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This article focuses on the properties of a fine grained reconfigurable transistor array currently under test at the Jet Propulsion Laboratory (JPL).
Abstract The Stiff Stalk heterotic pool is a foundation of US maize seed parent germplasm and has been heavily utilized by both public and private maize breeders since its inception in the 1930s. Flowering time and plant height are critical characteristics for both inbred parents and their test crossed hybrid progeny. To study these traits, a 6-parent multiparent advanced generation intercross population was developed including maize inbred lines B73, B84, PHB47 (B37 type), LH145 (B14 type), PHJ40 (novel early Stiff Stalk), and NKH8431 (B73/B14 type). A set of 779 doubled haploid lines were evaluated for flowering time and plant height in 2 field replicates in 2016 and 2017, and a subset of 689 and 561 doubled haploid lines were crossed to 2 testers, respectively, and evaluated as hybrids in 2 locations in 2018 and 2019 using an incomplete block design. Markers were derived from a practical haplotype graph built from the founder whole genome assemblies and genotype-by-sequencing and exome capture-based sequencing of the population. Genetic mapping utilizing an update to R/qtl2 revealed differing profiles of significant loci for both traits between 635 of the DH lines and 2 sets of 570 and 471 derived hybrids. Genomic prediction was used to test the feasibility of predicting hybrid phenotypes based on the per se data. Predictive abilities were highest on direct models trained using the data they would predict (0.55–0.63), and indirect models trained using per se data to predict hybrid traits had slightly lower predictive abilities (0.49–0.55). Overall, this finding is consistent with the overlapping and nonoverlapping significant quantitative trait loci found within the per se and hybrid populations and suggests that selections for phenology traits can be made effectively on doubled haploid lines before hybrid data is available.
SUMMARY Single‐parent expression (SPE) is defined as gene expression in only one of the two parents. SPE can arise from differential expression between parental alleles, termed non‐presence/absence (non‐PAV) SPE, or from the physical absence of a gene in one parent, termed PAV SPE. We used transcriptome data of diverse Zea mays (maize) inbreds and hybrids, including 401 samples from five different tissues, to test for differences between these types of SPE genes. Although commonly observed, SPE is highly genotype and tissue specific. A positive correlation was observed between the genetic distance of the two inbred parents and the number of SPE genes identified. Regulatory analysis showed that PAV SPE and non‐PAV SPE genes are mainly regulated by cis effects, with a small fraction under trans regulation. Polymorphic transposable element insertions in promoter sequences contributed to the high level of cis regulation for PAV SPE and non‐PAV SPE genes. PAV SPE genes were more frequently expressed in hybrids than non‐PAV SPE genes. The expression of parentally silent alleles in hybrids of non‐PAV SPE genes was relatively rare but occurred in most hybrids. Non‐PAV SPE genes with expression of the silent allele in hybrids are more likely to exhibit above high parent expression level than hybrids that do not express the silent allele, leading to non‐additive expression. This study provides a comprehensive understanding of the nature of non‐PAV SPE and PAV SPE genes and their roles in gene expression complementation in maize hybrids.
Genetic Algorithms meets Deep RL for Hyperparameters Hyperparameter optimization and architecture search can easily become cumbersome and finding the right hyperparameters can seriously impact the robustness of the deep RL application being developed. We use genetic algorithms to evolve optimum deep RL architectures in a scalable manner. HPS-RL is designed to work with multiple gym enviornments, allow users to test their own optimization functions and tune multi-objective parameters in multiple deep RL algorithms. HPS-RL uses multi-threading and is being extended with mpipy for distributed processing on HPC. https://arxiv.org/abs/2201.11182
Genetic algorithms (GAs) are finding increased application in difficult search, optimization, and machine learning problems in science and engineering. Increasing demands are being placed on algorithm performance, and the remaining challenges of genetic algorithm theory and practice are becoming increasingly unavoidable. Perhaps the most difficult of these challenges is the so-called linkage problem. Messy GAs were created to overcome the linkage problem of simple genetic algorithms by combining variable-length strings, gene expression, messy operators, and a nonhomogeneous phasing of evolutionary processing. Results on a number of difficult deceptive test functions are encouraging with the mGA always finding global optima in a polynomial number of function evaluations. Theoretical and empirical studies are continuing, and a first version of a messy GA is ready for testing by others. A Common LISP implementation called mGA1.0 is documented and related to the basic principles and operators developed by Goldberg et. al. (1989, 1990). Although the code was prepared with care, it is not a general-purpose code, only a research version. Important data structures and global variations are described. Thereafter brief function descriptions are given, and sample input data are presented together with sample program output. A source listing with comments is also included.
