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At least 1,045 records · Page 58

NASA's Rodent Research Project on ISS: Validation of a New Platform for Conducting Biomedical and Basic Research into the Consequences of Long Duration Habitation in Space

Rodent research has played a key role in advancing biomedical discoveries both on Earth and in space. The National Research Counsel’s Decadal survey(1) emphasized the importance of expanding NASAs life sciences research to perform long duration, rodent experiments on the International Space Station (ISS). To accomplish this objective, flight hardware, operations, and science capabilities were developed at NASA ARC to support both commercial and government-sponsored rodent research.Rodent Research-1 (RR-1) was the first mission in which animals were delivered and maintained in the ISS for a long duration mission in modified Animal Enclosure Module hardware. Both RR validation and commercial science objectives were pursued on the RR-1 mission. Adult female mice (20 total Flight, FLT) were launched Sept 21, 2014 in RR hardware within a Dragon Capsule (SpaceX4), then after 4 days in transit, were transferred for habitation on the ISS for 17 days (commercial) or 33 days (validation), when animals were euthanized and select tissues recovered on orbit. Various controls groups consisted of: 1) Basal mice from the same cohorts as FLT mice, but tissues were recovered at time of launch, 2) Vivarium (VIV) were housed in standard cages 3) Ground Controls (GC) were housed in flight hardware within an environmental chamber at Kennedy Space Center. The health and behavior of all mice on the ISS were monitored by video feed on a daily basis. Mice were euthanized by injection of Euthasol, then either fast frozen intact or dissected to preserve livers (fast frozen) and spleens (RNAlater). Samples were stored at ≤ -80˚C until their return to Earth for later analyses.Hardware performed nominally throughout the mission and the planned in-flight science operations were completed successfully. FLT mice appeared generally more physically active on orbit than respective GC groups. After 33 days on the ISS, mean body weights of FLT mice did not differ from GC, with both groups showing a 6% rise compared to time of launch, while VIV mice showed an 8% rise over the same period. Importantly, there were no significant differences in body weights between groups at the end of 33 days on the ISS, providing an indication that the RR hardware supported the health of the mice both on Earth and in space. Based on the preliminary data obtained from the livers and spleens of mice after 17 days on the ISS, purified RNA was of high quality (RIN values of spleen: FLT=9.48 +0.40, GC=9.28 +0.44, n=5/group); therefore, RNA quality from samples retrieved on orbit was acceptable for even the most demanding transcriptomic analyses. In addition, liver enzyme activity levels (units/mg protein) of FLT mice (after 17d on ISS) and all control mice were similar in magnitude to samples that were optimally prepared by freezing in liquid nitrogen in the laboratory (enzymes analyzed included catalase, glutathione reductase and glyceraldehyde-3-phosphate dehydrogenase). Validation analyses still in progress include behavior and tissue biochemistries, as well as optimization of science return by post-flight recovery of tissues for biospecimen sharing and global expression analyses.Together, these preliminary findings demonstrate new capability for supporting long duration rodent research on the ISS to achieve both basic science and biomedical objectives.

