A Metallurgical Investigation of a Large Forged Disc of 19-9 DL Alloy
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STEEL ALLOYS-UNIVERSAL-CYCLOPS 19-9 DL↗
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Optical resolution of DL-aspartic acid in the presence of optically active amino acid and copper /II/ ion.
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Harada, K.↗
Reliability of Maximal Strength Testing in Novice Weightlifters
The one repetition maximum (1RM) is a criterion measure of muscle strength. However, the reliability of 1RM testing in novice subjects has received little attention. Understanding this information is crucial to accurately interpret changes in muscle strength. To evaluate the test-retest reliability of a squat (SQ), heel raise (HR), and deadlift (DL) 1RM in novice subjects. Twenty healthy males (31 plus or minus 5 y, 179.1 plus or minus 6.1 cm, 81.4 plus or minus 10.6 kg) with no weight training experience in the previous six months participated in four 1RM testing sessions, with each session separated by 5-7 days. SQ and HR 1RM were conducted using a smith machine; DL 1RM was assessed using free weights. Session 1 was considered a familiarization and was not included in the statistical analyses. Repeated measures analysis of variance with Tukey fs post-hoc tests were used to detect between-session differences in 1RM (p.0.05). Test-retest reliability was evaluated by intraclass correlation coefficients (ICC). During Session 2, the SQ and DL 1RM (SQ: 90.2 }4.3, DL: 75.9 }3.3 kg) were less than Session 3 (SQ: 95.3 }4.1, DL: 81.5 plus or minus 3.5 kg) and Session 4 (SQ: 96.6 }4.0, DL: 82.4 }3.9 kg), but there were no differences between Session 3 and Session 4. HR 1RM measured during Session 2 (150.1 }3.7 kg) and Session 3 (152.5 }3.9 kg) were not different from one another, but both were less than Session 4 (157.5 }3.8 kg). The reliability (ICC) of 1RM measures for Sessions 2-4 were 0.88, 0.83, and 0.87, for SQ, HR, and DL, respectively. When considering only Sessions 3 and 4, the reliability was 0.93, 0.91, and 0.86 for SQ, HR, and DL, respectively. One familiarization session and 2 test sessions (for SQ and DL) were required to obtain excellent reliability (ICC greater than or equal to 0.90) in 1RM values with novice subjects. We were unable to attain this level of reliability following 3 HR testing sessions therefore additional sessions may be required to obtain an ICC of greater than or equal to 0.90. Future resistive exercise studies should consider the reliability of specific measures to ensure that changes in strength with training are attributable to training and not learning effects associated with 1RM testing.
Loehr, James A.↗
Towards an Implementation of Differential Dynamic Logic in PVS
This paper describes an ongoing effort to embed and verify differential dynamic logic (dL) in the Prototype Verification System (PVS). dL is a logic for specifying and formally reasoning about hybrid systems, which employ both continuous and discrete dynamics. There are several benefits of this effort. First, the embedding of dL in PVS offers an independent formal verification of the semantics and rules of dL. Second, the embedding is fully operational within PVS, giving PVS practitioners the ability to use dL in the formal specification and verification process. Third, the rich specification language, type system, and powerful interactive prover of PVS can be used on dL objects. In addition to the embedding and verification of dL, a custom extension for Visual Studio Code has been developed, so that a stylized dL syntax can be used to specify hybrid programs and their properties.
Differential Dynamic Logic↗
Embedding Differential Dynamic Logic in PVS
Differential dynamic logic (dL) is a formal framework for specifying and reasoning about hybrid systems, i.e., dynamical systems that exhibit both continuous and discrete behaviors. These kinds of systems arise in many safety- and mission-critical applications. This paper presents a formalization of dL in the Prototype Verification System (PVS) that includes the semantics of hybrid programs and dL’s proof calculus. The formalization embeds dL into the PVS logic, resulting in a version of dL whose proof calculus is not only formally verified, but is also available for the verification of hybrid programs within PVS itself. This embedding, called Plaidypvs (Properly Assured Implementation of dL for Hybrid Program Verification and Specification), supports standard dL style proofs, but further leverages the capabilities of PVS to allow reasoning about entire classes of hybrid programs. The embedding also allows the user to import the well-established definitions and mathematical theories available in PVS.