We present an evolved X-band antenna design and flight prototype currently on schedule to be deployed on NASA s Space Technology 5 spacecraft in 2004. The mission consists of three small satellites that wall take science measurements in Earth s magnetosphere. The antenna was evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly-polarized wave and wide bandwidth. Two genetic algorithms were used: one allowed branching an the antenna arms and the other did not. The highest performance antennas from both algorithms were fabricated and tested. A handdesigned antenna was produced by the contractor responsible for the design and build of the mission antennas. The hand-designed antenna is a quadrifilar helix, and we present performance data for comparison to the evolved antennas. As of this writing, one of our evolved antenna prototypes is undergoing flight qualification testing. If successful, the resulting antenna would represent the first evolved hardware in space, and the first deployed evolved antenna.
The different advances in the Micro Ecological Life Support System Alternative project (MELISSA), fostered and coordinated by the European Space Agency, as well as in other associated technologies, are integrated and demonstrated in the MELISSA Pilot Plant laboratory. During the first period of operation, the definition of the different compartments at an individual basis has been achieved, and the complete facility is being re-designed to face a new period of integration of all these compartments. The final objective is to demonstrate the potentiality of biological systems such as MELISSA as life support systems. The facility will also serve as a test bed to study the robustness and stability of the continuous operation of a complex biological system. This includes testing of the associated instrumentation and control for a safe operation, characterization of the chemical and microbial safety of the system, as well as tracking the genetic stability of the microbial strains used. The new period is envisaged as a contribution to the further development of more complete biological life support systems for long-term manned missions, that should be better defined from the knowledge to be gained from this integration phase. This contribution summarizes the current status of the Pilot Plant and the planned steps for the new period. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.
Evolutionary methods are powerful tools in discovering solutions for difficult continuous tasks. When such a solution is encoded over multiple genes, a genetic algorithm faces the difficult credit assignment problem of evaluating how a single gene in a chromosome contributes to the full solution. Typically a single evaluation function is used for the entire chromosome, implicitly giving each gene in the chromosome the same evaluation. This method is inefficient because a gene will get credit for the contribution of all the other genes as well. Accurately measuring the fitness of individual genes in such a large search space requires many trials. This paper instead proposes turning this single complex search problem into a multi-agent search problem, where each agent has the simpler task of discovering a suitable gene. Gene-specific evaluation functions can then be created that have better theoretical properties than a single evaluation function over all genes. This method is tested in the difficult double-pole balancing problem, showing that agents using gene-specific evaluation functions can create a successful control policy in 20 percent fewer trials than the best existing genetic algorithms. The method is extended to more distributed problems, achieving 95 percent performance gains over tradition methods in the multi-rover domain.
