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Introduction: The interaction of genotype with the environment provides the possibility of selecting stable genotypes for a wide range of environments. Evaluation of the interaction of genotype with the environment is necessary to increase the efficiency of selecting varieties with high and stable performance in a wide range of different environments. The objectives of this paper are: (1) evaluating the stability of 60 potato genotypes for tuber yield in two years using parametric and non-parametric stability methods, (2) identifying genotypes with good and stable performance when evaluated in variable environments and (3) investigating the relationship and correlation between stability statistics of tuber performance under water deficit conditions in Iran. Materials and methods: To evaluate the performance stability and adaptability of the 60 potato genotypes, two cultivars and 58 advanced clones, 17 parametric and non-parametric statistics were evaluated for tuber yield across eight environments during the 2018-2019 growing seasons. The genotypes were evaluated under normal and water deficit conditions in Karaj and Ardabil. In this study, the parametric analysis for yield was determined by such parameters as regression coefficient (bi), environmental variance (Si2), coefficient of variation (CVi), deviation from regression (sdi2), and Wricke's ecovalence (Wi2), The non-parametric analysis included Nassar and Huhn's statistics (S(1) and S(2)), Huhn's equation (S(3) and S(6)), Shukla's stability variance (σ2i), Plaisted and Peterson's (θi), Thennarasu's non-parametric (NP(1), NP(2), NP(3), and NP(4)), and Kang's rank-sum (KR) parameter. Parametric and non-parametric statistics are used by agronomists and plant breeders. Currently, researchers are interested in applying several stability statistics to obtain the desired results, crucial for the selection of stable varieties Results and discussion: Composite variance analysis showed that the effect of place and year as well as the effect of genotype are significant. The interaction effect of year × location × genotype was significant at the probability level of 1%. The genotype effect was also significant at the 1% probability level. The interaction effect of genotype x location and genotype x year was not significant, which indicates that the average performance of genotypes is not different in different locations and years. Grouping of genotypes based on average performance and parametric and non-parametric stability statistics showed that genotypes are divided into four main groups. In general, based on the average rank of parametric and non-parametric stability parameters, genotypes G31, G21 and G36 had the least changes and were recognized as the most stable genotypes, and therefore they can be introduced as stable genotypes. The results of stability statistics and cluster analysis showed that G31, G21 and G36 genotypes can be introduced as stable and compatible genotypes. Conclusion: Our results showed that G21, G31 and G36 genotypes contributed the least to the genetic × environment interaction (G*E) and were considered as stable genotypes under water deficit conditions. The different parametric and non-parametric stability procedures can be proposed to select drought tolerant genotypes under different environments conditions; these procedures could be useable for recognition of the best genotypes under drought stress conditions. Therefore, the analysis of yield stability could be utilized in combination with parametric and non-parametric methods to evaluate and identify drought tolerance genotypes. Dendrogram results confirmed each other with the results of parametric and nonparametric statistics. While G49, G51, G53 and G56 genotypes with the highest values were the most unstable genotypes. [ABSTRACT FROM AUTHOR]

In the present work, a stochastic framework, which combines the well‐recognized linear damage rule (LDR) and the uncertainty of material properties under constant amplitude loading, is proposed to improve the accuracy in prediction of remaining fatigue life. The uncertainty quantification approach based on parametric statistics is employed together with the Monte‐Carlo simulation method for modeling the randomness of remaining fatigue life. The impact of several commonly‐used parametric statistics is investigated, and the pertinent one is identified utilizing the Kullback‐Leibler divergence. The proposed framework is validated through the use of experimental fatigue life data under two‐step loading test. [ABSTRACT FROM AUTHOR]

The Delphi technique is a unique survey method that involves an iterative process to gain consensus when consensus is challenging to establish. Survey participants typically rate a variety of statements using a specified rating scale. The survey is repeated for several rounds, and at each round statements that do not reach a predefined level of consensus are advanced to the next round while giving the participants information about the responses of other participants for their comparison. The final statements are then ranked in order of the average rating. The statistical methods to analyze Delphi studies are not well described. This study investigates the use of a 1–7 linear rating scale along with parametric summary statistics for assessment of consensus and ranking of statements. A study set of 9297 individual ratings on the 1–7 scale were obtained from previously performed Delphi studies and used to create 490,000 simulated Delphi ratings with various numbers of participants. While the overall distribution of ratings was strongly left skewed the sampling distribution was near normally distributed for studies with five or more participants. The average difference between the standard deviation and interquartile range was −0.26/7. The overall risk of falsely concluding consensus using the standard deviation as a summary statistic was 7.3% when compared to using the interquartile range. The average difference between mean and median was −0.20/7. The risk of falsely ranking the statements by a value of 0.5 or more was near zero for all sample sizes when the mean was compared to the median. This study suggests that the use of the 1–7 linear rating scale in combination with the parametric summary statistics of standard deviation and mean is a valid method to analyze ratings from Delphi studies. [ABSTRACT FROM AUTHOR]

