Processing Method for Complex Characteristics of Electromagnetic Devices

ProfCom is a web-based tool for the functional interpretation of a gene list that was identified to be related by experiments. A trait which makes ProfCom a unique tool is an ability to profile enrichments of not only available Gene Ontology (GO) terms but also of ‘complex functions’. A ‘Complex function’ is constructed as Boolean combination of available GO terms. The complex functions inferred by ProfCom are more specific in comparison to single terms and describe more accurately the functional role of genes. ProfCom provides a user friendly dialog-driven web page submission available for several model organisms and supports most available gene identifiers. In addition, the web service interface allows the submission of any kind of annotation data. ProfCom is freely available at http://webclu.bio.wzw.tum.de/profcom/.

Statement of the problem. The analysis of the deplanation hypothesis for linear and angular deformations in reinforced concrete in bending with torsion has been carried out. Results. A simple new method from the method of grids has been developed for complex functions under the combined action of bending and torsion. Diagrams of linear and angular deformities and a new hypotheses have been analyzed. Complex functions have been found to determine the linear and angular deformations, bending and torque moments in the compressed area of concrete. The author has found the projection coefficients of linear and angular deformations using diagrams of compressed concrete and main reinforcement. Deplanations from a special geometric figure of deformations in a complex function have been found. There are vectors of several triangles and gradients where the proportions of a hypotenuse, cathetuses and vectors have been derived. Conclusions. It is also important that similar relative values can be obtained for any vectors. A field of small squares have been plotted and jumps from lateral, normal and other cracks appear have been determined. Fillings of the deformation (stress) diagrams have been obtained for the analysis of the moments in bending with torsion using simple expressions or in a full form. The research on the complex function of linear and angular deformations and the approximation of a function from Timoshenko-Goodyear have been carried out. The error of the deformations value of using the complex function at the points under study is 2% and is 7% at any points of the cross section.

The orthonormal basis for the space spanned by a given signal set can be chosen in many different ways. However, when the basis is truncated to fewer dimensions, the quality of the approximated signals differs, depending on the choice of the original basis. We study two energy-related quality measures and show that the optimal lower-dimensional approximation is given by the principal components (PC) method, which is also a simple and efficient alternative to Gram-Schmidt techniques. In addition, we derive and bound the average decrease in squared Euclidean distances over one symbol interval caused by the PC method. This measure is relevant in serially concatenated continuous phase modulation, where a manifold of signal pairs contributes to the bit-error rate for low-to-medium signal-to-noise ratios. By a numerical evaluation, we find that for this measure, the decrease is lower than that of a previous method by J. Huber and W. Liu (see IEEE J. Select. Areas Commun., vol.7, p.1437-49, 1989). Finally, we compare the minimum squared Euclidean distance for error events, which is relevant for uncoded CPM systems. Here, the loss with the PC method is generally larger than with Huber and Liu's method, although examples of the opposite exist.

Limit summability of functions was introduced as a new approach to extensions of the summation of real and complex functions, and also evaluating antidifferences. Also, limit summand functions generalize the (logarithm of) Gamma-type functions satisfying the functional equation F(x + 1) = f(x)F(x). Recently, another approach to the topic entitled analytic summability of functions, has been introduced and studied by the author. Since some functions are neither limit nor analytic summable, several types of summabilities are needed for improving the problem. Here, I introduce and study functional sequential summability of real and complex functions for obtaining multiple approaches to them. We not only show that the analytic summability is a type of functional sequential summability but also obtain trigonometric summability (for functions with a fourier series) as another its type. Hence, we arrive at a class of real and complex function spaces with various properties. Thereafter, we prove several properties of functional sequential, and also many criteria for trigonometric summability. Finally, we state many problems and future directions for the researches.

The Cauchy integral formula is a fundamental and critical formula in the complex function, and it has a variety of applications in many aspects such as physics, engineering, and mathematics. This article focuses on the applications in the functions and two main theorems in the Cauchy integral formula. It explores the formula and provides corresponding proofs of the formula. To this end, the aim of the article is to use the Cauchy integral formula to cope with some difficult and complex integrals and functions. As a result, this article denotes to use the formula to simplify the complex and difficult problems and provides a practical and fast method. Hence, the article lists some examples to demonstrate the functions’ use of the Cauchy integral formula. At last, the article may serve as a reference for relevant research and survey in the field of the complex functions and provide insights into the Cauchy integral formula’s uses.

