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Hybrid network via instantaneous photoradiation: High efficient design of 100% bio-based thermosets with remoldable and recyclable capabilities after UV curing

Conventional thermoset materials cannot be remolded and recycled due to the covalent bonds existing in the network. The reversible nature of secondary interactions between monomeric units renders supramolecular structure reconfigurable abilities upon exposure to appropriate stimuli. This feature is quite useful in the design of novel thermoset materials with remoldability and recyclability, but its widespread adoption is limited due to the low strength and high cost of supramolecular structure. Here, we present a new class of thermoset with a distinct recycling mechanism and structural design. It is strong, transparent, recyclable, 100% biorenewable, creep resistance after thermal treatment, and produced in a ultrafast, scalable and cost-effective manner. These characteristics come from a purpose-designed hybrid network via a ultraviolet radiation in seconds. As a result, this technology provides a new way to obtain thermosets with remoldability and recyclability. It is green and efficient, not limited in lab, and can be scaled up for industry.

36 MATERIALS SCIENCE↗

Evaluating Transactive Controls of Integrated Transmission and Distribution Systems using the Framework for Network Co-Simulation

With an ever-evolving power grid, concerns regarding how to maintain system stability, efficiency, and reliability remain constant because of increasing uncertainties and decreasing rotating inertia. To alleviate some of these concerns, demand response represents a viable solution and is virtually an untapped resource in the current power grid. This work describes a hierarchical control framework that allows coordination between distributed energy resources and demand response. This control framework is composed of two control layers: a coordination layer that ensures aggregations of resources are coordinated to achieve system objectives and a device layer that controls individual resources to assure the predetermined power profile is tracked in real time. Large-scale simulations are executed to study the hierarchical control, requiring advancements in simulation capabilities. Technical advancements necessary to investigate and answer control interaction questions, including the Framework for Network Co-Simulation platform and Arion modeling capability, are detailed. Insights into the interdependencies of controls across a complex system and how they must be tuned, as well as validation of the effectiveness of the proposed control framework, are yielded using a large-scale integrated transmission system model coupled with multiple distribution systems.

demand response↗

Pan- and core- network analysis of co-expression genes in a model plant

Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of ‘pan-network’ and ‘core-network’ representing union and intersection between a sizeable fractions of individual networks, respectively. Here, we showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.

60 APPLIED LIFE SCIENCES↗

Machine-Learning-Driven, Site-Specific Weather Forecasting for Grid-Interactive Efficient Buildings: Preprint

Emerging grid-interactive efficient buildings (GEBs) have great potential to provide much-needed demand flexibility to electric grids while fulfilling their own control targets by co-optimizing smart appliances, solar photovoltaics, electric vehicles, and energy storage at buildings. To enable the optimal operation of GEBs, site-specific weather information—such as temperature, solar irradiance, relative humidity, and wind speed—is crucial; however, this information is generally unavailable or expensive to obtain. This paper develops advanced machine learning methods to provide precise weather forecasts for individual building sites using readily available weather station data. Support vector regression and artificial neural networks have been employed to learn the spatiotemporal correlations between the weather conditions at nearby weather stations and the individual building site. The proposed site-specific weather forecasting methods have been validated using 1-year actual weather measurement data collected in the Denver metro area. Results show that the developed machine-learning-driven methods can accurately forecast the temperature at the target building site 1 hour ahead with mean absolute error less than 0.72°C and a 48% improvement over the persistence method. Site-specific weather forecasts will improve the understanding of the microclimate effect and its impact on building energy consumption. This information will drive efficiency upgrades and adjustments of building control strategies to improve energy savings and increase flexibility in building loads.

30 DIRECT ENERGY CONVERSION↗

RNi{sub 8}Si{sub 3} (R=Gd,Tb): Novel ternary ordered derivatives of the BaCd{sub 11} type

