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Genetic Algorithms that are used for learning as one way to control the combinational explosion associated with the generation of new rules are discussed. The Genetic Algorithm approach tends to work best when it can be applied to a domain independent knowledge representation. Applications to real time control in space systems are discussed.
Iterative error correction of asymptotically large associative memories is equivalent to a one-step learning rule. This rule is the inverse of the activation function of the memory. Spectral representations of nonlinear activation functions are used to obtain the inverse in closed form for Sparse Distributed Memory, Selected-Coordinate Design, and Radial Basis Functions.
In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial instruction presented here has two parts. First, a computational model of an intelligent agent, the problem space computational model, indicates the types of knowledge that determine an agent's performance, and thus, that should be acquirable via instruction. Second, a learning technique, called situated explanation specifies how the agent learns general knowledge from instruction. The theory is embodied by an implemented agent, Instructo-Soar, built within the Soar architecture. Instructo-Soar is able to learn hierarchies of completely new tasks, to extend task knowledge to apply in new situations, and in fact to acquire every type of knowledge it uses during task performance - control knowledge, knowledge of operators' effects, state inferences, etc. - from interactive natural language instructions. This variety of learning occurs by applying the situated explanation technique to a variety of instructional interactions involving a variety of types of instructions (commands, statements, conditionals, etc.). By taking seriously the requirements of flexible tutorial instruction, Instructo-Soar demonstrates a breadth of interaction and learning capabilities that goes beyond previous instructable systems, such as learning apprentice systems. Instructo-Soar's techniques could form the basis for future 'instructable technologies' that come equipped with basic capabilities, and can be taught by novice users to perform any number of desired tasks.
There are numerous problems associated with accommodating complex biological systems in microgravity in the flexible laboratory systems installed in the Orbiter cargo bay. This presentation will focus upon some of the lessons learned along the way from the University laboratory to the IML-1 Microgravity Laboratory. The First International Microgravity Laboratory (IML-1) mission contained a large number of specimens, including: 72 million nematodes, US-1; 3 billion yeast cells, US-2; 32 million mouse limb-bud cells, US-3; and 540 oat seeds (96 planted), FOTRAN. All five of the experiments had to undergo significant redevelopment effort in order to allow the investigator's ideas and objectives to be accommodated within the constraints of the IML-1 mission. Each of these experiments were proposed as unique entities rather than part of the mission, and many procedures had to be modified from the laboratory practice to meet IML-1 constraints. After a proposal is accepted by NASA for definition, an interactive process is begun between the Principal Investigator and the developer to ensure a maximum science return. The success of the five SLSPO-managed experiments was the result of successful completion of all preflight biological testing and hardware verification finalized at the KSC Life Sciences Support Facility housed in Hangar L. The ESTEC Biorack facility housed three U.S. experiments (US-1, US-2, and US-3). The U.S. Gravitational Plant Physiology Facility housed GTHRES and FOTRAN. The IML-1 mission (launched from KSC on 22 Jan. 1992, and landed at Dryden Flight Research Facility on 30 Jan. 1992) was an outstanding success--close to 100 percent of the prelaunch anticipated science return was achieved and, in some cases, greater than 100 percent was achieved (because of an extra mission day).
Here, this work presents an efficient approach for accelerating multilevel Markov Chain Monte Carlo (MCMC) sampling for large-scale problems using low-fidelity machine learning models. While conventional techniques for large-scale Bayesian inference often substitute computationally expensive high-fidelity models with machine learning models, thereby introducing approximation errors, our approach offers a computationally efficient alternative by augmenting high-fidelity models with low-fidelity ones within a hierarchical framework. The multilevel approach utilizes the low-fidelity machine learning model (MLM) for inexpensive evaluation of proposed samples thereby improving the acceptance of samples by the high-fidelity model. The hierarchy in our multilevel algorithm is derived from geometric multigrid hierarchy. We utilize an MLM to accelerate the coarse level sampling. Training machine learning model for the coarsest level significantly reduces the computational cost associated with generating training data and training the model. We present an MCMC algorithm to accelerate the coarsest level sampling using MLM and account for the approximation error introduced. We provide theoretical proofs of detailed balance and demonstrate that our multilevel approach constitutes a consistent MCMC algorithm. Additionally, we derive the expression for cost reduction due to machine learning model to facilitate cost analysis of the hierarchical sampling algorithm. Our technique is demonstrated on a standard benchmark inference problem in groundwater flow, where we estimate the probability density of a quantity of interest using a four-level MCMC algorithm. Our proposed algorithm accelerates multilevel sampling by a factor of two while achieving similar accuracy compared to sampling using the standard multilevel algorithm.