Extragalactic water maser emissions at 22 GHz have been playing vital roles in astrophysics. The limited detection rate of these masers has been motivating researchers to find clues that can help characterise them. The physical environments 22 GHz masers formed in are still ambiguous. Accordingly, statistical studies have been thoroughly used to resolve these favourable environments. This work goes through the essential parameter of Active Galactic Nuclei (AGN), namely, the mass of the central supermassive black hole (M BH) of the maser host galaxy. We study the correlation between maser luminosity (L H 2 O) and M BH in sub-samples of megamasers (MMs), kilomasers (KMs), and disc masers. The regression line of the relation is also calculated for these sub-samples. Unlike the results of previous works, dividing the maser sample into MMs and KMs gives no privilege to MM galaxies. Contrary to expectation, KMs have weak and low significant L H 2 O - M BH correlation, while MMs show no correlation. The positive correlation in KMs can be explained by the role of AGN therein, while the diversity of MMs types, with some of which are not strongly related to AGN, may explain the correlation missing. Surprisingly, the 28 disc maser sample, where tight correlation is expected, shows a very weak and low significant L H 2 O -M BH correlation. Future VLBI studies will eventually lead to a specific classification of a good number of maser galaxies, which is essential to establishing the L H 2 O -M BH relation. [ABSTRACT FROM AUTHOR]

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Experimental statistics are a key element for innovation in the agricultural sector. Commonly used statistical methods in experimentation are relatively simple, reliable, and widely used. However, the many problems in the quality of statistical analyses reported in the agricultural science literature highlight a need for continuing discussion on and updating of this topic. This article reviews critical points about classic linear models procedures commonly used in agricultural statistics, frequent procedures in publications in the agricultural sciences. Due to the evolution of statistical science some common recommendations from the past should no longer be followed. [ABSTRACT FROM AUTHOR]

Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates. [ABSTRACT FROM AUTHOR]

This study compared of univariate outlier data detection methods among vegetation data in a study of the effect of grazing intensity in the rangelands of arid regions. For this purpose, after measuring the vegetation cover in the rangeland and before the statistical analysis, the presence of outlier data was examined as the assumption of parametric comparison tests. In this study, eight methods including the boxplot and IQR (Tukey method), standard deviation of the mean (three-sigma rule), median absolute deviation (Hampel method), trimmed mean, 1st percentile and 99th percentile, The Chi Square test (χ²), the Grubbs Test (ESD) and the Rosner test (generalized ESD) were used. The results showed that the vegetation cover of rangelands with light and moderate grazing intensity was not normally distributed (Shapiro-Wilk test: p≤0.05). Even deletion of outliers did not lead to a normal distribution, but it resulted in the homogeneity of variances (Levene's test: p≥0.05). The modified Z-score and the Grubbs and Rosner tests (p≥0.05) did not identify outliers from the vegetation cover data. Among the methods evaluated, the boxplot and MAD method, which are not dependent on the mean, are more suitable for the vegetation cover. Therefore, before performing any comparison test, a combination of visual and statistical methods is recommended to evaluate the presence of outliers. [ABSTRACT FROM AUTHOR]

Understanding complicated city traffic patterns has been recognized as a critical goal by twenty-first-century urban planners and traffic management systems, resulting in a significant rise in the quantity and variety of traffic data gathered. For example, in a growing number of large cities, taxi firms have begun collecting metadata for each vehicle trip, such as origin, destination, and travel duration. Taxi data offer information on traffic patterns, allowing the study of urban flow—what will traffic look like between two sites on a particular day and time in the future? This paper proposes a method based on sparse GPS probe data, that focuses on allocating travel time data to the different links traveled between GPS observations. This model incorporates the progressive spatial correlations between the links in a network. The main goal of this work is to show how we can consider progressive spatial correlations and improve our results more realistically with a simple adjustment in the previously known parametric methods. For estimating arterial travel time, the methodology is applied to a case study for the partial network of New York City-based on the data collected from the taxicabs in New York City, providing the locations of origins, destinations, and travel times. The model estimates quarter-hourly averages of urban link travel times using OD trip data. This study proposes a more accurate approach for estimating link travel times, that fully utilizes the partial information received from taxi data in cities.

