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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Buyers Collusion in Incentivized Forwarding Networks: A Multi-Agent Reinforcement Learning Study

We present the issue of monetarily incentivized forwarding in a multi-hop mesh network architecture from an economic perspective. It is anticipated that credit-incentivized forwarding and relaying will be a simple method of exchanging transmission power and spectrum for connectivity. However, gateways and forwarding nodes, like any other free market, may create an oligopolistic market for the users they serve. In this study, a coalition scheme between buyers aims to address price control by gateways or nodes closer to gateways. In a Stackelberg competition game, buyer agents (users) and sellers (gateways) make decisions using reinforcement learning (RL), with decentralized Deep Q-Networks to buy and sell forwarding resources. We allow communication links between the buyers with a limited messaging space, without defining a collusion mechanism. The idea is to demonstrate that through messaging, and RL tacit collusion can emerge between agents in a decentralized setup. The multi-agent reinforcement learning (MARL) system is presented and analyzed from a machine-learning perspective. Moreover, MARL dynamics are discussed via mean field analysis to better understand divergence causes and make implementation recommendations for such systems. Finally, the simulation results show the results of coordination among the users.

42 ENGINEERING↗
DOE OSTI2021

Data-Driven Multi-agent Deep Reinforcement Learning for Distribution System Decentralized Voltage Control with High Penetration of PVs

This paper proposes a novel model-free/data-driven centralized training and decentralized execution multi-agent deep reinforcement learning (MADRL) framework for distribution system voltage control with high penetration of PVs. The proposed MADRL can coordinate both the real and reactive power control of PVs with existing static var compensators and battery storage systems. Unlike the existing DRL-based voltage control methods, our proposed method does not rely on a system model during both the training and execution stages. This is achieved by developing a new interaction scheme between the surrogate modeling of the original system and the multi-agent soft actor critic (MASAC) MADRL algorithm. In particular, the sparse pseudo-Gaussian process with a few-shots of measurements is utilized to construct the surrogate model of the original environment, i.e., power flow model. This is a data-driven process and no model parameters are needed. Furthermore, the MASAC enabled MADRL allows to achieve better scalability by dividing the original system into different voltage control regions with the aid of real and reactive power sensitivities to voltage, where each region is treated as an agent. This also serves as the foundation for the centralized training and decentralized execution, thus significantly reducing the communication requirements as only local measurements are required for control. Comparative results with other alternatives on the IEEE 123-nodes and 342-nodes systems demonstrate the superiority of the proposed method.

14 SOLAR ENERGY↗
DOE OSTI2014

Modeling the transmission of community-associated methicillin-resistant Staphylococcus aureus: a dynamic agent-based simulation

Background: Methicillin-resistant Staphylococcus aureus (MRSA) has been a deadly pathogen in healthcare settings since the 1960s, but MRSA epidemiology changed since 1990 with new genetically distinct strain types circulating among previously healthy people outside healthcare settings. Community-associated (CA) MRSA strains primarily cause skin and soft tissue infections, but may also cause life-threatening invasive infections. First seen in Australia and the U.S., it is a growing problem around the world. The U.S. has had the most widespread CA-MRSA epidemic, with strain type USA300 causing the great majority of infections. Individuals with either asymptomatic colonization or infection may transmit CA-MRSA to others, largely by skin-to-skin contact. Control measures have focused on hospital transmission. Limited public health education has focused on care for skin infections. Methods: We developed a fine-grained agent-based model for Chicago to identify where to target interventions to reduce CA-MRSA transmission. An agent-based model allows us to represent heterogeneity in population behavior, locations and contact patterns that are highly relevant for CA-MRSA transmission and control. Drawing on nationally representative survey data, the model represents variation in sociodemographics, locations, behaviors, and physical contact patterns. Transmission probabilities are based on a comprehensive literature review. Results: Over multiple 10-year runs with one-hour ticks, our model generates temporal and geographic trends in CA-MRSA incidence similar to Chicago from 2001 to 2010. On average, a majority of transmission events occurred in households, and colonized rather than infected agents were the source of the great majority (over 95%) of transmission events. The key findings are that infected people are not the primary source of spread. Rather, the far greater number of colonized individuals must be targeted to reduce transmission. Conclusions: Our findings suggest that current paradigms in MRSA control in the United States cannot be very effective in reducing the incidence of CA-MRSA infections. Furthermore, the control measures that have focused on hospitals are unlikely to have much population-wide impact on CA-MRSA rates. New strategies need to be developed, as the incidence of CA-MRSA is likely to continue to grow around the world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The development and use of decision support framework for informing selection of select agent toxins with modelling studies to inform permissible toxin amounts

