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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deep reaction network exploration at a heterogeneous catalytic interface

Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga 3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Improved Mechanical Performance Fracture Properties and Reliability of Radical-Cured Thermosets

The aim of this study was to alter polymerization chemistry to improve network homogeneity in free-radical crosslinked systems. It was hypothesized that a reduction in heterogeneity of the network would lead to improved mechanical performance. Experiments and simulations were carried out to investigate the connection between polymerization chemistry, network structure and mechanical properties. Experiments were conducted on two different monomer systems - the first is a single monomer system, urethane dimethacrylate (UDMA), and the second is a two-monomer system consisting of bisphenol A glycidyl dimethacrylate (BisGMA) and triethylene glycol dimethacrylate (TEGDMA) in a ratio of 70/30 BisGMA/TEGDMA by weight. The methacrylate systems were crosslinked using traditional radical polymeriza- tion (TRP) with azobisisobutyronitrile (AIBN) or benzoyl peroxide (BPO) as an initiator; TRP systems were used as the control. The monomers were also cross-linked using activator regenerated by electron transfer atom transfer radical polymerization (ARGET ATRP) as a type of controlled radical polymerization (CRP). FTIR and DSC were used to monitor reac- tion kinetics of the systems. The networks were analyzed using NMR, DSC, X-ray diffraction (XRD), atomic force microscopy (AFM), and small angle X-ray scattering (SAXS). These techniques were employed in an attempt to quantify differences between the traditional and controlled radical polymerizations. While a quantitative methodology for characterizing net- work morphology was not established, SAXS and AFM have shown some promising initial results. Additionally, differences in mechanical behavior were observed between traditional and controlled radical polymerized thermosets in the BisGMA/TEGDMA system but not in the UDMA materials; this finding may be the result of network ductility variations between the two materials. Coarse-grained molecular dynamics simulations employing a novel model of the CRP reaction were carried out for the UDMA system, with parameters calibrated based on fully atomistic simulations of the UDMA monomer in the liquid state. Detailed metrics based on network graph theoretical approaches were implemented to quantify the bond network topology resulting from simulations. For a broad range of polymerization parameters, no discernible differences were seen between TRP and CRP UDMA simulations at equal conversions, although clear differences exist as a function of conversion. Both findings are consistent with experiments. Despite a number of shortcomings, these models have demonstrated the potential of molecular simulations for studying network topology in these systems.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Fast Transient Simulations for Multi-Segment Transmission Lines with a Graphical Model

This paper studies a computationally efficient algebraic graph theory engine for simulating time-domain one-dimensional waves in a multi-segment transmission line, such as for reflectometry applications. Efficient simulation of time-domain signals in multi-segment transmission lines is challenging because the number of propagation paths (and therefore the number of operations) increases exponentially with each new interface. We address this challenge through the use of a frequency-domain, algebraic graphical model of wave propagation, which is then converted to the time domain via the Fourier transform. We use this model to achieve an exact, stable, and computationally efficient (O(NQ), where N is the number of segments and Q is the bandwidth) approach for studying one-dimensional wave propagation. Our approach requires the reflection and transmission coefficients for each interface and each segment's complex propagation constant. Furthermore, we compare our simulation results with known analytical solutions.

42 ENGINEERING↗
DOE OSTI2018

Aggregation and data analysis of corrosion studies in molten chloride and fluoride salts

Corrosion studies in molten fluoride and chloride salts were surveyed, and key data were aggregated into a single dataset. Studies were graphed by salt purity, temperature, sample material, container material, and experimental method (loop, capsule, crucible). To elucidate and quantify the factors that affect corrosion in molten salts, a data analysis using a newly-defined corrosion resistance score and a correlation analysis using techniques borrowed from machine learning were performed. It is shown that salt purity had the strongest correlation with corrosion rates in molten chlorides and fluorides. As a result, data analysis across varied works was inconsistent due to the lack of standardization among molten salt corrosion studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Determining the Most Influencing Medical Conditions in MEDPRAT’s SIN Directed Graph