PVS↗
A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing
Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.
Bayesian Deep Learning↗
16 Weeks of Training with the International Space Station Advanced Resistive Exercise Device (aRED) Is not Different than Training with Free Weights
The advanced Resistive Exercise Device (aRED) is a resistive exercise system designed to maintain muscle mass and strength in microgravity by simulating free weight (FW) exercise. aRED utilizes vacuum cylinders and inertial flywheels to replicate the constant mass and inertial components, respectively, of FW exercise in normal gravity. PURPOSE: To compare the effectiveness of aRED and FW resistive exercise training in ambulatory subjects. METHODS: Untrained subjects were assigned to two groups, FW (6 males, 3 females) and aRED (8 males, 3 females), and performed squat (SQ), heel raise (HR), and deadlift (DL) exercises 3 d wk-1 for 16 wks. SQ, HR and DL strength (1RM) were measured using FW hardware pre-, mid- and post-training. Subjects participated in a periodized training protocol with the exercise prescription based on a percentage of 1RM. Thigh and lower leg muscle volume were assessed using Magnetic Resonance Imaging (MRI), and leg (LLM) and total body lean mass (BLM) were measured using Dual Energy X-ray Absorptiometry (DXA) pre- and post-training. RESULTS: SQ 1RM increased in both FW (48.9+/-6.1%) and aRED (31.2+/-3.8%) groups, and there was a greater training response in FW compared with aRED (p=0.01). HR and DL 1RM increased in FW (HR: 12.3+/-2.4%, DL: 23.3+/-4.4%) and aRED (HR: 18.0+/-1.6%, DL: 23.2+'-2.8%), but there were no differences between groups. Thigh muscle volume was greater following training in both groups (FW: 9.8+/-0.9%, aRED: 7.1+/-1.2%) but lower leg muscle volume increased only in the FW group (3.0+/-1.1%). Lean tissue mass increased in both FW (LLM: 3.9+/-1.1%, BLM: 2.5+/-0.7%) and aRED (LLM: 4.8+/-0.7%, BLM: 2.6 0.7%). There were no between group differences in muscle volume or lean mass in response to training. CONCLUSIONS: In general, the increase in muscle strength, muscle volume, and lean tissue mass when training with aRED was not different than when using the same training protocol with FW. The smaller increase in SQ 1RM in the aRED group may be the result of undersizing the aRED flywheels which were intended to mimic the inertial component of the SQ movement when performing FW exercises. However, the biomechanical differences observed in body position during the performance of the aRED SQ, which may have affected training and testing, cannot be excluded as a factor that may have affected SQ 1RM results. PRACTICAL APPLICATIONS: Improvements in muscle strength, muscle volume and lean mass similar to FW exercise training may be elicited using an alternative source of resistance during exercise training. The acceleration of a mass during resistive exercise may result in greater muscle tension when changing the direction of movement resulting in enhanced strength gains. Therefore, to maximize the benefits of resistive exercise, the inertial components of FW exercise should be considered during exercise selection and hardware design. ACKNOWLEDGEMENT: This investigation was supported by NASA-JSC s Exercise Countermeasures Project.