Globus, R. K.↗

Diagnostics Adapted for Heat-Treating Furnace Environment

Diagnostics developed for the in situ monitoring of rocket combustion environments were adapted for use in heat-treating furnaces. Simultaneous, in situ monitoring of the carbon monoxide, carbon dioxide, methane, water, and hydrogen concentrations in the endothermic gas of a heat-treating furnace were demonstrated under a Space Act Agreement between the NASA Lewis Research Center, the Heat Treating Network, and Akron Steel Treating Company. This endothermic gas, or "endogas," is produced in a catalytic process, where natural gas is "cracked" in the presence of air. Variations in the composition of the natural gas supplied lead to variations in the composition of the endothermic gas. These variations could lead to an unacceptable quality of steel products that are hardened through the carborization process that uses this gas. Conventional methods of monitoring the endogas include measuring the dew point of the gas and the oxygen concentration. From these data, the carbon monoxide content of the gas can be calculated. This carbon monoxide concentration creates the carbon potential needed for carburization. Several weak links are present in this approach. The oxygen monitor deteriorates over time, and the measurement might be inaccurate by 50 percent. Also, the chemistry equations, which are based on several assumptions, such as secondary species concentrations, provide only an approximate estimate of the carbon monoxide concentration. To address these weaknesses, we investigated a new method based on ordinary Raman spectroscopy, in which the carbon monoxide concentration is measured directly and in situ. This method measures the laser light scattered from the molecules. Each species interacts with the light and scatters the light at a different frequency. Spectral monitoring of the scattered light intensity at each molecular frequency of interest provides the species concentrations. One advantage over the conventional method is that several species can be monitored simultaneously. A second advantage is that the measurement is direct; there is no need to make assumptions, to filter the gas, or to calibrate the instrument. An instrument was designed consisting of a laser and a detection system within an enclosure, connected to an optical probe by fibers. For determining carbon monoxide concentration, the probe is mounted on the endothermic gas line, close to the generator. Optical fibers with a length of 150 ft have been used to transmit laser light from the instrument to the probe. There, the light is focused into the gas, and the scattered light is collected and transmitted back to the instrument where it is analyzed with a photomultiplier and lock-in amplifier. Laboratory tests have shown that with the current system the concentration of carbon monoxide, water, nitrogen, oxygen, and hydrogen in the air can be monitored with an accuracy of 1 percent. The concentration of carbon dioxide in the air can be monitored with an accuracy of 0.5 percent, and the concentration of methane with an accuracy of 0.2 percent. This instrument was taken to the Akron Steel Treating Plant, where field tests are in progress to verify the system capabilities. Planned developments are improving the accuracy, monitoring multiple locations, and reducing instrument size and cost.

Source record↗

Information Technology and the Autonomous Control of a Mars In-Situ Propellant Production System

With the rapidly increasing performance of information technology, i.e., computer hardware and software systems, as well as networks and communication systems, a new capability is being developed that holds the clear promise of greatly increased exploration capability, along with dramatically reduced design, development, and operating costs. These new intelligent systems technologies, utilizing knowledge-based software and very high performance computer systems, will provide new design and development tools, scheduling mechanisms, and vehicle and system health monitoring capabilities. In addition, specific technologies such as neural nets will provide a degree of machine intelligence and associated autonomy which has previously been unavailable to the mission and spacecraft designer and to the system operator. One of the most promising applications of these new information technologies is to the area of in situ resource utilization. Useful resources such as oxygen, compressed carbon dioxide, water, methane, and buffer gases can be extracted and/or generated from planetary atmospheres, such as the Martian atmosphere. These products, when used for propulsion and life-support needs can provide significant savings in the launch mass and costs for both robotic and crewed missions. In the longer term the utilization of indigenous resources is an enabling technology that is vital to sustaining long duration human presence on Mars. This paper will present the concepts that are currently under investigation and development for mining the Martian atmosphere, such as temperature-swing adsorption, zirconia electrolysis etc., to create propellants and life-support materials. This description will be followed by an analysis of the information technology and control needs for the reliable and autonomous operation of such processing plants in a fault tolerant manner, as well as the approach being taken for the development of the controlling software. Finally, there will be a brief discussion of the verification and validation process so crucial to the implementation of mission-critical software.

Gross, Anthony R.↗

Probing the Kinetic Origin of Varying Oxidative Stability of Ethyl- vs. Propyl-spaced Amines for Direct Air Capture

Amine-based adsorbents are promising for direct air capture of CO 2 , yet oxidative degradation remains a key unmitigated risk hindering wide-scale deployment. Borrowing wisdom from the basic auto-oxidation scheme, insights are gained into the underlying degradation mechanisms of polyamines by quantum chemical, advanced sampling simulations, adsorbent synthesis, and accelerated degradation experiments. The reaction kinetics of polyamines are contrasted with that of typical aliphatic polymers and they elucidate for the first time the critical role of aminoalkyl hydroperoxide decomposition in the oxidative degradation of amino-oligomers. The experimentally observed variation in oxidative stability of polyamines with different backbone structures is explained by the relationship between the local chemical structure and the free energy barrier of aminoalkyl hydroperoxide decomposition, suggesting that its energetics can be used as a descriptor to screen and design new polyamines with improved stability. In conclusion, the developed computational capability sheds light on radical-induced degradation chemistry of other organic functional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Seismic source characterization of the Arabian Peninsula and Zagros Mountains from regional moment tensor and coda envelopes