INTRODUCTION: The Susceptibility Inference Network (SIN) is a network of medical conditions, part of the Medical Extensible Probabilistic Risk Assessment Tool (MEDPRAT) developed by NASA to assess human health and medical risk to space exploration missions. The SIN is subject matter expert informed and acts as a prototype that provides relationships and dependencies between events modeled by MEDPRAT. Each vertex in the SIN has a weight which evaluates the severity of having the condition regardless of the progression from or to that condition. In this presentation, we consider two statistics to measure that stand alone risk: Quality Time Lost (QTL) and Loss of Crew Life (LOCL). Our goal is to identify the medical conditions that contribute the most to crew members QTL and LOCL risks due to progression of conditions in the network. We investigate how different computation parameters result in different condition rankings and address the choice of parameters that allows appropriate interventions to ensure space mission success. METHODS: The Katz score, one of many centrality measures created for ranking purposes in network analysis, takes into account all possible walks through the network, penalizing each additional step in a walk by a factor α called the Katz parameter. The literature does not provide specific values for the choice of α. We derive an analytical relationship between α and the maximum path length which has influence on the Katz score and ranking. Based on the probability of progression of each condition in the SIN, we identify that maximum path length of interest and calculate α that is then used in the Katz formula to rank the conditions in the SIN. RESULTS AND CONCLUSION: The effective probabilities of the SIN matrix generally fall below 10−6, which is below the level of the least influencing condition in the set. This corresponds to the probability of at most six consecutive progressions of a condition. Consequently, we calculate the Katz Parameter α and get 0.32. We rank the medical conditions and find that Acute Radiation Symptom is the condition the most prone to contribute to quality time loss due to progression.

risk analysis↗
DOE OSTI2022

On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Computational workflows that combine molecular dynamics (MD) simulations and emerging data-centric (DC) methods can accelerate the screening and analysis of solvent systems experimentally and computationally. Here, MD simulations provide atomic positions and velocities of reactant, solvent, and catalyst materials that can be manipulated into data representations that in turn can be used by DC techniques to conduct predictive modeling, feature extraction, and experimental design. For liquid-phase catalytic applications, emerging DC techniques such as Convolutional and Graph Neural Networks (CNN/GNN), Topological Data Analysis (TDA), and Active Learning (AL) can leverage MD and experimental data to quickly predict solvent effects on reaction outcomes. For instance, in recent studies, 3D solvent environments obtained with MD have been exploited by CNNs to predict experimental reaction rates for homogeneous acid-catalyzed lignocellulosic processes. In this perspective, we discuss basic principles of DC methods and how these can be combined with MD to enable high-throughput screening of solvent selection for diverse catalysis applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Lastly, overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A spatial superstructure approach to the optimal design of modular processes and supply chains

Modularity is a design principle that aims to provide flexibility for spatio-temporal assembly/disassembly and reconfiguration of systems. This design principle can be applied to multiscale (hierarchical) manufacturing systems that connect units, processes, facilities, and entire supply chains. Designing modular systems is challenging because of the need to capture spatial interdependencies that arise between system components due to product exchange/transport between components and due to product transformation in such components. In this work, we propose an optimization framework to facilitate the design of modular manufacturing systems. Central to our approach is the concept of a spatial superstructure, which is a graph that captures all possible system configurations and interdependencies between components. The spatial superstructure is a generalization of the notion of a superstructure and of a p-graph used in process design, in that it encodes spatial (geographical) context of the system components. Here, we show that this generalization facilitates the simultaneous design and analysis of processes, facilities, and of supply chains. Our framework aims to select the system topology from the spatial superstructure that minimizes design cost and that maximizes design modularity. We show that this design problem can be cast as a mixed-integer, multi-objective optimization formulation. We demonstrate these capabilities using a case study arising in the design of a plastic waste upcycling supply chain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Building a Consistent and Reproducible Database for Adsorption Evaluation in Covalent–Organic Frameworks

We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent-organic frameworks (COFs) reported in the literature, to characterize their CO 2 adsorption properties using the following steps: (1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory, (2) fitting atomic point charges based on the electron density, (3) characterizing the pore geometry of the structures before and after optimization, (4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally, (5) assessing the CO 2 parasitic energy via process modeling. The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1995

