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Survey on stochastic distribution systems: A full probability density function control theory with potential applications
Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.
Copper complexes: cytotoxicity and transport possibilities
A copper complex with N-(5,6-dihydro-4H-1,3-thiazin-2-yl)benzamide was synthesized for the first time and characterized by {sup 1}H NMR spectroscopy, LETDY mass spectrometry, and elemental analysis. A comparative study of the obtained complex and copper 2-aminopyrimidine complex was performed by flow cytometry on normal lymphocytes and Jurkat cells. Complexes with radionuclides {sup 64,67}Cu (with and wihout a carrier) were prepared, and distribution of the labeled complexes in the organs of mice was considered. Packing in carboxymethylcellulose-based microgels was proposed as a transport form for the complexes. The results show a promise of the obtained materials as potential radiopharmaceutical agents.
Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis
The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existing graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.
SU-G-BRB-02: An Open-Source Software Analysis Library for Linear Accelerator Quality Assurance
Purpose: Routine linac quality assurance (QA) tests have become complex enough to require automation of most test analyses. A new data analysis software library was built that allows physicists to automate routine linear accelerator quality assurance tests. The package is open source, code tested, and benchmarked. Methods: Images and data were generated on a TrueBeam linac for the following routine QA tests: VMAT, starshot, CBCT, machine logs, Winston Lutz, and picket fence. The analysis library was built using the general programming language Python. Each test was analyzed with the library algorithms and compared to manual measurements taken at the time of acquisition. Results: VMAT QA results agreed within 0.1% between the library and manual measurements. Machine logs (dynalogs & trajectory logs) were successfully parsed; mechanical axis positions were verified for accuracy and MLC fluence agreed well with EPID measurements. CBCT QA measurements were within 10 HU and 0.2mm where applicable. Winston Lutz isocenter size measurements were within 0.2mm of TrueBeam’s Machine Performance Check. Starshot analysis was within 0.2mm of the Winston Lutz results for the same conditions. Picket fence images with and without a known error showed that the library was capable of detecting MLC offsets within 0.02mm. Conclusion: A new routine QA software library has been benchmarked and is available for use by the community. The library is open-source and extensible for use in larger systems.
Capturing the Physics of MaNGA Galaxies with Self-supervised Machine Learning
As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important. In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of increasing high dimensionality and complex data sets, which challenges the traditional methods used to extract the physical information they contain. Here, we present the use of a novel self-supervised machine-learning method to visualize the multidimensional information on stellar population and kinematics in the MaNGA survey in a 2D plane. Our framework is insensitive to nonphysical properties such as the size of the integral field unit and is therefore able to order galaxies according to their resolved physical properties. Using the extracted representations, we study how galaxies distribute based on their resolved and global physical properties. We show that even when exclusively using information about the internal structure, galaxies naturally cluster into two well-known categories, rotating main-sequence disks and massive slow rotators, from a purely data-driven perspective, hence confirming distinct assembly channels. Low-mass rotation-dominated quenched galaxies appear as a third cluster only if information about the integrated physical properties is preserved, suggesting a mixture of assembly processes for these galaxies without any particular signature in their internal kinematics that distinguishes them from the two main groups. The framework for data exploration is publicly released with this publication, ready to be used with the MaNGA or other integral field data sets.
Enriched immersed finite element and isogeometric analysis: algorithms and data structures
Immersed finite element methods provide a convenient analysis framework for problems involving geometrically complex domains, such as those found in topology optimization and microstructures for engineered materials. However, their implementation remains a major challenge due to, among other things, the need to apply nontrivial stabilization schemes and generate custom quadrature rules. This article introduces the robust and computationally efficient algorithms and data structures comprising an immersed finite element preprocessing framework. The input to the preprocessor consists of a background mesh and one or more geometries defined on its domain. The output is structured into groups of elements with custom quadrature rules formatted such that common finite element assembly routines may be used without or with only minimal modifications. The key to the preprocessing framework is the construction of material topology information, concurrently with the generation of a quadrature rule, which is then used to perform enrichment and generate stabilization rules. While the algorithmic framework applies to a wide range of immersed finite element methods using different types of meshes, integration, and stabilization schemes, the preprocessor is presented within the context of the extended isogeometric analysis. This method utilizes a structured B-spline mesh, a generalized Heaviside enrichment strategy considering the material layout within individual basis functions’ supports, and face-oriented ghost stabilization. Using a set of examples, the effectiveness of the enrichment and stabilization strategies is demonstrated alongside the preprocessor’s robustness in geometric edge cases. Additionally, the performance and parallel scalability of the implementation are evaluated.