Reliable estimates of moment magnitude and source mechanism for seismic events in the Middle East can be challenging due to a small number of openly available stations, the complex tectonic setting, and regions of high attenuation. Access to high-quality waveform data from well-calibrated regional seismic stations is fundamental in producing robust and stable estimates of earthquake source parameters, particularly when measurements of absolute ground motion amplitudes are required. Earthquake source mechanism and moment magnitude are invaluable information in the assessment of seismic hazard, plate motions, and the characterization of faults and regional stress field. The expansion of the Kingdom of Saudi Arabia’s national seismic network in the past several decades provides an opportunity to develop the capabilities of routine focal mechanism and moment magnitude estimations. In this study, we use time-domain full waveform moment tensor inversion and coda envelope–derived amplitude measurements to solve for earthquake source mechanism, moment magnitudes, and their source type. Furthermore, we compared the moment magnitudes calculated from the two methods and publicly available earthquake catalogs and discuss the implications of the obtained source parameters.

58 GEOSCIENCES↗

Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

14 SOLAR ENERGY↗

Effect of Zr and Sc additions on coarsening- and creep resistance of AlSi10Mg fabricated by laser powder bed fusion

Microstructure and creep properties are studied in a eutectic AlSi10Mg alloy modified with Zr and Sc additions (Al-9.8Si-0.32Mg-0.70Zr-0.23Sc, wt%) manufactured through laser powder-bed fusion (L-PBF). Three types of Zr/Sc-bearing powders - elemental, master-alloy, and pre-alloyed - are employed in the fabrication process, with the pre-alloyed powders providing the highest incorporation of Zr and Sc in the Al matrix. Here, the as-printed alloy exhibits a fine cellular Al-Si eutectic structure which fragments and coarsens into micron-sized Si particles during aging at 300 ºC, leading to a steady drop in alloy microhardness between 0.1 and 1000 h. Coarsening of the eutectic Si phase during aging is not measurably affected by Zr and Sc in solid solution, which precipitate during aging and increases strength, compensating weakening from Si coarsening between 1 and 200 h at 300 ºC. Atom-probe analysis in the peak-aged condition (96 h/300 °C) confirms the presence of Al 3 (Sc,Zr) secondary nano-precipitates with an average radius of 1.0 nm and some Si solubility. Micron-sized grains are present throughout the alloy, with ultra-fine-grained regions at the melt pool boundaries, neither of which coarsen during long-term aging at 300 ºC. Under creep conditions at 300 °C, the Zr/Sc-bearing alloy with Al 3 (Sc,Zr) nano-precipitates exhibits power-law behavior, with a high apparent stress exponent (n a = 9) and a high threshold stress (σ th = 43 MPa), exhibiting nearly double the strength of a Zr/Sc-free AlSi10Mg control alloy (σ th = 22 MPa).

36 MATERIALS SCIENCE↗

Innovative accident tolerant fuel concept enabled through direct manufacturing technology