Loehr, J. A.↗
Muscle Volume Increases Following 16 Weeks of Resistive Exercise Training with the Advanced Resistive Exercise Device (ARED) and Free Weights
Space flight-induced muscle atrophy, particularly in the postural and locomotorymuscles, may impair task performance during long-duration space missions and planetary exploration. High intensity free weight (FW) resistive exercise training has been shown to prevent atrophy during bed rest, a space flight analog. NASA developed the Advanced Resistive Exercise Device (ARED) to simulate the characteristics of FW exercise (i.e. constant mass, inertial force) and to be used as a countermeasure during International Space Station (ISS) missions. PURPOSE: To compare the efficacy of ARED and FW training to induce hypertrophy in specific muscle groups in ambulatory subjects prior to deploying ARED on the ISS. METHODS: Twenty untrained subjects were assigned to either the ARED (8 males, 3 females) or FW (6 males, 3 females) group and participated in a periodizedtraining protocol consisting of squat (SQ), heel raise (HR), and deadlift(DL) exercises 3 d wk-1 for 16 wks. SQ, HR, and DL muscle strength (1RM) was measured before, after 8 wks, and after 16 wks of training to prescribe exercise and measure strength changes. Muscle volume of the vastigroup (V), hamstring group (H), hip adductor group (ADD), medial gastrocnemius(MG), lateral gastrocnemius(LG), and deep posterior muscles including soleus(DP) was measured using MRI pre-and post-training. Consecutive cross-sectional images (8 mm slices with a 2 mm gap) were analyzed and summed. Anatomical references insured that the same muscle sections were analyzed pre-and post-training. Two-way repeated measures ANOVAs (p<0.05) were used to test for differences in muscle strength and volume between training devices. RESULTS: SQ, HR, and DL 1RM increased in both FW (SQ: 49+/-6%, HR: 12+/-2%, DL: 23+/-4%) and ARED (SQ: 31+/-4%, HR: 18+/-2%, DL: 23+/-3%) groups. Both groups increased muscle volume in the V (FW: 13+/-2%, ARED: 10+/-2%), H (FW: 3+/-1%, ARED: 3+/-1 %), ADD (FW: 15=/-2%, ARED: 10+/-1%), LG (FW: 7+/-2%, ARED: 4+/-1%), MG (FW: 7+/-2%, ARED: 5+/-2%), and DP (FW: 2+/-1%; ARED: 2+/-1%) after training. There were no between group differences in muscle strength or volume. CONCLUSIONS: The increase in muscle volume and strength following ARED training is not different than FW training. With the training effects similar to FW and a 600 lb load capacity, ARED likely will protect against muscle atrophy in microgravity.
Nash, R. E.↗
A Temporal Differential Dynamic Logic Formal Embedding
Differential dynamic logic is a formal framework to specify and reason about hybrid programs (HPs). The core of dL is a proof calculus that contains a collection of axioms and rules for the rigorous verification of properties of HPs. Recently, dL has been embedded within the theorem prover Prototype Verification System (PVS) resulting in the tool Plaidypvs2. The integration of dL into PVS expands its expressive power; user defined functions, such as trigonometric and other transcendental functions, can be used inside the dL framework, and meta-reasoning about HPs can be performed, including reasoning about entire classes of HPs, specified using dependent types in PVS. The differential temporal dynamic logic (dTL2) extends dL with temporal logic operators to reason about all the states reachable during the execution of an HP. This paper presents a work in progress focusing on embedding dTL2 in PVS as an extension of Plaidypvs. Plaidypvs is expanded with the formalization of a trace semantics for HPs, the definition of the LTL temporal operators eventually and globally, and the implementation of the proof calculus for dTL2. This new embedding has the same capabilties as Plaidypvs, which allows user defined functions and meta-reasoning of properties of HPs. To the best of the authors’ knowledge this is the first implementation of dTL2.
differential dynamic logic↗
Explainable deep learning for insights in El Niño and river flows
The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.
SST↗
Experimental observations of strong double layers
A computer simulation is applied to the production of strong electric potential double layers (DL) in a triple plasma device. The simulation is intended to represent DL in the low magnetosphere above the auroral zones. The DL are described as standing electrostatic shocks with different energy coefficients in their strong and weak forms. The strong DL was generally found to be unstable, but stability could be imparted if a population of trapped electrons was presented. Stability increased with the length of the system. A schematic for the system is presented, and a phase-space plot of electrons (indicating system stability) is graphed.