Radiation and reduced gravity pose biological risks to crewed deep space exploration. At the cellular level, radiation damage can be amplified by reduced gravity. Empirical evidence on cellular responses to beyond low Earth orbit (BLEO) environments is imperative to develop effective countermeasures for crew health and in-space biomanufacturing. The Lunar Explorer Instrument for Space Biology Applications (LEIA) project is developing an instrument suite to be delivered to the south polar region of the Moon by the Commercial Lunar Payload Services (CLPS) program. This presentation will provide an overview of the LEIA hardware, experiments, and mission timeline. The LEIA instruments include the BioSensor, the ARES charged particle detector, and the Mini-FND. The BioSensor is an autonomous light emitting diode (LED)-based spectrophotometer and microfluidic incubator. The BioSensor activates yeast cultures and can measure cell growth, metabolic activity, and carotenoid production. The ARES is a Timepix-based charged particle radiation detector that measures dose, dose rate, and linear energy transfer spectra. The Mini-FND is a fast neutron detector that measures albedo neutron flux and energy spectra. Combined, these instruments will be used for yeast genetics experiments to quantify growth, metabolism, and synthetic biology-enabled production of human nutrients, while taking real time measurements of biologically relevant radiation exposure on the lunar surface. These data will be used to test the importance of selected DNA damage repair and reactive oxygen species defense pathways in mitigating cellular damage from lunar surface radiation.
GMO dwarf plum (Prunus domestica) is being evaluated as a candidate food crop for long duration space flight missions. A project was undertaken to develop a protocol for transferring selected genetic lines of GMO plum (previously maintained in pots and propagated by cuttings at NASA's Kennedy Space Center in Florida) into in vitro tissue culture. In vitro culture may reduce the space, materials, and labor required to maintain the current lines of GMO plum and better preserve them for future study. Fresh plant material from three selected GMO plum lines (NASA-5, NASA-10, and NASA-11) and a non-modified control line (Control-5) were processed aseptically into in vitro culture on four separate occasions. The impact of multiple treatments on the successful growth of GMO plum tissue in vitro were tested: Parent explant tissue type (leaf petioles, stem nodes containing buds and internodes without buds), tissue sterilization method [soaking in 10 bleach only (5 min for petioles or 10 min for nodesinternodes), or soaking in 70 EtOH (30 sec) followed by 10 bleach (5 min for petioles and 10 min for nodesinternodes)], and media type [three Murashige and Skoog-based medias (SGM, SRM, and SRM+2,4-D) and one recipe containing woody plant media (WPM)]. 22.2 of the plates containing tissue sterilized with bleach alone developed microbial contamination after two weeks, while only 11.8 of plates containing tissue sterilized sequentially with EtOH and bleach developed contamination. Node bud tissue from all four genetic lines of plum produced leafy plantlets on SGM and SRM media after 4-6 weeks. The most numerous and well-developed plantlets were present on SGM. Upon reaching suitable size, plantlets were transferred to larger media containers for further growth. Some node bud growth occurred on SRM+2,4-D and WPM 2.5 weeks after plating, however as of yet no pieces on SRM+2,4-D have adequate development for transferring. Tissue pieces from NASA-5 plated on WPM are developing leaves and will be ready for transferring soon. Petioles and internode tissue lacking bud meristem failed to produce any plantlets on any plates, however petioles developed large masses of undifferentiated callus tissue on SRM+2,4-D media. These callused pieces were then transferred to SRM+TDZ media, which resulted in even larger callus growth but no differentiation. All four selected plum lines were successfully transitioned into in vitro culture. Nodes from NASA-5 and NASA-10 lines produced the most numerous and well-developed leafy plantlets in vitro, while those from NASA-11 and Control-5 were generally smaller, slower growing and less numerous. The best method overall was to use young stem node tissue with buds, surface sterilize the pieces sequentially with 70 EtOH and 10 bleach, and then plate them onto SGM media. Future areas of study will include introducing additional genetic lines of GMO plum into in vitro culture, attempting to induce shoot growth in petiole callus tissue, testing methods (such as cold storage) that extend the time interval between transferring explants into new media, and testing viability of plantlets transferred from in vitro culture back to traditional pot culture.