Experimental statistics are a key element for innovation in the agricultural sector. Commonly used statistical methods in experimentation are relatively simple, reliable, and widely used. However, the many problems in the quality of statistical analyses reported in the agricultural science literature highlight a need for continuing discussion on and updating of this topic. This article reviews critical points about classic linear models procedures commonly used in agricultural statistics, frequent procedures in publications in the agricultural sciences. Due to the evolution of statistical science some common recommendations from the past should no longer be followed.

This article proposes a novel electromagnetic (EM) parametric modeling technique using combined neural networks and resistance, inductance, conductance, and capacitance (RLGC)-based eigenfunctions (neuro-EF) for microwave components with two-port microstrip structures. In the proposed technique, neural networks are used to learn the unknown relationship between the parameters of RLGC of the eigenfunction and geometrical parameters. The generation of training data depends on obtaining the correct eigenvalues of different modes. However, for different geometrical parameter samples, there is no uniform correspondence between the calculated eigenvalues and the modes. The incorrect correspondence may cause the two modes to be swapped, defined as the mode-swap issue. We propose a mode-matching method based on eigenvectors to solve this mode-swap issue. After the training data of eigenfunction parameters is obtained, a preliminary training of the neural networks and a two-step refinement training of the neuro-EF model are proposed to develop the overall EM parametric model. By the proposed modeling technique, the trained model can provide a fast and accurate prediction of EM responses for two-port microstrip structures as geometrical parameters change. For the parametric modeling of microwave components with microstrip structures, the proposed technique can obtain better accuracy in larger geometrical variations compared with the existing methods. Two examples of microstrip structures are used to illustrate the proposed technique. [ABSTRACT FROM AUTHOR]

This article proposes a novel parametric modeling technique incorporating a joint polynomial-transfer function with neural networks (short for neuro-PTF) for electromagnetic (EM) behaviors of microwave filters. In the proposed technique, the polynomial function is introduced together with the pole-residue-based transfer function to represent the EM responses. The pole-residue-based transfer function is used to represent the whole EM response at the beginning and is subsequently divided into multiple subtransfer functions where each subtransfer function contains one pair of pole/residue. A novel smoothness-discriminating algorithm is proposed to judge the smoothness of each subtransfer function response and separate the pole/residue pairs whose subtransfer function response is determined to be smooth. The proposed method introduces low nonlinear polynomial functions to re-fit the smooth parts of subresponse and remains the nonsmooth parts of subresponse for the highly nonlinear transfer functions to represent. By this way, the proposed method avoids the discontinuity problems of nonunique parameter extraction caused by fitting smooth curves using the highly nonlinear transfer function. Neural networks are proposed to learn the relationship between the polynomial coefficients/poles/residues and the geometrical parameters. Utilizing both advantages of the polynomial functions and transfer functions, the proposed method can produce more accurate models than the existing neuro-TF methods, especially with large geometrical variations. The accuracy and robustness of the proposed technique are demonstrated using three EM application examples of microwave filters. [ABSTRACT FROM AUTHOR]

