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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

GPU remote communication with triggered operations

Methods, devices, and systems for transmitting data over a computer communications network are disclosed. A queue of communications commands can be pre-generated using a central processing unit (CPU) and stored in a device memory of a network interface controller (NIC). Thereafter, if a graphics processing unit (GPU) has data to communicate to a remote GPU, it can store the data in a send buffer, where the location in the buffer is pointed to by a pre-generated command. The GPU can then signal to the interface device that the data is ready, triggering execution of the pre-generated command to send the data.

LeBeane, Michael W.↗
DOE OSTI2016

Utilization of mixed ligands to construct diverse Ni(II)-coordination polymers based on terphenyl-2,2′,4,4′-tetracarboxylic acid and varied N-donor co-ligands

Three new coordination polymers, namely, {[Ni(H_2L)(bix)(H_2O)_2]·2h_2O}{sub n} (1), {[Ni(HL)(Hdpa)(H_2O)_2]·H_2O}{sub n} (2), {[Ni(L)_0_._5(bpp)(H_2O)]·H_2O}{sub n} (3) (H{sub 4}L=terphenyl-2,2′,4,4′-tetracarboxylic acid; bix=1,4-bis(imidazol-1-ylmethyl)benzene; dpa =4,4′-dipyridylamine; bpp=1,3-bis(4-pyridyl)propane), based on rigid H{sub 4}L ligand and different N-donor co-ligands, have been synthesized under hydrothermal conditions. Compound 1 features a 3D 4-connected 6{sup 6}-dia-type framework with H{sub 4}L ligand adopts a μ{sub 2}-bridging mode with two symmetry-related carboxylate groups in μ{sub 1}-η{sup 1}:η{sup 0} monodentate mode. Compound 2 displays a 1D [Ni(HL)(Hdpa)]{sub n} ribbon chains motif, in which the H{sub 4}L ligand adopts a μ{sub 2}-bridging mode with two carboxylate groups in μ{sub 1}-η{sup 1}:η{sup 1} and μ{sub 1}-η{sup 1}:η{sup 0} monodentate modes, while 3 possesses a (4,4)-connected 3D frameworks with bbf topology, with H{sub 4}L ligand displays a μ{sub 4}-bridging coordination mode. The H{sub 4}L ligand displays not only different deprotonated forms but also diverse coordination modes and conformations. The structural diversities among 1–3 have been carefully discussed, and the roles of N-donor co-ligands in the self-assembly of coordination polymers have been well documented. - Graphical abstract: Three nickel coordination polymers with different architectures based on mixed ligand system were synthesized and structurally characterized. Topology analyses indicate that 1 shows the 4-connected 6{sup 6}-dia net, 1D ribbon chains for 2 and 3D (4,4)-connected bbf network for 3. Display Omitted - Highlights: • Three Ni-based coordination polymers with distinct features have been prepared. • Compound 1 features a 3D 4-connected 66-dia-type framework, 2 displays a 1D [Ni(HL)(Hdpa)]{sub n} ribbon chains motif, while 3 possesses a (4,4)-connected 3D frameworks with bbf topology. • The “mixed ligand assembled” strategy is significant potential for network design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

GUI Control System for the Mu2e Electrostatic Septum High Voltage at Fermilab

The Mu2e Experiment has stringent beam structure requirements; namely, its proton bunches with a time structure of 1.7 $\mu$s in the Fermilab Delivery Ring. This beam structure will be delivered using the Fermilab 8-GeV Booster, the 8-GeV Recycler Ring, and the Delivery Ring. The 1.7-$\mu$s period of the Delivery Ring will generate the required beam structure by means of a third order resonant extraction system operating on a single circulating bunch. The electrostatic septum (ESS) for this system is particularly challenging, requiring mechanical precision in a ultra high vacuum of 1 x 10$^-8$ Torr to generate 100 kV across 15 mm. This paper describes a graphical user interface that has been developed to automate the conditioning and commissioning process for the electrostatic septa. It is based on an interface to the Fermilab ACNET system using the ACSys Python Data Pool Manager (DPM) Client produced and maintained by Fermilab Accelerator Controls. Network interfacing between data pool managers made by the application and ACNET devices introduce an inherent (approximately 1 s) latency in throughput of the readouts. This delay is utilized to process and graph incoming data events of devices crucial to conditioning of a electrostatic septum (ESS). 'Ramping' and 'Monitoring' modes adjust settings of the power supply based on internal logic to efficaciously increase and maintain the high voltage (HV) in the ESS, easing the voltage setting on incidence of sparking or other possibly damaging events. A timestamped log file is produced as the application runs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Artificial Reasoning System for Symptom-Based Conditional Failure Probability Estimation Using Bayesian Network