The title compounds have been synthesized and characterized both from the structural and magnetic point of view. Both crystallize in a new monoclinic structure strictly related to the tetragonal BaCd{sub 11} type. The structure was solved by means of X-ray single-crystal techniques for GdNi{sub 8}Si{sub 3} and confirmed for TbNi{sub 8}Si{sub 3} on powder data; the corresponding lattice parameters (obtained from Guinier powder patterns) are a=6.3259(2), b=13.7245(5), c=7.4949(3) Å, β=113.522(3)°, V{sub cell}=596.64(3) Å{sup 3} and a=6.3200(2), b=13.6987(4), c=7.4923(2) Å, β=113.494(2)°, V{sub cell}=594.88(2) Å{sup 3}. The symmetry relationship between the tI48-I4{sub 1}/amd BaCd{sub 11} aristotype and the new ordered mS48-C2/c GdNi{sub 8}Si{sub 3} derivative is described via the Bärnighausen formalism within the group theory. The large Gd–Gd (Tb–Tb) distances, mediated via Ni–Si network, likely lead to weak magnetic interactions. Low-field magnetization vs temperature measurements indicate weak and field-sensitive antiferromagnetic ground state, with ordering temperatures of 3 K in GdNi{sub 8}Si{sub 3} and about 2–3 K in TbNi{sub 8}Si{sub 3}. On the other hand, the isothermal field-dependent magnetization data show the presence of competing interactions in both compounds, with a field-induced ferromagnetic behavior for GdNi{sub 8}Si{sub 3} and a ferrimagnetic-like behavior in TbNi{sub 8}Si{sub 3} at the ordering temperature T{sub C/N} of about (or slightly higher than) 3K. The magnetocaloric effect, quantified in terms of isothermal magnetic entropy change ΔS{sub m}, has the maximum values of –19.8 J(kg K){sup −1} (at 4 K for 140 kOe field change) and −12.1 J(kg K){sup −1} (at 12 K for 140 kOe field change) in GdNi{sub 8}Si{sub 3} and TbNi{sub 8}Si{sub 3}, respectively. - Graphical abstract: GdNi{sub 8}Si{sub 3} and TbNi{sub 8}Si{sub 3} compounds are isostructural, and crystallize in a new monoclinic type strictly related to the tetragonal BaCd{sub 11} structure. The large R–R interatomic distances mediated via Ni–Si network lead to a weak magnetism in both compounds. - Highlights: • Novel RNi{sub 8}Si{sub 3} (R=Gd, Tb) compounds have been synthesized and characterized. • GdNi{sub 8}Si{sub 3} and TbNi{sub 8}Si{sub 3} are isostructural compounds, belonging to a new monoclinic structure type. • The monoclinic GdNi{sub 8}Si{sub 3} type is an ordered derivative of the tetragonal BaCd{sub 11} type. • The large R–R interatomic distances mediated via Ni–Si network lead to a weak magnetism. • Both compounds GdNi{sub 8}Si{sub 3} and TbNi{sub 8}Si{sub 3} show antiferromagnetic-like order around 3 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Interfacing Space Communications and Navigation Network Simulation with Distributed System Integration Laboratories (DSIL)

NASA's planned Lunar missions will involve multiple NASA centers where each participating center has a specific role and specialization. In this vision, the Constellation program (CxP)'s Distributed System Integration Laboratories (DSIL) architecture consist of multiple System Integration Labs (SILs), with simulators, emulators, testlabs and control centers interacting with each other over a broadband network to perform test and verification for mission scenarios. To support the end-to-end simulation and emulation effort of NASA' exploration initiatives, different NASA centers are interconnected to participate in distributed simulations. Currently, DSIL has interconnections among the following NASA centers: Johnson Space Center (JSC), Kennedy Space Center (KSC), Marshall Space Flight Center (MSFC) and Jet Propulsion Laboratory (JPL). Through interconnections and interactions among different NASA centers, critical resources and data can be shared, while independent simulations can be performed simultaneously at different NASA locations, to effectively utilize the simulation and emulation capabilities at each center. Furthermore, the development of DSIL can maximally leverage the existing project simulation and testing plans. In this work, we describe the specific role and development activities at JPL for Space Communications and Navigation Network (SCaN) simulator using the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) tool to simulate communications effects among mission assets. Using MACHETE, different space network configurations among spacecrafts and ground systems of various parameter sets can be simulated. Data that is necessary for tracking, navigation, and guidance of spacecrafts such as Crew Exploration Vehicle (CEV), Crew Launch Vehicle (CLV), and Lunar Relay Satellite (LRS) and orbit calculation data are disseminated to different NASA centers and updated periodically using the High Level Architecture (HLA). In addition, the performance of DSIL under different traffic loads with different mix of data and priorities are evaluated.