Neural networks (NN) are used in high-performance computing and high-throughput analysis to extract knowledge from datasets. Neural architecture search (NAS) automates NN design by generating, training, and analyzing thousands of NNs. However, NAS requires massive computational power for NN training. To address challenges of efficiency and scalability, we propose PENGUIN, a decoupled fitness prediction engine that informs the search without interfering in it. PENGUIN uses parametric modeling to predict fitness of NNs. Existing NAS methods and parametric modeling functions can be plugged into PENGUIN to build flexible NAS workflows. Through this decoupling and flexible parametric modeling, PENGUIN reduces training costs: it predicts the fitness of NNs, enabling NAS to terminate training NNs early. Early termination increases the number of NNs that fixed compute resources can evaluate, thus giving NAS additional opportunity to find better NNs. We assess the effectiveness of our engine on 6,000 NNs across three diverse benchmark datasets and three state of the art NAS implementations using the Summit supercomputer. Augmenting these NAS implementations with PENGUIN can increase throughput by a factor of 1.6 to 7.1. Furthermore, walltime tests indicate that PENGUIN can reduce training time by a factor of 2.5 to 5.3. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient method for robust and accurate 3D self-portraits using a single RGBD camera. Our method can generate detailed and realistic 3D self-portraits in seconds and shows the ability to handle subjects wearing extremely loose clothes. To achieve highly efficient and robust reconstruction, we propose PIFusion, which combines learning-based 3D recovery with volumetric non-rigid fusion to generate accurate sparse partial scans of the subject. Meanwhile, a non-rigid volumetric deformation method is proposed to continuously refine the learned shape prior. Moreover, a lightweight bundle adjustment algorithm is proposed to guarantee that all the partial scans can not only “loop” with each other but also remain consistent with the selected live key observations. Finally, to further generate realistic portraits, we propose non-rigid texture optimization to improve the texture quality. Additionally, we also contribute a benchmark for single-view 3D self-portrait reconstruction, an evaluation dataset that contains 10 single-view RGBD sequences of a self-rotating performer wearing various clothes and the corresponding ground-truth 3D models in the first frame of each sequence. The results and experiments based on this dataset show that the proposed method outperforms state-of-the-art methods on accuracy, efficiency, and generality. [ABSTRACT FROM AUTHOR]

One fundamental difficulty in multiphysics numerical simulation is the complex interactions between different physics domains leading to plenty of computational costs. Although neural networks have recently been introduced in multiphysics simulations, the modeling complexity and the enormous amount of training data required may still pose significant challenges to researchers. In this work, we introduce a low-cost, electromagnetic-centric, multiphysics modeling approach to simulate microwave filters. With ground-truth datasets being generated from the finite element method, a novel deep hybrid neural network (DHNN) model structure is introduced, which uses the sigmoid and the ReLU functions as activators to mimic the diversity of biological neurons. A new, more feasible training algorithm is proposed for the efficient development of the DHNN model. The algorithm adopts the design-of-experiment (DOE) sampling technique and is specifically designed for the simulation of multiphysics responses. The strong approximation ability of the DHNN can lead to high-accuracy modeling with fewer training data and less resource consumption. Another advantage of this approach is that the modeling process is more concise and easier to apply compared with other modeling technologies. Numerical examples show that the DHNN can achieve higher accurate results with much less training data compared to traditional ANNs. The advantages of the proposed method in computational efficiency are more pronounced, especially when the amount of input data increases. [ABSTRACT FROM AUTHOR]

We analyze the performance of the Borda counting algorithm in a non-parametric model. The algorithm needs to utilize probabilistic rankings of the items within $m$ -sized subsets to accurately determine which items are the overall top- $k$ items in a total of $n$ items. The Borda counting algorithm simply counts the cumulative scores for each item from these partial ranking observations. This generalizes a previous work of a similar nature by Shah et al. using probabilistic pairwise comparison data. The performance of the Borda counting algorithm critically depends on the associated score separation $\Delta _{k}$ between the $k$ -th item and the $(k+1)$ -th item. Specifically, we show that if $\Delta _{k}$ is greater than certain value, then the top- $k$ items selected by the algorithm is asymptotically accurate almost surely; if $\Delta _{k}$ is below certain value, then the result will be inaccurate with a constant probability. In the special case of $m=2$ , i.e., pairwise comparison, the resultant bound is tighter than that given by Shah et al., leading to a reduced gap between the error probability upper and lower bounds. These results are further extended to the approximate top- $k$ selection setting. Numerical experiments demonstrate the effectiveness and accuracy of the Borda counting algorithm, compared with the spectral MLE-based algorithm, particularly when the data does not necessarily follow an assumed parametric model. [ABSTRACT FROM AUTHOR]

The Fourier series is introduced as a transfer function (TF) in the artificial neural network (ANN) for parametric modeling of microwave filters in this letter. The reported pole-residue-based TF leads to an order-changing problem of input samples from vector fitting, which is usually solved with an order-tracking technique or data classification. The proposed Fourier series-based TF does not have to carry out the time-consuming operation because the only coefficient order can be determined for all input samples in an iterative process. Compared with the pole-residue-based TF, moreover, the ANN training involves a small number of TF coefficients in the proposed method. The predicted electromagnetic (EM) response is obtained from the coefficients of the ANN output. An example of the ultrawideband (UWB) filter is employed to verify the effectiveness of the Fourier series-based TF. [ABSTRACT FROM AUTHOR]