Nuclear energy is one of the largest sources of carbon-free electricity in the world. Some countries are looking at new ways to support and revitalize the nuclear sector since the Fukushima disaster. The accident tolerant fuel program is geared toward improving the safety of nuclear energy by investigating materials that can replace or modify the current uranium-dioxide nuclear fuel and zirconium-based cladding. This research program is being supported by all major nuclear countries since 2011. The practical limitations on allowable uranium enrichment has taken the focus away from the most promising fuels with high radioactivity retention such as tristructural isotropic particle fuel. To overcome such enrichment limitation, a new fuel concept is proposed using advanced ceramic direct manufacturing with laser-induced chemical vapor deposition. The fuel-as-fiber concept is an accident-tolerant fuel design that features high thermal conductivity, strong capability of radioactivity retention and most importantly requires reasonable enrichment levels with uranium nitride as the fuel. In this work, the initial fabrication of uranium-based fuel with laser-induced chemical vapor deposition technology is demonstrated. Then an advanced multi-physics guided modeling approach based on finite element analysis codes and informed by the manufacturing capabilities is developed to accelerate the advancement of fuel-as-fiber concept for use in current light-water reactor technology. The detailed thermomechanical analysis showed promising results for viability of the innovative fuel-as-fiber concept. The predicted stresses in the fuel structural materials were similar to the case of tristructural isotropic particle fuel experience base that has shown excellent reliability in retention of fuel radioactivity at high temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

UrbanPop: A spatial microsimulation framework for exploring demographic influences on human dynamics

Ensuring the social equity of planning measures in social systems requires an understanding of human dynamics, particularly how individual relationships, activities, and interactions intersect with individual needs. Spatial microsimulation models (SMSMs) support planning for human security goals by representing human dynamics through realistic, georeferenced synthetic populations, that a) provide a complete representation of social systems while b) also protecting individual privacy. In this paper, we present UrbanPop, an open and reproducible SMSM framework for analysis of human dynamics with high spatial, temporal, and demographic resolution. UrbanPop creates synthetic populations of demographically detailed worker and student agents, positioning them first at probable nighttime locations (home), then moving them to probable daytime locations (work/school). Summary aggregations of these populations match the granular detail available at the census block group level in the American Community Survey Summary File (SF), providing realistic approximations of the actual population. UrbanPop users can select particular demographic traits important in their application, resulting in a highly tailored agent population. We first lay out UrbanPop's baseline methodology, including population synthesis, activity modeling, and diagnostics, then demonstrate these capabilities by developing case studies of shifting population distributions and high-risk populations in Knox County, TN during the global COVID-19 pandemic.

60 APPLIED LIFE SCIENCES↗

Simulation of hardened cement degradation and estimation of uncertainty in predicted failure times with peridynamics

Modeling the degradation of cement-based infrastructure due to aqueous environmental conditions continues to be a challenge. In order to develop a capability to predict concrete infrastructure failure due to chemical degradation, here we created a chemomechanical model of the effects of long-term water exposure on cement paste. The model couples the mechanical static equilibrium balance with reactive–diffusive transport and incorporates fracture and failure via peridynamics (a meshless simulation method). The model includes fundamental aspects of degradation of ordinary Portland cement (OPC) paste, including the observed softening, reduced toughness, and shrinkage of the cement paste, and increased reactivity and transport with water induced degradation. This version of the model focuses on the first stage of cement paste decalcification, the dissolution of portlandite. Given unknowns in the cement paste degradation process and the cost of uncertainty quantification (UQ), we adopt a minimally complex model in two dimensions (2D) in order to perform sensitivity analysis and UQ. We calibrate the model to existing experimental data using simulations of common tests such as flexure, compression and diffusion. Then we calculate the global sensitivity and uncertainty of predicted failure times based on variation of eleven unique and fundamental material properties. We observed particularly strong sensitivities to the diffusion coefficient, the reaction rate, and the shrinkage with degradation. Also, the predicted time of first fracture is highly correlated with the time to total failure in compression, which implies fracture can indicate impending degradation induced failure; however, the distributions of the two events overlap so the lead time may be minimal. Extension of the model to include the multiple reactions that describe complete degradation, viscous relaxation, post-peak load mechanisms, and to three dimensions to explore the interactions of complex fracture patterns evoked by more realistic geometry is straightforward and ongoing.