Many countries have worked diligently to establish and implement policies and processes to regulate high consequence pathogens and toxins that could have a significant public health impact if misused. In the United States, the Antiterrorism and Effective Death Penalty Act of 1996 (Public Law 104-132, 1996), as amended by the Bioterrorism Preparedness and Response Act of 2002 (Public Law 107-188, 2002) requires that the Department of Health and Human Services (HHS) [through the Centers for Disease Control and Prevention (CDC)] establish a list of bacteria, viruses, and toxins that have the potential to pose a severe threat to public health and safety. Currently, this list is reviewed and updated on a biennial basis using input from subject matter experts (SMEs). We have developed decision support framework (DSF) approaches to facilitate selection of select toxins and, where toxicity data are known, conducted modelling studies to inform selection of toxin amounts that should be excluded from select agent regulations. Exclusion limits allow laboratories to possess toxins under an established limit to support their research or teaching activities without the requirement to register with the Federal Select Agent Program. Fact sheets capturing data from a previously vetted SME workshop convened by CDC, literature review and SME input were developed to assist in evaluating toxins using the DSF approach. The output of the DSF analysis agrees with the current select toxin designations, and no other toxins evaluated in this study were recommended for inclusion on the select agent and toxin list. To inform the selection of exclusion limits, attack scenarios were developed to estimate the amount of toxin needed to impact public health. Scenarios consisted of simulated aerosol releases of a toxin in high-population-density public facilities and the introduction of a toxin into a daily consumable product supply chain. Using published inhalation and ingestion median toxic dose (TD 50 ) and median lethal dose (LD 50 ) values, where available, a range of toxin amounts was examined to estimate the number of people exposed to these amounts in these scenarios. Based on data generated by these models, we proposed toxin exclusion values corresponding to levels below those that would trigger a significant public health response (i.e., amounts estimated to expose up to ten people by inhalation or one hundred people by ingestion to LD 50 or TD 50 levels of toxin in the modeled scenarios).

60 APPLIED LIFE SCIENCES↗
NASA NTRS2004

The EO-1 Autonomous Science Agent Architecture

An Autonomous Science Agent is currently flying onboard the Earth Observing One Spacecraft. This software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. The package includes software systems that perform science data analysis, deliberative planning, and run-time robust execution. Because of the deployment to a remote spacecraft, this Autonomous Science Agent has stringent constraints of autonomy, reliability, and limited computing resources. We describe these constraints and how they are reflected in our agent architecture.

Earth Observing One (EO-1)↗
NASA NTRSDate not supplied

Clutter Assessment for an Autonomous Multi-Agent Search Mission

This paper presents a method for evaluating the amount of clutter in a region where autonomous vehicles in a multi-agent system must operate based on LIDAR point cloud measurements. The point cloud is used to generate an occupancy grid which is then projected onto a 2D plane of vehicle motion, constituting an image. A series of Gaussian radial basis functions (GRBFs) is created, each centered at an occupied pixel in a 2D image, and summed together to form the clutter field. The clutter field is a representation of the density and permeability of the space at each coordinate. The clutter field is then approximated such that iso-clutter contours are simple geometric objects so that intelligent machine assets can easily query the distance between them and any given point in an environment. In this way, agents are able to determine whether to enter into or steer away from areas of interest. Each vehicle has a clutter threshold representing the clutter value of the space in which it can safely maneuver. The iso-clutter contour corresponding to a vehicle’s clutter threshold is treated as the boundary of an obstacle to be avoided. A simulation is presented where a multi-agent system is tasked with persistent observation of a cluttered area. Each vehicle in the simulation has a different clutter threshold. The vehicles use a potential field-based guidance algorithm, and an allocation of vehicles to specific regions of the space emerges.