The Influence of Temperature on the Cyclic Properties of the Transversely Isotropic Nanocomposite System Under Kinematic Harmonic Loading
A micromechanical model is developed to determine the effective inelastic properties of nanocomposites under monoharmonic deformation by taking into account the detailed microstructural geometries and constitutive models of the constituents. By using the correspondence principle in viscoelasticity and the modified Mori–Tanaka method, the effects of interface between the inclusion and the matrix are taken into account. By applying the developed model, we perform the numerical analysis aimed at the determination of complex moduli for a polymeric nanocomposite reinforced with nanofibers composed from carbon nanotubes under the isothermal conditions. The dependences of complex moduli on temperature and the amplitude of strain intensity are analyzed. The composites reinforced with unidirectionally aligned nanofibers are considered. The accumulated results demonstrate a strong dependence of the storage and loss moduli on temperature within a broad temperature range. The storage and loss moduli are found to monotonically increase with the volume fraction of nanofibers. At the same time, they decrease as temperature increases. The obtained results show that the strength of material decreases as temperature increases in the elastic and inelastic regions and the inelastic behavior occurs for lower strain amplitude as temperature increases.
Ultrafast Energy Transfer Involving the Red Chlorophylls of Cyanobacterial Photosystem I Probed through Two-Dimensional Electronic Spectroscopy
Photosystem I (PSI) is a naturally occurring light-harvesting complex that drives oxygenic photosynthesis through a series of photoinitiated transmembrane electron transfer reactions that occur with a high quantum efficiency. Understanding the mechanism by which this process occurs is fundamental to understanding the near-unity quantum efficiency of PSI and in turn could lead to further insight into PSI-based technologies for solar energy conversion. In this article, we have applied two-dimensional electronic spectroscopy to PSI complexes isolated from two different cyanobacterial strains to gain further insight into the ultrafast energy transfer in PSI. The PSI complexes studied differ in the number and absorption of the red chlorophylls, chlorophylls that lie to lower energies than the reaction center. By applying a global analysis to the 2D electronic spectra of the PSI complexes we extract 2D decay associated spectra (2D-DAS). Through analysis of the 2D-DAS we observe a 50 fs relaxation among the bulk antenna chlorophylls in addition to two pathways of energy equilibration involving the red chlorophylls: a fast 200 fs equilibration followed by a 2–4 ps equilibration. As demonstrated with a model system, the λ1, λ3 coordinates of the cross-peaks in the 2D-DAS spectra indicate that the two equilibration pathways involve different chlorophyll molecules.
A numerical study of process complexity in permafrost dominated regions
Numerical modeling of permafrost dynamics requires adequate representation of atmospheric and surface processes, a reasonable parameter estimation strategy, and site-specific model development. The three main research objectives of the study are: (i) to propose a novel methodology that determines the required level of surface process complexity of permafrost models by conducting parameter sensitivity and calibration, (ii) to design and compare three numerical models of increasing surface process complexity, and (iii) to calibrate and validate the numerical models at the Yakou catchment on the Qinghai-Tibet Plateau as an exemplary study site. The calibration was carried out by coupling the Advanced Terrestrial Simulator (numerical model) and PEST (calibration tool). Simulation results showed that (i) A simple numerical model that considers only subsurface processes can simulate active layer development with the same accuracy as other more complex models that include surface processes. (ii) Peat and mineral soil layer permeability, Van Genuchten alpha, and porosity are highly sensitive. (iii) Liquid precipitation aids in increasing the rate of permafrost degradation. (iv) Deposition of snow insulated the subsurface during the thaw initiation period. We have developed and released an integrated code that couples the numerical software ATS to the calibration software PEST. The numerical model can be further used to determine the impacts of climate change on permafrost degradation.
Nested polynomial trends for the improvement of Gaussian process-based predictors
The role of simulation keeps increasing for the sensitivity analysis and the uncertainty quantification of complex systems. Such numerical procedures are generally based on the processing of a huge amount of code evaluations. When the computational cost associated with one particular evaluation of the code is high, such direct approaches based on the computer code only, are not affordable. Surrogate models have therefore to be introduced to interpolate the information given by a fixed set of code evaluations to the whole input space. When confronted to deterministic mappings, the Gaussian process regression (GPR), or kriging, presents a good compromise between complexity, efficiency and error control. Such a method considers the quantity of interest of the system as a particular realization of a Gaussian stochastic process, whose mean and covariance functions have to be identified from the available code evaluations. In this context, this work proposes an innovative parametrization of this mean function, which is based on the composition of two polynomials. This approach is particularly relevant for the approximation of strongly non linear quantities of interest from very little information. After presenting the theoretical basis of this method, this work compares its efficiency to alternative approaches on a series of examples.