Traffic Measuring and Monitoring↗

Structure and Energetics of Nanometer Size Clusters of Sulfuric Acid with Ammonia and Dimethylamine

The structures of positively and negatively charged clusters of sulfuric acid with ammonia and/or dimethylamine ((CH{sub 3}){sub 2}NH or DMA) are investigated using a combination of Monte Carlo configuration sampling, semiempirical calculations, and density functional theory (DFT) calculations. Positively charged clusters of the formula [(NH{sub 4}{sup +}){sub x}(HSO{sub 4}{sup -}){sub y}]{sup +}, where x = y + 1, are studied for 1 {le} y {le} 10. These clusters exhibit strong cation-anion interactions, with no contribution to the hydrogen-bonding network from the bisulfate ion protons. A similar hydrogen-bonding network is found for the [(DMAH{sup +}){sub 5}(HSO{sub 4}{sup -}){sub 4}]{sup -} cluster. Negatively charged clusters derived from the reaction of DMA with [(H{sub 2}SO{sub 4}){sub 3}(NH{sub 4}{sup +})(HSO{sub 4}{sup -}){sub 2}]{sup -} are also studied, up to the fully reacted cluster [(DMAH{sup +}){sub 4}(HSO{sub 4}{sup -}){sub 5}]{sup -}. These clusters exhibit anion-anion and ion-molecule interactions in addition to cation-anion interactions. While the hydrogen-bonding network is extensive for both positively and negatively charged clusters, the binding energies of ions and molecules in these clusters are determined mostly by electrostatic interactions. The thermodynamics of amine substitution is explored and compared to experimental thermodynamic and kinetic data. Ammonia binds more strongly than DMA to sulfuric acid due to its greater participation in hydrogen bonding and its ability to form a more compact structure that increases electrostatic attraction between oppositely charged ions. However, the greater gas-phase basicity of DMA is sufficient to overcome the stronger binding of ammonia, making substitution of DMA for ammonia thermodynamically favorable. For small clusters of both polarities, substitutions of surface ammonium ions are facile. As the cluster size increases, an ammonium ion becomes encapsulated in the center of the cluster, making it inaccessible to substitution.

08 HYDROGEN↗

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of nonKEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

59 BASIC BIOLOGICAL SCIENCES↗

Nonlinear behavior of urban flood peaks in the U.S. Mid-Atlantic region

Urbanization, i.e., increasing urban development areas in a watershed, is well known as a major cause of increasing flood magnitudes. This study analyzes the observed flood peaks at 262 watersheds in the U.S. Mid-Atlantic region with varying levels of urban development and free from reservoir impacts. Our analysis reveals an interesting, V-shaped nonlinear behavior: flood peaks first decrease and then increase with increasing percentage of urban development area at the watershed scale (PDAW), with the shift occurring at a PDAW threshold of around 10%. Regression analyses suggest that the V-shaped pattern primarily results from complex interactions among climate conditions (e.g., storm-event rainfall) and landscape properties (e.g., elevation, distance to the coast). A neural network model was then developed to capture such interactions, satisfactorily reproducing the V-shaped pattern with an R-squared value of 0.58, RMSE of 6.72 mm/day, and NSE of 0.55. These findings highlight the need to account for nonlinear dynamics in flood prediction and management in the coastal environment.

flood peaks↗

Synthesis, characterization and crystal structure of a novel Strandberg-type polyoxoselenomolybdate Rb{sub 4}[Se{sub 2}Mo{sub 5}O{sub 21}]{center_dot}2H{sub 2}O

Graphical abstract: Crystal structure of Rb{sub 4}[Se{sub 2}Mo{sub 5}O{sub 21}]{center_dot}2H2O. Highlights: Black-Right-Pointing-Pointer Synthesis of the first Strandberg-type polyoxoselenomolybdate Rb{sub 4}[Se{sub 2}Mo{sub 5}O{sub 21}]{center_dot}2H{sub 2}O. Black-Right-Pointing-Pointer Characterization of the novel compound. Black-Right-Pointing-Pointer Chirality of the space group C2 of compound implies that it is expected to have nonlinear optical (NLO) properties -- Abstract: An inorganic compound formulated as Rb{sub 4}[Se{sub 2}Mo{sub 5}O{sub 21}]{center_dot}2H{sub 2}O (1) has been isolated by conventional solution method and structurally characterized by single-crystal X-ray diffraction methods, scanning electron microscopy (SEM), powder XRD, IR, UV-vis spectra, and cyclic voltammetry measurements. This compound crystallizes in the monoclinic system, space group C2 with unit a = 19.701 (3) Angstrom-Sign , b = 10.296 (2) Angstrom-Sign , c = 12.134 (4) Angstrom-Sign , {beta} = 106.96 (2) Degree-Sign and Z = 4. The crystal structure of (1) is built up from a Strandberg clusters connected through hydrogen-bonding interactions into a three-dimensional supramolecular network.