This letter proposes a novel systematic order fitting technique for parametric modeling using a combined neural network and transfer function (neuro-TF) technique for electromagnetic (EM) behaviors of microwave components. The proposed technique can not only systematically determine the minimum order of the transfer function, but also systematically change the orders. By using the proposed technique, the problem of pole/residue discontinuity due to the improperly fitting can be solved, which results in a better neuro-TF modeling accuracy over the existing order-changing technique. The proposed method is demonstrated by two microwave examples. [ABSTRACT FROM AUTHOR]

Mathai's pathway model is playing an increasingly prominent role in statistical distributions. As a generalization of a great variety of distributions, the pathway model allows the studying of several non-linear dynamics of complex systems. Here, we construct a model, called the Pareto–Mathai distribution, using the fact that the earthquakes' magnitudes of full catalogues are well-modeled by a Mathai distribution. The Pareto–Mathai distribution is used to study artificially induced microseisms in the mining industry. The fitting of a distribution for entire range of magnitudes allow us to calculate the completeness magnitude ( M c ). Mathematical properties of the new distribution are studied. In addition, applying this model to data recorded at a Chilean mine, the magnitude M c is estimated for several mine sectors and also the entire mine. [ABSTRACT FROM AUTHOR]

The reliable determination of earthquake source parameters is a relevant task of seismological investigations which ground nowadays on high-quality seismic waveforms collected by near-source dense arrays of ground motion sensors. Here, we propose a parametric modeling technique which analyzes the time-domain P-wave signal recorded in the near-source range of small-to-large-size earthquakes. Assuming a triangular moment rate function, a uniform speed, and circular rupture model, we develop the equations to estimate the seismic moment, rupture radius, and stress drop from the corner time and plateau level of the average logarithm of the P-wave displacement versus time curves (LPDT). The constant- $Q$ , anelastic attenuation effect, is accounted by a postprocessing procedure that evaluates the $Q$ -unperturbed moment rate triangular shape. The methodology has been validated through application to the acceleration records of the 2016–2017 Central Italy and 2007–2019 Japan earthquake sequences covering a wide moment magnitude range (Mw 2.5–6.5) and recording distance <100 km. After correcting for the anelastic attenuation function, the estimated average stress drop and the confidence interval ($\langle \Delta \sigma \rangle =0.60$ (0.42–0.87) MPa and $\langle \Delta \sigma \rangle =1.53$ (1.01–2.31) for crustal and subcrustal events of Japan and $\langle \Delta \sigma \rangle =0.36$ (0.30–0.44) MPa for Central Italy) show for both regions a self-similar, constant stress drop scaling of the rupture duration/radius with seismic moment. The smaller sensitivity of the spatially averaged, time-varying peak displacement amplitude to the radiation from localized high slip patch on the fracture surface could explain the retrieved smaller average stress drops for subcrustal earthquakes in Japan and $M >5.5$ events in Central Italy relative to previous estimates using spectral methods. [ABSTRACT FROM AUTHOR]

Matching local features between two overlapped images is a fundamental task in photogrammetry and remote sensing. However, images acquired by multiple sensors often differ substantially in properties, thus posing a great challenge to the robustness and flexibility of feature matching methods. In this article, we propose a locally non-linear affine verification (LAV) method for robust multisensor image matching. The main idea of the LAV is the development of a nonlinear regression formulation that practically models the nonlinear deviation of a real surface around a point from its tangent plane during affine verification. Specifically, we start by selecting a restricted set of reliable and well-distributed putative matches as the matching seeds and assign them with neighbors to construct search spaces. In each search space, the regression seeks the smoothest affine model consistent with the latent correct matches, thereby deriving a set of affine parameters to verify correspondence hypotheses for true matches. The verification can be extended to all nearest neighbor matches to discover additional inlier matches. Evaluation on multisensor image datasets with different extents of variations in viewpoint, scale, illumination, and appearance shows that the proposed LAV consistently outperforms existing methods. LAV can achieve a considerable number of high-quality matches, in cases where existing methods provide few or no correct matches. [ABSTRACT FROM AUTHOR]