Advances in nuclear power technologies require enhanced capabilities for operator advice and autonomous control. One of the first tasks in the development of such capabilities is the formulation of symptom-based conditional failure probabilities for structures, systems, and components (SSCs) of interest, for which the primary goal is to aid plant personnel in deducing the probabilistic performance status of the monitored SSCs and in detecting impending faults/failure. The task of conditional failure probability estimation is a bidirectional inference problem and shall be logically tackled by the Bayesian network (BN) approach. As a knowledge-based artificial intelligence tool and a probabilistic graphical model, BN offers the capability of reasoning under uncertainty and graphical representation emulating the physical behavior of the target SSC. This paper provides a systematic overview of the BN technique and the software tools for handling implementation of BN models, along with the associated knowledge representation and reasoning paradigm. Both operational data and expert judgement can be readily incorporated into the knowledge base of a BN model. The challenges with data availability are highlighted, and the general approach to target SSC identification is presented. Our focus is upon failure-prone and risk-important balance of plant assets, especially cases having strong operator involvement. An exemplary case study on the failure of a motor-driven centrifugal pump is also conducted to demonstrate the usefulness and technical feasibility of the proposed artificial reasoning system using an expert system shell.

Zhao, Xingang↗
DOE OSTI2016

Selectively catalytic activity of metal–organic frameworks depending on the N-position within the pyridine ring of their building blocks

Iron metal–organic frameworks (MOFs) [Fe(L){sub 2}(SCN){sub 2}]{sub ∝} (L1: 4-bpdh=2,5-bis(4-pyridyl)-3,4-diaza-2,4-hexadiene for 1Fe; and L2: 3-bpdh=2,5-bis(3-pyridyl)-3,4-diaza-2,4-hexadiene for 2Fe) were assembled in a MeOH–H{sub 2}O solvent system. 1Fe exhibits a two-dimensional extended-grid network, whereas 2Fe exhibits a stair-like double-chain; the N-position within the pyridine ring of the complexes was observed to regulate the MOF structure as layers or chains. Furthermore, selectively catalytic activity was observed for the layered MOF but not the chain-structured MOF; micro/nanoparticles of the layered MOF were therefore investigated for new potential applications of micro/nano MOFs. - Graphical abstract: Iron metal–organic frameworks (MOFs) [Fe(L){sub 2}(SCN){sub 2}]{sub ∝} (L1: 4-bpdh=2,5-bis(4-pyridyl)-3,4-diaza-2,4-hexadiene for 1Fe; and L2: 3-bpdh=2,5-bis(3-pyridyl)-3,4-diaza-2,4-hexadiene for 2Fe) were assembled in a MeOH–H{sub 2}O solvent system. The N-position within the pyridine ring of the complexes was observed to regulate the MOF structure as layers or chains. Selectively catalytic activity was observed for the layered MOF but not the chain-structured MOF. - Highlights: • Synthesis and structure of metal–organic framework [Fe(L){sub 2}(SCN){sub 2}]{sub ∝}. • Selectively catalytic activity depending on the N-position within the pyridine ring. • The degradation and conversion of methyl orange.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Machine Learning Tools Set for Natural Gas Fuel Cell System Design

This study is focusing on leveraging the system design tools set for the next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design, and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides a user-friendly graphic user interface built within Jupyter Notebooks, and the complete tools set is open-source public available.

Wang, Dewei↗
DOE OSTI2022

GraphAlign: Graph-Enabled Machine Learning for Seismic Event Filtering

This report summarizes results from a 2 year effort to improve the current automated seismic event processing system by leveraging machine learning models that can operated over the inherent graph data structure of a seismic sensor network. Specifically, the GraphAlign project seeks to utilize prior information on which stations are more likely to detect signals originating from particular geographic regions to inform event filtering. To date, the GraphAlign team has developed a Graphical Neural Network (GNN) model to filter out false events generated by the Global Associator (GA) algorithm. The algorithm operates directly on waveform data that has been associated to an event by building a variable sized graph of station waveforms nodes with edge relations to an event location node. This builds off of previous work where random forest models were used to do the same task using hand crafted features. The GNN model performance was analyzed using an 8 week IMS/IDC dataset, and it was demonstrated that the GNN outperforms the random forest baseline. We provide additional error analysis of which events the GNN model performs well and poorly against concluded by future directions for improvements.