multi-agent↗
DOE OSTI2012

Resilient Control System Execution Agent (ReCoSEA)

In an increasingly networked world, critical infrastructure systems suffer from two types of vulnerability. The first is the traditionally recognized problem of monitoring the systems for faults and failures, recognizing and analyzing data, and responding with real understanding to the problems of the system. Increasingly complex systems create the opportunity for single points of failure to cascade when inaccurate assessment of system health increases response time or leads to faulty analysis of the problems involved. A second problem involves vulnerability to cyber intrusion, in which bad actors can mask system deterioration or present false data about system status. A resilient system will protect stability, efficiency, and security. To ensure these three states, the system must react to changing conditions within the system with coordination: no one component of the system can be allowed to react to problems without real consideration of the effects of that action on other components within the system. Systems with multi-agent design typically have three layers of action, a management layer, a coordination layer, and an execution layer. A resilient multi-agent system will emphasize functions of the execution layer, which has the responsibility of initiating actions, monitoring, analyzing, and controlling its own processes, while feeding information back to the higher levels of management and coordination. The design concept of a resilient control system execution agent (ReCoSEA) grows out of these underpinnings, and through the use of computational intelligence techniques, this paper suggests an associated design methodology.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Volttron: An Agent Platform for the Smart Grid

VOLLTRON platform enables the deployment of intelligent sensors and controllers in the smart grid and provides a stable, secure and flexible framework that expands the sensing and control capabilities. VOLTTRON platform provides services fulfilling the essential requirements of resource management and security for agent operation in the power grid. The facilities provided by the platform allow agent developers to focus on the implementation of their agent system and not on the necessary "plumbing' code. For example, a simple collaborative demand response application was written in less than 200 lines of Python.

agents↗
DOE OSTI2022

Agent-Based Modeling for the Circular Economy: Lessons Learned From Three Case Studies

The circular economy (CE) aims at decoupling human activities from resource use, creating wealth in the process. Recently, many scholars have questioned the link between increased circularity and sustainability, resulting in many methodological approaches being developed for that purpose. This presentation summarizes the insights gained from the application of agent-based modeling (ABM) to study the techno-economic and social conditions promoting circularity and sustainability of three technologies: photovoltaic (PV) modules, hard disk drives (HDDs), and wind blades. Four main categories of agents are defined in the ABM: asset owners, service providers (e.g., refurbishers), recyclers, and manufacturers. Two main CE strategies are represented: lifetime extension (through repair or reuse) and recycling. The developed models start by projecting installed capacities and end-of-life (EOL) quantities. Then the theory of planned behavior - a social psychology model explaining behavior adoption based on attitude, peer influence, and costs - is used to model the asset owners' EOL decision (i.e., landfill, recycle or extend the lifetime of the asset). Then, the quantities of assets flowing to the recycler and service provider agents and quantities of materials flowing to manufacturers are computed. Recyclers' economies of scale are dynamically modeled, and the value generated by the CE strategies for the recyclers, service providers, and manufacturers is computed within the model. When data are available, avoided greenhouse gas emissions resulting from the CE strategies adoption are calculated exogenously from the ABM simulations. Results show that with improved used PV modules warranties, the reuse CE strategy adoption increases from 1% to 23% between 2020 and 2050. Similarly, improved standards could enhance HDDs end-users trust in data-wiping - a prerequisite to reuse - leading to a 3-fold increase in the reuse rate and avoid about 5 million tons of CO2 eq by 2050. Regarding wind blades, 5-15 years lifetime extension could reduce EOL blade quantities by 13%. High costs and logistic issues prevent blades from being recycled in greater quantities. One insight from the case studies is the necessity to have mature secondary markets for reuse to be a viable option. Interestingly, PV reuse is limited by the willingness of PV owners to purchase used modules (on the demand side), while HDDs reuse is constrained by the lack of trust toward data-wiping (limiting the supply of used HDDs). The six limits of the CE concepts presented by Korhonen et al. (2018) are finally used to interpret the results. The HDD case study is an exemplary lock-in, where the first accepted practice (shredding) retains most of the market. The PV results illustrate the technical limitation to reuse, as the growing demand cannot be supplied entirely with used PV modules. The wind case study shows the relevance of clearly defining physical flows - what type of waste should wind blades be considered, how should they be transported and landfilled? - a crucial consideration that also applies to PV modules. Finally, the three case studies highlight the relevance of studying a technology's technical, economic, and market material efficiency potentials altogether and the potential benefit of coupling ABM to life cycle assessment.

