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At least 451 records · Page 25

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DOE OSTI2010

Testing photons' Bose-Einstein statistics with Compton scattering

It is an empirical question whether photons always obey Bose-Einstein statistics, but devising and interpreting experimental tests of photon statistics can be a challenge. The nonrelativistic cross section for Compton scattering illustrates how a small admixture {nu} of wrong-sign statistics leads to a loss of gauge invariance; there is a large anomalous amplitude for scattering timelike photons. Nevertheless, one can interpret the observed transparency of the solar wind plasma at low frequencies as a bound {nu}<10{sup -25} if Lorentz symmetry is required. If there is instead a universal preferred frame, the bound is {nu}<10{sup -14}, still strong compared with previous results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2011

Joule-Thomson coefficient of ideal anyons within fractional exclusion statistics

The analytical expressions of the Joule-Thomson coefficient for homogeneous and harmonically trapped three-dimensional ideal anyons which obey Haldane fractional exclusion statistics are derived. For an ideal Fermi gas, the Joule-Thomson coefficient is negative, which means that there is no maximum Joule-Thomson inversion temperature. With careful study, it is found that there exists a Joule-Thomson inversion temperature in the fractional exclusion statistics model. Furthermore, the relations between the Joule-Thomson inversion temperature and the statistical parameter g are investigated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

The Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation-based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theory with the classical CKF, which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviate from the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.

42 ENGINEERING↗

DOE OSTI2026

Statistical generic design of glass and optimization: Selective review on oxide glasses

Designing a single glass composition for a multidimensional property space is challenging, and the difficulty increases with the number of design criteria. Traditionally, the task is accomplished using multiple statistical models that describe the relationships between composition (C) and property (P) values, i.e., C-P models. Recently, the structure (S)-property (P) statistical modeling has emerged as a complementary approach. The S-P modeling approach has also been shown to be a preferred method for modeling glass properties, particularly when a small data set is available, such as in single-component studies, or when strong nonlinearities exist between composition and properties. The combined model package, C-S-P, implements the concept of generic glass design, i.e., designing glass for performance by first selecting a specific or optimized set of glass network structural groups using S-P models and then transferring the designed structures (genes) to a particular composition using C-S models. This article reviews a set of supporting cases from the previous C-S-P modeling studies of phosphate, silicate, and borosilicate glasses, which are relevant for many critical commercial applications. The methodology for developing the statistical C-S-P database is presented, enabling the application of P?S?C to achieve a generic glass design and optimization, targeting multiple design criteria for both performance and processing properties simultaneously.

Network structure↗

DOE OSTI2019

Statistics for Testing Under Adverse Conditions

Here we develop a statistical basis for limited adverse testing. This type of testing simultaneously evaluates system performances against minimum requirements and minimizes costs, particularly for large scale engineering projects. Because testing is often expensive and narrow in scope, the data obtained is relatively limited—precisely the opposite of the recent “large data” movement but no less compelling. Although a remarkably common approach for industrial and large-scale government projects, a statistical basis for adverse testing remains poorly explored. Here we prove mathematically, under specific conditions, that setting each independent variable to an adverse condition leads to a similar level of adversity in the dependent variable. For example, setting all normally distributed independent variables to at least their 95th percentile values leads to a result at the 95th percentile. The analysis considers sample size estimates to clarify the value of replicates in this type of testing, determines how many of the independent variables must be set to adverse condition values, and highlights the essential assumptions, so that engineers, statisticians, and subject matter experts know when this statistical framework may be applied successfully.

42 ENGINEERING↗

DOE OSTI2010

Segmenting CT prostate images using population and patient-specific statistics for radiotherapy

