Statistical Characterization Research Articles

Investigation of sodium bicarbonate media blasting technology for achieving the cleaner production of graphite deposition reactor cleaning process: Process optimization and simulation

The periodic cleaning of silicon nitride residues on the graphite deposition reactor surface is a critical process for producing silicon wafers in the photovoltaic manufacturing industry. A green, non-erosive and sustainable sodium bicarbonate media blasting technology (SBMBT) was investigated in this research, aiming for providing an alternative to the widely-used polluting liquid hydrofluoric acid soaking method in the industry. By comparing the cleaning behavior of different blasting media (sodium bicarbonate, alumina, glass bead and walnut shell) under various blasting angles and pressures, the most appropriate cleaning process parameters (30° and 0.3 MPa) and efficient removal rate (0.085 cm2/s) of SBMBT were determined based on the statistical characterization of surface roughness data. Under the most appropriate cleaning process parameters, the mean value of roughness (Ra = 2.119 μm) and cleanliness of graphite deposition reactor surface that was cleaned by SBMBT could meet industry standards. Based on the stress sample data provided by the six identical simulations on each of the nine parameter combinations, the multiple-impact cleaning mechanism of SBMBT was revealed using the LSTC dynamics (LS-DYNA) particle impact model and a high-speed camera. Based on five sets of the recovery experiments data, the mean value of blasting media recovery rate obtained in the validation of the designed sodium bicarbonate media recovery scheme was 79.98%, and the consistency of the media crystal form before and after recovery was confirmed by PXRD. As the experimental results indicate, SBMBT is a green technology with low damage, no residue, and can effectively alleviate the problem of solid waste treatment, which is worthy of being applied in graphite deposition reactor cleaning processes.

Deep Neural Networks Guided Ensemble Learning for Point Estimation

In modern statistics, interests shift from pursuing the uniformly minimum variance unbiased estimator to reducing mean squared error (MSE) or residual squared error. Shrinkage-based estimation and regression methods offer better prediction accuracy and improved interpretation. However, the characterization of such optimal statistics in terms of minimizing MSE remains open and challenging in many problems, for example, estimating the treatment effect in adaptive clinical trials with pre-planned modifications to design aspects based on accumulated data. From an alternative perspective, we propose a deep neural network based automatic method to construct an improved estimator from existing ones. Theoretical properties are studied to provide guidance on applicability of our estimator to seek potential improvement. Simulation studies demonstrate that the proposed method has considerable finite-sample efficiency gain compared to several common estimators. In the Adaptive COVID-19 Treatment Trial (ACTT) as a motivating example, our ensemble estimator essentially contributes to a more ethical and efficient adaptive clinical trial with fewer patients enrolled. The proposed framework can be generally applied to various statistical problems, and can serve as a reference measure to guide statistical research.

Stochastic Turing patterns of trichomes in Arabidopsis leaves

Biological patterns that emerge during the morphogenesis of multicellular organisms can display high precision at large scales, while at cellular scales, cells exhibit large fluctuations stemming from cell-cell differences in molecular copy numbers also called demographic noise. We study the conflicting interplay between high precision and demographic noise in trichome patterns on the epidermis of wild-type Arabidopsis thaliana leaves, as a two-dimensional model system. We carry out a statistical characterization of these patterns and show that their power spectra display fat tails-a signature compatible with noise-driven stochastic Turing patterns-which are absent in power spectra of patterns driven by deterministic instabilities. We then present a theoretical model that includes demographic noise stemming from birth-death processes of genetic regulators which we study analytically and by stochastic simulations. The model captures the observed experimental features of trichome patterns.

Epidemic Spread Modeling for COVID-19 Using Cross-Fertilization of Mobility Data

We present an individual-centric model for COVID-19 spread in an urban setting. We first analyze patient and route data of infected patients from January 20, 2020, to May 31, 2020, collected by the Korean Center for Disease Control & Prevention (KCDC) and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of mobility habits and patterns of individuals at the beginning of the pandemic. While the KCDC data offer a wealth of information, they are also by their nature limited. To compensate for their limitations, we use detailed mobility data from Berlin, Germany after observing that mobility of individuals is surprisingly similar in both Berlin and Seoul. Using information from the Berlin mobility data, we cross-fertilize the KCDC Seoul data set and use it to parameterize an agent-based simulation that models the spread of the disease in an urban environment. After validating the simulation predictions with ground truth infection spread in Seoul, we study the importance of each input parameter on the prediction accuracy, compare the performance of our model to state-of-the-art approaches, and show how to use the proposed model to evaluate different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">what-if</i> counter-measure scenarios.