36 MATERIALS SCIENCE↗

Long term oxidation of NiCoCrAlY coated Ni-based superalloys: A comparison of observed and simulated interdiffusion

Lifetimes of MCrAlY-type coatings can easily surpass 25 kh when the criteria of ß-depletion is used. To reduce experimental effort, it’s necessary to develop models capable of simulating interdiffusion and oxidation. In the present study, NiCoCrAlY coatings were high velocity oxyfuel deposited on PWA 1483, MARM247, and CMSX-4 substrates, and samples were exposed at 900 °C for 5–20 kh in air+10 vol%H 2 O to study interdiffusion rates. Here, Thermo-Calc/DICTRA was used to reproduce the observed differences, and microstructures were compared. Based on the agreement between calculated and observed rates, simulations were projected past the experimental exposures, utilizing ß-depletion as lifetime criteria.

36 MATERIALS SCIENCE↗

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Probing dense plasmas for HEDS and ICF*

This paper is a retrospective of almost four decades of conceptualization and development of active x-ray probing of dense plasmas, involving multiple teams. In hindsight it was a surprisingly nonlinear and nonsystematic progression, with cycles of key epiphanies followed by multi-step technique optimization, spanning years.Further, each new idea or endeavor, whether or not realized / successful, spawned the next, and not necessarily in order of difficulty. The journey can be summarized by adapting/paraphrasing what Edward Teller said about NIF, that we develop new capabilities precisely because we don't know what those capabilities will enable.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Correlation between microstructure and residual stress formation in friction stir welded armor steels characterized by neutron diffraction

Friction stir welding (FSW) is a solid-state joining process that minimizes the heat-affected zone (HAZ) compared with fusion-based arc welding, making it well suited for joining martensitic armor steels where hardness and ballistic resistance are critical. This study investigates residual stress formation in three defect-free FSW butt-joint configurations relevant to armored-vehicle fabrication: similar rolled homogeneous armor (RHA–RHA, Case 1), similar high-hardness armor (HHA–HHA, Case 2), and dissimilar HHA–RHA (Case 3) joints produced under temperature-controlled conditions (770 °C). Neutron diffraction was employed to quantify the magnitude and spatial distribution of residual stresses in the longitudinal, transverse, and normal directions and to correlate them with weld microstructure and hardness. Tensile residual stresses were concentrated in the softened HAZ, reaching approximately 300 MPa for Case 2 and 400 MPa for Case 1 (≈50–70 % of the base-metal yield strength; ∼581 MPa for RHA and ∼566 MPa for HHA), while compressive residual stresses dominated the stir zone. The spatial extent of tensile stresses scaled with the width of the softened HAZ, which was largest in the dissimilar HHA–RHA joint and smallest in the HHA–HHA joint. Full-width-at-half-maximum (FWHM) analysis revealed low microstrain in overtempered HAZ regions and high microstrain in the stir zone associated with severe plastic deformation and fresh martensite formation. This work demonstrates that residual stress evolution in FSW of martensitic armor steels is governed not primarily by peak temperature or thermal contraction, as inferred from fusion-welding analogies, but by the competition between transformation-induced volumetric expansion and tempering-induced stress relaxation. The relative dominance of these mechanisms is shown to depend on alloy hardenability and local thermal history, leading to more extensive HAZ softening and broader tensile stress regions in the lower-hardenability RHA steel. These findings establish a transferable mechanistic framework for optimizing solid-state joining strategies in high-strength steels and other transformation-hardening alloys beyond armor applications.

Friction stir welding↗

Machine Learning Models for Binary Molecular Classification using VUV Absorption Spectra