Purpose: In the segmentation of sequential treatment-time CT prostate images acquired in image-guided radiotherapy, accurately capturing the intrapatient variation of the patient under therapy is more important than capturing interpatient variation. However, using the traditional deformable-model-based segmentation methods, it is difficult to capture intrapatient variation when the number of samples from the same patient is limited. This article presents a new deformable model, designed specifically for segmenting sequential CT images of the prostate, which leverages both population and patient-specific statistics to accurately capture the intrapatient variation of the patient under therapy. Methods: The novelty of the proposed method is twofold: First, a weighted combination of gradient and probability distribution function (PDF) features is used to build the appearance model to guide model deformation. The strengths of each feature type are emphasized by dynamically adjusting the weight between the profile-based gradient features and the local-region-based PDF features during the optimization process. An additional novel aspect of the gradient-based features is that, to alleviate the effect of feature inconsistency in the regions of gas and bone adjacent to the prostate, the optimal profile length at each landmark is calculated by statistically investigating the intensity profile in the training set. The resulting gradient-PDF combined feature produces more accurate and robust segmentations than general gradient features. Second, an online learning mechanism is used to build shape and appearance statistics for accurately capturing intrapatient variation. Results: The performance of the proposed method was evaluated on 306 images of the 24 patients. Compared to traditional gradient features, the proposed gradient-PDF combination features brought 5.2% increment in the success ratio of segmentation (from 94.1% to 99.3%). To evaluate the effectiveness of online learning mechanism, the authors carried out a comparison between partial online update strategy and full online update strategy. Using the full online update strategy, the mean DSC was improved from 86.6% to 89.3% with 2.8% gain. On the basis of full online update strategy, the manual modification before online update strategy was introduced and tested, the best performance was obtained; here, the mean DSC and the mean ASD achieved 92.4% and 1.47 mm, respectively. Conclusions: The proposed prostate segmentation method provided accurate and robust segmentation results for CT images even under the situation where the samples of patient under radiotherapy were limited. A conclusion that the proposed method is suitable for clinical application can be drawn.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-FG-201-03: Automatic Pre-Delivery Verification Using Statistical Analysis of Consistencies in Treatment Plan Parameters by the Treatment Site and Modality

Purpose: A novel computer software system, namely APDV (Automatic Pre-Delivery Verification), has been developed for verifying patient treatment plan parameters right prior to treatment deliveries in order to automatically detect and prevent catastrophic errors. Methods: APDV is designed to continuously monitor new DICOM plan files on the TMS computer at the treatment console. When new plans to be delivered are detected, APDV checks the consistencies of plan parameters and high-level plan statistics using underlying rules and statistical properties based on given treatment site, technique and modality. These rules were quantitatively derived by retrospectively analyzing all the EBRT treatment plans of the past 8 years at authors’ institution. Therapists and physicists will be notified with a warning message displayed on the TMS computer if any critical errors are detected, and check results, confirmation, together with dismissal actions will be saved into database for further review. Results: APDV was implemented as a stand-alone program using C# to ensure required real time performance. Mean values and standard deviations were quantitatively derived for various plan parameters including MLC usage, MU/cGy radio, beam SSD, beam weighting, and the beam gantry angles (only for lateral targets) per treatment site, technique and modality. 2D-based rules of combined MU/cGy ratio and averaged SSD values were also derived using joint probabilities of confidence error ellipses. The statistics of these major treatment plan parameters quantitatively evaluate the consistency of any treatment plans which facilitates automatic APDV checking procedures. Conclusion: APDV could be useful in detecting and preventing catastrophic errors immediately before treatment deliveries. Future plan including automatic patient identify and patient setup checks after patient daily images are acquired by the machine and become available on the TMS computer. This project is supported by the Agency for Healthcare Research and Quality (AHRQ) under award 1R01HS0222888. The senior author received research grants from ViewRay Inc. and Varian Medical System.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Statistics of resonance fluorescence of a pair of atoms in a feedback loop

The statistics of photoemission events of a pair of closely spaced two-level atoms is calculated in a classical light field whose phase is changed by {pi} after the detection of each spontaneous photon. This statistics is compared with the statistics in the case when the feedback is missing. In both cases, one can observe noticeable antibunching of photons in the range of parameters where no antibunching is observed in a single-atom system. The feedback substantially increases the antibunching. This effect manifests itself more strongly in relatively weak fields and for considerable frequency detunings.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2012

Fluctuations and correlations in statistical models of hadron production

An extension of the standard concept of the statistical ensembles is suggested. Namely, the statistical ensembles with extensive quantities fluctuating according to an externally given distribution are introduced. Applications in the statistical models of multiple hadron production in high energy physics are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2013