Statistical optimization, characterization, antioxidant and antibacterial properties of silver nanoparticle biosynthesized by saw palmetto seed phenolic extract

On the global market, silver nanoparticles (Ag-NPs) are in high demand for their various applications in biomedicine, material engineering, and consumer products. This study highlighted the biosynthesis of the Ag-NPs using saw palmetto seed phenolic extract (SPS-phenolic extract), which contained vital antioxidant-phenolic compounds. Herein, central composite statistical design, response surface methodology, and sixteen runs were conducted to optimize Ag-NPs biosynthesis conditions for maximizing the production of Ag-NPs and their phenolic content. The best-produced SPS-Ag-NPs showed a surface plasmon resonance peak at 460 nm and nano-spherical sizes ranging from 11.17 to 38.32 nm using the UV spectrum analysis and TEM images, respectively. The produced SPS-Ag-NPs displayed a high negative zeta-potential value (− 32.8 mV) demonstrating their high stability. The FTIR analysis demonstrated that SPS-phenolic compounds were involved in sliver bio-reduction and in stabilizing, capping, and preventing Ag-NP aggregation. The thermogravimetric investigation revealed that the produced SPS-Ag-NPs have remarkable thermal stability. The produced SPS-Ag-NP exceeded total antioxidant activity (13.8 µmol Trolox equivalent) more than the SPS-phenolic extract (12.0 µmol Trolox equivalent). The biosynthesized SPS-Ag-NPs exhibited noticeably better antibacterial activity against multidrug-resistant Gram-negative E. coli and Gram-positive S. aureus compared to SPS-phenolic extract. Hence, the bio-synthesized SPS-Ag-NPs demonstrated great potential for use in biomedical and antimicrobial applications.

Statistical characterization of a shock interacting with an inclined gas column

We present a statistical characterization of the interaction between a planar shock and a finite-diameter, cylindrical column of dense gas based on three-dimensional, large-eddy simulation results. In the simulation, the column of gas is initially inclined at an angle $\alpha _0$ with respect to the shock plane. Effects of the initial column angle on the mixing characteristics are examined at Mach number 2.0 for column incline angles $1^\circ$ , $5^\circ$ , $10^\circ$ and $30^\circ$ . Mean velocity profiles show that the column angle affects the gas velocity components in the vertical plane, but not in the spanwise direction. The gas undergoes higher initial upward acceleration at larger initial column incline angles. With time, the gas motion tends to become one-dimensional in the streamwise direction. Initially, velocity fluctuations are most intense within the interior of the column, but concentrate near the column leading edge over time. At high wavenumbers $\kappa$ , the turbulent kinetic energy spectra follow a power-law scaling of $\kappa ^{-1}$ . The structure functions of the mass fraction do not clearly demonstrate power-law scaling except at early times for $\alpha _0=30^\circ$ , manifesting overall trends very similar to those observed in earlier experiments. Probability distributions of the mass fraction show independence of the mean and the standard deviation of the mixed gas on $\alpha _0$ . The column angle was also found to have little effect on the mixing efficiency characterized by the molecular mixedness. Velocity components in the streamwise and transverse directions tend towards a bimodal distribution for larger $\alpha _{0}$ .

On the Effect of Imperfect Reference Signal Phase Recovery on Performance of PSK System Influenced by TWDP Fading.

We examine the effects of imperfect phase estimation of a reference signal on the bit error rate and mutual information over a communication channel influenced by fading and thermal noise. The Two-Wave Diffuse-Power (TWDP) model is utilized for statistical characterization of propagation environment where there are two dominant line-of-sight components together with diffuse ones. We derive novel analytical expression of the Fourier series for probability density function arising from the composite received signal phase. Further, the expression for the bit error rate is presented and numerically evaluated. We develop efficient analytical, numerical and simulation methods for estimating the value of the error floor and identifying the range of acceptable signal-to-noise ratio (SNR) values in cases when the floor is present during the detection of multilevel phase-shift keying (PSK) signals. In addition, we use Monte Carlo simulations in order to evaluate the mutual information for modulation orders two, four and eight, and identify its dependence on receiver hardware imperfections under the given channel conditions. Our results expose direct correspondence between bit error rate and mutual information value on one side, and the parameters of TWDP channel, SNR and phase noise standard deviation on the other side. The results illustrate that the error floor values are strongly influenced by the phase noise when signals propagate over a TWDP channel. In addition, the phase noise considerably affects the mutual information.