Coakley, P.↗
Creep-rupture and fractographic analysis of Stirling engine superalloys tested in air and 15 MPa hydrogen
A brief comparative analytical and microstructural evaluation of creep-rupture performance of two iron-base superalloys in air and 15 MPa of hydrogen, is presented. Creep rupture data are presented for the sheet alloy 19-9DL and the cast alloy XF-818, including temperature, initial stress, rupture life, minimum creep rate, time to reach one percent creep strain, and total elongation. In 19-9DL, both rupture life and minimum creep rate are more sharply dependent on small stress changes than in XF-818 in the given environment, and 19-9DL appears to become a more creep-resistant material with increasing Q (apparent activation energy) while the opposite is noted for XF-818. There appears to be no environmental effect on minimum creep rate for 19-9DL, whereas Q becomes less negative for XF-818 for 15 MPa of H2. Multiple cracks leading to rupture are observed on the fracture surfaces, with sheet specimens showing many more cracks close to the fracture surface than cast specimens.
Bhattacharyya, S.↗
Atmospheric measurements of carbonyl sulfide, dimethyl sulfide, and carbon disulfide using the electron capture sulfur detector
Measurements of atmospheric dimethyl sulfide (DMS), carbonyl sulfide (COS), and carbon disulfide (CS2) were conducted over the Atlantic Ocean on board the NASA Electra aircraft during the Chemical Instrumentation Test and Evaluation (CITE 3) project using the electron capture sulfur detector (ECD-S). The system employed cryogenic preconcentration of air samples, gas chromatographic separation, catalytic fluorination, and electron capture detection. Samples collected for DMS analysis were scrubbed of oxidants with NaOH impregnated glass fiber filters to preconcentration. The detection limits (DL) of the system for COS, DMS, and CS2 were 5, 5, and 2 ppt, respectively. COS concentrations ranged from 404 to 603 ppt with a mean of 489 ppt for measurements over the North Atlantic Ocean (31 deg N to 41 deg N), and from 395 to 437 ppt with a mean of 419 ppt for measurements over the Tropical Atlantic Ocean (11 deg S to 2 deg N). DMS concentrations in the lower marine boundary layer, below 600-m altitude, ranged from below DL to 150 ppt from flights over the North Atlantic, and from 9 to 104 ppt over the Tropical Atlantic. CS2 concentrations ranged from below DL to 29 ppt over the North Atlantic. Almost all CS2 measurements over the Tropical Atlantic were below DL.
Johnson, James E.↗
Building Multi-Discipline, Multi-Format Digital Libraries Using Clusters and Buckets
Our objective was to study the feasibility of extending the Dienst protocol to enable a multi-discipline, multi-format digital library. We implemented two new technologies: cluster functionality and publishing buckets. We have designed a possible implementation of clusters and buckets, and have prototyped some aspects of the resultant digital library. Currently, digital libraries are segregated by the disciplines they serve (computer science, aeronautics, etc.), and by the format of their holdings (reports, software, datasets, etc.). NCSTRL+ is a multi-discipline, multi-format digital library (DL) prototype created to explore the feasibility of the design and implementation issues involved with created a unified, canonical scientific and technical information (STI) DL. NCSTRL+ is based on the Networked Computer Science Technical Report Library (NCSTRL), a World Wide Web (WWW) accessible DL that provides access to over 80 university departments and laboratories. We have extended the Dienst protocol (version 4.1.8), the protocol underlying NCSTRL, to provide the ability to cluster independent collections into a logically centralized DL based upon subject category classification, type of organization, and genre of material. The concept of buckets provides a mechanism for publishing and managing logically linked entities with multiple data formats.