Statistical analysis of AFM topographic images of self-assembled quantum dots

To obtain statistical data on quantum-dot sizes, AFM topographic images of the substrate on which the dots under study are grown are analyzed. Due to the nonideality of the substrate containing height differences on the order of the size of nanoparticles at distances of 1-10 {mu}m and the insufficient resolution of closely arranged dots due to the finite curvature radius of the AFM probe, automation of the statistical analysis of their large dot array requires special techniques for processing topographic images to eliminate the loss of a particle fraction during conventional processing. As such a technique, convolution of the initial matrix of the AFM image with a specially selected matrix is used. This makes it possible to determine the position of each nanoparticle and, using the initial matrix, to measure their geometrical parameters. The results of statistical analysis by this method of self-assembled InAs quantum dots formed on the surface of an AlGaAs epitaxial layer are presented. It is shown that their concentration, average size, and half-width of height distribution depend strongly on the In flow and total amount of deposited InAs which are varied within insignificant limits.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning

Internal atmospheric variability fundamentally limits predictability of climate and obscures evidence of anthropogenic climate change regionally and on time scales of up to a few decades. Dynamical adjustment techniques estimate and subsequently remove the influence of atmospheric circulation variability on temperature or precipitation. The residual component is expected to contain the thermodynamical signal of the externally forced response but with less circulation-induced noise. Existing techniques have led to important insights into recent trends in regional (hydro-) climate and their drivers, but the variance explained by circulation is often low. Here, we develop a novel dynamical adjustment technique by implementing principles from statistical learning. We demonstrate in an ensemble of Community Earth System Model (CESM) simulations that statistical learning methods, such as regularized linear models, establish a clearer relationship between circulation variability and atmospheric target variables, and need relatively short periods of record for training (around 30 years). The method accounts for, on average, 83% and 78% of European monthly winter temperature and precipitation variability at gridcell level, and around 80% of global mean temperature and hemispheric precipitation variability. We show that the residuals retain forced thermodynamical contributions to temperature and precipitation variability. Accurate estimates of the total forced response can thus be recovered assuming that forced circulation changes are gradual over time. Overall, forced climate response estimates can be extracted at regional or global scales from approximately 3–5 times fewer ensemble members, or even a single realization, using statistical learning techniques. We anticipate the technique will contribute to reducing uncertainties around internal variability and facilitating climate change detection and attribution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

09 BIOMASS FUELS↗

DOE OSTI2023

Statistical methods for design and testing of 3D-printed polymers

Different statistical methods are used in various fields to qualify processes and products, especially in emerging technologies like Additive Manufacturing (AM) or 3D printing. Since several statistical methods are being employed to ensure quality production of the 3D-printed parts, an overview of these methods used in 3D printing for different purposes is presented in this paper. The advantages and challenges, to understanding the importance it brings for design and testing optimization of 3D-printed parts are also discussed. The application of different metrology methods is also summarized to guide future researchers in producing dimensionally-accurate and good-quality 3D-printed parts. Here, this review paper shows that the Taguchi Methodology is the commonly-used statistical tool in optimizing mechanical properties of the 3D-printed parts, followed by Weibull Analysis and Factorial Design. In addition, key areas such as Artificial Intelligence (AI), Machine Learning (ML), Finite Element Analysis (FEA), and Simulation require more research for improved 3D-printed part qualities for specific purposes. Future perspectives are also discussed, including other methods that can help further improve the overall quality of the 3D printing process from designing to manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2011

Gyrokinetic Statistical Absolute Equilibrium and Turbulence

A paradigm based on the absolute equilibrium of Galerkin-truncated inviscid systems to aid in understanding turbulence [T.-D. Lee, "On some statistical properties of hydrodynamical and magnetohydrodynamical fields," Q. Appl. Math. 10, 69 (1952)] is taken to study gyrokinetic plasma turbulence: A finite set of Fourier modes of the collisionless gyrokinetic equations are kept and the statistical equilibria are calculated; possible implications for plasma turbulence in various situations are discussed. For the case of two spatial and one velocity dimension, in the calculation with discretization also of velocity v with N grid points (where N + 1 quantities are conserved, corresponding to an energy invariant and N entropy-related invariants), the negative temperature states, corresponding to the condensation of the generalized energy into the lowest modes, are found. This indicates a generic feature of inverse energy cascade. Comparisons are made with some classical results, such as those of Charney-Hasegawa-Mima in the cold-ion limit. There is a universal shape for statistical equilibrium of gyrokinetics in three spatial and two velocity dimensions with just one conserved quantity. Possible physical relevance to turbulence, such as ITG zonal flows, and to a critical balance hypothesis are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Statistically-driven Experimental Design to Improve Reference-free Quantification of Small Molecules by Liquid Chromatography-Mass Spectrometry