58 GEOSCIENCES↗
DOE OSTI2019

Urban Deployment Model (UDM) v1.0

The software, called the Urban Deployment Model (UDM), is an integrated set of tools for the simulation and analysis of static and mobile radiation detection systems in complex urban environments. The UDM includes a graphical user interface through which the workflow associated with setting up and executing a simulation may be established. The modular toolsets combine aspects of radiation transport, navigation, graph theory, optimization, and visualization to allow the complete end-to-end simulation of large scale, highly-distributed sensor networks.

Abgrall, Nicolas↗
DOE OSTI2017

Software Comparison for Renewable Energy Deployment in a Distribution Network

The main objective of this report is to evaluate different software options for performing robust distributed generation (DG) power system modeling. The features and capabilities of four simulation tools, OpenDSS, GridLAB-D, CYMDIST, and PowerWorld Simulator, are compared to analyze their effectiveness in analyzing distribution networks with DG. OpenDSS and GridLAB-D, two open source software, have the capability to simulate networks with fluctuating data values. These packages allow the running of a simulation each time instant by iterating only the main script file. CYMDIST, a commercial software, allows for time-series simulation to study variations on network controls. PowerWorld Simulator, another commercial tool, has a batch mode simulation function through the 'Time Step Simulation' tool, which obtains solutions for a list of specified time points. PowerWorld Simulator is intended for analysis of transmission-level systems, while the other three are designed for distribution systems. CYMDIST and PowerWorld Simulator feature easy-to-use graphical user interfaces (GUIs). OpenDSS and GridLAB-D, on the other hand, are based on command-line programs, which increase the time necessary to become familiar with the software packages.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Assessing electrogenetic activation via a network model of biological signal propagation

Introduction: Molecular communication is the transfer of information encoded by molecular structure and activity. We examine molecular communication within bacterial consortia as cells with diverse biosynthetic capabilities can be assembled for enhanced function. Their coordination, both in terms of engineered genetic circuits within individual cells as well as their population-scale functions, is needed to ensure robust performance. We have suggested that “electrogenetics,” the use of electronics to activate specific genetic circuits, is a means by which electronic devices can mediate molecular communication, ultimately enabling programmable control. Methods: Here, we have developed a graphical network model for dynamically assessing electronic and molecular signal propagation schemes wherein nodes represent individual cells, and their edges represent communication channels by which signaling molecules are transferred. We utilize graph properties such as edge dynamics and graph topology to interrogate the signaling dynamics of specific engineered bacterial consortia. Results: We were able to recapitulate previous experimental systems with our model. In addition, we found that networks with more distinct subpopulations (high network modularity) propagated signals more slowly than randomized networks, while strategic arrangement of subpopulations with respect to the inducer source (an electrode) can increase signal output and outperform otherwise homogeneous networks. Discussion: We developed this model to better understand our previous experimental results, but also to enable future designs wherein subpopulation composition, genetic circuits, and spatial configurations can be varied to tune performance. We suggest that this work may provide insight into the signaling which occurs in synthetically assembled systems as well as native microbial communities.

Chun, Kayla↗
DOE OSTI2016

Assembly of 4-, 6- and 8-connected Cd(II) pseudo-polymorphic coordination polymers: Synthesis, solvent-dependent structural variation and properties

The reaction of Cd(NO{sub 3}){sub 2}·4H{sub 2}O, 2,5-thiophenedicarboxylic acid (H{sub 2}tdc) and 1,2-bis(imidazol-1′-yl)methane (bimm) by modulating solvent systems yielded three highly connected pseudo-polymorphic coordination polymers based on different dinuclear [Cd{sub 2}(CO{sub 2}){sub 2}] subunits bridged by carboxylate groups. Single crystal structural analyses reveal structural variation from 4-connected 2D sql layer, 6-connected 2-fold interpenetrated 3D pcu to 8-connected 3D bcu-type network in compounds 1–3. The structural dissimilarity in the structures dependent on the coordination environments of Cd(II) ions and linking modes of mixed ligand influenced by different solvent systems during the synthesis process. Moreover, thermogravimetric and photoluminescence behaviors of 1–3 were also investigated for the first time, and all the complexes emit blue luminescence in the solid state. - Graphical abstract: Key Topic. Different solvent systems modulated three Cd(II) pseudo-polymorphic coordination polymers based on thiophene-2,5-dicarboxylate and 1,2-bis(imidazol-1′-yl)methane mixed ligands. Display Omitted - Highlights: • Three solvent-dependent Cd(II) pseudo-polymorphic coordination polymers have been synthesized. • Structural variation from 4-connected 2D layer, 6-connected 2-fold interpenetrated 3D net to 8-connected 3D net. • All complexes emit blue luminescence.

