Application Of Analysis Method Research Articles

Finite-Horizon Robustness Analysis of an Automatic Landing System Under Probabilistic Uncertainty

This paper demonstrates the utility of novel probabilistic robustness analysis tools on an industry-sized autolanding system of a civil transport aircraft. The closed-loop aircraft model is representative of an Airbus A330 during final approach and landing. It is modeled as a linear time-varying system by linearizing with respect to a nominal reference trajectory. Disturbances due to random parametric uncertainty, atmospheric turbulence, and static wind shear are considered. Probabilistic uncertainty in the aerodynamic coefficients and mass parameters is quantified efficiently by applying a polynomial chaos series expansion. This generalized Fourier series is applied after employing a linear fractional transformation. The computational benefits of expanding uncertain interconnections in linear fractional representation build an appealing advantage of the applied analysis methods. Probabilistic and worst-case perturbations around the reference trajectory are quantified. The analysis results are compared with nonlinear Monte Carlo simulations of the complete closed-loop system.

A comparison between Robust regression analysis methods Applied statistical study of the most important demographic factors on the total fertility rate in Yemen for the period (1990-2013).

the research aims to compare the estimators of robust regression methods (the robustM method - the robust MM method - the robustS method), the detection of the nature of the distribution of data, the discovery of abnormal and extreme data of the study variables, in addition to revealing the stability of time chains, and testing a relationship Joint integration by analyzing the relationship between the studied demographic variables represented in (infant mortality - the number of maternal deaths - raw death rate - the rate of widespread malnutrition - female death rate - child mortality rate under the age For every 1,000 neighborhood births) as independent variables, and the total fertility rate as a variable in the Republic of Yemen for the period (1990-2015). the research aims to compare the estimators of robust regression methods (the robustM method - the robust MM method - the robustS method), the detection of the nature of the distribution of data, the discovery of abnormal and extreme data of the study variables, in addition to revealing the stability of time chains, and testing a relationship Joint integration by analyzing the relationship between the studied demographic variables represented in (infant mortality - the number of maternal deaths - raw death rate - the rate of widespread malnutrition - female death rate - child mortality rate under the age For every 1,000 neighborhood births) as independent variables, and the total fertility rate as a variable in the Republic of Yemen for the period (1990-2015).

Testing methods of neural systems understanding

Neuroscientists apply a range of analysis tools to recorded neural activity in order to glean insights into how neural circuits drive behavior in organisms. Despite the fact that these tools shape the progress of the field as a whole, we have little empirical proof that they are effective at identifying the mechanisms of interest. At the same time, deep learning systems are trained to produce intelligent behavior using neural networks, and the resulting models are impressive but also largely impenetrable. Can the tools of neuroscience be applied to artificial neural networks (ANNs) and if so what would this process tell us about ANNs, brains, and – most importantly – the tools themselves? Here we argue that applying analysis methods from neuroscience to ANNs will provide a much-needed test of the abilities of these tools. It would also encourage the development of a unified field of neural systems understanding, which can identify shared concepts and methods for studying distributed information processing in artificial and biological systems. To support this argument, we review methods commonly used in neuroscience, along with work that has demonstrated how these methods can be applied to ANNs and what we learn from this, and related efforts from interpretable AI.

Enigmatic Surface Ruptures at Cape Rytyi and Surroundings, Baikal Rift, Siberia: Seismic Hazard Implication

The geomorphic expression of active faulting and distinction of paleoseismic events in areas that are rapidly obscured by erosion/sedimentation still remains a considerable scientific problem. The present article discusses the revealing of surface faulting ruptures and their parameters to identify capable faults without trenching and to estimate the magnitude of earthquakes. The case study was at Cape Rytyi, located in Baikal-Lena Nature Reserve on the northwestern shore of Lake Baikal. Based on unmanned aerial photography, GPR, and structural observations, we mapped and investigated the relation between geomorphological forms and ruptures. The obtained results show that past landslides and paleoruptures at Cape Rytyi and its surroundings are associated with at least two earthquakes. The Mw of the earlier event was 7.3 (Ms = 7.4); the Mw of the later one was 7.1 (Ms = 7.3). The paleoruptures in the distal part of the delta of the Rita River and on the southeastern slope of the Baikal Ridge were included in the seismogenic rupture zone, which traces some 37 km along the Kocherikovsky fault. The approximate intervals in which earthquakes occurred are 12–5 ka and 4–0.3 ka, respectively. The applied analysis methods can be useful for paleoseismology and assessing seismic hazards in similar regions elsewhere.