Radiation and reduced gravity pose biological risks to crewed deep space exploration. At the cellular level, radiation damage can be amplified by reduced gravity. Empirical evidence on cellular responses to beyond low Earth orbit (BLEO) environments is imperative to develop effective countermeasures for crew health and in-space biomanufacturing. The Lunar Explorer Instrument for Space Biology Applications (LEIA) project is developing an instrument suite to be delivered to the south polar region of the Moon by the Commercial Lunar Payload Services (CLPS) program. This presentation will provide an overview of the LEIA hardware, experiments, and mission timeline. The LEIA instruments include the BioSensor, the ARES charged particle detector, and the Mini-FND. The BioSensor is an autonomous light emitting diode (LED)-based spectrophotometer and microfluidic incubator. The BioSensor activates yeast cultures and can measure cell growth, metabolic activity, and carotenoid production. The ARES is a Timepix-based charged particle radiation detector that measures dose, dose rate, and linear energy transfer spectra. The Mini-FND is a fast neutron detector that measures albedo neutron flux and energy spectra. Combined, these instruments will be used for yeast genetics experiments to quantify growth, metabolism, and synthetic biology-enabled production of human nutrients, while taking real time measurements of biologically relevant radiation exposure on the lunar surface. These data will be used to test the importance of selected DNA damage repair and reactive oxygen species defense pathways in mitigating cellular damage from lunar surface radiation.
The goal of the 3P Program was to establish integrated, real-time phenotyping and to analyze above- and below-ground plant architecture and total carbon partitioning and allocation to predict heterosis and develop superior crop hybrids by fully leveraging the Sorghum gene pool. There were two overarching themes: 1) the development of a new crop improvement approach utilizing advances in high-throughput phenotyping (HTP), computing, and genomics for public dissemination and 2) leveraging this platform for sorghum crop improvement and commercialization. The Clemson team worked on creating genomic resources and using both statistical learning and high-throughput phenotyping in genomics-assisted breeding. Research was broadly interested in the genetics of carbon partitioning, with the aim of improving crop performance and achieving sustainability. The technology and resources created can be readily found in the public domain and serve to advance scientific understanding of crop genomics and breeding. Genomic prediction was able to identify top crosses to be made, and a hybrid prediction pipeline is in place to drive year-over-year genetic gain. Roots have long been ignored by plant breeders and agronomists, not because they are unimportant but because they are hard to measure. This is an untapped white space of potential insight and innovation. To address this, Hi Fidelity Genetics developed the RootTracker to measure roots in the field on a continuous basis. A database system called RootTracker Tracker was developed to handle data coming from the RootTrackers. In using this device, valuable data was observed for plant breeding, hydrochemical development, and other agricultural biology applications. Carnegie Mellon’s goal was developing new techniques to generate high-resolution 3D models of plants from data collected in the field. The idea was that more useful and more informative phenotypes could be extracted by resolving small features, such as seeds and flowers, and that by modeling in 3D, the spatial structure of plants could be examined. To achieve this, multiple images collected by a new small format structured light stereo imager were fused together. A sorghum panicle modeling pipeline was developed to allow the collection and processing of data. Carolina Seed Systems is an agricultural technology company focused on decarbonizing the agricultural system. Their technology pipeline serves to drive fundamental progress towards creation and distribution of carbon negative crops. The genomic and the engineering technology developed through the 3P Program was leveraged to deliver both value and sustainability from the grower to the consumer. Promising sorghum hybrids were scaled up and commercialized. The overall goal of our research was to integrate, create, and deploy genetic and engineering concepts and technologies to enhance crop productivity in a sustainable fashion. The combination of public and private partners allowed the basic research and hypothesis testing to be quickly accelerated for commercial application by the companies yet maintained that the core framework and academic insights remain in the public domain for continued market disruption, competition, and innovation.
This grant aimed to accelerate the development of specialized enzymes—biological catalysts essential for sustainable manufacturing and medicine—by integrating traditional laboratory evolution with cutting-edge artificial intelligence. To achieve this, we developed a suite of high-throughput sequencing tools and a centralized database to bridge the gap between a protein’s genetic "code" and its physical function. By training machine learning models on large datasets, we also demonstrated the ability to move beyond slow, trial-and-error testing to a "generative" approach, where AI can independently design new, versatile enzymes like tryptophan synthases. Ultimately, these findings demonstrate that combining laboratory data with computer-guided design enables the engineering of highly efficient biological tools with unprecedented speed and precision.