Nelson, Michael L.↗
Robust and Mass Efficient Thermal Protection Systems for Future Venus Missions
Several international Venus missions, both orbiter, and in-situ probe are in the works after a prolonged absence. Future missions are expected to be long-duration lander missions and balloon missions to investigate the cloud layer. The entry mass for these in-situ missions will be significantly higher, and the aeroshell will be larger, than previous missions. Two cardinal requirements govern the selection and use of thermal protection systems (TPS), namely, robustness to assure mission safety during entry and mass efficiency so that the useful mass for science is maximized. One cannot trade mission safety for mass when it comes to TPS. The robustness of the TPS is a paramount requirement as it is a single point of system failure. At the same time, TPS mass is carried for the entire mission duration prior to entry, and any excess TPS mass is at the cost of science payload. Future missions will benefit enormously from TPS options with mass and performance benefits far beyond advanced carbon-carbon (ACC) that is currently proposed for the DAVINCI mission. NASA STMD, and SMD jointly invested in the development of 3-D woven thermal protection systems in the last decade, and as a result, the Heat-shield for Extreme Entry Environment Technology (HEEET) TPS has been matured to TRL 6. It is ready for future mission use including Venus. The HEEET project focused on developing a broad technology base, applicable for missions not only to Venus, but also to Saturn, the Ice Giants, and higher speed sample return missions such as Mars Sample Return requiring extreme robustness due to bio-hazards. The dual-layer HEEET (DL-HEEET) is proven to be extremely robust. DL-HEEET performed well in arc jet and laser testing conditions where heritage carbon-phenolic failed. HEEET also proved to be more mass efficient compared to Carbon-Phenolic. Recently, the principal author was invited to present his perspectives to the 2023-2032 Planetary Sciences Decadal Venus subcommittee on the current state of TPS for future Venus missions [1]. In addition to pointing out successful TPS and other developments in the last decade in support of entry systems, the principal author made two findings: 1) importance of and need to sustain TPS capabilities that took nearly a decade to develop and 2) the opportunity to further optimize TPS mass without sacrificing robustness to further enable future in-situ missions. This talk will focus on the above two recommendations and provide the rationale for them. The intent of the talk is to seek advocacy from the VEXAG community for the criticality of TPS sustainment and the benefits of 3D woven TPS optimization. TPS Capability Sustainment: HEEET development was necessitated by the atrophy of heritage carbon-phenolic. Atrophy has impacted other TPS materials as well. Avcoat, the TPS that successfully allowed astronauts to explore the Moon in the 1960s and 70s, took nearly a decade and $10’sM to recover in support of Orion/Artemis. One of the most used TPS materials, SLA 561V, had to be recovered prior to its use on Mars Pathfinder. Phenolic Impregnated Carbon Ablator (PICA), the TPS that replaced SLA 561V on MSL, has undergone multiple replacements and recovery due to constituent rayon changes. Recently, NASA invested in a domestic rayon replacement program and also invested in FMI to consolidate PICA capability for NASA missions, as a result of FMI’s decision to discontinue commercial FiberForm, which is needed for PICA. TPS used for planetary missions are unique and have no other commercial or defense use. In addition, low mission cadence is also a driver for TPS atrophy. Hence, NASA, as the steward, must take steps to ensure and sustain TPS capabilities. In this talk, we outline steps NASA can take to keep abreast of emerging risks and target risk mitigation steps to ensure TPS capability sustainment for Venus and other extreme environment missions. Next Generation of Mass efficient and Robust TPS: NASA invested in an alternate TPS to PICA based on felt-technology called Conformal-PICA which has the potential to save 30% - 50% mass over PICA. The development was discontinued at a Technology Readiness Level (TRL) of ~ 5. 3D Mid-Density Carbon Phenolic (3MDCP) is a single layer variant of HEEET, based on the insulating layer only (SL-HEEET) and currently baselined for the Mars Sample Return Mission Earth Entry System due to its mass efficiency (30% more mass efficient than DL- HEEET). Currently, SL-HEEET is limited to aeroshell diameters of < 1.3m. The SL-HEEET was compared to DL-HEEET in the recent ADVENT flag-ship class mission study in support of the Planetary Science Decadal. SL HEEET was the recommended TPS based on 30% mass savings for both the balloon and lander missions. Given C-PICA and SL-HEEET have superior reliability and mass efficiency, advocacy from VEXAG is sought for completing their further development to TRL 5/6 in this decade so as to enable TPS readiness for future missions.