Spectroscopic Time Series Performance of the Mid-infrared Instrument on the JWST

We present here the first ever mid-infrared spectroscopic time series observation of the transiting exoplanet L 168-9 b with the Mid-Infrared Instrument (MIRI) on the James Webb Space Telescope. The data were obtained as part of the MIRI commissioning activities, to characterize the performance of the Low Resolution Spectroscopy (LRS) mode for these challenging observations. To assess the MIRI LRS performance, we performed two independent analyses of the data. We find that with a single transit observation we reached a spectro-photometric precision of ∼50 ppm in the 7–8 μm range at R = 50, consistent with ∼25 ppm systematic noise. The derived band averaged transit depth is 524 ± 15 ppm and 547 ± 13 ppm for the two applied analysis methods, respectively, recovering the known transit depth to within 1σ. The measured noise in the planet’s transmission spectrum is approximately 15%–20% higher than random noise simulations over wavelengths 6.8 ≲ λ ≲ 11 μm. We observed an larger excess noise at the shortest wavelengths of up to a factor of two, for which possible causes are discussed. This performance was achieved with limited in-flight calibration data, demonstrating the future potential of MIRI for the characterization of exoplanet atmospheres.

Spatial distribution of educational services in the city of Faw

This topic is concerned with the field study and the spatial signature of educational services in the city of Al-Faw for all levels of study so that the researcher can apply the tools of spatial analysis using the GIS ARC MAP10 to use it in smart cities. The Research problem is the lack of spatial organization of educational services in the city of Faw. The Research Hypothesis There is an imbalance in the distribution of educational services in the city of Faw. The aim of the research is to study the reality of educational services in the city of Faw and to activate the role of the spatial organization method in planning policies, and in aspects related to urban planning in general and the distribution of educational services in the Faw region in particular, and to determine the actual need for educational services. Research Methodology: The study adopted an analytical approach using geographic information systems and spatial analysis tools. Geographical information systems in topics related to urban planning and the study of services save time and cost and give accurate results that support decision-makers in reaching treatments and planning alternatives and come up with analytical results through spatial maps that show the applied analysis methods and through which Putting the most important conclusions reached by the research through the results of the analysis and thus supporting the research and verifying its hypothesis, leading to the most important proposals that would make the best use of educational services to meet the needs of the population.

TECHNO-ORIENTALISM: AN INTERTEXTUAL ANALYSIS OF THE WINDUP GIRL

The concept of ‘Orientalism’ as suggested by Said (1978) is not only the stereotyped representation of east but it has deeper significance and approach. By the stereotyping of east the west has always maintained its position at the center of binarism. This idea may be of self-conceit or an urge to develop the third world countries (east), the question of ‘Orientalism’ is persistent throughout the sagas of history. Present study is an endeavor to analyze Orientalism by Said (1978) along with Techno-orientalism by Morley and Robins (1995) in order to understand the contemporary concept of orientalist regarding the exotic east. For in the current scenario east is no longer underdeveloped or primitive still it is ‘the exotic east’ with all its possessions and gluts to be explored by west. Applied method for analysis is Critical Discourse Analysis from which intertextuality by Fairclough (1992) is chosen as a tool of analysis. Findings of research asserts that although west has changed its attitude towards east still there are glaring evidences that Orientalism is prevalent with some varying degrees. Techno-orientalism is one of these new forms of Orientalism that depicts a new kind of orient/occident binarism.

