOLO-NAS. The study aims to assess their effectiveness in early detection of wildfires using the Foggia dataset, comprising 8,974 images specifically designed for this purpose. Performance evaluation employs metrics such as Recall, Precision, F1-score, and mean Average Precision to provide a holistic assessment of the models’ performance. The study follows a rigorous methodology involving fixed epochs, continuous performance tracking, and unbiased testing. Results show that YOLOv5, YOLOv7, and YOLOv8 exhibit a balanced performance across all metrics in both validation and testing. YOLOv6 performs slightly lower in recall during validation but achieves a good balance on testing. YOLO-NAS variants excel in recall, making them suitable for critical applications. However, precision performance is lower for YOLO-NAS models. Visual results demonstrate that the top-performing models accurately identify most instances in the test set. However, they struggle with distant scenes and poor lighting conditions, occasionally detecting false positives. In favorable conditions, the models perform well in identifying relevant instances. We conclude that no single model excels in all aspects of smoke and wildfire detection. The choice of model depends on specific application requirements, considering accuracy, recall, and inference time. This research contributes to the field of computer vision in smoke and wildfire detection, providing a foundation for improving detection systems and mitigating the impact of wildfires. Researchers can build upon these findings to propose modifications and enhance the effectiveness of wildfire detection systems.

The four spacecraft of the NASA Magnetospheric Multiscale (MMS) mission carry instruments to reduce the positive potential by means of indium ion beams. Since the start of the nominal mission in September 2015 and until the end of 2021, the instruments active spacecraft potential control (ASPOC) have been actively operating for more than 16 000 h at a nominal emission current of 20 $\mu$ A per spacecraft. Based on data from more than six years in orbit with more than 50 000 h in regions of scientific interest, statistical results regarding the potential’s interdependencies with ambient plasma were obtained. This article reports on the derivation of the photo electron energy spectrum from the correlation between the potential and the plasma data obtained by the fast plasma instrument with and without controlled potential. Finally, the time constants during the relaxation of the controlled potential when the active control instrument is turned off, if measured at high time resolution, allow to estimate the electric capacitance of the spacecraft system.

Between 2017 and 2020, a team of researchers from the George Washington University collaborated with NASA and Freelancer.com to design and launch the “Astrobee Challenge Series,” a large-scale field experiment that aimed to generate data to characterize the relationship among how a technical problem is formulated and who is able and willing to solve, and the quality of solutions they generate. The core experimental manipulation was of the architecture of the problem posed; the typical open innovation process was instrumented to collect unusually rich data but otherwise untouched. In all, 17 individual contests were run over a period of 12 months. Over the course of the challenge series, we tracked a population of 16,249 potential solvers, of which 6,219 initiated solving, and a subset of 147 unique solvers submitted 263 judgeable solutions. The resultant dataset is unique because it captures demographic and expertise data on the full population of potential solvers and links their activity to their solving processes and solution outcomes. Moreover, in addition to winning designs (the typical basis of analysis), it captures design outcomes for all submitted design artifacts allowing analysis of the variety of solutions to the same problem. This data explainer documents the research design and implementation process and provides a detailed explanation of each data record, carefully characterizing potential limitations associated with research design choices. This data should be useful for researchers interested in studying the design and innovation process, particularly those focused on novelty, variety, feasibility of solutions or expertise, diversity and capability of solvers.

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. One big challenge is that new aerial vehicles (AV) will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe air operations. In this paper, we consider an air transportation system where multiple AVs are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) must be achieved at all times to ensure flight safety. Our objective is to minimize the total mission completion time by jointly optimizing the velocities and spectrum allocation for all AVs. We formulate the optimization problem as a multi-stage Markov game where the optimization variables are coupled together. A multi-agent deep reinforcement learning VD3QN algorithm is proposed to enable cooperative learning among AVs. Additionally, we propose a heuristic greedy algorithm (HGA) and an orthogonal multiple access (OMA) solution as baseline solutions. Extensive simulation results show that our learning-based solution outperforms the baseline solutions under different network configurations.

Device-level failure probabilities derived from historical, similar radiation datasets can be inputted into a system-level radiation reliability model to provide insight into that system’s failure probability. A linear voltage regulator reliability model that utilizes historical TID (<100 krad(SiO2) and DDD measurements (<9×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> equivalent 1 MeV neutrons/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) is used as a demonstration system. This methodology aims to reduce engineering uncertainty at early design stages before radiation test data is available, or on quick turn-around projects without radiation test budgets.

Background: Ventricular mass responds to changes in physical activity and loading, with cardiac hypertrophy after exercise training, and cardiac atrophy after sustained inactivity. Ventricular wall stress (ie, loading) decreases during microgravity. Cardiac atrophy does not plateau during 12 weeks of simulated microgravity but is mitigated by concurrent exercise training. Objectives: The goal of this study was to determine whether the current exercise countermeasures on the International Space Station (ISS) offset cardiac atrophy during prolonged space flight. Methods: We measured left ventricular (LV) and right ventricular (RV) mass and volumes (via magnetic resonance imaging) in 13 astronauts (4 females; age 49 ± 4 years), between 75 and 60 days before and 3 days after 155 ± 31 days aboard the ISS. Furthermore, we assessed total cardiac work between 21 and 7 days before space flight and 15 days before the end of the mission. Data were compared via paired-samples t-tests. Results: Total cardiac work was lower during space flight (P = 0.008); however, we observed no meaningful difference in LV mass postflight (pre: 115 ± 30 g vs post: 118 ± 29 g; P = 0.053), with marginally higher LV stroke volume (P = 0.074) and ejection fraction postflight (P = 0.075). RV mass (P = 0.999), RV ejection fraction (P = 0.147), and ventricular end-diastolic (P = 0.934) and end-systolic volumes (P = 0.145) were not different postflight. There were strong positive correlations between the relative change in LV mass with the relative changes in total cardiac output (r = 0.73; P = 0.015) and total cardiac work (r = 0.53; P = 0.112). Conclusions: The current exercise countermeasures used on the ISS appear effective in offsetting reductions in cardiac mass and volume, despite overall reductions in total cardiac work, during prolonged space flight.