36 MATERIALS SCIENCE↗

Support for Reactor Operators in Case of Cyber-Security Threats (NEUP Final Report)

Increasing use of digital technology in nuclear power plants (NPPs) poses cyber-security as a crucial threat to public safety and to continuous energy production. Cyber-security risks are comprised of complex known and unknown interactions between various entities, system vulnerabilities, network protocols, human users and malicious attacks. There is little understanding or research geared towards plant operators’ response under cyber-security threats and operational procedures to cope with such threats. This is particularly critical when a cyber-security event masquerades as a safety incident or an evolving accident. Instead of leading operators to the remediation of the accident, the masked cyber event may lead them to circumvent the safety mechanisms of the plant. Given these challenges, the objective of this project is to develop a tool to characterize abnormal NPP events as “cyber” or “safety” incidents, and to further develop this tool as a real-time operator aid to assist in response to the incident. This NEUP Final Report summarizes the research efforts towards the above goals and the results in the following three aspects. First, a methodology for classifying abnormal NPP events as cyber-attacks or safety incidents was developed. This provides the basis for responding to potential cyber-attacks. In this methodology, the causal relationships between various variables (i.e., physical variables, network variables, and control variables) for a nuclear system under consideration are modeled using dynamic Bayesian networks. Based on observations of certain variables (i.e., the evidence), inference is performed to identify the state of hidden variables, which helps indicate the occurrence of cyber-attacks or safety incidents. The proposed methodology was applied to the full scope NPP simulator system at The Ohio State University as a case study. The result shows that the proposed methodology is capable of distinguishing between cyber-attacks and safety incidents. Second, a game-theoretic approach for responding to cyber-attacks on nuclear power plants was developed. This is the second major goal of the project, and the method is used to recommend optimal responses to reactor operators in the case of cyber-attacks. We consider two cases in this research. In the first case, the interaction between the defender (i.e., the reactor operator) and the attacker is modeled as a two-player, nonzero-sum, Markov game with an infinite time horizon. In the second case, we consider a semi-Markov game between the defender and the attacker within a finite time horizon. The model in the second case enables us to consider the time sensitive response of the defender. In each case, the method for deriving the Nash equilibrium is developed. The Nash Equilibrium of the game provides the valid prediction of both players’ actions because no single player can benefit from unilaterally deviating from the equilibrium policy if the other player adheres to his/hers, hence it provides the best response of the defender to cyber-attacks. The proposed method in each case is demonstrated through a simplified benchmark digital feedwater control system. Third, a prototype display is developed to integrate the above information. The prototype display depicts the communication information to the operator and incorporates the cyber-security tool developed by the team into the interface. It consists of the regular display in current control rooms, as well as a panel used to indicate the occurrence of cyber-attacks or safety events and a panel used to guide the operator’s response to detected cyber-attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Unified Approach to Modeling and Simulation of Space Communication Networks and Systems

Network simulator software tools are often used to model the behaviors and interactions of applications, protocols, packets, and data links in terrestrial communication networks. Other software tools that model the physics, orbital dynamics, and RF characteristics of space systems have matured to allow for rapid, detailed analysis of space communication links. However, the absence of a unified toolset that integrates the two modeling approaches has encumbered the systems engineers tasked with the design, architecture, and analysis of complex space communication networks and systems. This paper presents the unified approach and describes the motivation, challenges, and our solution - the customization of the network simulator to integrate with astronautical analysis software tools for high-fidelity end-to-end simulation. Keywords space; communication; systems; networking; simulation; modeling; QualNet; STK; integration; space networks

Barritt, Brian↗

Optimizing Transportation Networks for E-Waste Reverse Logistics: A Multi-Modal Cost Allocation and Pricing Strategy