Statistical Analysis Methods Applied to Early Outpatient COVID-19 Treatment Case Series Data

When confronted with a public health emergency, significant innovative treatment protocols can sometimes be discovered by medical doctors at the front lines based on repurposed medications. We propose a statistical framework for analyzing the case series of patients treated with such new protocols, that enables a comparison with our prior knowledge of expected outcomes, in the absence of treatment. The goal of the proposed methodology is not to provide a precise measurement of treatment efficacy, but to establish the existence of treatment efficacy, in order to facilitate the binary decision of whether the treatment protocol should be adopted on an emergency basis. The methodology consists of a frequentist component that compares a treatment group against the probability of an adverse outcome in the absence of treatment, and calculates an efficacy threshold that has to be exceeded by this probability, in order to control the corresponding p-value and reject the null hypothesis. The efficacy threshold is further adjusted with a Bayesian technique, in order to also control the false positive rate. A random selection bias threshold is then calculated from the efficacy threshold to control for random selection bias. Exceeding the efficacy threshold establishes the existence of treatment efficacy by the preponderance of evidence, and exceeding the more demanding random selection bias threshold establishes the existence of treatment efficacy by the clear and convincing evidentiary standard. The combined techniques are applied to case series of high-risk COVID-19 outpatients that were treated using the early Zelenko protocol and the more enhanced McCullough protocol.

Multilevel probit regression for 3-alternative forced choice audibility testing

A recent psychoacoustic test at NASA Langley generated a dataset of 3-alternative forced-choice responses for 40 subjects that measured the audibility of a tone complex in a shaped broadband masker. The task was completed by four subjects at a time in a small theatre-like environment using predetermined stimuli levels. These data were subject to 4 forms of probit regression: a “complete pooling” analysis in which all data from the test was fit with one curve, two forms of “no pooling” analyses in which subjects’ data were treated individually (using both packaged and custom software), and a “partial pooling” analysis in which multilevel-regression software fit both individual curves as well as population-level parameters at the same time. The results of the analyses are compared in terms of both individual- and population-level parameters. Partial pooling appears to give the most consistent results at both levels, as well provide the most robustness among the packaged approaches (albeit with added complexity over single-level regression). These results mirror those contained in a recent NASA technical memorandum entitled “Comparisons of Analysis Methods Applied to Alternative Forced Choice Audibility Data.”

An Inverse Analysis Method Applied to Optimization of Specimen’s Shape for Performing Hot Rapid Crushing Tests from Homogeneous Initial Temperature Field

Specific experimental tests with loadings conditions close to those of industrial fast forming processes as rapid forging, rapid stamping or high speed machining, characterized by large plastic strains, localized deformations and important gradients of strain rates, strain and temperature, requires to analyses material flow behavior at different initial temperatures. One of the more important conditions to obtain intrinsic rheological constitutive equations is to have a quasi-homogenous initial temperature distribution and especially to keep constant the material microstructure during the specimens heating. The rapid induction heating seems to be one of the most reliable processes. This scientific study proposes an inverse analysis technique based on numerical finite element modelling to define on the thermal point of view, optimal specimen shapes for performing hot rapid crushing tests from homogenous initial temperature field.

Proposal of User Experience Analysis Method Applied Customer Satisfaction

Proposal of User Experience Analysis Method Applied Customer Satisfaction

An Overview on Reliability Analysis and Evaluation Methods Applied to Smart Grids

The reliability can be defined as to carry out predefined requirements in a predefined duration. The importance of reliability analysis increases day by day as the customers become more conscious about a product or service which they buy. The application area of reliability analysis is very wide. Any product\ system or service including smart grids can be subject of the reliability analysis. As power system technology and computer science improving, the concept of smart grids begins to take part in our lives. Therefore, it is very essential to make reliability analysis for smart grids. In this paper, reliability analysis methods applied to smart grids are focused on and the classifications in reliability analysis have been explained. Besides, distribution networks reliability concept and different methods such as simulation and analytical approaches to assess the reliability have been introduced. The applications of these methods on smart grids are well explained. In addition to these, smart grids and conventional grid is compared. Different approaches such as tree analysis, failure mode effect analysis, Markov process and Monte Carlo simulation methods are carried out with wind turbines.

Image analysis methodology for a quantitative evaluation of coating abrasion resistance

Superhydrophobic materials possess great potential in a range of fields. However, the utilisation of such coatings in real-life applications remains limited, mainly due to their low physical resilience. The fabrication of high-strength superhydrophobic materials is a highly active area in the literature, and the development of techniques to assess their resilience is highly important. Existing resilience analysis of superhydrophobic materials is able to provide some level of understanding. However, reported approaches are often wildly different, in the type of degradation used and the applied analysis method. Comprehensive analysis is rarely carried out as this requires complex operations or equipment, and/or specific testing requirements, which are not widely accessible. As a result, many studies provide limited or solely qualitative insight. Here, we report a methodology for a quantitative assessment of the resilience of superhydrophobic coatings. The method combines physical abrasion with optical imaging (electronic scanner) and mass-loss tracking. Combined examination using image processing and mass-loss allows for achieving a greater understanding of degradation behaviour, including differentiation between cohesive/adhesive failures.