The figure is a schematic diagram of a complementary metal oxide/semiconductor (CMOS) electronic circuit that performs one of three different logic functions, depending on the level of an externally applied control voltage, V(sub sel). Specifically, the circuit acts as A NAND gate at V(sub sel) = 0.0 V, A wire (the output equals one of the inputs) at V(sub sel) = 1.0 V, or An AND gate at V(sub sel) = -1.8 V. [The nominal power-supply potential (VDD) and logic "1" potential of this circuit is 1.8 V.] Like other multifunctional circuits described in several prior NASA Tech Briefs articles, this circuit was synthesized following an automated evolutionary approach that is so named because it is modeled partly after the repetitive trial-and-error process of biological evolution. An evolved circuit can be tested by computational simulation and/or tested in real hardware, and the results of the test can provide guidance for refining the design through further iteration. The evolutionary synthesis of electronic circuits can now be implemented by means of a software package Genetic Algorithms for Circuit Synthesis (GACS) that was developed specifically for this purpose. GACS was used to synthesize the present trifunctional circuit. As in the cases of other multifunctional circuits described in several prior NASA Tech Briefs articles, the multiple functionality of this circuit, the use of a single control voltage to select the function, and the automated evolutionary approach to synthesis all contribute synergistically to a combination of features that are potentially advantageous for the further development of robust, multiple-function logic circuits, including, especially, field-programmable gate arrays (FPGAs). These advantages include the following: This circuit contains only 9 transistors about half the number of transistors that would be needed to obtain equivalent NAND/wire/AND functionality by use of components from a standard digital design library. If multifunctional gates like this circuit were used in the place of the configurable logic blocks of present commercial FPGAs, it would be possible to change the functions of the resulting digital systems within shorter times. For example, by changing a single control voltage, one could change the function of thousands of FPGA cells within nanoseconds. In contrast, typically, the reconfiguration in a conventional FPGA by use of bits downloaded from look-up tables via a digital bus takes microseconds.
The present paper details the design of the counter rotating fans for a Turboelectric Distributed Propulsion (TeDP) system. Sixteen propulsors installed in mail-slot-shape nacelles are embedded on an aerodynamically optimized hybrid wing-body configuration. The hybrid-wing/body (HWB) configuration which was previously designed to satisfy the conditions of trim, longitudinally static stability and specific cargo space is employed as the baseline configuration in pursuing an optimal distributed propulsion system. A set of distributed propulsors is conceptually designed and the collective performance is evaluated against the target thrust mandated by the mission requirements. The concept of the distributed propulsion allows the fan pressure ratio to be around 1.27~1.32 for the target thrust. In addition, further splitting of the fan pressure ratio by using the counter-rotating fans for each slot realizes the target pressure ratio with low tip speed. In the distributed propulsion system, the nature of the flow conditions and/or the thickness of the ingested boundary layer may differ and result in different propulsive reaction of each individual propulsor. The optimization is, thus, approached from both the propulsion system and individual propulsor perspectives. An optimal distribution of the thrust and power output is determined by how the system utilizes each passage's propulsive characteristics and its interaction with the airframe. These system level analysis and optimization are conducted using an actuator disk model to account for the propulsion-airframe integration numerically. With respect to the propulsor level, aerodynamic shape optimizations of the fan blades are performed in a sequential multi-objective optimization process for various design objectives, such as mass flow rate condition, fan pressure ratio, efficiency and the exit flow angle of the fan stage by using a genetic algorithm, NSGA-II. The radial chord distribution, and meanline distribution of the rotors are designed on the circumferentially averaged axi-symmetric inlet profiles and tested on the six inlet profiles from six divided sectors to reckon flow distortion. The performances of the counter rotating fans are, thus, evaluated accordingly for obtaining distortion tolerant fan. The performance of the distributed propulsion system is evaluated by two CFD tools, i.e., a multi-stage turbo-machinery CFD code and one propulsion-airframe integration flow solver coupled with a body-force model. The optimized boundary layer ingestion propulsion system of 16 distributed slots not only reaches the system target thrust, but also delivers a close to 20% fuel saving benefit against its counterpart 12 distributed clean inlet propulsion system.