PCA-Enhanced Methodology for the Identification of Partial Discharge Locations

Partial discharge (PD) that occurs due to insulation breakdown is a precursor to plant failure. PD emits electromagnetic pulses which radiate through space and can be detected using appropriate sensing devices. This paper proposed an enhanced radiolocation technique to locate PD. This approach depends on sensing the radio frequency spectrum and the extraction of PD location features from PD signals. We hypothesize that the statistical characterization of the received PD signals generates many features that represent distinct PD locations within a substation. It is assumed that the waveform of the received signal is altered due to attenuation and distortion during propagation. A methodology for the identification of PD locations based on extracted signal features has been developed using a fingerprint matching algorithm. First, the original extracted signal features are used as inputs to the algorithm. Secondly, Principal Component Analysis (PCA) is used to improve PD localization accuracy by transforming the original extracted features into s new informative feature subspace (principal components) with reduced dimensionality. The few selected PCs are then used as inputs into the algorithm to develop a new PD localization model. This work has established that PCA can provide robust PC representative features with spatially distinctive patterns, a prerequisite for a good fingerprinting localization model. The results indicate that the location of a discharge can be determined from the selected PCs with improved localization accuracy compared to using the original extracted PD features directly.

Automatic detection of sunspots from solar images using fractional-order derivatives and extraction of their attributes

A huge collection of solar images to visualize sunspot are acquired by various solar observatories spread across the globe. This necessitates efficient tools for detecting and analyzing the sunspots encompassing diverse solar features. Particularly sunspot characterization requires precise intensity refinement to distinguish the intricate sunspot structures the umbra and penumbra. Accordingly, this work delivers a sunspot detection module by forging Fractional-Order Derivative Mask (FODM) in segmentation for embracing the diverse solar regions present in solar images. The mechanism initially localizes the statistical intensity variations and bounds them followed by FODM generalization for effectively outlining the heterogenous regions concerned with diverse solar regions. Rigorous analysis of the developed mechanism in terms of the diverse Receiver Operating Characteristics (ROC) parameters was performed on images obtained from diverse benchmarked solar repositories. The proposed model’s nature of acutely packing high-frequency image features using the localized statistical intensity characterization supersedes the conventional Integer Order Differential Mask (IODM) investigated using the ROC parameters. Furthermore, relative ROC analysis with trending models reveals the consistent superiority of this scheme. Additionally, comparative analysis in terms of the diverse extracted solar features with relevant solar bulletins affirms the model’s efficacy.

Stochastic Dynamics of Two‐Dimensional Particle Motion in Darcy‐Scale Heterogeneous Porous Media

AbstractWe study the upscaling and prediction of ensemble dispersion in two‐dimensional heterogeneous porous media with focus on transverse dispersion. To this end, we study the stochastic dynamics of the motion of advective particles that move along the streamlines of the heterogeneous flow field. While longitudinal dispersion may evolve super‐linearly with time, transverse dispersion is characterized by ultraslow diffusion, that is, the transverse displacement variance grows asymptotically with the logarithm of time. This remarkable behavior is linked to the solenoidal character of the flow field, which needs to be accounted for in stochastic models for the two‐dimensional particle motion. Here, we derive an upscaled model based on the statistical characterization of the motion of solute particles. To this end, we analyze particle velocities and orientations through equidistant sampling along the particle trajectories obtained from direct numerical simulations. This sampling strategy respects the flow structure, which is organized on a characteristic length scale. Perturbation theory shows that the longitudinal particle motion is determined by the variability of travel times, while the transverse motion is governed by the fluctuations of the space increments. The latter turns out to be strongly anticorrelated with a correlation structure that leads to ultraslow diffusion. Based on this analysis, we derive a stochastic model that combines a correlated Gaussian noise for the transverse motion with a spatial Markov model for the particle speeds. The model results are contrasted with detailed numerical simulations in two‐dimensional heterogeneous porous media of different heterogeneity variances.