agent-based modeling↗
DOE OSTI2023

Multi-Agent Reinforcement Learning for Distribution System Critical Load Restoration: Preprint

Grid resilience has become a critical topic recently because of the increasing occurrence of extreme events and the growing integration of intermittent renewable energy sources. To build a resilient distribution system, this paper develops a multi-agent reinforcement learning-based (MARL) method to coordinate distribution energy resources (DERs) dispatch, load pickup, and network reconfiguration for load restoration after a system outage. With the help of two types of control agents, namely critical load restoration (CLR) and coordination (COR) agents, system loads can be restored efficiently, given available resources. The effectiveness and superiority of the proposed algorithm are demonstrated through simulations and comparative studies on a real distribution feeder in Western Colorado.

distribution system↗
DOE OSTI2025

Multi-agent AI collaboration for digital twin development and assessment

Developing a digital twin (DT) model involves different steps that encompass formulating requirements, model development, implementation, and assessment with respect to real applications. Human expertise is required to coordinate and implement different steps in the DT development and assessment process. However, certain parts of this process can be automated using artificial intelligence (AI) agents for efficient workflow development. In this work, we test and analyze a multiagent AI collaboration with humans in the loop to automate different elements of the DT development and assessment process. To implement the workflow for multiagent AI DT development and assessment, we use Autogen, a multiagent framework developed by Microsoft. Autogen offers a modular and flexible framework for configuring and designing task-specific multiagent workflows. In this framework, large language models (LLMs) form the core intelligence of the AI agents where the quality and performance of the automated element is governed by the inherent capabilities and knowledge base of the LLM. We use retrieval augmented generation to supplement the LLM with relevant domain-specific information for DT requirement formulation. We illustrate this multiagent workflow using a case study on a thermal energy storage system, focusing on how AI agents can collaborate with humans to expedite and optimize different elements of DT development and assessment process.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2011

A novel nanoparticle-based disposable electrochemical immunosensor for diagnosis of exposure to toxic organophosphorus agents

We present a novel disposable electrochemical immunosensor for highly selective and sensitive detection of organophosphorylated butyrylcholinesterase (OP-BChE), a specific biomarker for exposure to toxic organophosphorus agents. In our new approach, the zirconia nanoparticles (ZrO-2) were employed to selectively capture the OP moiety of OP-BChE adducts, and followed by quantum dot (QD)-tagged anti-BChE antibodies for amplified quantification. The captured CdSe-QD tags can be sensitively detected by stripping voltammetry using in situ bismuth-plating method. The OP agent, diisopropylfluorophosphate (DFP), was selected to prepare OP-BChE adducts in various matrices. The formation of OP-BChE adducts in plasma sample was confirmed using mass spectroscopy. The developed electrochemical immunosensor demonstrates a highly linear voltammetric response over the range of 0.1 to 30 nM OP-BChE. Moreover, the immunosensor has been successfully applied for the detection of OP-BChE adducts in the plasma samples. This novel nanoparticle-based electrochemical immunosensor thus provides an alternative way for designing simple, fast, sensitive, and cost-effective sensing platform for on-site screening/evaluating exposure to a variety of OP agents.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Interlaminar improvement of carbon fiber/epoxy composites via fluorine‐containing high‐epoxy‐value sizing agent