Constructal design for a boiler economizer

Replication of minute virus of mice (MVM) induces a sustained cellular DNA damage response (DDR) which the virus then exploits to prepare the nuclear environment for effective parvovirus takeover. An essential aspect of the MVM-induced DDR is the establishment of a potent premitotic block, which we previously found to be independent of activated p21 and ATR/Chk1 signaling. This arrest, unlike others reported previously, depends upon a significant, specific depletion of cyclin B1 and its encoding RNA, which precludes cyclin B1/CDK1 complex function, thus preventing mitotic entry. We show here that while the stability of cyclin B1 RNA was not affected by MVM infection, the production of nascent cyclin B1 RNA was substantially diminished at late times postinfection. Ectopic expression of NS1 alone did not reduce cyclin B1 expression. MVM infection also reduced the levels of cyclin B1 protein, and RNA levels normally increased in response to DNA-damaging reagents. We demonstrated that at times of reduced cyclin B1 expression during infection, there was a significantly reduced occupancy of RNA polymerase II and the essential mitotic transcription factor FoxM1 on the cyclin B1 gene promoter. Additionally, while total FoxM1 levels remained constant, there was a significant decrease of the phosphorylated, likely active, forms of FoxM1. Targeting of a constitutively active FoxM1 construct or the activation domain of FoxM1 to the cyclin B1 gene promoter via clustered regularly interspaced short palindromic repeats (CRISPR)-enzymatically inactive Cas9 in MVM-infected cells increased both cyclin B1 protein and RNA levels, implicating FoxM1 as a critical target for cyclin B1 inhibition during MVM infection.IMPORTANCE Replication of the parvovirus minute virus of mice (MVM) induces a sustained cellular DNA damage response (DDR) which the virus exploits to prepare the nuclear environment for effective takeover. An essential aspect of the MVM-induced DDR is establishment of a potent premitotic block. This block depends upon a significant, specific depletion of cyclin B1 and its encoding RNA that precludes cyclin B1/CDK1 complex functions necessary for mitotic entry. We show that reduced cyclin B1 expression is controlled primarily at the level of transcription initiation. Additionally, the essential mitotic transcription factor FoxM1 and RNA polymerase II were found to occupy the cyclin B1 gene promoter at reduced levels during infection. Recruiting a constitutively active FoxM1 construct or the activation domain of FoxM1 to the cyclin B1 gene promoter via CRISPR-catalytically inactive Cas9 (dCas9) in MVM-infected cells increased expression of both cyclin B1 protein and RNA, implicating FoxM1 as a critical target mediating MVM-induced cyclin B1 inhibition.

The article deals with the approaches to the study of population life quality in countries around the world. We considered the formation of the main Human Development Index, сalculated annually by the United Nations, its сomposition and method of сalculating the indicators, сhanges in the structure and calculation of the indicators of the HDI. According to the data of the 2014 United Nations Report, all countries of the world were divided into four groups: the countries with very high human development level, the countries with high human development level, the countries with medium human development level and the countries with low human development level. In the paper the complex analysis of human development is conducted for the group with very high level of development. In the study we suggested the method of selection of integrated, generalizing indicators using the method of dimension reduction – the method of principal components. By the method of principal components the generalizing integrated indicators, which allowed assessing living conditions of the population at the most generalized level, were identified. The principal factors were selected taking into account the total accumulated dispersion, Kaiser criterion and the scree plot. In the result of the recalculations with the axes rotation by the method of Varimax of initials, three principal components were obtained. The model of dependance of the Human Development Index (HDI) – 􀀀 on the selected principal factors f1, f2 and f3 was built. The countries, included to the first group with very high human development level were classified using cluster analysis. The classification was carried out by the selected generalizing factors. In the result of hierarchical procedures for combining, we identified four groups of countries – four clusters, which confirm the differences in the conditions of human development in the group of countries with very high human development level. DOI: 10.5901/mjss.2015.v6n3s7p79

The flow field in a wall attachment amplifier was visualized and analyzed numerically, and the flow pattern was made clear in the former studies (1) (2) (3). Also, the field-diving method using the k-εturbulent flow model was developed, and its effectiveness has been confirmed by the experiment of flow visualization and velocity measurement. In this study, the variations of the input and output static characteristics of a wall attachment device were made clear by numerical analysis and experiments changing the splitter distance, supply and control flow rate. In this case, a FLIP-FLOP-type logical device was used as the wall attachment device. The numerical method used was the flow-field diving method using the k-εturbulent model, as in the former reports. The input and output static characteristics were determined from the velocity and pressure distributions obtained by using this numerical analysis method. The experiments were performed by changing the Reynolds number, control flow rate and splitter distance. The results were compared with analytical values with which they were in good agreement. From these results, the effects that the variation of splitter distance, control flow rate and supply flow rate exerted on the characteristics of the device were made clear, and the effectiveness of the flow-field dividing method for the calculation of the characteristics was confirmed.

In this study, the method of moments (MoM, Method of Moments) has been used to calculate the electromagnetic characteristics of the quadcopter. The electromagnetic reflection characteristics of 4 sorties of multi-rotor aircraft in the air group flight situation have been carried out. It has been set that the frequency band is 433MHz that the incident wave magnitude is 1V/m and the polarization is a linearly polarized wave. The quadcopter was at a fixed distance of 3 meters and found under different observation angles (XY, XZ, YZ planes) that has been found that the electromagnetic reflection characteristics are peculiar. And under the convergence of simulation data, the near-field and far-field reflection fields have been discussed separately. The electromagnetic characteristics analysis of radar reflection cross-sectional area has been provided. Relevant data can be used as a reference for the formulation of flight plans when performing specific flight mission planning.