Neuro-transfer function (neuro-TF) methods are deemed as powerful tools in modeling the electromagnetic (EM) behavior of microwave passive components. Existing neuro-TF methods either endure the issue of “order-changing” or the issue of mismatch of poles/zeros, both calling for specific algorithms to process the data of the transfer function coefficients as a part of model development. This letter proposes a novel neuro-TF method to eliminate the two issues simultaneously by using combined neural networks and model-order reduction (MOR)-based rational transfer functions. In the proposed method, the coefficients of the transfer function are computed by the MOR technique instead of vector fitting. The use of MOR allows the order of the transfer function to remain constant in different regions in the design parameter space, thereby avoiding the issue of “order-changing.” Additionally, the coefficients of the rational transfer function resultant from MOR are naturally sorted according to the order of frequency, which eliminates the issue of mismatch of poles/zeros and subsequently improves modeling accuracy. Compared with existing neuro-TF methods, the proposed method achieves better modeling accuracy for the same geometrical variations. Two microwave examples are utilized to demonstrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

A new on-chip unequal Wilkinson power divider (WPD), developed on a silicon-based integrated passive device (IPD) process and parametric model, is proposed. The central operating frequency is at 4.8 GHz for sub-6G applications. The design process achieves a balance between circuit simulation efficiency and full-wave simulation accuracy based on the parametric model. The size of a fabricated sample for the proposed unequal WPD is $1.6\times2.1$ mm2. A good agreement between the simulated and measured results is achieved. [ABSTRACT FROM AUTHOR]

Multifunction radars (MFRs) are sophisticated sensors with significant intelligence, flexibility, and agility. It is important to analyze the system behavior and interpret the intentions of an MFR through the recognition of consecutive work modes in a pulse sequence. With the rapid development of MFRs, the agility capabilities in work mode modulations and the modulation parameters have increased, making the recognition task more challenging. Therefore, it is essential to develop new methods that are more adaptive and less depended on prior information. This article proposes a new method for the time series clustering of MFR work modes named as model-based radar time series clustering. The proposed method considers the variable modulation parameters of submode and employs three algorithms for different assumptions of progressive availability of the priors of a noncooperative MFR. The experiments with four typical pulse repetition interval modulations simulation samples validate the feasibility and the superior performance of the proposed method over the state-of-the-art pulse sequence clustering methods for recognition of MFR work modes. [ABSTRACT FROM AUTHOR]

Parametric models of video quality are designed for service and network planning as well as video quality monitoring. They are widely applied in a broad range of applications especially when video streams are encrypted or even unavailable at all. Designing metrics in these models remains challenge due to limited available information. In this paper, a spatio-temporal resolution-adaptive parametric (STRAP) model is proposed to evaluate the quality of video streaming services considering the spatial and temporal resolutions. This work serves as a follow-up study for ITU Rec. P.1203.1 that we were previously involved. The relationship between the content complexity and the spatial and temporal resolutions are analyzed and incorporated into the proposed model. Moreover, the effect of video up/down-scaling in display devices on the perceived video quality is further taken into consideration. Experimental results showed that the proposed model can be used as a reliable indicator for video streaming providers to improve their services performance. [ABSTRACT FROM AUTHOR]

Mathai’s pathway model is playing an increasingly prominent role in statistical distributions. As a generalization of a great variety of distributions, the pathway model allows the studying of several non-linear dynamics of complex systems. Here, we construct a model, called the Pareto–Mathai distribution, using the fact that the earthquakes’ magnitudes of full catalogues are well-modeled by a Mathai distribution. The Pareto–Mathai distribution is used to study artificially induced microseisms in the mining industry. The fitting of a distribution for entire range of magnitudes allow us to calculate the completeness magnitude (Mc). Mathematical properties of the new distribution are studied. In addition, applying this model to data recorded at a Chilean mine, the magnitude Mc is estimated for several mine sectors and also the entire mine.

This paper describes a method for rapid resolution of parametric failure during the development period. Since the number of samples is small, it is not possible to carry out general big data analysis of white defect failures of CMOS image sensors (CISs) during the development period in the same way as can be done during the mass production period. Our feature value analysis of white defects based on device physics revealed that both a development product and mass products using big data show similar fluctuations in dark current spectroscopy and in-plane distribution in the wafer. Process step/tool identification and tool sensor data analysis revealed the degree of vacuum within the implantation tool to have a strong correlation with white defect count. We developed a virtual metrology (VM) model for white defect count and varied the degree of vacuum. As a result of our experiments, we were able to reduce white defect count during the development period by 75%. The time required for resolution of failures was 10% that of the conventional method. This paper describes a detailed explanation of how the potential causative process step/tool was identified in addition to the conference paper presented in ISSM2020. [ABSTRACT FROM AUTHOR]