The exponential growth of electronic waste (e-waste) poses critical challenges for sustainable reverse logistics and transportation network optimization. This study develops a dual-channel transportation framework for e-waste logistics that integrates dynamic freight pricing, cost allocation mechanisms, and game-theoretic coordination. The model captures interactions between centralized hubs and distributed processing networks, accounting for freight rate elasticity, volume allocation, and capacity constraints. Using Stackelberg game theory and cost-sharing strategies, the framework optimizes transportation efficiency and profit distribution across logistics channels. Numerical simulations show that the dual-channel structure increases centralized hub profit by 226.8% compared to baseline single-channel operations, while boosting total transported volume by 1.2% and nearly doubling freight collector profit under cost-sharing. Scenario analyses across regional infrastructures reveal that network density, policy incentives, and logistics costs shape routing efficiency and profit allocation. These findings suggest that coordinated strategies combining dynamic pricing, targeted infrastructure investment, and strategic cost allocation are needed to design efficient, resilient, and regionally adaptable e-waste transportation systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions in Synechococcus elongatus PCC 7942 despite limited accuracy in predicting direct regulator-gene interactions

Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO 2 -to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO 2 .

59 BASIC BIOLOGICAL SCIENCES↗

New Material for Surface-Enhanced Raman Spectroscopy

A chemical method of synthesis and application of coating materials that are especially suitable for surface-enhanced Raman spectroscopy (SERS) has been developed. The purpose of this development is to facilitate the utilization of the inherently high sensitivity of SERS to detect chemicals of interest (analytes) in trace amounts, without need for lengthy sample preparation. Up to now, the use of SERS has not become routine because the methods available have not been able to reproduce sampling conditions and provide quantitative measurements. In contrast, the coating materials of the present method enable analysis with minimum preparation of samples, and SERS measurements made using these materials are reproducible and reversible. Moreover, unlike in methods investigated in prior efforts to implement SERS, sampling is not restricted to such specific environments as electrolytes or specific solvents. The coating materials of this method are porous glasses, formed in sol-gel processes, that contain small particles of gold or silver metal. Materials of this type can be applied to the sample-contact surfaces of a variety of sampling and sensing devices, including glass slides, glass vials, fiber-optic probes, and glass tubes. Glass vials with their insides coated according to this method are particularly convenient for SERS to detect trace chemicals in solutions: One simply puts a sample solution containing the analyte(s) into a vial, then puts the vial into a Raman spectrometer for analysis. The chemical ingredients and the physical conditions of the sol-gel process have been selected so that the porous glass formed incorporates particles of the desired metal with size(s) to match the wavelength(s) of the SERS excitation laser in order to optimize the generation of surface plasmons. The ingredients and processing conditions have further been chosen to tailor the porosity and polarity of the glass to optimize the sample flow and the interaction between the analyte(s) and the plasmon field that generates Raman photons. The porous silica network of a sol-gel glass creates a unique environment for stabilizing SERS-active metal particles. Relative to other material structures that could be considered for SERS, the porous silica network offers higher specific surface area and thus greater interaction between analyte molecules and metal particles. Efforts to perform SERS measurements with the help of sampling devices coated by this method have been successful. In tests, numerous organic and inorganic chemicals were analyzed in several solvents, including water. The results of the tests indicate that the SERS measurements were reproducible within 10 percent and linear over five orders of magnitude. One measure of the limits of detectability of chemicals in these tests was found to be a concentration of 300 parts per billion. Further development may eventually make it possible to realize the full potential sensitivity of SERS for detecting some analytes in quantities as small as a single molecule.

Farquharson, Stuart↗

Unified feature association networks through integration of transcriptomic and proteomic data

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks. We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm disease associated mechanisms of disease.

59 BASIC BIOLOGICAL SCIENCES↗

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's ν e -appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. Here, the accuracy achieved on the test sample is ≥ 99%. For full neutrino interaction simulations, the time for processing one image is ≈ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Cohesive instability in elastomers: insights from a crosslinked Van der Waals fluid model

Abstract The resistance to volumetric deformations displayed by polymer networks is largely due to secondary and tertiary interactions between neighboring polymer chains. These interactions are both entropic and enthalpic in nature but are fundamentally different from the entropic forces that resist shearing in these networks. In this paper, we introduce a new depiction of elastomers as a crosslinked Van der Waals fluid. Starting from first principles, we develop constitutive equations that are implemented in a continuum model as well as a discrete network model. Our models predict that the failure of polymer networks may be driven by an instability in the underlying polymer bulk ‘fluid’ or by the breaking of polymer chains, depending on the loading path taken. The results of this study indicate that material failure in elastomers exposed to a purely triaxial state, such as in a poker chip experiment, may be driven by an entirely different mode of instability than those deformed in pure shear, such as in a uniaxial tension experiment.

Lamont, Samuel C.↗