INTRODUCTION: Spaceflight Associated Neuro-ocular Syndrome (SANS) was first described in 2011 as a clinical process involving degradation of visual acuity and structural changes of the eye when exposed to microgravity. This syndrome poses a risk to astronaut vision, safety, and mission performance, especially during extended-duration missions. The modern astronaut cohort is a diverse group of men and women including scientists, engineers, teachers, pilots, business professionals, and artists. Given their diverse backgrounds, it is imperative all crews are educated on SANS risks and potential countermeasures. This education must be comprehensive and easily understood, regardless of academic background or specific expertise. TOPIC: A PowerPoint was created to serve as a presentation and stand-alone educational aid for all astronaut candidates (ASCANS). This medium was chosen as it can be updated dynamically as new information emerges. The brief was split into sections to facilitate organized delivery of complex content in easy-to-understand components. Sections included descriptive statistics, signs and symptoms, outcomes, myths and ongoing research, countermeasures, ongoing surveillance, and unknown and future directions. More research is necessary in order to understand this syndrome, and without continued ASCAN participation in research, acquisition of data will not be possible. These efforts will become increasingly critical with deep space and exploration missions. APPLICATION: We recommend regular use of this brief for all ASCANS, and potentially veteran crewmembers, given the high risks and potentially poor outcomes associated with SANS. For ASCANS, recommendations include that this brief be provided at the start of their training, with a bi-annual refresher course for all astronauts. This ensures they are current on SANS information, given the dynamic nature of this syndrome. Additionally, this brief will be made available to all as a self-study aid, utilized as a refresher immediately prior to launch. It is anticipated that this brief will promote the health, safety, and wellbeing of professional and private crews alike, during and after long-duration spaceflight. RESOURCES: Brunstetter, T., Tarver, B., & Tsung, A. (2022, October). Spaceflight Associated Neuro-ocular Syndrome (Sans) and its Risk to Nasa Astronauts. JSC Aerospace Medicine Clerkship. Houston, TX; Lyndon B. Johnson Space Center. Laurie, S. S., Macias, B. R., Pardon, L. P., Brunstetter, T., Tarver, W. J., Gibson, C. R., Greenwald, S. H., Jasien, K. V., Mason, S., & Tsung, A. (2022). (rep.). Evidence Report: Risk of Spaceflight Associated Neuro-ocular Syndrome (SANS). Houston, TX: National Aeronautics and Space Administration. Mader, T. H., Gibson, C. R., Pass, A. F., Kramer, L. A., Lee, A. G., Fogarty, J., ... & Polk, J. D. (2011). Optic disc edema, globe flattening, choroidal folds, and hyperopic shifts observed in astronauts after long-duration space flight. Ophthalmology, 118(10), 2058-2069. LEARNING OBJECTIVES: • The audience will be able to describe current Spaceflight Associated Neuro-ocular Syndrome (SANS) findings, significance, and countermeasures. • The audience will understand the importance of astronauts’ familiarity with SANS prior to flying and the large risks associated with this syndrome. CME QUESTIONS (3 QUESTIONS, MULTIPLE CHOICE A-D): 1. What is the approximate prevalence rate of SANS in astronauts? A. 22% B. 50% C. 66% D. 80% Answer: C. 2. Which of the ocular findings are NOT associated with SANS? A. Optic disc edema B. Retinal detachment C. Globe flattening D. Chorioretinal folds Answer: B. 3. Which of the following diagnostic technologies are NOT available in-flight? A. Ocular ultrasound B. Retinal photography C. MRI Orbit D. Optical Coherence Tomography (OCT) Answer: C.
Apollo was designed to carry astronauts safely back from the Moon at return speeds exceeding 11 km/s and requireddevelopment of a new ablative thermal protection system (TPS) to protect the capsule from entry heating. Mercuryand Gemini, that preceded Apollo, were focused on Earth orbiting system demonstration and lessons learned fromthem were used in Apollo. The ablative material and associated system development for Lunar return conditionsrequired considerable ground and flight testing. Mars Viking Lander missions required a new lighter weight ablatoras entry heating was benign compared to Apollo. Pioneer-Venus and Galileo Probe missions required a new and morecapable ablator than Apollo. After two decades, Mars Pathfinder followed by Mars Exploration Rover missions,smaller than Viking but more demanding, were able to use Viking ablative TPS. At the same time, advances in manufacturing and materials technology led to development of innovative lightweight ablators. These new ablators enabled Stardust and Genesis Sample Return Missions. Around the turn of this century, NASA decided on a scaled-upversion of the Apollo capsule for human exploration of Moon and Mars and the ablative heat shield to protect the CrewExploration Vehicle ended up being the Apollo ablative TPS. The Artemis 1 mission is currently fitted with tiledsystem, different than Orion EFT-1 but with the Apollo ablative material as a result of lessons learned. NASA iscurrently planning on sample return missions from Mars, and this will require robust ablative TPS that can providehigher reliability than any other past mission. There are still unexplored high scientific value destinations in the solarsystem. In situ exploration of Uranus, Neptune, Saturn and sample return missions with return speed much higher thanStardust will require ablators capable of withstanding extreme entry that are also efficient. New ablative TPS havebeen developed in anticipation of these future missions. This paper is intended to tell the story of these ablators,illustrated through examples. We see the use of flight proven ablators was sometimes a risky proposition and newablators perceived to be higher risk have proved otherwise. The history of ablators illustrates the challenges eachmission had to address, either through the use of flight proven or new ablative TPS, to be successful.