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

Accelerating high-order continuum kinetic plasma simulations using multiple GPUs

Kinetic plasma simulations solve the Vlasov-Poisson or Vlasov-Maxwell equations to evolve scalar-variable distribution functions in position-velocity phase space and vector-variable electromagnetic fields in configuration space. The immense computational cost of evolving high-dimensional variables, and their large number of degrees of freedom, often limits the utility of continuum kinetic simulations and presents a challenge when it comes to accurately simulating real-world physical phenomena. To address this challenge, we present techniques that accelerate and minimize the computational work required for a scalable Vlasov-Poisson solver. We show theoretical hardware compute and communication bounds for solving a fourth-order finite-volume Vlasov-Poisson system. These bounds are then used to inform and evaluate the design of performance portable algorithms for a multiple graphics processing unit (GPU) accelerated version of the Vlasov-Poisson solver VCK-CPU [1]. We demonstrate that the multi-GPU Vlasov solver implementation, VCK-GPU, simultaneously minimizes required inter-process data transfer while also being bounded by the machine network performance limits. This results in an overall strong scaling speedup per timestep of up to 40x in three-dimensional phase space (one position, two velocity coordinates) and 54x in four dimensional phase space (two position, two velocity coordinates) and a 341x increase in simulation throughput of the GPU accelerated code over the existing CPU code. The GPU code is also able to weak scale up to 256 compute nodes and 1024 GPUs. In conclusion, we demonstrate that the improved compute performance enables exploring configurations which were previously computationally infeasible, including resolving fine-scale distribution function filamentation and multi-species dynamics with realistic electron-proton mass ratios.

Continuum kinetics↗
DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

New vanadium tellurites: Syntheses, structures, optical properties of noncentrosymmetric VTeO{sub 4}(OH), centrosymmetric Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})

Two new vanadium tellurites, VTeO{sub 4}(OH) (1) and Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2), have been synthesized successfully with the use of hydrothermal reactions. The crystal structures of the two compounds were determined by single-crystal X-ray diffraction. Compound 1 crystallizes in the polar space group Pca2{sub 1} (No. 29) while compound 2 crystallizes in the centrosymmetric space group C2/c (No. 15). The topography of compound 1 reveals a two-dimensional, layered structure comprised of VO{sub 6} octahedral chains and TeO{sub 3}(OH) zig-zag chains. Compound 2, on the contrary, features a three-dimensional [V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})]{sup 4-} anionic framework with Ba{sup 2+} ions filled into the 10-member ring helical tunnels. The [V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})]{sup 4-} anionic network is the first 3D vanadium tellurite framework to be discovered in the alkaline-earth vanadium tellurite system. Powder second harmonic generation (SHG) measurements indicate that compound 1 shows a weak SHG response of about 0.3×KDP (KH{sub 2}PO{sub 4}) under 1064 nm laser radiation. Infrared spectroscopy, elemental analysis, thermal analysis, and dipole moment calculations have also been carried out. - Graphical abstract: VTeO{sub 4}(OH) (1) crystallizes in the noncentrosymmetric space group Pca2{sub 1} (No. 29) while Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2) crystallizes in the centrosymmetric space group C2/c (No. 15). - Highlights: • VTeO{sub 4}(OH) (1) and Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2) have been synthesized successfully with the use of hydrothermal reactions. • VTeO{sub 4}(OH) (1) crystallizes in the noncentrosymmetric space group Pca2{sub 1} and displays a weak SHG response. • VTeO{sub 4}(OH) (1) represents only the fourth SHG-active material found in vanadium tellurite systems. • Ba{sub 2}V{sub 4}O{sub 8}(Te{sub 3}O{sub 10}) (2) exhibits a novel three-dimensional [V{sub 4}O{sub 8}(Te{sub 3}O{sub 10})]{sup 4-} anionic framework.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Target tracking via recursive Bayesian state estimation in cognitive radar networks

To cope with complicated environments and stealthier targets, incorporating intelligence and cognition cycles into target tracking is of great importance in modern sensor network management. With remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. In this paper, we propose a general framework for single target tracking in cognitive networks of radars, including consideration of waveform design, path planning, and radar selection, which are separately but not jointly taken into account in existing work. The tracking procedure, built on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), is formulated as two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, an illustrative example introduces a specific scenario in 2-D space. Simulation results based on the scenario demonstrate that the proposed framework can accurately track the target under the management of the network of radars.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Web Extensible Display Manager