Effects of Non-Electrostatic Intermolecular Interactions on the Phase Behavior of pH-Sensitive Polyelectrolyte Complexes
Polyelectrolyte complexes (PECs) offer enormous material tunability and desirable functionalities, and consequently have found broad utility in biomedical and material industries. While poly(acrylic acid) (PAA) and poly( allylamine hydrochloride) (PAH) are a commonly used pairing, various aspects of the phase behavior of PAA-PAH complexes have not been sufficiently quantified. In this work, we present a comprehensive experimental study depicting the binodal phase boundaries for the PAA-PAH complexes prepared under acidic, neutral, and basic conditions using thermogravimetric analysis, turbidimetry, and optical microscopy. Under neutral and basic conditions, phase behaviors of the complexes were largely similar to one another and followed general expectations of PEC phase behavior, except for unusually high resistance to disruption of the complex with added salt. Stable complexes are observed up to 4 M NaCl concentrations. Under acidic conditions, strikingly different phase behaviors of the PAA-PAH complexes were observed. The polymer content in the complex phase increased initially, followed by an expected decrease as salt was added to the complexes. This behavior may result from a combination of associative phase separation of PAA and PAH chains, influenced by electrostatic interactions, and segregative phase separation, which can be ascribed to the influence of a combination of the hydrophobic interactions of the aliphatic polymer backbone and the interpolymer hydrogen bonding of unionized acrylic monomer units. Our systematic investigations over a range of pH detailing these discrepancies in the PAA-PAH phase behavior are expected to clarify the inconsistencies among the reports in the literature and provide the material design strategies for practical use of the PAA-PAH complexes and multilayer assemblies.
Structural and mechanistic insights into activation of the human RNA ligase RTCB by Archease
Abstract RNA ligases of the RTCB-type play an essential role in tRNA splicing, the unfolded protein response and RNA repair. RTCB is the catalytic subunit of the pentameric human tRNA ligase complex. RNA ligation by the tRNA ligase complex requires GTP-dependent activation of RTCB. This active site guanylylation reaction relies on the activation factor Archease. The mechanistic interplay between both proteins has remained unknown. Here, we report a biochemical and structural analysis of the human RTCB-Archease complex in the pre- and post-activation state. Archease reaches into the active site of RTCB and promotes the formation of a covalent RTCB-GMP intermediate through coordination of GTP and metal ions. During the activation reaction, Archease prevents futile RNA substrate binding to RTCB. Moreover, monomer structures of Archease and RTCB reveal additional states within the RNA ligation mechanism. Taken together, we present structural snapshots along the reaction cycle of the human tRNA ligase.
Preparation of the Multi-Site Data Processing at the Vera C. Rubin Observatory
The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) Camera is scheduled to start taking data in the summer of 2025. The Data Release Production will run the LSST Science Pipe software at data facilities in the US, France and the UK. The LSST Science Pipeline consists of complex directed acyclic graphs (DAGs) of tasks. Rubin will use the Production and Distributed Analysis (PanDA) workflow and workload management system to orchestrate this complex workflow and the distribution of workloads to the data facilities. When run end-to-end by a team of data production staff, this processing (the Science Pipelines, distributed by the workflow and workload management system) is referred to as a 'campaign'. This paper describes the central services and data facility specific services that support this multi-site data process model, including the service deployment infrastructure, the workload and workflow system, the Campaign Management tools, and connection to Rubin Data Management. This paper will also mention the experience of processing the Rubin Commissioning Camera data. All these are part of the effort to scale up the processing capabilities for the expected very large data volume from the LSST Camera.
An Experimental Investigation of Human Performance Differences Depending on Simulator Complexity
As a different approach to collect human reliability analysis (HRA) data compared to the full-scope simulator studies, Idaho National Laboratory (INL) has attempted to collect HRA data based on Simplified Human Error Experimental Program (SHEEP), which uses a simplified simulator and student participants. To date, INL has considered the SHEEP approach using simplified simulators such as Rancor Microworld and Compact Nuclear Simulator to complement – not replace – full-scope studies as well as to mainly collect HRA data for estimating nominal/basic human error probabilities (HEPs) needed in the HRA quantification process. This study is a part of the project aiming to suggest how to support full-scope data collection studies based on SHEEP. This paper first introduces major tasks within the SHEEP framework. Then, as one of the major tasks, why and how we have planned to experimentally investigate human performance differences depending on simulator complexity are mainly introduced in this paper.
Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design
We developed path-wise information theory-based and goal-oriented sensitivity analysis and parameter identification methods for complex high-dimensional dynamics and in particular of non-equilibrium extended molecular systems. The combination of these novel methodologies provided the first methods in the literature which are capable to handle UQ questions for stochastic complex systems with some or all of the following features: (a) multi-scale stochastic models such as (bio)chemical reaction networks, with a very large number of parameters, (b) spatially distributed systems such as Kinetic Monte Carlo or Langevin Dynamics, (c) non-equilibrium processes typically associated with coupled physico-chemical mechanisms, driven boundary conditions, hybrid micro-macro systems, etc. A particular computational challenge arises in simulations of multi-scale reaction networks and molecular systems. Mathematical techniques were applied to in silico prediction of novel materials with emphasis on the effect of microstructure on model uncertainty quantification (UQ). We outline acceleration methods to make calculations of real chemistry feasible followed by two complementary tasks on structure optimization and microstructure-induced UQ.
Recurrence analysis of friction based dry-couplant ultrasonic Lamb waves in plate-like structures
Here, in this study, the effect of friction on the generation of dry-coupled Lamb waves is experimentally investigated. Recurrence analysis is performed to analyze the complex behavior of friction based dry-coupled Lamb waves. In particular, the effect of the normal force, which is necessary for a stronger dry-coupled Lamb wave generation and the friction, on the transmission of mechanical energy and determinism characteristics of Lamb waves are investigated. The results verify that larger friction coefficient and friction force are crucial for generation and propagation of strong Lamb waves supporting the fact that the main mechanism to transfer mechanical energy using dry-couplant is friction. The sensitivity of Lamb waves to the friction coefficient, highlights the importance of designing specific pads with respect to condition of the surface. Besides, the results show that the normal force and friction coefficient can change the determinism characteristics behavior of multimode Lamb waves. Furthermore, it is shown that the determinism value is sensitive to the friction coefficient and normal force. A similar trend is observed in the determinism values and friction coefficient. In general, a smaller friction coefficient indicates smaller determinism value. Additionally, it is shown that a normal load can change the behavior of a system, as observed from recurrence plots, owing to changes in the Lamb waves trajectories in the phase-space domain. In addition, it is shown that recurrence plots enable the detection of mode transitions in multimode Lamb waves. Recurrence analysis is a complementary tool to frequency domain methods for accurate analysis of multimode Lamb waves behavior.
Second Coordination Sphere Effects in an Evolved Ru Complex Based on Highly Adaptable Ligand Results in Rapid Water Oxidation Catalysis
A new Ru complex containing the deprotonated 2,2':6',2"-terpyridine-6,6"-diphosphonic acid (H 4 tPa) and pyridine (py) of general formula [Ru II (H 3 tPa-κ-N 3 O)(py) 2 ] + , 2 + , has been prepared and thoroughly characterized by means of spectroscopic and electrochemical techniques, X-ray diffraction analysis, and density functional theory (DFT) calculations. Complex 2 + presents a dynamic behavior in the solution that involves the synchronous coordination and the decoordination of the dangling phosphonic groups of the tPa 4– ligand. Yet, at oxidation state IV, complex 2 + becomes seven coordinated with the two phosphonic groups now bonded to the metal center. Further, at this oxidation state at neutral and basic pH, the Ru complex undergoes the coordination of an exogenous OH – group from the solvent that leads to an intramolecular aromatic O atom insertion into the CH bond of one of the pyridyl groups, forming the corresponding phenoxo-phosphonate Ru complex [Ru III (tPaO-κ-N 2 O P O C )(py) 2 ] 2– , 4 2– , where tPaO 5– is the 3-(hydroxo-[2,2':6',2"-terpyridine]-6,6"-diyl)bis(phosphonate) ligand. This new in situ generated Ru complex, 4 2– , has been isolated and spectroscopically and electrochemically characterized. In addition, a crystal structure has been also obtained using single-crystal X-ray diffraction techniques. Complex 4 2– turns out to be an exceptional water oxidation catalyst achieving record maximum turnover frequencies (TOF max ) on the order of 16 000 s –1 . A mechanistic analysis complemented with DFT calculations has further been carried out, showing the critical role of intramolecular second coordination sphere effects exerted by the phosphonate groups in lowering the activation energy at the rate-determining step.