The investigation of a complex productivity indicator based on the main components method for evaluating sheep of North Caucasian Meat and Wool breed

Improving the productive traits of animals through the use of genetic potential is impossible without a systematic evaluation of productivity characteristics and conformation traits. Special attention has always been paid to the study of the physique of animals in association with their productivity. The priorities in the selection of farm animals today are the parameters of meat productivity. For breeding work in order to increase productivity indicators in sheep, it is necessary to use the most informative parameters. The above dictates the need to apply new approaches to the evaluation of phenotypic parameters. Each trait has certain significance in the description of the phenotype, which can be evaluated on the basis of mathematical methods of analysis. Mathematical analysis, as well as mathematical modeling methods, is successfully used in animal husbandry. A new approach to the lifetime evaluation of sheep meat productivity using a multidimensional analysis method as the analysis of the main components has been presented in the article. The main purpose of the applied analysis method is to reduce the dimensionality for a multidimensional matrix of initial data. As a result of this analysis, a certain number of new variables are obtained which cannot be isolated during the direct study of the object, but has a high degree of correlation with the actual traits. We have described one of the possible ways to solve the problem of forming a complex numerical indicator using the principal component method. This indicator is called a complex indicator of productivity. At the same time, it is expected that the indicator under consideration does not impose restrictions on the number of parameters used in its formation. It is assumed that the high value of the complex productivity indicator obtained on the basis of lifetime measurements of the physique of animals will correspond to the same high value of the complex indicator obtained on the basis of slaughter parameters. As the result of the analysis, the mathematical and statistical method for calculating the correlation between lifetime traits and slaughter indicators of animal productivity has been carried out. On the base of conducted method of mathematical analysis, it can be assumed that the complex indicator can be used as the criterion for evaluating the productivity of sheep.

Review of Impedance-Based Analysis Methods Applied to Grid-Forming Inverters in Inverter-Dominated Grids

As the use of distributed generation with power electronics-based interfaces increases, the separation between DC and AC parts of the grid is reduced. In such inverter-dominated AC grids, impedance-based analysis methods are proving to be more powerful than traditional state-space-based analysis methods. Even the conventional parameters and standards used to estimate the stability of generators and stronger grids cannot fully capture the dynamics of weaker, inverter-dominated grids. It then stands to reason that system impedances that are commonly used to analyze DC systems will be useful in the analysis of grid-forming inverters in these hybrid systems. To understand the value of studying the impedances of inverters and other elements in weak AC grids, this article reviews and describes the various ways in which impedance-based analyses can be used to define, assess, and improve the performance of grid-forming inverter controllers. An exemplary case using the conventional P-f/Q-V droop control demonstrates the application of impedance-based analyses to determine the impact of the controller on the input and output stability of the inverter.

Dynamics of a Simulated Demonstration March: An Efficient Sensitivity Analysis

Protest demonstrations are a manifestation of fundamental rights. Authorities are responsible for guiding protesters safely along predefined routes, typically set in an urban built environment. Microscopic crowd simulations support decision-makers in finding sustainable crowd management strategies. Planning routes usually requires knowledge about the length of the demonstration march. This case study quantifies the impact of two uncertain parameters, the number of protesters and the standard deviation of their free-flow speeds, on the length of a protest march through Kaiserslautern, Germany. Over 1000 participants walking through more than 100,000 m2 lead to a computationally demanding model that cannot be analyzed with a standard Monte Carlo ansatz. We select and apply analysis methods that are efficient for large topographies. This combination constitutes the main novelty of this paper: We compute Sobol’ indices with two different methods, based on polynomial chaos expansions, for a down-scaled version of the original set-up and compare them to Monte Carlo computations. We employ the more accurate of the approaches for the full-scale scenario. The global sensitivity analysis reveals a shift in the governing parameter from the number of protesters to the standard deviation of their free-flow speeds over time, stressing the benefits of a time-dependent analysis. We discuss typical actions, for example floats that reduce the variation of the free-flow speed, and their effectiveness in view of the findings.