Non-targeted analysis of small molecules and metabolites in unknown, complex samples using liquid chromatography-tandem mass spectrometry remains challenging. One of the main bottlenecks is the extensive unannotated regions of metabolomics mass spectrometry data, resulting in knowledge gaps. Small molecule annotation in mass spectrometry data has conventionally relied on reference standards and libraries for compound identification and confirmation, which can constrain compound identification to those molecules already known, thus limiting the ability to discover new knowledge and new markers. Retention time prediction can facilitate and expedite unknown compound identification in non-targeted analysis of complex metabolomics samples. Additionally, accurate retention time predictions can also inform sample mixture design for LC-MS/MS analyses. However, current machine learning-based methods for retention time prediction are typically developed for specific chromatographic platforms and are not generalizable across scales. And while technologies and methods to improve reference-free metabolite identification for more comprehensive annotation of unknowns has received much attention, development of the same for quantitation without reference standards has been much more limited, despite its importance in toxicological, environmental, food safety, forensics, and clinical applications. We believe that a reference-free quantitation strategy that exploits mass spectrometry data already collected for reference-free identification can provide much more insight on unknowns, and move the metabolomics field for more complete unknowns characterization. As such, we pursue two efforts to improve upon current state-of-the-art methods in non-targeted analysis: (1) machine learning-based retention time prediction and (2) statistical design of experiments framework for reference-free quantitation. In this work, we develop and demonstrate (1) a generalizable retention time prediction capability across chromatographic conditions and scales, and (2) a statistical design-based framework for response factor contribution elucidation and reference-free quantitation. Evaluation of our retention time prediction model, PrediToR, showed approximately 24% improvement over current models, and we observed approximately 10X improvement in concentration estimation accuracy from our statistical design-based response factor model over a primarily ionization efficiency-based model. We expect that future efforts to improve upon these new capabilities will further advance non-targeted analysis of small molecules towards truly reference-free metabolomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1998

Predicting lettuce canopy photosynthesis with statistical and neural network models

An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future).

Non-NASA Center↗

NASA NTRS1995

Statistical properties of DNA sequences

We review evidence supporting the idea that the DNA sequence in genes containing non-coding regions is correlated, and that the correlation is remarkably long range--indeed, nucleotides thousands of base pairs distant are correlated. We do not find such a long-range correlation in the coding regions of the gene. We resolve the problem of the "non-stationarity" feature of the sequence of base pairs by applying a new algorithm called detrended fluctuation analysis (DFA). We address the claim of Voss that there is no difference in the statistical properties of coding and non-coding regions of DNA by systematically applying the DFA algorithm, as well as standard FFT analysis, to every DNA sequence (33301 coding and 29453 non-coding) in the entire GenBank database. Finally, we describe briefly some recent work showing that the non-coding sequences have certain statistical features in common with natural and artificial languages. Specifically, we adapt to DNA the Zipf approach to analyzing linguistic texts. These statistical properties of non-coding sequences support the possibility that non-coding regions of DNA may carry biological information.

Non-NASA Center↗

NASA NTRS1995

Systematic analysis of coding and noncoding DNA sequences using methods of statistical linguistics

We compare the statistical properties of coding and noncoding regions in eukaryotic and viral DNA sequences by adapting two tests developed for the analysis of natural languages and symbolic sequences. The data set comprises all 30 sequences of length above 50 000 base pairs in GenBank Release No. 81.0, as well as the recently published sequences of C. elegans chromosome III (2.2 Mbp) and yeast chromosome XI (661 Kbp). We find that for the three chromosomes we studied the statistical properties of noncoding regions appear to be closer to those observed in natural languages than those of coding regions. In particular, (i) a n-tuple Zipf analysis of noncoding regions reveals a regime close to power-law behavior while the coding regions show logarithmic behavior over a wide interval, while (ii) an n-gram entropy measurement shows that the noncoding regions have a lower n-gram entropy (and hence a larger "n-gram redundancy") than the coding regions. In contrast to the three chromosomes, we find that for vertebrates such as primates and rodents and for viral DNA, the difference between the statistical properties of coding and noncoding regions is not pronounced and therefore the results of the analyses of the investigated sequences are less conclusive. After noting the intrinsic limitations of the n-gram redundancy analysis, we also briefly discuss the failure of the zeroth- and first-order Markovian models or simple nucleotide repeats to account fully for these "linguistic" features of DNA. Finally, we emphasize that our results by no means prove the existence of a "language" in noncoding DNA.

NASA Discipline Number 14-10↗