Thermal Protection Systems↗
The Application of Artificial Intelligence Deep Learning to Visually Identify Micrometeoroid and Orbital Debris Impacts
Recent advances in Artificial Intelligence (AI) are changing the World. Novel approaches to training AI systems have led to dramatic reductions in the amount of time required. Training an AI system could take years and teams of people using traditional methods, but with the advancements of Deep Learning (DL) models this training can now be accomplished by an individual in a matter of minutes. The development of “fast AI” libraries has delivered AI to essentially everyone. Democratization of AI power has inspired many to revisit past problems that will benefit from DL approaches. For example, the application of AI has improved detection of breast cancer by 20% compared to traditional detection methods. Computer vision and machine learning are being used to identify soil deficiencies and provide planting recommendations to farmers. Success stories like these and many others have provided inspiration to see if AI can help improve one of our needed capabilities – that of visually identifying micrometeoroid and orbital debris (MMOD) impact damage to spacecraft from images of the spacecraft exterior. The need to visually locate and characterize spacecraft MMOD impact damage has been present since the early days of space travel. This is often done by either having a crew member take photographs of the spacecraft through a window using a hand-held camera or ground personnel directing externally-mounted cameras. The photographs are then transmitted back to Earth for visual analysis. This method of MMOD damage inspection works well and has been used on various spacecraft including the Space Shuttle and the International Space Station (ISS). One of the issues with the current method that we believe AI could improve is the speed and possibly the accuracy in identifying MMOD impacts. Note that detecting MMOD impacts in images can be very difficult. The visual appearance of an MMOD impact can change dramatically with lighting conditions, size of impact, depth of penetration, material types, surface waviness, fabric coverings, camera & lens, distance to surface, spacecraft orientation, analyst experience, and many other factors. Currently, this takes a team of highly-experienced specialists in both the fields of Image Analysis and MMOD impacts. This paper documents our initial research in training an AI DL model using the fast-AI library to identify actual and simulated MMOD impacts and perforations into exposed flat surfaces. While we recognize that this initial goal seems modest, it must be noted that what we have done would have taken teams of individuals and years of training just ten years ago. Our long-term goal is to add complexity and use-cases to the DL model being trained to expand the capabilities of this model so that it can be used to identify MMOD impacts on all types of spacecraft surfaces.
Cameron M Collins↗
Advancement of Deep Learning and Geometric Methods for Active Terrain Relative Navigation
To enhance NASA’s precision landing capabilities, in conjunction with the development of a novel active terrain relative navigation (ATRN) and terrain mapping system, denoted SHERIF, this work performed a comparative analysis between both deep-learning (DL) based and geometric approaches to hazard detection (HD) and safe-site-identification (SSI) through hardware-in-the loop testing on the Six degree-of-freedom Tendon Actuated Robot (STAR). The Standalone Hazard Evaluation and Refinement using Instrument Findings (SHERIF) system is capable of ingesting sensor data at an asynchronous rate, stitching successive terrain scans together to yield a high-resolution digital elevation map (DEM), performing absolute and relative localization using novel 3D feature extraction and matching methods, and HD/SSI activities. The DL-based HD/SSI algorithm provides a modular alternative to classical geometric approaches which have performance times that scale with map resolution. As the adoption of AI solutions become more prevalent for autonomous system decision making, it is prudent to explore the utility of such solutions in applications where they traditionally excel, such as image classification. Along with the development of a DL-based HD system, this work performed the first comparative analysis between DL and geometric approaches to HD/SSI using real sensor data from real-time testing in a relevant environment.
Davis Adams↗
Cyanamide mediated synthesis under plausible primitive earth conditions. VI - The synthesis of glycerol and glycerophosphates
The formation of glycerol occurs when a solution of DL-glyceraldehyde is heated in the presence of hydrogen sulfide at room temperature. DL-glyceraldehyde and dihydroxyacetone treated with hydrazine, as well as DL-glyceraldehyde incubated with formaldehyde are also partially converted to glycerol. The yields of the above reactions are from approximately 1% to about 3%. The formation of glycerophosphates occurs when glycerol is heated with ammonium dihydrogen phosphate and either urea or cyanamide. The yield of glycerophosphates is about 30%, most of which is sn-glycero-1(3)-phosphate. These findings indicate that glycerol and sn-glycero-3-phosphate, which are moieties of glycerolipids, could have been formed under conditions which may have prevailed on the primitive earth.
Epps, D. E.↗