Network Analysis and Kinetic Clustering Methods Applied to Molecular Dynamics Simulations of Proteins in Complex Environments

Molecular dynamics (MD) simulations are increasingly used to probe the structural dynamics of proteins in response to perturbations in their environment. Characterization of MD trajectories within the simulation time-scale of tens to hundreds of microseconds is hindered by the determination of relevant reaction coordinates that characterize macrostates and transition barriers among them. Over recent years, kinetic clustering methods have been successfully applied to analyze MD trajectories allowing to analyze and describe complex free-energy landscapes of proteins. These methods rely on the identification of a set of relevant features describing these landscapes; however, exhaustive feature identification is limited by the amount of memory available in standard computer clusters. Network analysis and community detection methods provide an alternative to generate coarse-grained features by identifying communities of protein residues that are grouped together according to their time-dependent association. Such communities can be used for coarse-graining the representation of relatively large proteins (several hundreds of residues) to a few communities, over a time-scale of several microseconds. Here, we implement the application of community detection methods to MD trajectories of proteins ranging from tens to several hundreds of residues solvated in explicit water and/or embedded in lipid bilayers. Kinetic clustering methods are applied on both the coarse-grained representation generated by network analysis and the all-atom representation and used to analyze the free-energy landscape of a diverse set of proteins.

ОБЩЕСТВЕННО-ПОЛИТИЧЕСКАЯ РОЛЬ ТЕРРИТОРИАЛЬНЫХ ИНТЕРНЕТ-СООБЩЕСТВ

The research is devoted to the study of the topic of networkization of the socio-political space that is relevant in the context of the informatization of modern society. The author analyzes one of the displays of modern politics networkization in the paper. Its demonstration is associated with the growing social and political role of territorial Internet communities. The functioning of one of the Nizhny Novgorod Internet communities is studied as an example. The theoretical basis of the study is the theory of the information society and several concepts: the concept of the network community and the concept of the network identity. The paper uses content-analysis and comparative analysis as the applied analysis methods. The results of the study do not confirm the assumption that network communities' functioning contributes to an increased degree of civic engagement. However, the analysis confirms another assumption that public and political communication using the Internet community increases the efficiency of interaction between government and society, as it helps to improve the quality of feedback from the authorities. The author supposes that the display of the network identity factor affects the change in the degree of citizens' involvement in interaction with the authorities.

A Comparative Study of Signal Analysis Methods Applied in the Detection of Instantaneous Frequency

The smart grid concept is being applied more and more frequently and this is due to the need to integrate all the components that are part of power systems today, starting from generation units, storage systems, communications and connected loads. Non-linear and non-stationary signals have been obtained in this type of systems, which have high penetration of non-conventional energy sources (NCSRE) and non-linear loads. The power quality criterion has had to be adapted to the new conditions of the electrical systems and this has led to the need to search for new analysis methodologies for the acquired signals. In this article we present a review on non-linear and non-stationary signal analysis methods in electrical systems with high NCSRE penetration. To this end we explore the application of the Hilbert-Huang Transform (HHT), Wavelet Transform (WT) and Wigner-Ville Distribution (WVD), exposing each of the advantages and disadvantages of these methods. To validate the methodology, we have selected some synthetic signals that adequately describe the typical behaviors in these systems.

Positive solutions of a nonlinear algebraic system with sign-changing coefficient matrix

Existence of positive solutions for the nonlinear algebraic system x=lambda GF ( x ) has been extensively studied when the ntimes n coefficient matrix G is positive or nonnegative. However, to the best of our knowledge, few results have been obtained when the coefficient matrix changes sign. In this case, some commonly applied analysis methods such as the cone theory, the Krein–Rutman theorem, the monotone iterative techniques, and so on cannot be directly applied. In this note, we prove the existence of positive solutions for the above nonlinear algebraic system with sign-changing coefficient matrix taking the advantages of the classical Brouwer fixed point theorem combined with a decomposition condition on the coefficient matrix. We provide an example in solving a second-order difference equation with periodic boundary conditions to illustrate the applications of the results.