Jefferson Lab's Web Extensible Display Manager (WEDM) allows staff to access EDM control system screens from a web browser in remote offices and from mobile devices. Native browser technologies are leveraged to avoid installing and managing software on remote clients such as browser plugins, tunnel applications, or an EDM environment. Since standard network ports are used firewall exceptions are minimized. To avoid security concerns from remote users modifying a control system, WEDM exposes read-only access and basic web authentication can be used to further restrict access. Updates of monitored EPICS channels are delivered via a Web Socket using a web gateway. The software translates EDM description files (denoted with the edl suffix) to HTML with Scalable Vector Graphics (SVG) following the EDM's edl file vector drawing rules to create faithful screen renderings. The WEDM server parses edl files and creates the HTML equivalent in real-time allowing existing screens to work without modification. Alternatively, the familiar drag and drop EDM screen creation tool can be used to create optimized screens sized specifically for smart phones and then rendered by WEDM.

Slominski, Ryan↗
DOE OSTI2022

Heterosynaptic plasticity in biomembrane memristors controlled by pH

Abstract In biology, heterosynaptic plasticity maintains homeostasis in synaptic inputs during associative learning and memory, and initiates long-term changes in synaptic strengths that nonspecifically modulate different synapse types. In bioinspired neuromorphic circuits, heterosynaptic plasticity may be used to extend the functionality of two-terminal, biomimetic memristors. In this article, we explore how changes in the pH of droplet interface bilayer aqueous solutions modulate the memristive responses of a lipid bilayer membrane in the pH range 4.97–7.40. Surprisingly, we did not find conclusive evidence for pH-dependent shifts in the voltage thresholds ( V* ) needed for alamethicin ion channel formation in the membrane. However, we did observe a clear modulation in the dynamics of pore formation with pH in time-dependent, pulsed voltage experiments. Moreover, at the same voltage, lowering the pH resulted in higher steady-state currents because of increased numbers of conductive peptide ion channels in the membrane. This was due to increased partitioning of alamethicin monomers into the membrane at pH 4.97, which is below the pKa (~5.3–5.7) of carboxylate groups on the glutamate residues of the peptide, making the monomers more hydrophobic. Neutralization of the negative charges on these residues, under acidic conditions, increased the concentration of peptide monomers in the membrane, shifting the equilibrium concentrations of peptide aggregate assemblies in the membrane to favor greater numbers of larger, increasingly more conductive pores. It also increased the relaxation time constants for pore formation and decay, and enhanced short-term facilitation and depression of the switching characteristics of the device. Modulating these thresholds globally and independently of alamethicin concentration and applied voltage will enable the assembly of neuromorphic computational circuitry with enhanced functionality. Impact statement We describe how to use pH as a modulatory “interneuron” that changes the voltage-dependent memristance of alamethicin ion channels in lipid bilayers by changing the structure and dynamical properties of the bilayer. Having the ability to independently control the threshold levels for pore conduction from voltage or ion channel concentration enables additional levels of programmability in a neuromorphic system. In this article, we note that barriers to conduction from membrane-bound ion channels can be lowered by reducing solution pH, resulting in higher currents, and enhanced short-term learning behavior in the form of paired-pulse facilitation. Tuning threshold values with environmental variables, such as pH, provide additional training and learning algorithms that can be used to elicit complex functionality within spiking neural networks. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2020

ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists

Recently, advances in machine learning and artificial intelligence have been playing more and more critical roles in a wide range of areas. For the last several years, industries have shown that how learning from data, identifying patterns and making decisions with minimal human intervention can be extremely useful to their business (e.g., image classification, recommending a product to a customer, finding friends in a social network, predicting customers actions, etc.). These success stories have been motivating scientists who study physics, chemistry, materials, medicine, and many other subjects, to explore a new pathway of utilizing machine learning techniques like regression and classification for their scientific activities. However, most existing machine learning tools, systems, and methodologies have been developed for programming experts but not for scientists (or any users) who have no or little knowledge of programming. ASCENDS is a toolkit that is developed to assist scientists (or any persons) who want to use their data for machine learning tasks, more specifically, correlation analysis, regression, and classification. ASCENDS does not require programming skills. Instead, it provides a set of simple but powerful CLI (Command Line Interface) and GUI (Graphic User Interface) tools for non-data scientists to be able to intuitively perform advanced data analysis and machine learning techniques. ASCENDS has been implemented by wrapping around opensource software including Keras, TensorFlow, and scikit-learn.

97 MATHEMATICS AND COMPUTING↗