In this paper, modeling of contours by a time-varying autoregressive (TVAR) model is considered. In general, a contour function is not a stationary process, and stationary models are often unsatisfactory in representing local features. Recently, there have been advances in time-series modeling of nonstationary processes. In a typical TVAR modeling approach, autoregressive coefficients are expanded as a linear combination of deterministic functions. In this paper, a low-order discrete Fourier transform (DFT) is used to expand autoregressive coefficients. It is based on the fact that a contour is represented by a complex function, and the model coefficients need to be expanded by a set of complex functions. The parameter estimation and order selection in TVAR models are also considered. A least squares estimator of the TVAR model parameters is presented, and the maximum likelihood approach for determining the model order is also presented. The efficacy of the TVAR modeling approach in modeling a closed contour is demonstrated by estimating the time-varying spectrum of a synthetic contour by different approaches, and is tested by detecting curvature extrema points of synthesized contours. For the classification of contours of planar shapes, the estimated TVAR model parameters are used as features. A neural network is used to classify TVAR features extracted from complex contour functions. In the classification experiment with contours of various planar shapes, about 97% of samples are correctly classified.

The Andean Amazon is an endangered biodiversity hot spot but its forest dynamics are less studied than those of the Amazon lowland and forests from middle or high latitudes. This is because its landscape variability, complex topography and cloudy conditions constitute a challenging environment for any remote-sensing assessment. Breakpoint detection with Landsat time-series data is an established robust approach for monitoring forest dynamics around the globe but has not been properly evaluated for implementation in the Andean Amazon. We analyzed breakpoint detection-generated forest dynamics in order to determine its limitations when applied to three different study areas located along an altitude gradient in the Andean Amazon in Ecuador. Using all available Landsat imagery for the period 1997–2016, we evaluated different pre-processing approaches, noise reduction techniques, and breakpoint detection algorithms. These procedures were integrated into a complex function called the processing chain generator. Calibration was not straightforward since it required us to define values for 24 parameters. To solve this problem, we implemented a novel approach using genetic algorithms. We calibrated the processing chain generator by applying a stratified training sampling and a reference dataset based on high resolution imagery. After the best calibration solution was found and the processing chain generator executed, we assessed accuracy and found that data gaps, inaccurate co-registration, radiometric variability in sensor calibration, unmasked cloud, and shadows can drastically affect the results, compromising the application of breakpoint detection in mountainous areas of the Andean Amazon. Moreover, since breakpoint detection analysis of landscape variability in the Andean Amazon requires a unique calibration of algorithms, the time required to optimize analysis could complicate its proper implementation and undermine its application for large-scale projects. In exceptional cases when data quality and quantity were adequate, we recommend the pre-processing approaches, noise reduction algorithms and breakpoint detection algorithms procedures that can enhance results. Finally, we include recommendations for achieving a faster and more accurate calibration of complex functions applied to remote sensing using genetic algorithms.

In the electromagnetic field formed by coaxial cables, electromagnetic waves of different frequencies are shown to propagate at the same speed (the speed of light c). Therefore, the variational method, which is based on the fact that electromagnetic waves of different frequencies have different propagation speeds, cannot be applied. In this session, the characteristics of light propagating through an electromagnetic field of glass, suitable for applying the variational method and for constructing lenses, are determined. Although the electromagnetic field is derived from the properties of dielectrics, it has been demonstrated to be equivalent to a circuit that represents the Schrödinger equation used in quantum mechanics. In other words, it may be possible to apply electromagnetic fields from classical physics (circuit theory) to quantum mechanics. Quaternions and wave functions that are considered to be related to complex functions will be the topic of future studies because the Schrödinger equation is a complex function.

The activation of macrophages, defined as acquisition of competence to complete a complex function such as the destruction of microbes or tumor cells, can now reasonably be viewed as a fundamental element in the regulation and life cycle of these cells (for reviews, see Refs. 1, 2). Monocytes, after entering the tissues, generally become down-regulated and remain so until they have received one or a combination of the multiple inductive signals to which these cells can respond. The cells then gain increased competence for one or more complex functions and retain this competence until application of inductive signals has ceased and/or the application of suppressive signals has begun.

Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy, and weak adaptive ability. This method separates image feature extraction and classification into two steps for classification operation. The deep learning model has a powerful learning ability, which integrates the feature extraction and classification process into a whole to complete the image classification test, which can effectively improve the image classification accuracy. However, this method has the following problems in the application process: first, it is impossible to effectively approximate the complex functions in the deep learning model. Second, the deep learning model comes with a low classifier with low accuracy. So, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of well multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping to complete the complex function approximation in the deep learning model. And a sparse representation classification method based on the optimized kernel function is proposed to replace the classifier in the deep learning model, thereby improving the image classification effect. Therefore, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. The experimental results show that the proposed method not only has a higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effect, thus further improving image classification accuracy.