Many distantly related organisms have convergently evolved traits and lifestyles that enable them to live in similar ecological environments. However, the extent of phenotypic convergence evolving through the same or distinct genetic trajectories remains an open question. Here, we leverage a comprehensive dataset of genomic and phenotypic data from 1,049 yeast species in the subphylum Saccharomycotina (Kingdom Fungi, Phylum Ascomycota) to explore signatures of convergent evolution in cactophilic yeasts, ecological specialists associated with cacti. We inferred that the ecological association of yeasts with cacti arose independently approximately 17 times. Using a machine learning–based approach, we further found that cactophily can be predicted with 76% accuracy from both functional genomic and phenotypic data. The most informative feature for predicting cactophily was thermotolerance, which we found to be likely associated with altered evolutionary rates of genes impacting the cell envelope in several cactophilic lineages. We also identified horizontal gene transfer and duplication events of plant cell wall–degrading enzymes in distantly related cactophilic clades, suggesting that putatively adaptive traits evolved independently through disparate molecular mechanisms. Notably, we found that multiple cactophilic species and their close relatives have been reported as emerging human opportunistic pathogens, suggesting that the cactophilic lifestyle—and perhaps more generally lifestyles favoring thermotolerance—might preadapt yeasts to cause human disease. This work underscores the potential of a multifaceted approach involving high-throughput genomic and phenotypic data to shed light onto ecological adaptation and highlights how convergent evolution to wild environments could facilitate the transition to human pathogenicity.
Here, we present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the nonperturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural network is trained to accurately predict physical properties associated with sampled phonon configurations, thus bypassing the time-consuming ab initio calculations. To incorporate the point-group symmetry of the electronic system into the ML model, group-theoretical methods are used to develop a symmetry-invariant descriptor for phonon configurations in the supercell. We apply our ML approach to compute the temperature dependent electronic energy gap of silicon based on density functional theory (DFT). We show that, with less than a hundred DFT calculations for training the neural network model, an order of magnitude larger number of sampling can be achieved for the computation of the vibrational thermal expectation values. Our work highlights the promising potential of ML techniques for finite temperature first-principles electronic structure methods.
The development of effective vaccines is crucial for combating current and emerging pathogens. Despite significant advances in the field of vaccine development there remain numerous challenges including the lack of standardized data reporting and curation practices, making it difficult to determine correlates of protection from experimental and clinical studies. Significant gaps in data and knowledge integration can hinder vaccine development which relies on a comprehensive understanding of the interplay between pathogens and the host immune system. In this review, we explore the current landscape of vaccine development, highlighting the computational challenges, limitations, and opportunities associated with integrating diverse data types for leveraging artificial intelligence (AI) and machine learning (ML) techniques in vaccine design. We discuss the role of natural language processing, semantic integration, and causal inference in extracting valuable insights from published literature and unstructured data sources, as well as the computational modeling of immune responses. Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. Through data harmonization and integration, the development of safe and effective vaccines can be accelerated to improve public health outcomes. Looking to the future, we highlight the need for collaborative efforts among researchers, data scientists, and public health experts to realize the full potential of AI-assisted vaccine design and streamline the vaccine development process.
An advanced, universally-mountable, integrated residential photovoltaic array concept was defined based upon an in-depth formulation and evaluation of three candidate approaches which were synthesized from existing or proposed residential array concepts. The impact of module circuitry and process sequence is considered and technology gaps and performance drivers associated with residential photovoltaic array concepts are identified. The actual learning experience gained from the comparison of the problem areas of the hexagonal shingle design with the rectangular module design led to what is considered an advanced array concept. Building the laboratory mockup provided actual experience and the opportunity to uncover additional technology gaps.
Increased complexity of space missions during the 1980s led to the introduction of expert systems and advanced automation techniques in mission operations. This paper describes several technologies in operational use or under development at the National Aeronautics and Space Administration's Goddard Space Flight Center. Several expert systems are described that diagnose faults, analyze spacecraft operations and onboard subsystem performance (in conjunction with neural networks), and perform data quality and data accounting functions. The design of customized user interfaces is discussed, with examples of their application to space missions. Displays, which allow mission operators to see the spacecraft position, orientation, and configuration under a variety of operating conditions, are described. Automated systems for scheduling are discussed, and a testbed that allows tests and demonstrations of the associated architectures, interface protocols, and operations concepts is described. Lessons learned are summarized.