Abstract To improve the interfacial bonding effect of carbon fibers (CFs) and epoxy resins, we prepared a fluorine‐containing high‐epoxy‐value sizing agent (named TS) in this article. It is an emulsion with a more uniform particle size distribution and higher stability better than a commercial Takemoto sizing agent. In addition, sulfonic acid groups can improve the hydrophilicity of the TS, and the presence of epoxy groups enhances the wettability between CFs and the matrix. The properties of the sizing agents were examined by Fourier transform infrared spectroscopy, dynamic light scattering, and thermogravimetric analysis. The sized CFs were evaluated by scanning electron microscopy, x‐ray photoelectron spectroscopy, monofilament tensile test, and dynamic contact angle. Afterward, CF/epoxy composites were prepared, and their fracture morphology and interlaminar shear strength (ILSS) were examined. Results indicated that the TS sizing significantly increased the content of activated carbon on the surface of the CFs and improved the bonding effect between the fiber and the matrix. After TS was sized, the ILSS increased from 49.64 to 58.74 MPa, 18.34% higher than unsized CF/epoxy composites, showing the prominent interfacial reinforcement better than commercial Takemoto.

Qiu, Zhijie↗
DOE OSTI2014

Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents

Building Energy Modeling (BEM) is an approach to model the energy usage in buildings for design and retrofit purposes. EnergyPlus is the flagship Department of Energy software that performs BEM for different types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters which have to be calibrated manually by an expert for realistic energy modeling. This makes it challenging and expensive thereby making building energy modeling unfeasible for smaller projects. In this paper, we describe the Autotune research which employs machine learning algorithms to generate agents for the different kinds of standard reference buildings in the U.S. building stock. The parametric space and the variety of building locations and types make this a challenging computational problem necessitating the use of supercomputers. Millions of EnergyPlus simulations are run on supercomputers which are subsequently used to train machine learning algorithms to generate agents. These agents, once created, can then run in a fraction of the time thereby allowing cost-effective calibration of building models.

Sanyal, Jibonananda↗
DOE OSTI2011

Comparison of the Efficacy of the Embolic Agents Acrylamido Polyvinyl Alcohol Microspheres and Tris-Acryl Gelatin Microspheres for Uterine Artery Embolization for Leiomyomas: A Prospective Randomized Controlled Trial

Objective: To evaluate the efficacy of acrylamido polyvinyl alcohol microspheres (a-PVAM) as an embolic agent for uterine artery embolization (UAE) compared with Tris-acryl gelatin microspheres (TAGM).Design, Setting, ParticipantsProspective randomized double-blind noninferiority trial. Conducted at two sites both with regional UAE practices. Forty-six women with symptomatic leiomyomas.InterventionUAE procedure was performed with either of the two embolic agents. Either 700-900-{mu}m a-PVAM or 500-700-{mu}m TAGM was used.Main Outcome MeasuresChanges in leiomyoma perfusion, overall uterine volume, and dominant leiomyomas volume measured by contrast-enhanced magnetic resonance imaging at 1 week, 3 months, and 6 months after UAE by a reader blinded to the embolic agent used. Changes in Uterine Fibroid Symptoms and Quality of Life questionnaire scores were measured at 3, 6, and 12 months after UAE. Results: Forty-six patients were randomized and treated under the study protocol (a-PVAM n = 22, TAGM n = 24). There were no procedure-related complications. Two patients were excluded from analysis (one technical failure of the procedure, one withdrawal from study). Successful (>90%) leiomyoma devascularization was observed in 81% of subjects at 1 week after UAE, 97% at 3 months after UAE, and 95% at 6 months after UAE. No significant differences were observed in 14 of 15 outcome measurements, consistent with noninferiority. TAGM was slightly superior to a-PVAM on one comparison (overall quality of life at 3 months after UAE).