Affect of Brush Seals on Wave Rotor Performance Assessed

The NASA Lewis Research Center's experimental and theoretical research shows that wave rotor topping can significantly enhance gas turbine engine performance levels. Engine-specific fuel consumption and specific power are potentially enhanced by 15 and 20 percent, respectively, in small (e.g., 400 to 700 hp) and intermediate (e.g., 3000 to 5000 hp) turboshaft engines. Furthermore, there is potential for a 3- to 6-percent specific fuel consumption enhancement in large (e.g., 80,000 to 100,000 lbf) turbofan engines. This wave-rotor-enhanced engine performance is accomplished within current material-limited temperature constraints. The completed first phase of experimental testing involved a three-port wave rotor cycle in which medium total pressure inlet air was divided into two outlet streams, one of higher total pressure and one of lower total pressure. The experiment successfully provided the data needed to characterize viscous, partial admission, and leakage loss mechanisms. Statistical analysis indicated that wave rotor product efficiency decreases linearly with the rotor to end-wall gap, the square of the friction factor, and the square of the passage of nondimensional opening time. Brush seals were installed to further minimize rotor passage-to-cavity leakage. The graph shows the effect of brush seals on wave rotor product efficiency. For the second-phase experiment, which involves a four-port wave rotor cycle in which heat is added to the Brayton cycle in an external burner, a one-dimensional design/analysis code is used in conjunction with a wave rotor performance optimization scheme and a two-dimensional Navier-Stokes code. The purpose of the four-port experiment is to demonstrate and validate the numerically predicted four-port pressure ratio versus temperature ratio at pressures and temperatures lower than those that would be encountered in a future wave rotor/demonstrator engine test. Lewis and the Allison Engine Company are collaborating to investigate wave rotor integration in an existing turboshaft engine. Recent theoretical efforts include simulating wave rotor dynamics (e.g., startup and load-change transient analysis), modifying the one-dimensional wave rotor code to simulate combustion internal to the wave rotor, and developing an analytical wave rotor design/analysis tool based on macroscopic balances for parametric wave rotor/engine analysis.

Source record↗
NASA NTRS2010

Rocket-Plume Spectroscopy Simulation for Hydrocarbon-Fueled Rocket Engines

The UV-Vis spectroscopic system for plume diagnostics monitors rocket engine health by using several analytical tools developed at Stennis Space Center (SSC), including the rocket plume spectroscopy simulation code (RPSSC), to identify and quantify the alloys from the metallic elements observed in engine plumes. Because the hydrocarbon-fueled rocket engine is likely to contain C2, CO, CH, CN, and NO in addition to OH and H2O, the relevant electronic bands of these molecules in the spectral range of 300 to 850 nm in the RPSSC have been included. SSC incorporated several enhancements and modifications to the original line-by-line spectral simulation computer program implemented for plume spectral data analysis and quantification in 1994. These changes made the program applicable to the Space Shuttle Main Engine (SSME) and the Diagnostic Testbed Facility Thruster (DTFT) exhaust plume spectral data. Modifications included updating the molecular and spectral parameters for OH, adding spectral parameter input files optimized for the 10 elements of interest in the spectral range from 320 to 430 nm and linking the output to graphing and analysis packages. Additionally, the ability to handle the non-uniform wavelength interval at which the spectral computations are made was added. This allowed a precise superposition of wavelengths at which the spectral measurements have been made with the wavelengths at which the spectral computations are done by using the line-by-line (LBL) code. To account for hydrocarbon combustion products in the plume, which might interfere with detection and quantification of metallic elements in the spectral region of 300 to 850 nm, the spectroscopic code has been enhanced to include the carbon-based combustion species of C2, CO, and CH. In addition, CN and NO have spectral bands in 300 to 850 nm and, while these molecules are not direct products of hydrocarbon-oxygen combustion systems, they can show up if nitrogen or a nitrogen compound is present as an impurity in the propellants and/or these can form in the boundary layer as a result of interaction of the hot plume with the atmosphere during the ground testing of engines. Ten additional electronic band systems of these five molecules have been included into the code. A comprehensive literature search was conducted to obtain the most accurate values for the molecular and the spectral parameters, including Franck-Cordon factors and electronic transition moments for all ten band systems. For each elemental transition in the RPSSC, six spectral parameters - Doppler broadened line width at half-height, pressure-broadened line width at half-height, electronic multiplicity of the upper state, electronic term energy of the upper state, Einstein transition probability coefficient, and the atomic line center - are required. Input files have been created for ten elements of Ni, Fe, Cr, Co, Cu, Ca, Mn, Al, Ag, and Pd, which retain only relatively moderate to strong transitions in 300 to 430 nm spectral range for each element. The number of transitions in the input files is 68 for Ni; 148 for Fe; 6 for Cr; 87 for Co; 1 for Ca; 3 for Mn; 2 each for Cu, Al, and Ag; and 11 for Pd.