Reliability assessment of steel‐framed industrial buildings

AbstractThe classical reliability problem seeking an estimation of the probability of failure is usually undefined due to: i) its scale (size of the design problems), and ii) deficient characterization of the randomness (lack of statistical characterization of the basic random variables). A common challenge in current approaches for the assessment of structural reliability adopted in EN 1990 is the lack of data examples that could support additional research, simulation, and experiments. As the failure probability in Eurocode is about 10‐4, millions of simulations are required, which limits the accuracy of commonly used Monte Carlo simulations (MCS). Nowadays, there are different intelligent data management approaches to augment existing data points by creating synthetic examples that can then be further used in specific behavior prediction. One such intelligent approach is the recently developed Generative Adversarial Networks (GAN), which has found its use mainly in image augmentation, but also in other applications where finding new data examples is challenging, e.g., credit card fraud detection, defect detection, etc. In this paper, a reliability assessment of steel‐framed industrial buildings is presented. For that purpose, a set of portal frames is first selected as a case study and designed using the actual design codes, whereby the critical structural members and failure modes are identified. Subsequently, based on these representative case studies, the database is expanded using GAN, and the current state of reliability for the selected type of buildings is assessed, aiming to provide a statement of the current safety of industrial sheds using the Eurocodes.

Statistical characterization of PIXL trace element detection limits

Trace element detection and mapping are key capabilities of the Planetary Instrument for X-ray Lithochemistry (PIXL) on board the Mars 2020 rover Perseverance. However, poor signal-to-noise ratios due to the short integration times required to make high-spatial-resolution map scans operationally feasible raise the possibility of misidentifying statistical noise as a signal from a trace element, which can cause many false detections of a trace element among the hundreds to thousands of data points in each PIXL scan. Here, we apply a statistical technique to quantify the likelihood of such misidentifications and determine what concentration of a trace element must be present to reach statistical confidence in a detection. This approach is anticipated to be applicable both when analyzing existing PIXL data to ensure that noise is not misinterpreted as signals from trace elements and operationally to inform scan parameters when trace elements of interest are anticipated to be present.

Performance assessment of the signal-in-space ranging errors of BDS-3: statistical characterization and integrity standard

Performance assessment of the signal-in-space ranging errors of BDS-3: statistical characterization and integrity standard

The dominant role of defects on fatigue behaviour of a SLM Ni-based superalloy at elevated temperature

This research investigates the impact of defects on the fatigue properties of a selective laser melted Ni-based superalloy at elevated temperatures. Results show that 78.6% of the fatigue failures are initiated by defects, with a larger scatter in fatigue life at lower stress levels. And the coalescence and growth of defects are captured by X-ray computed tomography (XCT). Besides, a quantitative analysis indicates that the fatigue life increases with defect location but decreases with defect size, which is influenced by defect shape. A modified stress intensity range, ΔKL, is proposed, considering the effect of the three factors. Statistical characterization shows larger extreme values for critical defects compared to XCT observation.

Satellite uplink interference measurements in the 437 MHz UHF amateur radio band onboard LUME-1

Satellite operators struggle to communicate with their satellites over Europe in the UHF amateur band (430–440 MHz) due to high power in-orbit interference. Statistical characterisation of the interference beyond average interference levels using in-orbit measurements is useful for the design of suitable countermeasures. Some recent studies have started to investigate both the frequency and time behaviour of the interference, but more measurements are needed to cover the whole UHF amateur band. In this paper, we use the Local Mean Envelope (LME) method to analyse the time and frequency characteristics of in-orbit radio interference from the LUME-1 satellite, measured in the 437 MHz band. Satellite measurements were performed on a TOTEM Software-Defined Radio (SDR), in orbit over Europe and Antarctica and the results were downloaded for analysis on the ground. The average power spectrum and the variability of the LME for different time windows in the 437 MHz band are presented. The data analysis also includes the coefficient of variation of the LME to study the dispersion of the interference. The results are compared to previous measurements in the 435 MHz band using the same method. The new results are found to differ compared with previous measurements on the 435 MHz band. The bandwidth of the band-limited signals measured at 435 MHz was found to be 300 kHz, and at 437 MHz, 200 kHz wide for this dataset. Also, the interference power was found to be higher for 437 MHz. In both cases the interference behaviour is highly non-Gaussian, suggesting that typical communication countermeasures will not be sufficient. Although more measurements are needed for better spatial resolution, our findings may contribute to explaining why satellite operations are difficult on these bands. A better knowledge of the time and frequency variability of the interference can indicate which mitigation techniques are required and are efficient to improve satellite communication in that band.