Increasing interest in novel aircraft propulsion systems and potential for unwanted heat generation, or capture and reuse of waste heat, may require increasingly lightweight and high performing heat exchangers. Advances in manufacturing technologies have shown potential to create complex designs, but design tools need more flexibility. This study utilizes genetic algorithm-driven topology optimization to develop high performance heat exchanger fins for critical applications such as aerospace. The solid domain is generated using voxel representation where a voxel value of 1 indicates the solid domain and a voxel value of 0 refers to the fluid domain. The use of voxel representation somewhat matches the digitization of a model that is required to fabricate using additive manufacturing, and also allows for a highly unconstrained geometry. To test the topology optimization approach, a three-dimensional(3D)baseline offset strip fin geometry in steady laminar flow(Reynolds number = 215)with conjugate heat transfer(simultaneous solution of solid and fluid temperature fields)is optimized. New designs are generated using the genetic algorithm (GA) and sent to evaluation by the CFD software OpenFOAM; then the GA sorts and selects the reproduction pool for the following generation. This process is repeated for 60 generations. The study also investigates the effect off in material on the performance of the GA and the resulting designs. The results show that the optimal designs have overall performance improvement of 18% relative to the baseline. Additionally, a fin constructed of a lower conductivity material (such as an Inconel super alloy that might be necessary for waste heat recovery applications)results in lower overall performance improvement (11%)and optimal designs with higher pressure drop relative to their baseline, and relative to optimal designs produced using higher conductivity materials.
In this manuscript we have examined an extension of BCB that encompasses a mix of continuous and quasi-discrete, as well as truly-discrete applications. FVe began by testing two refinements to the discrete version of BCB. The testing of midpoint versus fitness (Tables 1 and 2) proved inconclusive. The testing of discrete normal tails versus standard mutation showed was conclusive and demonstrated that the discrete normal tails are better. Next, we implemented these refinements in a combined continuous and discrete BCB and compared the performance of two discrete distance on the hub problem. Here we found when "order does matter" it pays to take it into account.
Active distribution grids can experience voltage fluctuations and violations due to the high penetration of variable distributed energy resources (DERs). These problems might occur because of the uncertain and variable generation natures of these resources, especially solar photovoltaic resources, during panel shadowing scenarios. Volt-VAR control (VVC) is an efficient method that controls the reactive power set-points of the inverters to regulate the voltage of distribution grids. Although several VVC approaches have been proposed recently, the performance of these approaches degrades significantly if behind-the-meter solar generation data are unobservable/missing. Therefore, it is necessary to impute missing/unobservable PV data accurately to be utilized in VVC approaches. Further, this paper proposes a model-free, data-driven, centrally trained, and decentrally executed multi-agent deep reinforcement learning-based VVC architecture to regulate the voltage of distribution networks. A generative adversarial network (GAN) is incorporated to impute the unobservable PV data accurately, which improves the performance of the proposed control architecture. The proposed multi-agent-soft-actor–critic algorithm (MASAC)-based VVC technique utilizes the actual PV dataset as well as the imputed dataset from the GAN framework to learn the optimal coordinated control policy for controlling the optimal reactive power set-points of PV inverters. The effectiveness of the proposed approach is analyzed on a modified IEEE 34-bus test case with added PV inverters. The results are compared and analyzed with a base case model with no VVC and VVC with a local droop control approach, genetic algorithm optimization, and a centralized soft actor–critic-based approach. Moreover, the performance of the proposed approach is compared with that of a multi-agent VVC framework without using the PV generation data and load information as the system state. The results illustrate that the proposed method with more state input improves the voltage profile and reduces the power loss of the network across various loading and PV generation scenarios.