Tejwani, Gopal D.↗
DOE OSTI2015

NONLINEAR EFFECTS IN PARTICLE TRANSPORT IN STOCHASTIC MAGNETIC FIELDS

Collisional particle transport in stochastic magnetic fields is studied using a semi-analytical method. The aim is to determine the influence of the nonlinear effects that occur in the magnetic field line random walk on particle transport. We show that particle transport coefficients can be strongly influenced by the magnetic line trapping. The conditions that correspond to these nonlinear regimes are determined. We also analyze the effects produced by the space variation of the large-scale magnetic field. We show that an average drift is generated by the gradient of the magnetic field, which strongly increases and reverses its orientation in the nonlinear regime.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Active causal learning for decoding chemical complexities with targeted interventions

Abstract Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task—finding molecules with a large dipole moment—our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗
DOE OSTI2023

Ensuring thermodynamic consistency with invertible coarse-graining

Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insights by isolating the essential degrees of freedom that dictate the thermodynamic properties of a complex, condensed-phase system. The reduced complexity of the model typically leads to lower computational costs and more efficient sampling compared with atomistic models. Designing “good” coarse-grained models is an art. Generally, the mapping from fine-grained configurations to coarse-grained configurations itself is not optimized in any way; instead, the energy function associated with the mapped configurations is. In this work, we explore the consequences of optimizing the coarse-grained representation alongside its potential energy function. We use a graph machine learning framework to embed atomic configurations into a low-dimensional space to produce efficient representations of the original molecular system. Because the representation we obtain is no longer directly interpretable as a real-space representation of the atomic coordinates, we also introduce an inversion process and an associated thermodynamic consistency relation that allows us to rigorously sample fine-grained configurations conditioned on the coarse-grained sampling. We illustrate that this technique is robust, recovering the first two moments of the distribution of several observables in proteins such as chignolin and alanine dipeptide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

SU-E-T-277: Dose Calculation Comparisons Between Monaco, Pinnacle and Eclipse Treatment Planning Systems

Purpose: Monaco treatment planning system (TPS) version 5.0 uses a Monte-Carlo based dose calculation engine. The aim of this study is to verify and compare the Monaco based dose calculations with both Pinnacle{sup 3} collapsed cone convolution superposition (CCC) and Eclipse analytical anisotropic algorithm (AAA) calculations. Methods: For this study, previously treated SBRT lung, head and neck and abdomen patients were chosen to compare dose calculations between Pinnacle, Monaco and Eclipse. Plans were chosen from those that had been treated using the Elekta VersaHD or a NovalisTX linac. The plans included 3D conventional and IMRT beams using 6MV and 6MV Flattening filter free (FFF) photon beams. The original plans calculated with CCCS or AAA along with the recalculated ones using MC from the three TPS were exported into Velocity software for inter-comparison. Results: To compare the dose calculations, Mean Lung Dose (MLD), lung V5 and V20 values, and PTV Heterogeneity indexes (HI) and Conformity indexes (CI) were all calculated and recorded from the dose volume histograms (DVH). For each patient, the CI values were identical but there were differences in all other parameters. The HI was computed higher by 5 and 4% for calculated plans AAA and CCCS respectively, compared to the MC ones. The DVH graphs showed large differences between the CCCS and AAA and Monaco for 3D FFF, VMAT and IMRT plans. Better DVH agreement between was observed for 3D conventional plans. Conclusion: Better agreement was observed between CCCS and MC calculations than AAA and MC calculations. Those differences were more profound as the field size was decreasing and in the presence of inhomogeneities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.

chemical space exploration↗
DOE OSTI2018

SHAD: the Scalable High-performance Algorithms and Data-structures Library

The unprecedented amount of data that needs to be processed in emerging data analytics applications poses novel challenges to industry and academia. Scalability and high performance become more than a desirable feature because, due to the scale and the nature of the problems, they draw the line between what is achievable and what is unfeasible. In this paper, we propose SHAD, the Scalable High-performance Algorithms and Data-structures library. SHAD adopts a modular design that confines low level details and promotes reuse. SHAD's core is built on an Abstract Runtime Interface which enhances portability and identifies the minimal set of features of the underlying system required by the framework. The core library includes common data-structures such as: Array, Vector, Map and Set. These are designed to accommodate significant amount of data which can be accessed in massively parallel environments, and used as building blocks for SHAD extensions, i.e. higher level software libraries. We have validated and evaluated our design with a performance and scalability study of the core components of the library. We have validated the design flexibility by proposing a Graph Library as an example of SHAD extension, which implements two different graph data-structures; we evaluate their performance with a set of graph applications. Experimental results show that the approach is promising in terms of both performance and scalability. On a distributed system with 320 cores, SHAD Arrays are able to sustain a throughput of 65 billion operations per second, while SHAD Maps sustain 1 billion of operations per second. Algorithms implemented using the Graph Library exhibit performance and scalability comparable to a custom solution, but with smaller development effort.

Castellana, Vito G.↗