Machine learning methods were combined with differential absorption spectroscopy measurements in the vacuum-ultraviolet region (5.167 – 9.920 eV) in order to develop predictive capabilities for inferring molecular structure from the spectra. Several types of species were analyzed and, for modeling purposes, were defined using a single classification: (1) alkane, (2) conjugation with oxygen (e.g. diacetyl, ethyl vinyl ether), (3) non-conjugated alkene (e.g. 1-butene, 1,4-cyclohexadiene), (4) oxygen-containing (e.g. 1-butanol, tetrahydrofuran), or (5) cyclic (e.g. cyclopentane, cyclohexanone). The latter molecular classification excluded cyclic ethers. Several modeling methods were employed in the analysis of 102 absorption spectra, 24 of which were measured for the first time. The primary objective was to identify suitable methods that enable accurate predictions of molecular structure classifications with minimized statistical uncertainties. Rather than identifying a single, unifying method to reliably predict molecular structure contributions to VUV absorption spectra, coordination is required among a particular method, the type of molecular structure detail (e.g. conjugation), and absorption region of interest. The latter is accomplished using a binning approach, wherein absorption regions of ~0.5 eV were utilized rather than the entire ~4.8 eV range. Photon energy binning enabled analysis of region-specific predictions of accuracy, precision, and recall. The outcome from the binning approach is that, rather than utilizing the entire spectrum, optimal determination of molecular structure using machine learning methods depends on the absorption region. Furthermore, the present work provides separate machine learning models for each molecular classification, which enables the identification of multi-functional species relevant to atmospheric chemistry and combustion chemistry, where isomer-resolved speciation is critical to understanding complex reaction networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Seismic tomography of compressional wave velocity and attenuation structure for Makushin Volcano, Alaska

Located in the central portion of the Alaska-Aleutian subduction zone, Makushin Volcano is among the most active volcanoes in the United States and has been classified as a very high threat by the National Volcano Early Warning System based on its eruptive history and proximity to the City of Unalaska and international air routes. In 2015, five standalone seismic stations and three mini seismic arrays of 15 stations each were deployed on Unalaska Island to supplement the Alaska Volcano Observatory permanent seismic network. This temporary array was operational for one year. Taking advantage of the increased azimuthal coverage and the array's increased earthquake detection capability, we developed body-wave Vp and Vp/Vs seismic images of the velocity structure beneath the volcano. Our results show a complex structure with the upper 4 km of the crust. The shallow high-Vp features possibly delineate remnant magma pathways or conduits. Low-Vp regions are found east of the caldera at approximately 4–8 km depth. This is in agreement with previous seismic tomographic work and InSAR-based geodetic models, which had identified this region as a possible long-term source of magma. A three-dimensional compressional wave attenuation model complemented the velocity models. In general, we observe regions of corresponding low P-wave velocity and high attenuation east of the summit caldera between 5 and 7 km depth, supporting the presence of a melt-rich zone. The absolute Vp values (5.5–6.0 km/s) in this region are consistent with the presence of a magma body with a mafic signature. Here, we suggest that a triggering mechanism that involves mafic injections into a more evolved shallow magma chamber is representative of the current magmatic system below the volcano, similarly to what petrological studies have inferred for past eruptions.

58 GEOSCIENCES↗

Climatology of Melting Layer Heights Estimated From Cloud Radar Observations at Various Locations

A melting layer (ML) detection algorithm for cloud radar with polarimetric capability was developed and applied to the cloud radar data collected from five different sites around the world for several years. The retrieved melting layer top height (MLH) showed a very good correspondence with the ECMWF Reanalysis 5 zero degree level data for all five sites. The ML characteristics were distinctively different for different sites, revealing climatological characteristics of ML forming clouds in different regions. Generally, ML tended to occur more frequently in summer than in winter except for a maritime site, where low stratiform clouds formed frequently in summer, the top of which might be lower than the freezing level. In contrast, at two Arctic sites, ML occurred almost exclusively in summer because it was too cold to have an ML in the other seasons. The MLH also varied significantly from site to site but generally was higher during warmer seasons. Based on MLH, two new indices, bulk temperature lapse rate (BLR) and relative depth (RD) of liquid cloud below MLH (RD) were developed, which were useful to explain the environmental characteristics of the five sites. BLR generally increased with the surface temperature at all sites except at the marine site that showed an opposite trend, where a unique synoptic pattern in winter generated high BLR in this cold season. Finally, these findings confirm that studies on thermodynamic structures using cloud radars can be broadened, taking advantage of BLR and RD information, as these indices can represent environmental thermodynamic characteristics of the clouds that have ML.

54 ENVIRONMENTAL SCIENCES↗

SciPy 1.0: fundamental algorithms for scientific computing in Python

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

97 MATHEMATICS AND COMPUTING↗