Multidimensional nonlinear numerical simulation of post‐tensioned concrete girders with different prestressing levels

AbstractMost of the Italian infrastructures have been built since the mid‐twentieth century. Prestressed concrete (PC) has been widely used, especially for bridges and viaducts. These structures have proved effective over time, but their performance could be affected by degradation phenomena (i.e., corrosion of the tendons and residual prestressing) or construction defects (i.e., grouting of the ducts), which need to be accurately modeled. This paper focuses on the flexural response of two reduced‐scale, posttensioned, PC bridge girders, prestressed with two different jacking forces and tested under four‐point bending configuration. Four multidimensional numerical models were developed to simulate the experimental behaviors of the selected specimens, using two alternative computational strategies, namely, the finite element method (FEM) and applied element method (AEM). Three FEM modeling were considered, ranging from one‐dimensional lumped‐plasticity models to two‐dimensional and three‐dimensional (3D) spread‐plasticity models. Besides, 3D AEM models were developed into another software package. The accuracy and the computational costs of the two numerical strategies are discussed in this paper. Also, the main features of each numerical modeling technique are addressed, including comparisons between numerical and experimental global response data as well as the statistical characterization of the model error. Based on these different features, the authors also suggest the most suitable numerical strategy for future studies on PC girders.

Random Telegraph Noise and Bias Temperature Instabilities statistical characterization of Ω-gate FDSOI devices at low voltages

Random Telegraph Noise (RTN) and Bias Temperature Instabilities (BTI) are two mechanisms that can significantly reduce the performance and reliability of integrated circuits. In scaled devices, both phenomena are stochastic, so that a statistical analysis is required to accurately evaluate their impact on a particular technology. This study presents such analysis in scaled FD-SOI devices under various gate and drain voltages, ranging from near-threshold to nominal conditions. The combined effect of RTN and BTI is also modeled in a defect-centric context, and the main impact of the different bias conditions is discussed.

Screening of Mustard Varieties for Resistance against Mustard Aphid, (Kaltenbach)

The present investigation was conducted at Agronomy Farm and Laboratory, Department of Biochemistry, S.K.N. College of Agriculture, Jobner (Rajasthan) during Rabi, 2019-20 and Rabi, 2020-21 to screen out the varieties of mustard for their resistance to mustard aphid, Lipaphis erysimi. The overall pooled mean aphid population ranged from 42.99 to 80.55 per 10 cm terminal shoots of five plants on different varieties. The minimum aphid infestation (42.99 aphids/ 10 cm terminal shoot) was recorded on Varuna followed by Bio-902 (44.63 aphids/ 10 cm terminal shoot) and both were found statistically same in reaction to aphid incidence. The maximum infestation/ 10 cm terminal shoot was recorded on Kranti (80.55 aphids) followed by GM-2 (79.99 aphids), and RH-30 (79.46 aphids) which remained at par to harbour the aphid population. The variability of resistance in mustard varieties in the decreasing order was Varuna, Bio-902, NRCDR-2, Pusa Bold, Laxmi, NRCHB-101, Rohini, Durgamani, Vardan, RH-9304, GM-1, Maya, RH-30, GM-2 and Kranti. The statistical characterization of results revealed that out of fifteen mustard varieties, two varieties Varuna and Bio-902 were found less susceptible to the mustard aphid, L. erysimi. Nine varieties viz., NRCDR-2, Laxmi, Pusa Bold, NRCHB-101, Rohini, Vardan, RH-9304, Durgamani and GM-1 were categorized as moderately susceptible, whereas, four varieties viz., Maya, RH-30, GM-2 and Kranti were categorized as highly susceptible in both years of the study.

Wear Analysis of 3D-Printed Spur and Herringbone Gears Used in Automated Retail Kiosks Based on Computer Vision and Statistical Methods.

This paper focuses on a wear evaluation conducted for prototype spur and herringbone gears made from PET-G filament using additive manufacturing. The main objective of this study is to verify if 3D-printed gears can be considered a reliable choice for long-term exploitation in selected mechanical systems, specifically automated retail kiosks. For this reason, two methods were applied, utilizing: (1) vision-based inspection of the gears' cross-sectional geometry and (2) the statistical characterization of the selected kinematic parameters and torques generated by drives. The former method involves destructive testing and allows for identification of the gears' operation-induced geometric shape evolution, whereas the latter method focuses on searching for nondestructive kinematic and torque-based indicators, which allow tracking of the wear. The novel contribution presented in this paper is the conceptual and experimental application of the identification of the changes of 3D-printed parts' geometric properties resulting from wear. The inspected exploited and non-exploited 3D-printed parts underwent encasing in resin and a curing process, followed by cutting in a specific plane to reveal the desired shapes, before finally being subjected to a vision-based geometric characterization. The authors have experimentally demonstrated, in real industrial conditions, on batch production parts, the usefulness of the presented destructive testing technique providing valid indices for wear identification.

Statistical characterization of medical images of bone

Statistical characterization of medical images of bone