Fire is one of the many potentially catastrophic hazards associated with the operation of crewed spacecraft. A major lesson learned by NASA from the Apollo 204 fire in 1966 was that ignition sources in an electrically powered vehicle should and can be minimized, but can never be eliminated completely. For this reason, spacecraft fire control is based on minimizing potential ignition sources and eliminating materials that can propagate fire. Fire extinguishers are always provided on crewed spacecraft, but are not considered as part of the fire control process. "Eliminating materials that can propagate fire" does not mean eliminating all flammable materials - the cost of designing and building spacecraft using only nonflammable materials is extraordinary and unnecessary. It means controlling the quantity and configuration of such materials to eliminate potential fire propagation paths and thus ensure that any fire would be small, localized, and isolated, and would self-extinguish without harm to the crew. Over the years, NASA has developed many solutions for controlling the configuration of flammable materials (and potentially flammable materials in commercial "off-the-shelf" hardware) so that they can be used safely in air and oxygen-enriched environments in crewed spacecraft. This document describes and explains these design solutions so payload customers and other organizations can use them in designing safe and cost-effective flight hardware. Proper application of these guidelines will produce acceptable flammability configurations for hardware located in any compartment of the International Space Station or other program crewed vehicles and habitats. However, use of these guidelines does not exempt hardware organizations of the responsibility for safety of the hardware under their control.
The 16th anniversary of the launch of NASA's Terra Spacecraft was marked on December 18, 2015, with the Measurements of Pollution in the Troposphere (MOPITT) instrument being a successful contributor to the NASA EOS flagship. MOPITT has been enabled by a large suite of mechanisms, allowing the instrument to perform long-duration monitoring of atmospheric carbon monoxide, providing global measurements of this important greenhouse gas for 16 years. Mechanisms have been successfully employed for scanning, cooling of detectors, and to optically modulate the gas path length within the instrument by means of pressure and gas cell length variation. The instrument utilizes these devices to perform correlation spectroscopy, enabling measurements with vertical resolution from the nadir view, and has thereby furthered understanding of source and global transport effects of carbon monoxide. Given the design requirement for a 5.25-year lifetime, the stability and performance of the majority of mechanisms have far surpassed design goals. With 16 continuously operating mechanisms in service on MOPITT, including 12 rotating mechanisms and 4 with linear drive elements, the instrument was an ambitious undertaking. The long life requirements combined with demands for cleanliness and optical stability made for difficult design choices including that of the selection of new lubrication processes. Observations and lessons learned with regards to many aspects of the mechanisms and associated monitoring devices are discussed here. Mechanism behaviors are described, including anomalies, long-term drive current/power, fill pressure, vibration and cold-tip temperature trends. The effectiveness of particular lubrication formulations and the screening method implemented is discussed in relation to continuous rotating mechanisms and stepper motors, which have exceeded 15 billon rotations and 2.5 billion steps respectively. Aspects of gas cell hermeticity, optical cleanliness, heater problems and SEU effects on accelerometers are also discussed.
Extreme heat is the deadliest weather-related event in the United States, as well as one of the least discussed. Climate change has raised the frequency, intensity, and duration of severe heat events. The built, urban environment is often hotter than neighboring rural areas due to a denser concentration of pavement, metal and other building materials absorbing and retaining heat, as well as a low amount of vegetation – creating a phenomenon commonly known as the urban heat island effect. This effect contributes to a wide range of public health issues, associated with heat strokes, dehydration, loss of work productivity, decreased learning, respiratory difficulties, and heat-related mortality. The average summer temperature in Wichita has increased 1.3 degrees (F) since 1970, and the number of days over 100 degrees (F) has historically increased, from 40 days in 1934 to 53 days in 2011.
Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.
Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.
This is the final report of a program that examined the fundamentals of education associated with space activities, promoted educational policy development in appropriate forums, and developed pathfinder products and services to demonstrate the utility of advanced communication technologies for space-based education. Our focus was on space astrophysics and planetary exploration, with a special emphasis on the themes of the Origins Program, with which the Principal Investigator (PI) had been involved from the outset. Teaching, Learning, and Planetary Exploration was also the core funding of the Space Telescope Science Institute's (ST ScI) Special Studies Office (SSO), and as such had provided basic support for such important NASA studies as the fix for Hubble Space Telescope (HST) spherical aberration, scientific conception of the HST Advanced Camera, specification of the Next-Generation Space Telescope (NGST), and the strategic plan for the second decade of the HST science program.