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Chitosan–Sodium Tetradecyl Sulfate Hydrogel: Characterization and Preclinical Evaluation of a Novel Sclerosing Embolizing Agent for the Treatment of Endoleaks

PurposeTo compare the efficacy of an embolization agent with sclerosing properties (made of chitosan and sodium tetradecyl sulfate, CH–STS) with a similar embolization agent but without sclerosing properties (made of chitosan, CH) in treating endoleaks in a canine endovascular aneurysm repair model.MethodsTwo chitosan-based radiopaque hydrogels were prepared, one with STS and one without STS. Their rheological, injectability, and embolizing properties were assessed in vitro; afterwards, their efficacy in occluding endoleaks was compared in a canine bilateral aneurysm model reproducing type I endoleaks (n = 9 each). The primary endpoint was endoleak persistence at 3 or 6 months, assessed on a CT scan and macroscopic examination. Secondary endpoints were the occurrence of stent-graft (SG) thrombosis, the evolution of the aneurysm mean diameter, as well as aneurysm healing and inflammation scores in pathology examinations.ResultsIn vitro experiments showed that both products gelled rapidly and presented initial storage moduli greater than 800 Pa, which increased with time. Both gels were compatible with microcatheter injection and occlude flow up to physiological pressure in vitro. In a type I endoleak model, the injection of CH–STS sclerosing gel tended to reduce the risk of occurrence of endoleaks, compared to CH non-sclerosing agent (2/9 vs. 6/9, p = 0.069). No case of SG thrombosis was observed. Moderate inflammation was found around both gels, with a comparable intensity score in both CH and CH–STS groups (2.6 ± 0.9 and 2.7 ± 0.9, respectively; p = 0.789).ConclusionsFlow occlusion combined with chemical endothelial denudation appears promising for the treatment of endoleaks.Level of EvidenceN/A.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Contrast Agents Based on Iron Oxide Nanoparticles for Clinical Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is one of the most perspective methods of noninvasive visualization in medicine, and use of contrast agents significantly its potentialities extends. Iron oxide nanoparticles are promising contrast agents, but in fact all the data on their efficiency were obtained in high-field tomographs for experimental animals. We studied the possibility of using magnetic nanoparticles for MRI visualization of rat brain glioblastoma at the most common clinical field 1.5 T The data indicate the efficiency of iron oxide magnetic nanoparticles as contrast agents for 1.5 T MR tomographs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Fischer–Tropsch Synthesis: Effect of Reducing Agent for Aqueous-Phase Synthesis Over Ru Nanoparticle and Supported Ru Catalysts

The effect of the reducing agent on the performance of a ruthenium nanoparticle catalyst was investigated during aqueous-phase Fischer–Tropsch synthesis using a 1 L stirred tank reactor in the batch mode of operation. For the purpose of comparison, the activity and selectivity of NaY zeolite supported Ru catalyst were also studied. NaBH 4 and hydrogen were used as reducing agents in our study, and hydrogen reduced catalysts exhibited higher activities than the NaBH 4 reduced catalysts, because of higher extent of reduction and a relatively lower tendency toward agglomeration of Ru particles. The Ru nanoparticle catalyst displayed higher activities than the NaY zeolite supported Ru catalyst for both reducing agents. NaBH 4 reduced catalysts are less active and the carbon dioxide selectivity is higher than the hydrogen reduced catalysts. The activity of the supported Ru catalyst (Ru/NaY) was 75 % of that of the Ru nanoparticle catalyst, and has the benefit of easy wax/catalyst slurry separation by filtration. Finally, the hydrogen reduced supported Ru catalyst exhibited superior selectivity towards hydrocarbons (higher C 5+ selectivity and lower selectivity to methane) than all other catalysts tested.

77 NANOSCIENCE AND NANOTECHNOLOGY↗